research about stress of students

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• Published: 10 November 2022

Understanding the role of stress, personality and coping on learning motivation and mental health in university students during a pandemic

• Chris Gibbons   ORCID: orcid.org/0000-0001-6631-721X 1  

BMC Psychology volume  10 , Article number:  261 ( 2022 ) Cite this article

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The aims explored the associations between stress, personality and coping on student mental health and compared defensive-pessimism and optimism as influences on learning motivation. Most research construes ‘stress’ as ‘distress’, with little attempt to measure the stress that enhances motivation and wellbeing. Undergraduate psychology students (N = 162) were surveyed on student and pandemic-related stressors, personality, support, control, mental health and learning motivation. Overall, adverse mental health was high and the lack of motivation acute. While positive ratings of teaching and optimistic thinking were associated with good mental health, context control was key. Adverse ratings of teaching quality lowered learning motivation. Support and conscientiousness bolstered learning motivation and conscientiousness buffered against the adverse impact of stress on motivation. Openness was associated with the stress involved in learning. For those anxious-prone, defensive-pessimism was as effective as optimism was in stimulating learning motivation. Developing context control, support and strategies linked to personality could bolster student resilience during and post Covid-19.

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Introduction

Stress has historically been defined as a physiological and psychological response [ 1 , 2 ], and as the external stimuli that trigger or result in that reaction [ 3 ]. This early stimulus–response framework saw psychological factors as largely a consequence of the stress response. In contrast, in the Transactional model of stress, psychological and social factors are front and centre in recognizing and interpreting demands (the primary appraisal) and in managing those demands (the secondary appraisal). Adopting this model, stress is defined as the demands that exceed one’s capacity to cope [ 4 ].

The primary appraisal refers to the initial perception and assessment of the stressor. This can lead to the judgment that it is irrelevant (or benign), a challenge or a threat. As illustrated in Fig.  1 , sources of stress that are interpreted as demands in which one can achieve are called eustress (B) and those that are perceived as associated with apathy or boredom (A) or, more often, as exceeding one’s capacity to cope (C), are sources of distress [ 5 ]. The traditional health psychology approach construed stress in terms of degrees of distress. This study adopted a positive psychology perspective with university demands measured using an adapted National Student Survey (Higher Education Funding Council in England, 2017), with a response scale that allowed stress demands to be rated as hassles (that hold the potential to have an adverse effect on wellbeing) and as uplifts (that hold the potential to enhance wellbeing). This is consistent with the ‘threat’ and ‘challenge’ or distress and eustress primary appraisal judgments in the Transactional model. This study measured daily and ongoing demands, rather than life-events. This is consistent with Moos and Swindle’s (1990) argument that daily and ongoing stressors are important influences on wellbeing [ 6 ].

Adapted from the Yerkes–Dodson curve (1908) [ 7 ]

Sources of student stress

Sources of student stress include academic demands, such as coursework, assessment, exams and work-life balance [ 8 , 9 , 10 ]; to fear of failure and lack of timely feedback on assessments and to the quality of teaching [ 11 , 12 , 13 ]. Personal sources of stress include financial concerns, managing apparent free time, frequently working part-time while studying, and concerns about future careers [ 13 ]. The changes students experience as they transition to university are frequently a source of acute stress. For most, they are learning to live independently, meet new people and often live in close confines with strangers, as well as managing their own finances, and all along with the challenges posed by a course that may leave them feeling overwhelmed [ 14 ].

Stress effects in students

Wellbeing is defined as: ‘…a state of complete physical, mental and social wellbeing and not merely the absence of disease and infirmity’ [ 15 ]. While critics question the assumption of ‘completeness’ as integral to wellbeing, the definition highlights the critical role of psychology in wellbeing. Perceived stress can affect student wellbeing, including depression [ 16 ]; happiness [ 14 ] and even suicidal ideation [ 17 ]. Macaskill (2012) reports that students under 26 report more adverse wellbeing because they are still transitioning into adulthood [ 18 ].

A widely used measure of self-reported mental health is the General Health Questionnaire (GHQ), with approximately, 15–19% of the general population categorised as ‘at risk’ of developing a stress-related illness based on this measure [ 19 ]. This is not a life-threatening illness but complaints ranging from tension headaches, back problems, mouth ulcers and cold sores to digestive and intestinal problems, mood swings and irritability. Among student populations, this can range from 30% to over 60% [ 11 , 20 ]. These stress effects have been observed in students in the UK; in North America [ 21 , 22 ]; Australia [ 23 ] and Sweden [ 24 ] and the experience of stress has been directly linked to student attrition and retention issues [ 25 ].

Pandemic stressors and effects

Following the global spread of the Covid-19 virus, a UK national lockdown was declared on 23 rd March 2020. This led to a dramatic change in students’ university experience. Learning and teaching became a virtual experience, with students, in this sample, receiving online pre-recorded lectures, live virtual seminars and tutorials. Rogowska, Kusnierz and Bokszczanin (2020) examined stress, coping and wellbeing in Polish students (n = 914) during Covid lockdown. Self-rated health and anxiety were poorer compared to normative data and those high in perceived stress more frequently used emotion-based coping [ 26 ]. Awoke, Mamo, Abdu and Terefe (2021), reported that over a third of health-professional students (n = 337) in Ethiopia, surveyed during the pandemic, reported high perceived stress [ 27 ]. However, neither study was longitudinal. Elmer, Mepham and Stadfeld (2020) measured the sources of stress and wellbeing in students (n = 212) before and after the onset of the pandemic in Sweden [ 28 ]. Within sample comparisons showed marked increases in depression, anxiety, loneliness and distress. Key sources of stress included the health of family and friends and uncertainty about their future, along with physically and emotionally isolation.

In a survey of 69,054 quarantined students in France, between April and May 2020, Wathelet et al., (2020) found that students worried more about any symptom of illness, indicating high anticipatory anxiety and the loss in part-time income was associated with higher anxiety, depression and even suicidal ideation [ 29 ]. In a longitudinal survey of 454 students in Italy, higher rates of mental health symptoms, related to depression, anxiety and obsessive–compulsive tendencies, were reported during lockdown, compared to when restrictions were lifted and females suffered disproportionately more [ 30 ]. A similar sex difference and overall deterioration in physical and mental health was observed in a longitudinal test in May and June, 2020, in university students in Germany (n = 917), [ 31 ].

What seems to add to the weight of stress and mental health concerns is not just the impact Covid might have on students but on their family and friends too. Of 7,143 students surveyed in China in January and February 2020, those whose family and friends had Covid, scored higher on anxiety [ 32 ], and similar results were found among students in Spain [ 33 ].

Since October 2020, the Office for National Statistics in England (ONS) have carried out three pilot surveys of university students (in mid-October and the start and end of November) with over 100 000 students in England and Scotland invited to participate via emails from the National Union of Students [ 34 ]. Between 2016- to pre-pandemic 2020, wellbeing measures (operationalised through life satisfaction, life worthwhile, happiness and anxiety), had already declined for students compared with similar aged non-students in the general population [ 35 ]. The differences are likely to be influenced not just by the increased demands and life changes students faced but that students are typically more willing, than non-students, to share mental health issues and university cultures are more supportive and focused on addressing student mental health.

In the Student Academic Experience Survey, a UK wide UCAS survey of first year students, taken in March, after most students had stopped face-to-face teaching, there was a drop in happiness scores [ 35 ]. The results from these and other large surveys [ 34 , 36 ] lays testament to the adverse impact Covid-19 has had on students’ lives and mental health.

Coping with stress

The secondary appraisal refers to individual coping resources, personality and the past experiences drawn on to perceive and manage stress demands. Key student coping resources include support [ 37 ] and control [ 13 , 38 , 39 ]. While trait-related control is a strong predictor of good coping, so is context control or the skills one acquires to feel in control in a given situation [ 11 , 40 , 41 ]. Given the potential context control has over trait-related control in improving coping, it is this type that is measured. Important personality ingredients, related to coping, include those measured by the Big Five [ 42 ], including extraversion [ 43 ] and conscientiousness, levels of emotional stability and openness [ 44 ]—in education contexts, openness is important if learning is to expand; and optimistic thinking strategies have been associated with improved wellbeing, performance and health [ 45 , 46 , 47 ]. Those scoring high on optimism construe stress demands in a way that makes success more likely. They tend to perceive change and stress demands as opportunities to grow and achieve, for example, those who cope well more frequently score stress demands as higher on uplifts and lower when rated as hassles [ 12 ]. They are biased to attend more to positive events over negative events (called defensive optimism) and they are more active in learning from their coping mistakes [ 4 ].

Norem and Cantor (1986) dispute the claim that adopting optimistic thinking strategies offers a panacea to the downside of stress [ 48 ]. They argue that for those anxious-prone, a more effective strategy is defensive pessimism. This involves setting yourself unrealistically low expectations in situations that cause you anxiety. Setting a high expectation of success could add to already heightened anxiety and inhibit performance, tipping you past the peak of the curve in Fig.  1 .

Aims and hypotheses

Most of the research into student stress and coping comes from pre-pandemic findings and the pandemic forced universities to turbo-charge their digital learning provision, providing a different environment to explore the role of stress and coping on mental health and learning motivation. Despite the education potential that digital and remote learning holds, its impact on learning motivation in higher education is mixed [ 49 , 50 , 51 ]. This underscores the need to explore its effect on student motivation during a pandemic, along with the coping and moderating influence played by personality, support and control on motivation and mental health. The factors affecting the rating of stress associated with achievement (i.e., eustress) is a relatively under-researched area in students [ 13 , 41 ]. This study, therefore, aims to explore the relationship between sources of stress (rated as hassles and again as uplifting opportunities) and influences on coping (ratings on support, context control and personality) on mental health and learning motivation. A second aim was to see if defensive pessimism, compared to optimism, was an effective strategy to harness anxiety as motivation towards learning goals.

The following hypotheses were tested:

H1: There will be a difference in the mental health of students studying during the pandemic compared to pre-pandemic norms.

H2: There will be a difference in stress ratings (on hassles and uplifting ratings and pandemic-related stress) between those ‘at risk’ and ‘not at risk’ of a stress-related illness.

H3: There will be correlations between sources of stress, support, control and personality and the outcomes – mental health and learning motivation.

H4: Support, control and personality will have a moderating influence on the impact stress has on mental health and learning motivation.

H5: There will be no difference in learning motivation between those high on defensive pessimism and optimism.

A survey-based, correlational design was employed. The predictor variables were: course-related demands (rated as hassles and as uplifts), amended from the National Student Survey; pandemic-related stressors, including social media use and changes in diet and exercise; and, finally, aspects and influences on coping, namely support, context control and personality.

Participants

A sample of 162 university students (81% of the second-year cohort) were recruited from the second-year of a psychology BSc programme. The inclusion criteria were second year full-time psychology students. Part-time students and those first year were excluded to avoid conflating the different, additional demands they face with those measured (e.g. related to time management and transitioning to university). On demographics, 86.4% were female (n = 140) and 13% male (n = 21). Participants’ average age was 22 years (SD = 4.55 and range 18–59 years).

Students completed an online survey that included a brief and instructions and 89 items gathering information on demographics, sources of student stress, influences on coping—control, support and personality and on anxiety, course satisfaction, learning motivation and mental health.

The cohort was made aware of the study via email and in links on their course homepage to a google survey link. Participation was voluntary and respondents were told they could stop at any time without penalty. The survey took approximately 12 min to complete. They were given the opportunity to complete this in class.

The national student survey (NSS) [ 52 ]

NSS items were adapted so participants could rate each item twice – once as a “hassle” (a perceived source of distress) and once as an “uplift” (a perceived source of eustress). A continuous response scale, from 0 to 5, was used to rate each item as a hassle or uplift – 0 indicating that the item caused no source of distress or eustress and 5 indicating an extreme source. A range of factors were measured using 23 items from the NSS, such as teaching demands, assessment and feedback, time management etc. An example item is: ‘The extent to which teaching staff explain things’. Banked items from the NSS were selected to measure learning motivation . This was a two-item measure with a 5-point Likert scale. An example item is: ‘I have found the course motivating’. The Alpha coefficients for all factors ranged from .64–.85.

Pandemic-related stressors (generated by the author)

This scale contained six items that split into two sub-scales: time on devices and lack of motivation . They were generated following focus group interviews with three groups of second year students. Respondents rated each item on a 10-point response scale from 1 (Not at all True) to 10 (Very True). Sample items included: ‘During the period of Covid-19 restrictions, have you found that you have been: ‘…using social media more than usual’ (time on devices), ‘…losing your mojo’ (lack of motivation) The Alpha coefficient for time on devices was .67 and lack of motivation .85.

Context control [ 12 ]

This scale, of three items, aimed to measure how much participants had developed control in specific contexts. A 5-point Likert scale was used. A sample item is: ‘The pace of learning often leaves me with little feeling of control.’ Two of the three items are reverse scored. The Alpha coefficient was .80.

The values in action scale [ 53 ]

This eight-item scale measures levels of optimistic thinking. Participants respond on a five-point Likert scale. A sample item is: ‘I always look on the bright side’. The Alpha coefficient was .81.

Big five inventory-10 (BFI-10) [ 54 ]

This is a ten-item scale using a 5-point Likert scale. Respondents are asked to rate statements that describe their personality. A sample item is: ‘I see myself as someone who is reserved’. Two items measure each of the Big Five traits, with one of those two being reversed. Alpha coefficient ranged from .61–.74.

Defensive pessimism scale [ 48 ]

This is a twelve-item scale using a 7-point response scale from ‘Not at all true of me’ (1) to ‘Very true of me’ (7). A sample item is: ‘I often start out expecting the worst, even though I will probably do okay’. The Cronbach’s alpha was .87.

General health questionnaire (GHQ) [ 55 ]

This a twelve-item scale and respondents answer on a four-point frequency scale. GHQ measures general levels of self-confidence, happiness, anxiety, depression and sleep disturbance and, taken together, this comprises a general measure of mental health. An example item is: ‘Have you recently been able to concentrate on whatever you’re doing?’ Response options include: ‘Better than usual’, ‘Same as usual’, ‘Less than usual’, ‘Much less than usual’. The scale measures transitory distress. A scoring key of 0–3 was used to determine totals for the analysis and a scoring key of 0, 0, 1, 1 was used to determine ‘caseness’ or those ‘at risk’, where totals on the measure above 3 indicated a risk of developing a stress-related illness [ 50 ]. The Alpha coefficient was .89.

Hospital anxiety and depression scale (HADS) [ 56 ]

The anxiety sub-scale of the HADS was used to measure anxiety. Respondents rated seven statements, each on a scale from 0–3, where 0 is “not at all” and 3 is “most of the time”. An example item is: “I feel tense or wound up”. The Alpha coefficient was .87.

The course satisfaction scale (abridged from the national student survey, [ 52 ]

This is a three-item scale, using a 5-point Likert scale. Respondents are asked to rate statements that describe their course, such as: ‘I enjoy my studies.’ The Alpha coefficient was .89.

The study received ethical approval from the Ethics committee at the host university. All participants received a brief and a point of contact for further clarifications. All were informed that participation was voluntary and they were free to stop at any time and all acknowledged informed consent before participating, in accordance with the Declaration of Helsinki.

One sample t -tests were carried out to compare the mental health of the students studying during the pandemic compared to pre-pandemic normative data (H1). Independent sample t -tests were carried out between those ‘at risk’ and ‘not at risk’ on sources of stress (H2) and between those identified as high in defensive pessimism and optimism on learning motivation (H5). Multiple hierarchical regressions were run using SPSS version 27. Predictors were entered in line with the Transactional model—Sources of stress (primary appraisal factors) were entered in block one and personality and the influences on coping (secondary appraisal factors) in block two, along with demographics. The regression tables illustrate the final block for each model. Regression assumptions were checked and confirmed, and the guidelines proposed by Baron and Kenny (1986) were followed to arrive at the most parsimonious model and in testing for moderation [ 57 ].

The GHQ results in this sample (M = 18.44, SD = 7.40) were compared with and significantly higher than normative data from James, Yates and Ferguson (2013) with a cohort (n = 251) of UK (medical) students (M = 13.39, SD = 5.77), t (159) = 8.63, p  < .001 [ 58 ]. The scores were computed for caseness and 68.5% (n = 111) were ‘at risk’ and 30.2% (n = 49) ‘not at risk’. This compares with 19% ‘at risk’ in the Health Survey for England report (n = 8034) [ 19 ]. This supports H1.

Table 1 compares those ‘at risk’ and ‘not at risk’ on stress ratings for the different sources of stress from the NSS scale, for example, those ‘at risk’ more frequently perceived ‘Teaching on my course’ (one of the demands they were asked to rate) as a hassle, compared to those ‘not at risk’:

There were significant differences in nine out of eleven stress demands, when rated as a hassle, with those ‘at risk’ rating the demands higher than those ‘not at risk’. There were no significant differences in the uplifting ratings between these two groups. This supports H2 for the difference in hassle ratings for the two groups, but not for the difference in uplifting ratings.

Pandemic-related stressors

Those ‘at risk’ (n = 110), (M = 15.55, SD = 4.01), compared to those ‘not at risk’ (n = 49), (M = 12.93, SD = 4.98), spent more time on their devices, t (157) = 3.51, p  = .001, and the ‘at risk’ group (n = 110), (M = 16.79, SD = 3.58), compared to those ‘not at risk’ (n = 49), (M = 11.08, SD = 4.80), scored higher on lack of motivation, t (157) = 8.32, p  < .001. This supports H2.

The regression model explained, 63.4% of the variance in scores on GHQ (Table 2 ). The results of the regression indicated that there was a collective significant effect between lack of motivation, neuroticism, context control, optimism and openness on mental health, F (6, 145) = 44.54, p  < .001, R2 = .648, Adjusted R2 = .634). The individual predictors were examined further and indicated that: Lack of Motivation, Beta = .33, ( p  < .001); neuroticism, Beta = .28, ( p  < .001); context control, Beta = − .26, ( p  < .001); and optimism, Beta = − .14, ( p  = .024) and openness, Beta = .10, ( p  = .041) were predictors in the model and offered partial support for H3.

Optimism and pessimism as predictors of learning motivation

Following the procedure first adopted by Norem and Cantor (1986), the participants in the upper quartile on defensive pessimism were identified and, from this group, those in the upper quartile on anxiety and course satisfaction were selected and they were compared with those in the upper quartile on optimism. Results of the independent sample t -tests indicated no significant differences in learning motivation between the 11 participants selected for being in the upper quartile on defensive pessimism, anxiety and course satisfaction (M = 7.6, SD = 1.2) compared with the 44 participants in the upper quartile on optimism (M = 7.2, SD = 2.1), ( t (53) = .56, p  = .159). There were no significant differences between the defensive pessimism group (M = 12.86, SD = 2.07) and the optimism group (M = 12.83, SD = 2.50) on course satisfaction ( t (53) = .04, p  = 967), with the defensive pessimism group scoring higher (M = 18.55, SD = 2.50) than the optimism group (M = 8.45, SD = 4.83) on anxiety ( t (53) = 6.68, p  < .001). This supports H5 – defensive pessimism was just as effective as optimism on learning motivation for those anxious-prone.

However, significant differences were reported between the defensive pessimism group (M = 3.6, SD = 1.5) and the optimistic group (M = 5.1, SD = .9) on satisfaction in life, ( t (53) = 3.97, p  < .05) and between the defensive pessimism group (M = 10.6, SD = 4.5) and the optimistic group (M = 16.9, SD = 2.4) on happiness, ( t (53) = 6.40, p  < .05).

The optimistic group scored higher on context control (M = 6.94, SD = 1.75) than the defensive pessimism group (M = 4.45, SD = 1.06), ( t (53) = 4.49, p  < .001). However, there was no evidence that context control played a mediating role between optimism and life satisfaction or between optimism and happiness.

The regression model explained 40.9% of the variance in scores on learning motivation (Table 3 ). The results of the regression indicated that there was a collective significant effect between teaching demands rated as hassle, social opportunities rated as an uplift, lack of motivation, conscientiousness and teaching on my course hassle-conscientiousness moderator on learning motivation, F (5, 144) = 21.59, p  < .001, R2 = .43, Adjusted R2 = .41). The individual predictors were examined further and indicated that: teaching demands rated as hassle, Beta = − .37 ( p  < .0001); social opportunities rated as an uplift, Beta = .22 ( p  < .001); lack of motivation, Beta = − .22 ( p  < .001); and conscientiousness and teaching on my course hassle-conscientiousness moderator, Beta = .13 ( p  < .05) were predictors in the model and offered partial support for H3.

The results indicate that high levels of conscientiousness moderated the effects of teaching demands students found disruptive during a pandemic on learning motivation (Fig. 2 ). This supports H4.

Slope graph testing the interaction between conscientiousness and teaching demands rated as hassle on learning motivation

The ‘at risk’ caseness analysis

A striking finding, related to H1, was that 68.5% of respondents were ‘at risk’ of a stress-related illness. This exceeded that reported in pre-pandemic populations of students and non-students of similar age [ 13 , 19 , 20 ] and the average GHQ score was higher compared with pre-pandemic normative student populations [ 58 ]. This reflects the marked stress associated with living and learning during a pandemic and the pattern of results is consistent with that found across Europe and the international comparisons made earlier [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ].

For H2, the NSS items were sub-divided into eleven factors or stress demands, rated once as a hassle and again as an uplift. For nine of eleven of these factors, those ‘at risk’ scored higher on hassles ratings compared with those ‘not at risk’ (Table 1 ). Some of these differences may, in part, be attributable to actual differences in the demands. For example, there may be differences in the quality of support offered between peers or from tutor to tutor or from one’s family and friends.

However, for other demands, such as the teaching experience, workload and course resources (a source of intellectual stimulation), these were the same or similar. That is, students followed the same modules, received the same pre-recorded lectures and faced the same assignments. So, the differences in hassles ratings for these demands was more likely to be attributable to differences in student appraisals, with those ‘at risk’ and, by implication, not coping well, more likely to interpret those demands as distressing. It is possible that some of those ‘at risk’ were not, de facto, bad at coping but given these different appraisals to the same stressors, the contention offered here is that most in the ‘at risk’ group could improve in how they cope. That it was differences in the individual coping rather than material differences in the stressors faced, is supported by the finding in relation to pandemic-related stress: Those ‘at risk’ spent more time on their devices and they were more likely to struggle to find the motivation to be productive and they more often reported changes in sleeping habits i.e., they engaged in behaviours that impacted on their coping or reflected poor coping.

The mental health regression analysis

For H3, in this analysis (Table 2 ), lack of motivation was the strongest predictor of adverse mental health and it referred to the loss of mojo or motivation towards learning demands during a pandemic. It appears that apathy and a lack of energy to undertake necessary tasks was a major source of stress. Procrastination is a perennial problem for most people from time to time and frequently for students. It is a state that is negatively reinforcing but avoidance adversely impacts on learning and wellbeing [ 13 , 59 ]. The challenge of studying during a pandemic has created a set of circumstances where, despite one’s aspirations, struggling to overcome a state of procrastination proved especially difficult and this was the strongest predictor of adverse mental health.

Those students that are worry-prone or anxious by nature appear to suffer most. This was suggested by the positive correlation between neuroticism and GHQ. Consistent with earlier research [ 13 , 41 ], developing a sense of control in specific contexts is a powerful coping mechanism – high scores on context control were associated with low scores on GHQ.

Optimism was predictive in the same way but weaker than context control. Several studies lay testament to the value of optimism, but others show that it either does not feature or is only weakly related to measures of wellbeing [ 13 , 38 ]. Context control is a frequent robust predictor of health and wellbeing and this measure of control is learnt, not dispositional [ 40 ]. Developing context control holds potential to help students cope in the face of changing pandemic challenges. Both context control and optimistic strategies can be developed through learnt strategies, however, based on these findings, it is the former that may offer more benefits.

Openness was a significant predictor. Higher scores on openness were associated with higher scores on GHQ. On the face of it, this suggests that openness has an adverse effect on mental health. However, it is important to remember that stress is not always distress and if one is to learn and develop new knowledge and new skills, one has to be willing to move out of one’s comfort zone. Feeling vulnerable and accepting that one might get things wrong and make mistakes and accepting that one’s self-esteem may take a hit in some disappointing marks or critical feedback, for example, are best interpreted as the growing pains of a growth mindset [ 60 ]. Hand-in-hand with this, is the feeling that one may occasionally doubt that one can meet the learning challenge. This is consistent with the large number of students who eventually succeed, if not shine, in their performance but who experience imposter syndrome en route [ 61 ]. Being open-minded is integral if one is to effectively master new learning, and so too is its association with heightened stress.

‘Teaching on my course’, when rated as an uplift, was associated with lower scores on GHQ. This is likely to reflect the efforts by faculty to engage their students remotely and to provide effective teaching through pre-recordings, live seminars and more frequent live tutorials (these were held weekly instead of fortnightly, the pre-pandemic format). It also reflects the tendency by those scoring high on optimism, to more readily interpret stress demands as opportunities to achieve.

Openness and high scores on idealism, as opposed to cynicism, have been associated with more frequent and more intense experiences of ‘elevation’. This is an uplifting emotion, where one feels inspired, experiences awe or a general feeling of emotional warmth [ 62 ]. Teaching and learning experiences are more likely to be elevating if one adopts an open-minded perspective and this might be part of the explanation behind the dominance of the uplifting ratings for teaching and the openness predictor in the model.

Support, as a coping resource, was removed in the process of arriving at the most parsimonious regression model. This is not to suggest that support is not important. The literature supporting its efficacy is strong [ 37 ]. Its absence here could be attributed to the lack of in-person support during the pandemic and its significance was over-shadowed by the importance of those predictors in the model. In the second analysis it did feature, in the form of social opportunities. This suggests that it remains important, but less so in predicting mental health, as measured by the GHQ.

Is there a place for defensive pessimism in coping?

For H5, defensive pessimism was helpful for those anxious-prone in relation to learning motivation: There was no difference in levels of learning motivation between those respondents high on defensive pessimism and anxiety compared with those high on optimism. This suggests that for individuals who are anxious-prone, rather than adopt those ubiquitous optimistic thinking strategies, setting unrealistically low learning expectations, might relieve them of the pressure to achieve and actually (ironically) enhance performance. Only those in the upper quartile on defensive pessimism, anxiety and course satisfaction were selected and compared against those in the upper quartile on optimism. Selecting those high in course satisfaction was used because it made it more likely that their pessimism was defensive not realistic – the satisfaction rating was an indicator that they had been achieving. Had those with lower scores in course satisfaction been included it would make it more likely that their pessimism was, for some, a realistic reflection of a disappointing course performance.

A cautionary note

It was noteworthy that while not hypothesized, defensive pessimism did not offer the same dividends for happiness and satisfaction with life. The defensive pessimist group scored significantly lower than the optimists on these measures. Moreover, optimism remained a significant predictor of happiness and life satisfaction when context control was tested as a potential mediator. This suggests that for anxious-prone individuals, defensive pessimism offers an effective strategy for harnessing motivation towards learning goals, but optimistic thinking strategies and context control should be employed to help bolster these other wellbeing ingredients.

Consistent with the tenets of positive psychology, one does not always need to work directly on one’s coping deficits, such as trying to lower measures of neuroticism. Rather, if one focuses on building one’s coping strengths, such as improved techniques in context control and in optimistic thinking strategies, and in defensive pessimism for those anxious-prone, it can buffer against the costs of neuroticism on mental health [ 47 , 48 ].

Regression analysis for learning motivation

For H3, in this analysis (Table 3 ), teaching demands was the strongest predictor of learning motivation but not in the positive way observed in the first regression – the more these demands were rated as a hassle, the more learning motivation declined. The benefit of asking participants to identify the distress and eustress elements of demands allows one to identify their subtle and disparate influences. The nature of learning and teaching took on a new meaning when students did it virtually and in isolation, and in a way that involved many more hours sat in front of a computer screen. Where faculty introduced changes that helped, it significantly improved mental health (Table 2 ). However, so dramatic were the changes in learning that this inevitable shift in practice is likely to be associated with added hassle ratings. If there are other added disappointments, perhaps related to teaching variability or in the levels of effort faculty engaged in to support students, then it is understandable that these combined influences had an adverse impact on learning motivation.

Previous research justified testing the role of personality, support and control but several key influences—extraversion, neuroticism, control and optimism, did not feature in this second analysis. Conscientiousness did however, and it was the most effective in maintaining learning motivation. It is likely that the isolation of the pandemic meant there was little scope to derive the same wellbeing benefits (for example in happiness and general motivation) that extraversion is normally associated with [ 63 ]. Studying remotely and virtually put an increased importance on how learning and teaching was delivered and rated and, not unsurprisingly, when the experience was positive it was rated very favourably (its uplifting rating in the first regression) and when it was disappointing, it had a greater adverse impact on learning motivation because the pandemic-induced isolation took away most of the coping benefits that come from being extraverted.

The items underpinning the social opportunities predictor asked respondents to rate opportunities to interact with other students on the course and in university clubs and societies. The predictor represents a proxy for support. Its positive relationship with learning motivation shows that, despite the restricted opportunities imposed by the pandemic, having the contact and support of other students, whether course-related or recreationally, increased learning motivation.

Consistent with the mental health regression, students who reported losing general motivation as a fall-out of the prolonged Covid restrictions, found this carried over to the motivation towards their studies. In both regressions, as part of H4, all the predictors were tested for moderation effects and the slope graph in Fig.  2 illustrates the moderating influence of conscientiousness on learning motivation in response to teaching demands: For those low in conscientiousness (the bottom line), the more teaching demands were experienced as a hassle the more dramatically learning motivation declined. For those average in conscientiousness (the middle line) the decline in learning motivation was less dramatic. For those high in conscientiousness (the top line), motivation was higher and increased ratings of teaching as a hassle had only a nominal influence on rates of learning motivation compared to the other two groups. This suggests conscientiousness was an important buffer for learning motivation against the adverse changes in the quality of teaching.

Limitations

The NSS was used because it is recognized as the, de facto, measure of student experience. However, the evidence of its validity does not yet match the frequency of its use [ 64 ]. The use of a survey method and volunteer sample are not without limitations and while the sample size was good, relative to target population, a larger sample across all cohorts in the psychology department would have allowed more insights into the difference demands faced in each year of study.

Norem and Cantor (1986) used upper quartile measures on GPA to benchmark those respondents whose pessimism was likely to be defensive not realistic. Here, course satisfaction was used. While past performance is likely to be an influence on course satisfaction, it is not the only influence – so is the quality of teaching and how engaging learning resources might be. This may question the validity of using course satisfaction alone to identify those that are defensive rather than realistic pessimists. Using course satisfaction and GPA, rather than either alone, would be a useful way to increase the confidence in identifying those whose pessimism was defensive.

Identifying the sources and experience of stress that are likely to enhance performance and are thereby uplifting as opposed to a hinderance or hassle, is a key challenge for those of us who explore this aspect of positive psychology. The stress that helps you achieve may be experienced as unpleasant and unwanted at the time and, because of that, be more likely to be rated as a hassle. This was the argument offered to explain the relationship between openness and GHQ. A fuller explanation on the distinction between the sources of stress that can help and that can inhibit performance was added to the participant brief in this study, compared to similar earlier studies, but, as an online survey, it was difficult to drive home this distinction. An improvement might be to adopt different labels for ‘hassles’ and ‘uplifts’ such as sources of stress that ‘hinder’ performance and that are ‘necessary to facilitate’ performance.

Overall, the lack of motivation was acute. While positive ratings of teaching and optimistic thinking were associated with good mental health, context control was a stronger predictor. Support and conscientiousness were positively associated with learning motivation, and conscientiousness buffered against the adverse impact of stress on motivation. Openness was associated with the stress involved in learning and, for those anxious-prone, defensive-pessimism was as effective as optimism in stimulating learning motivation.

Recommendations

Studying during a pandemic imposed dramatic and significant changes in student learning and coping. The interpretation offered suggests specific pointers to help students cope; to improve mental health and learning motivation. During induction and early in their studies, students could be offered resilience training that includes tips on the thinking strategies adopted by optimists (for example, that change can be construed as a challenge even if one’s initial reaction is one of threat; in defensive optimism, active disputing, problem-based coping) and in defensive pessimism for those high in anxiety or who experience situations associated with high anxiety, such as early in the semester for new and returning students. It would be useful to raise awareness to re-interpret ‘stress and change’ in a positive light. Understanding our evolved tendency to perceive change as a threat is, to that end, likely to improve coping.

Control, in an education context, could be developed by empowering students with an HE skill-set that goes beyond exercises in time and task management, important though they are, and that incorporates apps that imbed daily and weekly schedules anchored around assignment deadlines; for better time management, and that utilize evidence-based positive psychology techniques. Students can be supported in their learning independence by using some of the psychology-based apps designed for this purpose; along with selected subject-specific podcasts to help enthuse them in their learning and to help move them from a lay understanding to a progressively more academic and in-depth understanding at a pace that leaves them feeling in control.

As universities move to return to in-person teaching, they are more likely to retain some elements of virtual learning. Both regression analyses showed this can be associated with uplifting and hassle ratings. It is important, therefore, to look to maximise its positive impact. For example, by recording virtual learning for students to revisit; using transcript options to facilitate (not replace) student note-taking; allowing student participation through chat features and break-out rooms. Many faculty drew on these elements and are getting better at doing this. However, during this study, there was a mixed take-up in encouraging students to turn on their cameras during learning and where some educators did not turn on their camera when presenting. Evidence in multi-sensory processing [ 65 ] and the animacy effect in memory [ 66 ], support the benefit to learners if they can see as well as hear each other and the presenter. Finally, support opportunities should continue to be enhanced through extended freshers’ fayre events; student inductions with a strong peer-networking focus, along with peer-mentoring initiatives.

These are just some suggestions to help develop specific personality ingredients; student control and support and which, in turn, increases the likelihood that a conscientious approach is one that quickly translates into effective learning and coping. These initiatives hold the potential to combat procrastination, improve learning motivation and mental health.

Availability of data and materials

The data set is available at: https://orcid.org/0000-0001-6631-721X . The question items are subject to copyright but the sources for all the measures used are referenced and interested parties can contact any of these sources. The authors vary on their decisions to make their tests available for free for educational purposes.

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Dr Chris Gibbons is associate professor in psychology at Queen’s University Belfast. His research focus is on health psychology, positive psychology, including the influences on student wellbeing and performance in higher education. He has been Chair of the Association for Psychology Teachers (https://www.associationforpsychologyteachers.com/) since it was founded in 1995 and is the recipient of numerous teaching awards. In August 2021 he received a National Teaching Hero Award from the National Forum For The Enhancement Of Teaching And Learning In Higher Education.

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Previous research has largely failed to separate the between- and within-person effects in the longitudinal associations between academic stress, academic self-efficacy, and psychological distress (symptoms of anxiety and depression). Filling this research gap, this study investigated if academic self-efficacy mediated the relationship between academic stress and psychological distress at the intraindividual level during 3 years of upper secondary school. Gender moderation was also examined in the hypothesised model. The present sample consisted of 1508 Norwegian adolescents (baseline M age = 16.42; 52.9% high perceived family wealth; 70.6% Norwegian-born). The random intercept cross-lagged panel model results indicated (1) positive and time-invariant direct effects from academic stress to psychological distress, (2) academic self-efficacy partially mediated these effects, and (3) psychological distress impacted later academic stress. Academic stress was more strongly related to academic self-efficacy and psychological distress at the interpersonal level for boys, while the intraindividual impact of academic stress on psychological distress was stronger for girls. The study findings might have implications for school-based implementation strategies and theoretical development.

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Introduction

School-related stress affects young people’s quality of life (Berdida & Grande, 2022 ). Studies show that academic stress (Högberg et al., 2020 ), including demands and pressure from school (Wiklund et al., 2012 ) and school-related worry (Sweeting et al., 2010 ), impacts psychological distress (i.e. symptoms of anxiety and depression: Drapeau et al., 2012 ; Mirowsky & Ross, 2002 ) (Torsheim & Wold, 2001 ) over and beyond previous depressive symptoms (Murberg & Bru, 2005 ) on an interpersonal, between-person level. However, the intraindividual (i.e. within-person) relationship between academic stress and psychological distress, including relevant explanatory mechanisms and moderators, has largely been ignored. This study employs a moderated random intercept cross-lagged panel model (RI-CLPM) to examine the intraindividual, longitudinal associations between academic stress, academic self-efficacy, and psychological distress in a cohort of upper secondary school students.

During late secondary school, people experience increasing academic pressure from significant adults such as parents (Deb et al., 2015 ) and teachers (Song et al., 2015 ). In addition, comparing oneself to and competing with peers intensifies during this period (Eccles et al., 2003 ), and a series of final examinations that decide future work and educational prospects are on the horizon. In other words, students experience many day-to-day hassles related to their education, such as different pressures and demands to perform well academically during late secondary school (Dewald et al., 2014 ; Pascoe et al., 2020 ), and stressful feelings (Leonard et al., 2015 ; McGraw et al., 2008 ; Moeller et al., 2020 ). How adolescents experience stress is highly individual and varies in terms of duration and intensity (Moksnes, Byrne, et al., 2010 ). Motivation, performance, and well-being can increase if stressors feel challenging due to goal relevance and manageability, resulting in positive stress (eustress: Selye, 1974 ) (Travis et al., 2020 ). However, if people lack resources to cope with the various pressures and demands, the stressors are perceived as threatening and can be detrimental to psychological health and well-being (Murberg & Bru, 2005 ). When adolescents cannot handle a situation, negative stress and accompanying adverse feelings arise (Lazarus, 1966 ; Sarafino & Smith, 2022 ).

An increasing secular trend of adolescent psychological distress has been observed during the past decades, internationally (Collishaw, 2015 ) and particularly in northern Europe (Potrebny et al., 2017 ) and Norway (von Soest & Wichstrøm, 2014 ). In Norway, adolescent psychological distress has approximately doubled from 2006 to 2019, increasing from 15 to 30% (Krokstad et al., 2022 ). A recent study found that academic stress partly explains the rising trend of psychological distress during adolescence (Högberg et al., 2020 ). The ‘educational stressors hypothesis’ has been put forth as a possible explanation for this association (West & Sweeting, 2003 ). The educational stressors hypothesis argues that there is a societal development of increasing emphasis on and value of educational attainment, which comes with an increase in school-related stressors (West & Sweeting, 2003 ). The rising pressure to perform academically and a more prominent focus on normative testing are accompanied by adverse experiences associated with being evaluated, negatively affecting young people’s health (Karvonen et al., 2005 ). Girls are more likely to experience stress due to these pressures and demands because they place more value on schoolwork and are more susceptible to stressors in their educational environment than boys (Landstedt et al., 2009 ; Schraml et al., 2011 ).

Academic self-efficacy (i.e. a person’s belief regarding their capabilities to perform academically: Bandura, 1997 ) might constitute an explanatory mechanism in the relationship between academic stress and psychological distress (Lazarus, 2006 ). When people perceive their school-and homework as stressful, their academic self-efficacy might decrease due to the adverse affective state that characterises the negative evaluation (Bandura, 1997 ). In support of this assumption, studies indicate that school-related stress negatively impacts academic self-efficacy (McKay et al., 2014 ; Ye et al., 2018 ). Further, low academic self-efficacy has been established as a predictor of psychological distress cross-sectionally (Karademas & Kalantzi-Azizi, 2004 ) and longitudinally (Bandura et al., 1999 ). A reduction in academic self-efficacy might impede individuals’ ability and drive to handle the academic pressures, demands, and difficulties that instigated stressful feelings in the first place, which could result in negative emotions. If individuals do not believe in their academic capabilities enough to cope with their perceived academic stress, feelings of hopelessness and anxiety are promoted (Flett et al., 2011 ).

Self-efficacy and the Transactional Theory of Stress and Coping

Lazarus and Folkman ( 1984 ) argue that people continuously go through primary and secondary cognitive appraisals, evaluating their situations and the resources available to handle them. A primary appraisal concerns the personal implications of a situation. In late secondary school, students continuously appraise their workload, namely if their school- and homework have implications for their personal well-being. There are three types of situational implications: irrelevant, benign-positive, and stressful (Lazarus & Folkman, 1984 , p. 32). A stressful appraisal concerns feelings of harm/loss, threat, or challenge. Feelings of threat and challenge are most relevant to evaluating school- and homework as an implication for personal well-being. Threat concerns anticipation of loss or harm, such as being unable to do school- and homework and consequently receiving poor grades. A challenge is a positive situation that could lead to personal growth, such as favourable consequences for school success. Students who evaluate their school-and homework as challenging likely experience eagerness, excitement, and exhilaration. On the other hand, students who consider their school workload threatening focus on the potential harms of the situation and characteristically experience negative emotions (Lazarus & Folkman, 1984 ).

When students perceive their school- and homework as stressful, they must do something to cope (Lazarus & Folkman, 1984 ). In this case, the second appraisal becomes salient and intricately interacts with the primary appraisal to shape individuals’ emotional reactions (Lazarus & Folkman, 1984 ). The second appraisal is an evaluation of whether the individual can manage the stressful situation. In other words, what biological, social, and cognitive resources are available to meet and cope with the contextual demands? An example of this evaluation is context-specific self-efficacy (Lazarus, 2006 ). Perceiving a situation as a threat might negatively inform self-efficacy through the affective/physiological state experienced in the specific setting (Bandura, 1997 ). The stressful reaction to school- and homework might decrease self-efficacy in the same context (i.e. academic self-efficacy), resulting in increased psychological distress (Bandura, 1997 ). In contrast, if the academic workload is perceived as challenging, academic self-efficacy might increase due to the positive feelings associated with school-and homework, thus reducing psychological distress.

The transactional relationship between stress, coping, and emotions is a complex system, assumed to be recursive (Lazarus & Folkman, 1987 ). In other words, a precursor might become an outcome and vice versa as time progresses. Therefore, in addition to the assumed associations described above, it could be beneficial to investigate the possible recursive effects over time. Specifically, psychological distress might simultaneously be an outcome and an antecedent of academic stress and academic self-efficacy. Similarly, academic self-efficacy may be an outcome and precursor of academic stress. For example, psychological distress increases stress in general (Bandura, 1997 ; Hammen, 2005 , 2020 ) and reduces academic self-efficacy (Bandura, 1997 ; Grøtan et al., 2019 ; Usher & Pajares, 2008 ), which can result in heightened academic stress (Chee et al., 2019 ; Chemers et al., 2001 ).

Moderating effects

According to Lazarus and Folkman ( 1984 , p. 22), people in different groups have varying degrees of vulnerability and sensitivity to stressors and their understanding and response to them. In support of this assumption in an academic setting, Ye et al. ( 2018 ) found that gender moderated the association between academic stress and later academic self-efficacy. Specifically, they found that the association between academic stress and later academic self-efficacy was more salient for girls than boys. Moreover, studies imply that the relationship between academic stress and psychological distress is stronger for girls than boys in secondary school students (Liu & Lu, 2012 ; Moksnes, Moljord, et al., 2010 ). There is a lack of studies on the possible gender moderation of the relationship between academic self-efficacy and psychological distress. However, many studies have found gender differences in academic self-efficacy, wherein boys generally report higher levels than girls (for an overview, see Huang, 2013 ). These findings might imply the existence of gender differences in the association between academic self-efficacy and other factors.

Current Study

There is a lack of research on the longitudinal relationship between academic stress and psychological distress within adolescents. This study investigates the association between academic stress, academic self-efficacy, and psychological distress on an inter- and intrapersonal level throughout upper secondary school. Additionally, gender differences in these relationships are investigated. The following hypotheses are based on previous research and the assumptions of the transactional theory of stress and coping. First, academic self-efficacy will be negatively related to academic stress and psychological distress, and academic stress and psychological distress will be positively related on an interpersonal level (hypothesis 1). Second, fluctuations in academic stress will predict similar fluctuations in concurrent psychological distress (hypothesis 2). Third, fluctuations in academic self-efficacy will partially explain the association between the fluctuations in academic stress and psychological distress (hypothesis 3). Fourth, the associations between academic stress, academic self-efficacy, and psychological distress will be more salient for girls than boys (hypothesis 4). Fifth, fluctuations in psychological distress will predict opposite fluctuations in subsequent academic self-efficacy (hypothesis 5). Lastly, fluctuations in academic self-efficacy will predict an opposite fluctuation in subsequent academic stress (hypothesis 6). Due to a lack of previous research on the effect of psychological distress on later academic stress, there is no specific hypothesis regarding this relationship. However, the association is investigated in the model.

Procedure and Participants

The participants were part of the COMPLETE project (Larsen et al., 2018 ), a randomised controlled trial aiming to improve the psychosocial learning environment and reduce dropout rates in upper secondary school in Norway. Sixteen schools in four municipalities agreed to participate in the study. The project randomly assigned schools to one of two intervention conditions or the control group. All students who started in August 2016 in the mentioned schools were invited to participate. The participants in this study attended a general education programme, which spans 3 years of upper secondary school from grade 11 through grade 13. The study followed a cohort of students from the beginning to the end of this education. Participants ( N  = 1508) were adolescents who had recently started in grade 11. The respondents completed surveys in August 2016 (start of grade 11), March 2017 (end of grade 11), March 2018 (end of grade 12), and March 2019 (end of grade 13). Students who were part of the same cohort, but were absent at a previous data collection, were allowed to participate in the following data collections throughout the study. Please see Table 3 for more details on the number of participants across time points.

The Norwegian Centre for Research Data (NSD) approved that the COMPLETE project is in line with GDPR. Students under age 16 needed parental/guardian consent before participating, and respondents actively consented to participate. Ahead of participation, the students were informed about the study’s aims. Researchers and research assistants in the project collected survey data using tablets on the school grounds. Students not physically present during data collection were invited to participate via e-mail.

Concerning the participant’s age at baseline, they were 15 ( n  = 425, 28.2%), 16 ( n  = 955, 63.3%), 17 ( n  = 63, 4.2%), 18 ( n  = 23, 1.5%), 19 ( n  = 15, 1%), 20 ( n  = 8, 0.5%), 21 ( n  = 11, 0.7%), 22 ( n  = 4, 0.3%), 23 ( n  = 1, 0.1%), and 24 ( n  = 3, 0.2%) years old. Regarding gender, 60.7% were girls, and 39.3% were boys. The reason for the somewhat unequal distribution of gender is that girls comprise the majority of general education students in Norway. In contrast, approximately nine out of ten students in vocational education are boys (SSB, 2022 ). Most students were born in Norway (70.6%), and 5.5% had an immigrant background. Concerning perceived family wealth, a median split indicated that 52.9% thought their family were in a high socioeconomic position, and 22.5% believed their family were in a low socioeconomic position.

Instruments

• Academic stress

The student’s academic stress was measured using a single indicator from the study ‘Health Behaviour in School-Aged Children (HBSC)’ (Klinger et al., 2015 ; WHO, 2012 ). Participants answered how stressed they felt due to the schoolwork they must do (both work during school hours and homework). The response scale ranged from 1 (not at all) to 4 (a lot).

• Academic self-efficacy

The participants’ academic self-efficacy was assessed using the five-item academic efficacy scale from Patterns of Adaptive Learning Scales (PALS: Midgley et al., 2000 ). The scale is a context-specific measure of how capable individuals perceive themselves to be in performing and mastering schoolwork (i.e. classwork and homework). An item example is ‘even if the work is hard, I can learn it’. The participants responded to the items on a Likert scale ranging from 1 (not at all confident) to 5 (very confident). Earlier studies have found acceptable Cronbach’s alpha values (>0.78) (Midgley et al., 2000 ).

• Psychological distress

Participants’ psychological distress was measured by the Norwegian five-item short version of the Symptom Check List-90-R, based on the anxiety and depression subscales (Tambs & Moum, 1993 ). This measure is not a diagnostic tool for anxiety or depression disorders but a global indicator of mixed anxiety and depressive symptoms (Siqveland et al., 2016 ). Adolescents assessed how bothered or distressed they had been in the last 14 days on a scale ranging from 1 (not at all) to 4 (very much). Example indicators of depression and anxiety are ‘feeling blue and sad’ and ‘feeling tense and worried’, respectively. Previous research indicates acceptable Cronbach’s values (>0.83) (Gjerde et al., 2011 ; Skrove et al., 2013 ; Strand et al., 2003 ; Tambs & Moum, 1993 ).

Gender was retrieved from registry data, coded as 0 (boys) and 1 (girls). Of note, participants also answered a question on gender identification (female, male, or other) in the questionnaires. However, very few respondents identified as non-cis or other-gendered (14 respondents on baseline). Thus, multigroup comparisons were not viable using all groups (cis females, cis males, non-cis females, non-cis males, and other-gendered).

Control variables

The following variables were included as time-invariant covariates in the model. Two dummy variables were created based on intervention conditions —participants were either in an intervention group (coded as 1) or not (coded as 0). The study measured socioeconomic position using a single indicator question on perceived family wealth (Iversen & Holsen, 2008 ), which was dummy coded as 0 (low) and 1 (high) by a median split. Regarding country of origin, Norwegian-born participants were coded as 0, and participants born outside of Norway were coded as 1.

Analytical Plan

Preliminary analyses.

Initial analyses investigated omega reliability, descriptive statistics, and correlations using SPSS version 28 (IBM corp, 2021 ). M plus version 8 (Muthén & Muthén, 1998 – 2017 ) and maximum likelihood (ML) estimation were used for structural equation modelling (SEM). Several criteria were used to assess the model fit of the SEM models. Model fit was considered acceptable if CFI > 0.90, RMSEA < 0.08, and SRMR < 0.08 (Byrne, 2012 ; Hu & Bentler, 1999 ). When investigating measurement invariance, the following fit criteria were used between comparison and nested models: ΔCFI < 0.010, ΔRMSEA < 0.015, and ΔSRMR < 0.030 (Chen, 2007 ).

This study investigated measurement invariance across time and gender using the effects-coding approach by Little et al. ( 2006 ), which is preferable to other methods (Breitsohl, 2019 ). In effects-coding, the average factor loadings across all indicators are constrained to 1.0, and the sum of the indicator intercepts is constrained to 0.0. The configural models were otherwise freely estimated. Equal factor loading constraints were applied across time and gender to establish metric (weak) invariance for the multiple indicator RI-CLPM (Hamaker, 2018 ). The invariance constraints were retained in further modelling. The academic self-efficacy and psychological distress scales achieved partial weak invariance, wherein at least two indicators of each scale were invariant over time and gender (Byrne et al., 1989 ). For space constraints, the measurement invariance results are presented in Table 2 .

Primary analyses

The random intercept cross-lagged panel model (RI-CLPM) with academic stress, academic self-efficacy, and psychological distress was modelled following the approaches by Hamaker ( 2018 ) and Mulder and Hamaker ( 2021 ). First, each construct’s random intercept (interindividual, trait-like components) was specified by adding regression coefficients from the intercepts to corresponding latent factors at each time point, constrained to 1.0. Second, 12 second-order latent factors (state-like components) were specified (one latent factor for each of the four time points in three constructs), with regression coefficients to corresponding first-order latent factors constrained to 1.0. Third, to ensure the random intercepts and within-person variables capture all variance, the variances of the first-order latent factors were constrained to 0.0. Lastly, socioeconomic position, gender, country of origin, and intervention conditions were added as control variables in the model, regressed on the random intercepts.

Academic stress was specified as a predictor of concurrent academic self-efficacy and psychological distress on an intraindividual level throughout the study period (see Fig. 1 for model specification), mainly because the first and second stress appraisals happen roughly simultaneously within individuals (Lazarus & Folkman, 1984 ). Further, the effect of academic self-efficacy on later academic stress and the impact of psychological distress on subsequent academic self-efficacy and academic stress was examined. A freely estimated model was compared to a time-invariant model (i.e. coefficients are equal over time). The time-invariant constraints were retained if the model fit did not significantly deteriorate the chi-square. If the constraints significantly deteriorated model fit, the constraints were not tenable and removed. Next, this study examined the academic self-efficacy mediation between academic stress and psychological distress using the “model indirect” syntax in M plus .

Model Specification of the Random Intercept Cross-lagged Panel Model of Academic Stress, Academic Self-efficacy, and Psychological Distress. IC = intervention condition, CO = country of origin, G = gender, SEP = socioeconomic position, PD = psychological distress, ASE = academic self-efficacy, AS = academic stress

To investigate if gender moderated the effects in the RI-CLPM, time-invariant constraints were initially investigated for both genders separately. Then, a multigroup analysis on the RI-CLPM with 1000 bootstraps using gender as a grouping variable was conducted, and the model constraint function in M plus was used to compare estimates across groups.

Missingness

According to Little’s missing completely at random (MCAR) test, the patterns of missingness in the study’s variables were completely random ( χ 2 = 3092.302, df = 3031, p  = 0.215). Full information maximum likelihood (FIML) was used to handle potential missing data at the construct level (Newman, 2014 ). Detailed information regarding the number of respondents across time is in Table 3 .

Descriptive Statistics

The descriptive statistics of the study variables are presented in Table 1 . There were significant gender differences in all variables. Girls experienced significantly higher academic stress, psychological distress, and lower academic self-efficacy at all times than boys. The gender differences in terms of effect sizes were, according to Cohen ( 1988 ), moderate to large concerning academic stress, moderate regarding psychological distress, and negligible to small concerning academic self-efficacy.

Random Intercept Cross-lagged Panel Model of Academic Stress, Academic Self-efficacy, and Psychological Distress

The RI-CLPM of academic stress, academic self-efficacy, and psychological distress produced good model fit: χ 2  = 2241.786, df = 1031, p  < 0.001, RMSEA [95% CI] = 0.032 [0.030, 0.034], CFI = 0.954, SRMR = 0.039. The model included metric invariance constraints and socioeconomic position, country of origin, gender, and intervention conditions as time-invariant covariates. Next, a fully time-invariant model with identical constraints on the regression coefficients over time was investigated. A chi-square difference test showed that the model fit significantly deteriorated (Δ χ 2  = 40.658, Δdf = 21, p  = 0.006). The autoregressive constraints were removed, and the time-invariant, cross-lagged constraint model was compared to the freely estimated model. The model fit did not significantly deteriorate: Δ χ 2  = 24.326 Δdf = 15, p  = 0.060. Therefore, the constraints were deemed tenable, and the partially time-invariant model produced good fit ( χ 2  = 2266.112, df = 1046, p  < 0.001, RMSEA [95% CI] = 0.032 [0.030, 0.034], CFI = 0.954, SRMR = 0.040). The results are presented in Fig. 2 , and more details are in table 4 .

Random Intercept Cross-lagged Panel Model of Academic Stress, Academic Self-efficacy, and Psychological Distress. Standardised estimates are presented. The grey lines are non-significant. *** p  < 0.001, ** p  < 0.01

In support of hypothesis one, the correlation between academic stress and psychological distress was positive and moderate in effect size at the interindividual level (i.e. the random intercepts) ( r  = 0.49, p  < 0.001). Moreover, the interindividual association between psychological distress and academic self-efficacy was negative and moderate ( r  = −0.38, p  < 0.001). Lastly, the correlation between academic self-efficacy and academic stress intercepts was negative and small ( r  = −0.28, p  < 0.001). Thus, adolescents who experienced high academic stress throughout their upper secondary school education were also likely to experience high psychological distress and low academic self-efficacy during the same time. Additionally, individuals likely experienced opposite levels of psychological distress and academic self-efficacy during this period.

The autoregressive regression coefficients were positive and significant in academic stress from T1 to T2 ( β  = 0.14, p  < 0.01), T2 to T3 ( β  = 0.29, p  < 0.001), and T3 to T4 ( β  = 0.22, p  < 0.001). Similarly, there were positive and significant carry-over stability effects in academic self-efficacy from T1 to T2 ( β  = 0.36, p  < 0.001), T2 to T3 ( β  = 0.44, p  < 0.001), and T3 to T4 ( β  = 0.22, p  < 0.001). Lastly, fluctuations in psychological distress were positively and significantly associated with later fluxes in psychological distress from T1 to T2 ( β  = 0.33, p  < 0.001), T2 to T3 ( β  = 0.30, p  < 0.001), and T3 to T4 ( β  = 0.42, p  < 0.001). Thus, adolescents were increasingly likely to experience similar fluctuations at approximate time points in all three constructs.

In support of hypothesis two, individuals with a deviating level of academic stress were increasingly likely to experience the opposite deviation in concurrent academic self-efficacy on T1 ( β  = −0.18, p  < 0.001), T2 ( β  = −0.17, p  < 0.001), T3 ( β  = −0.17, p  < 0.001), and T4 ( β  = −0.12, p  < 0.001). In addition, fluctuations in academic stress were positively and significantly related to changes in concurrent psychological distress on T1 ( β  = 0.30, p  < 0.001), T2 ( β  = 0.31, p  < 0.001), T3 ( β  = 0.30, p  < 0.001), and T4 ( β  = 0.25, p  < 0.001).

Fluctuations in academic self-efficacy were predictive of oppositional fluctuations in concurrent psychological distress on T1 ( β  = −0.09, p  < 0.001), T2 ( β  = −0.09, p  < 0.001), T3 ( β  = −0.09, p  < 0.001), and T4 ( β  = −0.10, p  < 0.001). Supporting hypothesis three, the results showed that academic self-efficacy partially mediated the time-invariant association between concurrent academic stress and psychological distress on T1 ( β  = 0.02, p  < 0.01), T2 ( β  = 0.02, p  < 0.01), T3 ( β  = 0.02, p  < 0.01), and T4 ( β  = 0.01, p  < 0.01).

There was no support for hypotheses five or six. The results indicated a null effect between psychological distress and later academic self-efficacy. Similarly, academic self-efficacy did not impact later academic stress. However, the impact of psychological distress on subsequent academic stress was positive from T1 to T2 ( β  = 0.16, p  < 0.001), T2 to T3 ( β  = 0.15, p  < 0.001), and T3 to T4 ( β  = 0.19, p  < 0.001). Thus, fluctuations in psychological distress were consistently associated with similar fluxes in academic stress approximately 1 year later throughout the study.

Gender moderation model

Before the moderation analysis of the RI-CLPM of academic stress, academic self-efficacy, and psychological distress, the appropriateness of the time-invariant constraints enforced in the mediation model was separately examined for boys and girls. The chi-square in the freely estimated RI-CLPMs was compared to the chi-square in the time-invariant constraint models in both genders. The chi-square difference tests were non-significant for both genders ( p  > 0.05), indicating that the time-invariant constraints were tenable. Thus, the following nine parameters between boys and girls were compared: three intercept correlation coefficients and six time-invariant regression coefficients (academic stress on concurrent academic self-efficacy and psychological distress; academic self-efficacy on concurrent psychological distress; psychological distress on subsequent academic self-efficacy and academic stress; academic self-efficacy on subsequent academic stress).

The gender moderation RI-CLPM of academic stress, academic self-efficacy, and psychological distress achieved acceptable model fit: χ 2  = 3727.383, df = 2059, p  < 0.001, RMSEA [95% CI] = 0.038 [0.036, 0.040], CFI = 0.933, SRMR = 0.057. The results are presented in Fig. 3 and table 5 . In partial support of the fourth hypothesis, three parameters significantly differed across gender: the intercept correlation between academic stress and academic self-efficacy ( r difference  = 0.086, p  = 0.025), the intercept correlation between psychological distress and academic stress ( r difference  = –0.082, p  = 0.044), and the time-invariant regression coefficient from academic stress to concurrent psychological distress (B difference  = 0.164, p  = 0.000). Of note, the difference tests consider unstandardised estimates, while Fig. 3 shows the standardised results. Please see table 5 for further details on model estimates.

Random Intercept Cross-lagged Panel Model of Academic Stress, Academic Self-efficacy, and Psychological Distress Moderated by Gender. Boys on the upper line and girls on the lower line. Standardised estimates are presented. The grey lines are non-significant. *** p  < 0.001, ** p  < 0.01, * p  < 0.05

The significance of the indirect effects of academic stress on concurrent psychological distress through academic self-efficacy disappeared in the moderation analysis. There were no apparent gender differences in these effects (see table 6 for details). However, the 95% confidence interval of the indirect effect did not include zero for both genders. Thus, the mediation effect, albeit small, might still be relevant for both genders despite the lack of a significant p value.

There was a significantly stronger intercept correlations between academic stress and psychological distress for boys ( r  = 0.57, p  < 0.001) than girls ( r  = 0.37, p  > 0.05). Additionally, the interindividual association between academic stress and academic self-efficacy was significantly stronger for boys ( r  = −0.50, p  < 0.001) than girls ( r  = −0.08, p  > 0.05). Hence, boys who experienced a high (or low) level of academic stress in late secondary school were more likely to experience a similar level of psychological distress and oppositional level academic self-efficacy during the same time compared to girls. Girls had significantly larger direct effects from academic stress to concurrent psychological distress (T1: β  = 0.34, p  < 0.001; T2: β  = 0.34, p  < 0.001; T3: β  = 0.33, p  < 0.001; and T4: β  = 0.30, p  < 0.001) than boys (T1: β  = 0.19, p  < 0.001; T2: β  = 0.25, p  < 0.001; T3: β  = 0.20, p  < 0.001; and T4: β  = 0.14, p  < 0.001). Thus, girls with unusually high (or low) academic stress at each time point were more likely to experience unusually high (or low) psychological distress concurrently than boys.

Sensitivity Analyses

This study investigated several competing models, such as different time lags between the constructs, and examined the impact of missingness on the selected model. The final model was chosen because (1) the theoretical assumptions of the transactional theory of stress and coping argue that the first and second appraisals occur simultaneously, and (2) the AIC and BIC values in the final model were lower than competing models. Regarding missingness, the final model was compared across three groups in our sample: participants with complete data (no missingness), participants with intermittent missing data patterns (non-dropouts), and all participants. The models produced similar patterns of results in terms of coefficients and standard errors.

Few or none have investigated the associations between academic stress and psychological distress while separating inter- and intrapersonal effects. Consequently, there is little knowledge of possible explanatory mechanisms or moderators in the mentioned association on an intraindividual level. This study sought to fill that knowledge gap. The results implied that, during upper secondary school, the normative levels of academic stress, academic self-efficacy, and psychological distress were associated. Further, that academic stress consistently predicted psychological distress throughout the study and that academic self-efficacy partially mediated this relationship. Recursively, psychological distress impacted later academic stress. Lastly, the intraindividual association between academic stress and psychological distress was stronger for girls, while the interpersonal associations between academic stress, academic self-efficacy, and psychological distress were stronger for boys.

The Longitudinal Associations Between Academic Stress, Academic self-efficacy, and Psychological Distress

Aligning with the assumptions in the transactional theory of stress and coping (Lazarus & Folkman, 1984 ) and previous research, the association between academic stress and psychological distress was positive within adolescents during upper secondary school. Adolescents with, for them, unusually high (or low) academic stress at one time were increasingly likely to experience unusually high (or low) psychological distress simultaneously. Moreover, fluctuations in psychological distress were related to similar fluxes in academic stress on the following occasions. These findings indicate that interventions successful in decreasing levels of academic stress and psychological distress (e.g. Feiss et al., 2019 ) might lower levels in the other factor concurrently and over time, respectively. However, it might be beneficial for implementation research to investigate the effect of school-based measures on the intraindividual association between academic stress and psychological distress. For instance, are interventions designed on an interpersonal level effective in reducing unusually high academic stress or psychological distress at the intraindividual level? Such research might further important knowledge in the field.

Academic self-efficacy functioned as a mechanism, partially explaining the concurrent relationship between academic stress and psychological distress within adolescents over time. Indeed, fluctuations in academic stress were related to oppositional fluctuations in academic self-efficacy and similar fluxes in psychological distress simultaneously. This effect aligns with central assumptions on how self-efficacy changes within individuals, wherein adverse feelings in certain situations decrease self-efficacy in the same settings (Bandura, 1997 ). Because stress, as measured in this study, is an inherently negative affective state, the reduction in self-efficacy for the same context that induced the negative feeling has been explored in many instances (for an overview, see Usher & Pajares, 2008 ). However, the finding that fluctuations in academic self-efficacy partly explain changes in psychological distress during fluxes in academic stress is novel. Theoretical or conceptual models of stress and mental health problems might include this mechanism in adolescent samples. Even though the transactional theory of stress and coping (Lazarus & Folkman, 1984 ) and self-efficacy theory (Bandura, 1997 ) describe processes occurring within individuals, such as cognitive evaluations and change, and emotional responses, the frameworks have used research on the interpersonal level to postulate intraindividual psychological developments.

The impact of psychological distress on later academic stress was positive. Hence, fluctuations in psychological distress were associated with similar changes in academic stress ~1 year later throughout the study period. Little research has focused on the impact psychological distress has on academic stress, mainly because academic stress is assumed to be an antecedent in the relationship between the two (e.g. Murberg & Bru, 2005 ; Tian et al., 2019 ). However, psychologically distressed individuals often behave in manners that create situations they perceive as stressful (Hammen, 2020 ). The findings in this study suggest that this effect might also apply to the educational setting, particularly the perception of school- and homework as stressful. In other words, due to an unexpected rise in psychological distress, students might behave in ways that increase the likelihood of experiencing the school- and homework as stressful later. It is possible that unusually psychologically distressed students postpone or avoid the academic workload or even physically withdraw from school. Such behaviour might result in perceiving school- and homework as a threat instead of challenging, positive, or irrelevant to personal well-being. Thus, an adverse loop of school-related stress and hopelessness, sadness, and worry might arise.

Gender Differences

Regarding gender differences, fluctuations in academic stress were more strongly associated with concurrent fluxes in psychological distress for girls than boys. The stronger intraindividual association for girls might be related to the academic pressure and demands girls perceive by others and themselves. For example, girls experience more pressures and expectations concerning their school performances (Gådin & Hammarström, 2000 ) and are more worried and affected by the beliefs and judgments of other people (Rudolph, 2002 ) than boys. Indeed, one report indicated that 39% of Norwegian girls, compared to 14% of boys, who experienced school-related stress “very often” also felt “very bothered” by symptoms of anxiety and depression (Eriksen et al., 2017 ). On the other hand, academic stress was significantly more strongly related to academic self-efficacy and psychological distress on an interindividual level throughout upper secondary school for boys than girls.

Limitations

One limitation is that the sample is not nationally representative. However, the participants have typical characteristics of Norwegian and Western cultures, and the results are likely transferable to other late secondary school samples similar in age and demographics.

Another possible limitation is the single-item measurement of academic stress. Single-item measures have uncertain reliability and might not adequately capture a complex psychological construct (Allen et al., 2022 ). A latent factor with several indicators might have provided more information concerning academic stress as a construct. However, the single indicator has been validated previously and functions well as a measure of academic stress (Klinger et al., 2015 ). Additionally, based on comparisons with negative stress items in stress scales, such as the perceived stress scale (Cohen et al., 1983 ) and the educational stress scale for adolescents (Sun et al., 2011 ), the included indicator is expected to have strong face validity. The bivariate correlations between the indicator across time points were moderate to strong in effect size, according to Cohen ( 1988 ), ranging from r  = 0.36 ( p  < 0.001) to r  = 0.55 ( p  < 0.001).

Any bias associated with self-report measures, such as common method bias (Doty & Glick, 1998 ) or under- and overreporting (Hunt et al., 2003 ; Sigmon et al., 2005 ), might be considered another limitation, as all data was self-reported in this study. Regarding underreporting, one study found that the difference between self-reported and administrative health service data on mood and anxiety disorders has decreased over time, particularly in adolescence (O’Donnell et al., 2016 ). This finding might indicate improved mental health literacy or a positive societal change in the perceptions of mental health, such as reduced stigma (O’Donnell et al., 2016 ). Concerning common method bias, a post hoc Harman’s single factor test was performed on each time point to investigate if a latent factor was accountable for the variance in the study’s data (Chang et al., 2010 ). The results showed that a single factor did not account for the majority of the variance, and several factor solutions were more appropriate for each measurement occasion.

Lastly, the mediating effect of academic self-efficacy between academic stress and psychological distress was small. Therefore, caution in interpreting this finding is advised. However, within-person effects tend to be smaller than effects that include both between- and within-person variances. Furthermore, the model controls for prior levels of the predictive variables. Thus, the mediation effect is relevant even though it is small.

Future Directions

Academic self-efficacy was only a partial mediator in the concurrent association between academic stress and psychological distress, implying it only explains parts of the relationship. Future research should include other relevant mediators between stress and psychological distress (e.g. coping mechanisms) in a school setting to further unravel these associations over time. Notably, researchers are encouraged to separate between- and within-person effects to truly parse the associations between academic stress and adolescent psychological distress. Moreover, when investigating the associations between academic stress, academic self-efficacy (or other mediators), and psychological distress, researchers should consider the effect of gender.

There is a research gap on explanatory mechanisms and moderators in the intraindividual relationship between academic stress and psychological distress during adolescence. This study aimed to fill this gap. Specifically, the inter- and intraindividual associations between academic stress, academic self-efficacy, and psychological distress, and possible gender differences in these relationships, were investigated in an upper secondary school cohort. The results showed that academic stress, directly and indirectly through academic self-efficacy, impacted concurrent psychological distress consistently during 3 years in mid-late adolescence. Psychological distress systematically affected later academic stress. Intraindividual effects were more salient for girls, and interindividual effects were stronger for boys. The study findings imply the existence of an exacerbating feedback loop between academic stress and psychological distress in upper secondary school, which functions differently for boys and girls and is partly explained by fluctuations in academic self-efficacy.

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This study is part of the COMPLETE project ( https://complete.w.uib.no/ ) and has received funding from the Norwegian Ministry of Education and Research (20161789).

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S.M.K. conceived of the study, participated in its design and coordination, drafted the paper, performed the statistical analyses, and interpreted the data; T.B.L. conceived of the study, participated in its design and coordination, and helped draft the paper; H.B.U. conceived of the study, participated in its design and coordination, and helped draft the paper; A.G.D. conceived of the study, participated in its design and coordination, and helped draft the paper. All authors read and approved the final paper.

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Kristensen, S.M., Larsen, T.M.B., Urke, H.B. et al. Academic Stress, Academic Self-efficacy, and Psychological Distress: A Moderated Mediation of Within-person Effects. J Youth Adolescence 52 , 1512–1529 (2023). https://doi.org/10.1007/s10964-023-01770-1

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The Science Behind Student Stress

A new study shows how a growth mindset helps students cope with academic setbacks.

A new study finds that when students experience an academic setback such as a bad grade, the amount of cortisol—the so-called stress hormone—in their bodies typically spikes. For most students it drops back down to normal levels a day later, but for some it stays high. These students remain fixated on the setback and have difficulty moving forward.

The researchers analyzed the stress levels of students at two high schools in central Texas during an especially stressful time—the transition into high school. Students completed daily surveys asking about the stress they experienced, and daily saliva samples were collected to measure their cortisol levels.

A majority of these students—68 percent—experienced a drop in grades in the first semester and reported feeling stressed as a result. In how they handled that stress, two clear groups emerged. Students who believed that intelligence can be developed—a growth mindset—were more likely to see setbacks as temporary, and not only had lower overall cortisol levels but were able to return to lower levels shortly after a setback. Students who believed that intelligence is fixed, on the other hand, maintained high cortisol level for longer, said researchers—a stress response that tends to depress problem solving and intellectual flexibility.

“Declining grades may get ‘under the skin,’ as it were, for first-year high school students who believe intelligence is a fixed trait,” explains Hae Yeon Lee, the study’s lead author. “But believing, instead, that intelligence can be developed—or having what is called a growth mindset—may buffer the effects of academic stress.” The researchers speculate that students with a growth mindset may be more likely to seek out "resources to help them cope—such as talking with teachers, peers, or parents about how to study more effectively."

Stress isn’t always bad. Cortisol increases blood sugar, metabolism, and memory function, providing a temporary boost to physical and cognitive ability, and positive stress—called eustress —can boost motivation and decision-making, helping students achieve goals. The stress experienced over an upcoming test is a reminder to study, a way of raising the stakes so that students recognize the importance of being prepared.

But with chronic stress , high cortisol levels can instead impair brain functioning and suppress the immune system, causing long-term damage. During childhood, the neural circuits for dealing with stress are malleable, and chronic stress can rewire the brain to become overly reactive or slow to shut down when faced with threats. So too much stress can disrupt normal brain development and increase the risk of diseases even into adulthood, according to a 2014 Harvard report .

What can schools do to help? “For many young people, the transition to high school can seem like the start of a stressful, seemingly endless marathon,” the researchers write. They recommend that in addition to helping students develop a growth mindset, schools pay closer attention to the demands that students face in ninth grade, and provide more academic and emotional support during this transition year.

The takeaway: Stressed-out students aren’t thinking about solutions. If you want students to learn from their mistakes and overcome obstacles, think about ways to encourage them to adopt a growth mindset .

ORIGINAL RESEARCH article

Academic stress and mental well-being in college students: correlations, affected groups, and covid-19.

• 1 Department of Neurology, Rutgers New Jersey Medical School, Newark, NJ, United States
• 2 Rutgers New Jersey Medical School, Newark, NJ, United States
• 3 Office for Diversity and Community Engagement, Rutgers New Jersey Medical School, Newark, NJ, United States
• 4 Department of Biology, The College of New Jersey, Ewing, NJ, United States

Academic stress may be the single most dominant stress factor that affects the mental well-being of college students. Some groups of students may experience more stress than others, and the coronavirus disease 19 (COVID-19) pandemic could further complicate the stress response. We surveyed 843 college students and evaluated whether academic stress levels affected their mental health, and if so, whether there were specific vulnerable groups by gender, race/ethnicity, year of study, and reaction to the pandemic. Using a combination of scores from the Perception of Academic Stress Scale (PAS) and the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), we found a significant correlation between worse academic stress and poor mental well-being in all the students, who also reported an exacerbation of stress in response to the pandemic. In addition, SWEMWBS scores revealed the lowest mental health and highest academic stress in non-binary individuals, and the opposite trend was observed for both the measures in men. Furthermore, women and non-binary students reported higher academic stress than men, as indicated by PAS scores. The same pattern held as a reaction to COVID-19-related stress. PAS scores and responses to the pandemic varied by the year of study, but no obvious patterns emerged. These results indicate that academic stress in college is significantly correlated to psychological well-being in the students who responded to this survey. In addition, some groups of college students are more affected by stress than others, and additional resources and support should be provided to them.

Introduction

Late adolescence and emerging adulthood are transitional periods marked by major physiological and psychological changes, including elevated stress ( Hogan and Astone, 1986 ; Arnett, 2000 ; Shanahan, 2000 ; Spear, 2000 ; Scales et al., 2015 ; Romeo et al., 2016 ; Barbayannis et al., 2017 ; Chiang et al., 2019 ; Lally and Valentine-French, 2019 ; Matud et al., 2020 ). This pattern is particularly true for college students. According to a 2015 American College Health Association-National College Health Assessment survey, three in four college students self-reported feeling stressed, while one in five college students reported stress-related suicidal ideation ( Liu, C. H., et al., 2019 ; American Psychological Association, 2020 ). Studies show that a stressor experienced in college may serve as a predictor of mental health diagnoses ( Pedrelli et al., 2015 ; Liu, C. H., et al., 2019 ; Karyotaki et al., 2020 ). Indeed, many mental health disorders, including depression, anxiety, and substance abuse disorder, begin during this period ( Blanco et al., 2008 ; Pedrelli et al., 2015 ; Saleh et al., 2017 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ).

Stress experienced by college students is multi-factorial and can be attributed to a variety of contributing factors ( Reddy et al., 2018 ; Karyotaki et al., 2020 ). A growing body of evidence suggests that academic-related stress plays a significant role in college ( Misra and McKean, 2000 ; Dusselier et al., 2005 ; Elias et al., 2011 ; Bedewy and Gabriel, 2015 ; Hj Ramli et al., 2018 ; Reddy et al., 2018 ; Pascoe et al., 2020 ). For instance, as many as 87% of college students surveyed across the United States cited education as their primary source of stress ( American Psychological Association, 2020 ). College students are exposed to novel academic stressors, such as an extensive academic course load, substantial studying, time management, classroom competition, financial concerns, familial pressures, and adapting to a new environment ( Misra and Castillo, 2004 ; Byrd and McKinney, 2012 ; Ekpenyong et al., 2013 ; Bedewy and Gabriel, 2015 ; Ketchen Lipson et al., 2015 ; Pedrelli et al., 2015 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Academic stress can reduce motivation, hinder academic achievement, and lead to increased college dropout rates ( Pascoe et al., 2020 ).

Academic stress has also been shown to negatively impact mental health in students ( Li and Lin, 2003 ; Eisenberg et al., 2009 ; Green et al., 2021 ). Mental, or psychological, well-being is one of the components of positive mental health, and it includes happiness, life satisfaction, stress management, and psychological functioning ( Ryan and Deci, 2001 ; Tennant et al., 2007 ; Galderisi et al., 2015 ; Trout and Alsandor, 2020 ; Defeyter et al., 2021 ; Green et al., 2021 ). Positive mental health is an understudied but important area that helps paint a more comprehensive picture of overall mental health ( Tennant et al., 2007 ; Margraf et al., 2020 ). Moreover, positive mental health has been shown to be predictive of both negative and positive mental health indicators over time ( Margraf et al., 2020 ). Further exploring the relationship between academic stress and mental well-being is important because poor mental well-being has been shown to affect academic performance in college ( Tennant et al., 2007 ; Eisenberg et al., 2009 ; Freire et al., 2016 ).

Perception of academic stress varies among different groups of college students ( Lee et al., 2021 ). For instance, female college students report experiencing increased stress than their male counterparts ( Misra et al., 2000 ; Eisenberg et al., 2007 ; Evans et al., 2018 ; Lee et al., 2021 ). Male and female students also respond differently to stressors ( Misra et al., 2000 ; Verma et al., 2011 ). Moreover, compared to their cisgender peers, non-binary students report increased stressors and mental health issues ( Budge et al., 2020 ). The academic year of study of the college students has also been shown to impact academic stress levels ( Misra and McKean, 2000 ; Elias et al., 2011 ; Wyatt et al., 2017 ; Liu, C. H., et al., 2019 ; Defeyter et al., 2021 ). While several studies indicate that racial/ethnic minority groups of students, including Black/African American, Hispanic/Latino, and Asian American students, are more likely to experience anxiety, depression, and suicidality than their white peers ( Lesure-Lester and King, 2004 ; Lipson et al., 2018 ; Liu, C. H., et al., 2019 ; Kodish et al., 2022 ), these studies are limited and often report mixed or inconclusive findings ( Liu, C. H., et al., 2019 ; Kodish et al., 2022 ). Therefore, more studies should be conducted to address this gap in research to help identify subgroups that may be disproportionately impacted by academic stress and lower well-being.

The coronavirus disease 19 (COVID-19) pandemic is a major stressor that has led to a mental health crisis ( American Psychological Association, 2020 ; Dong and Bouey, 2020 ). For college students, the COVID-19 pandemic has resulted in significant changes and disruptions to daily life, elevated stress levels, and mental and physical health deterioration ( American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ). While any college student is vulnerable to these stressors, these concerns are amplified for members of minority groups ( Salerno et al., 2020 ; Clabaugh et al., 2021 ; McQuaid et al., 2021 ; Prowse et al., 2021 ; Kodish et al., 2022 ). Identifying students at greatest risk provides opportunities to offer support, resources, and mental health services to specific subgroups.

The overall aim of this study was to assess academic stress and mental well-being in a sample of college students. Within this umbrella, we had several goals. First, to determine whether a relationship exists between the two constructs of perceived academic stress, measured by the Perception of Academic Stress Scale (PAS), and mental well-being, measured by the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), in college students. Second, to identify groups that could experience differential levels of academic stress and mental health. Third, to explore how the perception of the ongoing COVID-19 pandemic affected stress levels. We hypothesized that students who experienced more academic stress would have worse psychological well-being and that certain groups of students would be more impacted by academic- and COVID-19-related stress.

Materials and Methods

Survey instrument.

A survey was developed that included all questions from the Short Warwick-Edinburgh Mental Well-Being ( Tennant et al., 2007 ; Stewart-Brown and Janmohamed, 2008 ) and from the Perception of Academic Stress Scale ( Bedewy and Gabriel, 2015 ). The Short Warwick-Edinburgh Mental Well-Being Scale is a seven-item scale designed to measure mental well-being and positive mental health ( Tennant et al., 2007 ; Fung, 2019 ; Shah et al., 2021 ). The Perception of Academic Stress Scale is an 18-item scale designed to assess sources of academic stress perceived by individuals and measures three main academic stressors: academic expectations, workload and examinations, and academic self-perceptions of students ( Bedewy and Gabriel, 2015 ). These shorter scales were chosen to increase our response and study completion rates ( Kost and de Rosa, 2018 ). Both tools have been shown to be valid and reliable in college students with Likert scale responses ( Tennant et al., 2007 ; Bedewy and Gabriel, 2015 ; Ringdal et al., 2018 ; Fung, 2019 ; Koushede et al., 2019 ). Both the SWEMWBS and PAS scores are a summation of responses to the individual questions in the instruments. For the SWEMWBS questions, a higher score indicates better mental health, and scores range from 7 to 35. Similarly, the PAS questions are phrased such that a higher score indicates lower levels of stress, and scores range from 18 to 90. We augmented the survey with demographic questions (e.g., age, gender, and race/ethnicity) at the beginning of the survey and two yes/no questions and one Likert scale question about the impact of the COVID-19 pandemic at the end of our survey.

Participants for the study were self-reported college students between the ages of 18 and 30 years who resided in the United States, were fluent in English, and had Internet access. Participants were solicited through Prolific ( https://prolific.co ) in October 2021. A total of 1,023 individuals enrolled in the survey. Three individuals did not agree to participate after beginning the survey. Two were not fluent in English. Thirteen individuals indicated that they were not college students. Two were not in the 18–30 age range, and one was located outside of the United States. Of the remaining individuals, 906 were full-time students and 96 were part-time students. Given the skew of the data and potential differences in these populations, we removed the part-time students. Of the 906 full-time students, 58 indicated that they were in their fifth year of college or higher. We understand that not every student completes their undergraduate studies in 4 years, but we did not want to have a mixture of undergraduate and graduate students with no way to differentiate them. Finally, one individual reported their age as a non-number, and four individuals did not answer a question about their response to the COVID-19 pandemic. This yielded a final sample of 843 college students.

Data Analyses

After reviewing the dataset, some variables were removed from consideration due to a lack of consistency (e.g., some students reported annual income for themselves and others reported family income) or heterogeneity that prevented easy categorization (e.g., field of study). We settled on four variables of interest: gender, race/ethnicity, year in school, and response to the COVID-19 pandemic ( Table 1 ). Gender was coded as female, male, or non-binary. Race/ethnicity was coded as white or Caucasian; Black or African American; East Asian; Hispanic, Latino, or of Spanish origin; or other. Other was used for groups that were not well-represented in the sample and included individuals who identified themselves as Middle Eastern, Native American or Alaskan Native, and South Asian, as well as individuals who chose “other” or “prefer not to answer” on the survey. The year of study was coded as one through four, and COVID-19 stress was coded as two groups, no change/neutral response/reduced stress or increased stress.

Table 1 . Characteristics of the participants in the study.

Our first goal was to determine whether there was a relationship between self-reported academic stress and mental health, and we found a significant correlation (see Results section). Given the positive correlation, a multivariate analysis of variance (MANOVA) with a model testing the main effects of gender, race/ethnicity, and year of study was run in SPSS v 26.0. A factorial MANOVA would have been ideal, but our data were drawn from a convenience sample, which did not give equal representation to all groupings, and some combinations of gender, race/ethnicity, and year of study were poorly represented (e.g., a single individual). As such, we determined that it would be better to have a lack of interaction terms as a limitation to the study than to provide potentially spurious results. Finally, we used chi-square analyses to assess the effect of potential differences in the perception of the COVID-19 pandemic on stress levels in general among the groups in each category (gender, race/ethnicity, and year of study).

In terms of internal consistency, Cronbach's alpha was 0.82 for the SMEMWBS and 0.86 for the PAS. A variety of descriptors have been applied to Cronbach's alpha values. That said, 0.7 is often considered a threshold value in terms of acceptable internal consistency, and our values could be considered “high” or “good” ( Taber, 2018 ).

The participants in our study were primarily women (78.5% of respondents; Table 1 ). Participants were not equally distributed among races/ethnicities, with the majority of students selecting white or Caucasian (66.4% of responders; Table 1 ), or years of study, with fewer first-year students than other groups ( Table 1 ).

Students who reported higher academic stress also reported worse mental well-being in general, irrespective of age, gender, race/ethnicity, or year of study. PAS and SWEMWBS scores were significantly correlated ( r = 0.53, p < 0.001; Figure 1 ), indicating that a higher level of perceived academic stress is associated with worse mental well-being in college students within the United States.

Figure 1 . SWEMWBS and PAS scores for all participants.

Among the subgroups of students, women, non-binary students, and second-year students reported higher academic stress levels and worse mental well-being ( Table 2 ; Figures 2 – 4 ). In addition, the combined measures differed significantly between the groups in each category ( Table 2 ). However, as measured by partial eta squared, the effect sizes were relatively small, given the convention of 0.01 = small, 0.06 = medium, and 0.14 = large differences ( Lakens, 2013 ). As such, there were only two instances in which Tukey's post-hoc tests revealed more than one statistical grouping ( Figures 2 – 4 ). For SWEMWBS score by gender, women were intermediate between men (high) and non-binary individuals (low) and not significantly different from either group ( Figure 2 ). Second-year students had the lowest PAS scores for the year of study, and first-year students had the highest scores. Third- and fourth-year students were intermediate and not statistically different from the other two groups ( Figure 4 ). There were no pairwise differences in academic stress levels or mental well-being among racial/ethnic groups.

Table 2 . Results of the MANOVA.

Figure 2 . SWEMWBS and PAS scores according to gender (mean ± SEM). Different letters for SWEMWBS scores indicate different statistical groupings ( p < 0.05).

Figure 3 . SWEMWBS and PAS scores according to race/ethnicity (mean ± SEM).

Figure 4 . SWEMWBS and PAS scores according to year in college (mean ± SEM). Different letters for PAS scores indicate different statistical groupings ( p < 0.05).

The findings varied among categories in terms of stress responses due to the COVID-19 pandemic ( Table 3 ). For gender, men were less likely than women or non-binary individuals to report increased stress from COVID-19 (χ 2 = 27.98, df = 2, p < 0.001). All racial/ethnic groups responded similarly to the pandemic (χ 2 = 3.41, df = 4, p < 0.49). For the year of study, first-year students were less likely than other cohorts to report increased stress from COVID-19 (χ 2 = 9.38, df = 3, p < 0.03).

Table 3 . Impact of COVID-19 on stress level by gender, race/ethnicity, and year of study.

Our primary findings showed a positive correlation between perceived academic stress and mental well-being in United States college students, suggesting that academic stressors, including academic expectations, workload and grading, and students' academic self-perceptions, are equally important as psychological well-being. Overall, irrespective of gender, race/ethnicity, or year of study, students who reported higher academic stress levels experienced diminished mental well-being. The utilization of well-established scales and a large sample size are strengths of this study. Our results extend and contribute to the existing literature on stress by confirming findings from past studies that reported higher academic stress and lower psychological well-being in college students utilizing the same two scales ( Green et al., 2021 ; Syed, 2021 ). To our knowledge, the majority of other prior studies with similar findings examined different components of stress, studied negative mental health indicators, used different scales or methods, employed smaller sample sizes, or were conducted in different countries ( Li and Lin, 2003 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Pascoe et al., 2020 ; Patsali et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ).

This study also demonstrated that college students are not uniformly impacted by academic stress or pandemic-related stress and that there are significant group-level differences in mental well-being. Specifically, non-binary individuals and second-year students were disproportionately impacted by academic stress. When considering the effects of gender, non-binary students, in comparison to gender-conforming students, reported the highest stress levels and worst psychological well-being. Although there is a paucity of research examining the impact of academic stress in non-binary college students, prior studies have indicated that non-binary adults face adverse mental health outcomes when compared to male and female-identifying individuals ( Thorne et al., 2018 ; Jones et al., 2019 ; Budge et al., 2020 ). Alarmingly, Lipson et al. (2019) found that gender non-conforming college students were two to four times more likely to experience mental health struggles than cisgender students ( Lipson et al., 2019 ). With a growing number of college students in the United States identifying as as non-binary, additional studies could offer invaluable insight into how academic stress affects this population ( Budge et al., 2020 ).

In addition, we found that second-year students reported the most academic-related distress and lowest psychological well-being relative to students in other years of study. We surmise this may be due to this group taking advanced courses, managing heavier academic workloads, and exploring different majors. Other studies support our findings and suggest higher stress levels could be attributed to increased studying and difficulties with time management, as well as having less well-established social support networks and coping mechanisms compared to upperclassmen ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ; Liu, X et al., 2019 ). Benefiting from their additional experience, upperclassmen may have developed more sophisticated studying skills, formed peer support groups, and identified approaches to better manage their academic stress ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ). Our findings suggest that colleges should consider offering tailored mental health resources, such as time management and study skill workshops, based on the year of study to improve students' stress levels and psychological well-being ( Liu, X et al., 2019 ).

Although this study reported no significant differences regarding race or ethnicity, this does not indicate that minority groups experienced less academic stress or better mental well-being ( Lee et al., 2021 ). Instead, our results may reflect the low sample size of non-white races/ethnicities, which may not have given enough statistical power to corroborate. In addition, since coping and resilience are important mediators of subjective stress experiences ( Freire et al., 2020 ), we speculate that the lower ratios of stress reported in non-white participants in our study (75 vs. 81) may be because they are more accustomed to adversity and thereby more resilient ( Brown, 2008 ; Acheampong et al., 2019 ). Furthermore, ethnic minority students may face stigma when reporting mental health struggles ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). For instance, studies showed that Black/African American, Hispanic/Latino, and Asian American students disclose fewer mental health issues than white students ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). Moreover, the ability to identify stressors and mental health problems may manifest differently culturally for some minority groups ( Huang and Zane, 2016 ; Liu, C. H., et al., 2019 ). Contrary to our findings, other studies cited racial disparities in academic stress levels and mental well-being of students. More specifically, Negga et al. (2007) concluded that African American college students were more susceptible to higher academic stress levels than their white classmates ( Negga et al., 2007 ). Another study reported that minority students experienced greater distress and worse mental health outcomes compared to non-minority students ( Smith et al., 2014 ). Since there may be racial disparities in access to mental health services at the college level, universities, professors, and counselors should offer additional resources to support these students while closely monitoring their psychological well-being ( Lipson et al., 2018 ; Liu, C. H., et al., 2019 ).

While the COVID-19 pandemic increased stress levels in all the students included in our study, women, non-binary students, and upperclassmen were disproportionately affected. An overwhelming body of evidence suggests that the majority of college students experienced increased stress levels and worsening mental health as a result of the pandemic ( Allen and Hiebert, 1991 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Yang et al., 2021 ). Our results also align with prior studies that found similar subgroups of students experience disproportionate pandemic-related distress ( Gao et al., 2020 ; Clabaugh et al., 2021 ; Hunt et al., 2021 ; Jarrett et al., 2021 ; Lee et al., 2021 ; Chen and Lucock, 2022 ). In particular, the differences between female students and their male peers may be the result of different psychological and physiological responses to stress reactivity, which in turn may contribute to different coping mechanisms to stress and the higher rates of stress-related disorders experienced by women ( Misra et al., 2000 ; Kajantie and Phillips, 2006 ; Verma et al., 2011 ; Gao et al., 2020 ; Graves et al., 2021 ). COVID-19 was a secondary consideration in our study and survey design, so the conclusions drawn here are necessarily limited.

The implications of this study are that college students facing increased stress and struggling with mental health issues should receive personalized and specific mental health services, resources, and support. This is particularly true for groups that have been disproportionately impacted by academic stress and stress due to the pandemic. Many students who experience mental health struggles underutilize college services due to cost, stigma, or lack of information ( Cage et al., 2020 ; Lee et al., 2021 ). To raise awareness and destigmatize mental health, colleges can consider distributing confidential validated assessments, such as the PAS and SWEMWBS, in class and teach students to self-score ( Lee et al., 2021 ). These results can be used to understand how academic stress and mental well-being change over time and allow for specific and targeted interventions for vulnerable groups. In addition, teaching students healthy stress management techniques has been shown to improve psychological well-being ( Alborzkouh et al., 2015 ). Moreover, adaptive coping strategies, including social and emotional support, have been found to improve the mental well-being of students, and stress-reduction peer support groups and workshops on campus could be beneficial in reducing stress and improving the self-efficacy of students ( Ruthig et al., 2009 ; Baqutayan, 2011 ; Bedewy and Gabriel, 2015 ; Freire et al., 2020 ; Green et al., 2021 ; Suresh et al., 2021 ). Other interventions that have been effective in improving the coping skills of college students include cognitive-behavioral therapy, mindfulness mediation, and online coping tools ( Kang et al., 2009 ; Regehr et al., 2013 ; Molla Jafar et al., 2015 ; Phang et al., 2015 ; Houston et al., 2017 ; Yusufov et al., 2019 ; Freire et al., 2020 ). Given that resilience has also been shown to help mediate stress and improve mental well-being during the COVID-19 pandemic, interventions focusing on enhancing resilience should be considered ( Surzykiewicz et al., 2021 ; Skalski et al., 2022 ). Telemental health resources across colleges can also be implemented to reduce stigma and improve at-risk students' access to care ( Toscos et al., 2018 ; Hadler et al., 2021 ). University campuses, professors, and counselors should consider focusing on fostering a more equitable and inclusive environment to encourage marginalized students to seek mental health support ( Budge et al., 2020 ).

Limitations

While our study has numerous strengths, including using standardized instruments and a large sample size, this study also has several limitations due to both the methodology and sample. First, the correlational study design precludes making any causal relationships ( Misra and McKean, 2000 ). Thereby, our findings should be taken in the context of academic stress and mental well-being, and recognize that mental health could be caused by other non-academic factors. Second, the PAS comprised only the perception of responses to academic stress, but stress is a multi-factorial response that encompasses both perceptions and coping mechanisms to different stressors, and the magnitude of stress varies with the perception of the degree of uncontrollability, unpredictability, or threat to self ( Miller, 1981 ; Hobfoll and Walfisch, 1984 ; Lazarus and Folkman, 1984 ; Wheaton, 1985 ; Perrewé and Zellars, 1999 ; Schneiderman et al., 2005 ; Bedewy and Gabriel, 2015 ; Schönfeld et al., 2016 ; Reddy et al., 2018 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Third, the SWEMSBS used in our study and the data only measured positive mental health. Mental health pathways are numerous and complex, and are composed of distinct and interdependent negative and positive indicators that should be considered together ( Margraf et al., 2020 ). Fourth, due to the small effect sizes and unequal representation for different combinations of variables, our analysis for both the PAS and SWEMSBS included only summed-up scales and did not examine group differences in response to the type of academic stressors or individual mental health questions.

An additional limitation is that the participants in our study were a convenience sample. The testing service we used, prolific.co, self-reports a sample bias toward young women of high levels of education (i.e., WEIRD bias) ( Team Prolific, 2018 ). The skew toward this population was observed in our data, as 80% of our participants were women. While we controlled for these factors, the possibility remains that the conclusions we draw for certain groups, such as nonbinary students, ethnic/racial minorities, and men, may not be as statistically powerful as they should be. Moreover, our pre-screening was designed to recruit undergraduate level, English-speaking, 18–30-year-olds who resided in the United States. This resulted in our participant demographics being skewed toward the WEIRD bias that was already inherent in the testing service we used. Future research will aim to be more inclusive of diverse races/ethnicities, sexual orientations, languages, educational backgrounds, socioeconomic backgrounds, and first-generation college students.

Another limitation of our study is the nature of satisficing. Satisficing is a response strategy in which a participant answers a question to satisfy its condition with little regard to the quality or accuracy of the answer ( Roberts et al., 2019 ). Anonymous participants are more likely to satisfice than respondents who answer the question face-to-face ( Krosnick et al., 2002 ). We sought to mitigate satisficing by offering financial incentives to increase response rates and decrease straight-lining, item skipping, total missing items, and non-completion ( Cole et al., 2015 ). Concerns of poor data quality due to surveys offering financial incentives found little evidence to support that claim and may do the opposite ( Cole et al., 2015 ). On the other hand, social desirability bias may have influenced the participant's self-reported responses, although our anonymous survey design aimed to reduce this bias ( Joinson, 1999 ; Kecojevic et al., 2020 ).

Future Studies

Future studies should replicate our study to validate our results, conduct longitudinal cohort studies to examine well-being and perceived academic stress over time, and aim for a more representative student sample that includes various groups, including diverse races/ethnicities, sexual orientations, socioeconomic backgrounds, languages, educational levels, and first-generation college students. Additionally, these studies should consider examining other non-academic stressors and students' coping mechanisms, both of which contribute to mental health and well-being ( Lazarus and Folkman, 1984 ; Freire et al., 2020 ). Further explorations of negative and other positive indicators of mental health may offer a broader perspective ( Margraf et al., 2020 ). Moreover, future research should consider extending our work by exploring group differences in relation to each factor in the PAS (i.e., academic expectations, workload and examinations, and self-perception of students) and SWEMBS to determine which aspects of academic stress and mental health were most affected and allow for the devising of targeted stress-reduction approaches. Ultimately, we hope our research spurs readers into advocating for greater academic support and access to group-specific mental health resources to reduce the stress levels of college students and improve their mental well-being.

Utilizing two well-established scales, our research found a statistically significant correlation between the perceived academic stress of university students and their mental well-being (i.e., the higher the stress, the worse the well-being). This relationship was most apparent among gender and grade levels. More specifically, non-binary and second-year students experienced greater academic burden and lower psychological well-being. Moreover, women, non-binary students, and upper-level students were disproportionately impacted by stress related to the COVID-19 pandemic.

Studies regarding broad concepts of stress and well-being using a questionnaire are limited, but our study adds value to the understanding of academic stress as a contributor to the overall well-being of college students during this specific point in time (i.e., the COVID-19 pandemic). Competition both for admission to college ( Bound et al., 2009 ) and during college ( Posselt and Lipson, 2016 ) has increased over time. Further, selective American colleges and universities draw applicants from a global pool. As such, it is important to document the dynamics of academic stress with renewed focus. We hope that our study sparks interest in both exploring and funding in-depth and well-designed psychological studies related to stress in colleges in the future.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board at Rutgers University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

GB and MB contributed to conceptualization, study design, IRB application, manuscript drafting, and revision. XZ participated in the conceptualization and design of the questionnaires. HB participated in subject recruitment and questionnaire collection. KP contributed to data analysis, table and figure preparation, manuscript drafting, and revision. XM contributed to conceptualization, study design, IRB application, supervision of the project, manuscript drafting, and revision. All authors contributed to the article and approved the submitted version.

This study was made possible by a generous donation from the Knights of Columbus East Hanover Chapter in New Jersey.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors wish to thank Shivani Mehta and Varsha Garla for their assistance with the study. We also thank all the participants for their efforts in the completion of the study.

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Keywords: academic stress, well-being, college students, Perception of Academic Stress, Short Warwick-Edinburgh Mental Well-Being Scale, COVID-19

Citation: Barbayannis G, Bandari M, Zheng X, Baquerizo H, Pecor KW and Ming X (2022) Academic Stress and Mental Well-Being in College Students: Correlations, Affected Groups, and COVID-19. Front. Psychol. 13:886344. doi: 10.3389/fpsyg.2022.886344

Received: 28 February 2022; Accepted: 20 April 2022; Published: 23 May 2022.

Reviewed by:

Copyright © 2022 Barbayannis, Bandari, Zheng, Baquerizo, Pecor and Ming. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Keith W. Pecor, pecor@tcnj.edu

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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In CDC survey, 37% of U.S. high school students report regular mental health struggles during COVID-19 pandemic

Many high school students have reported experiencing mental health challenges during the coronavirus outbreak, according to recently published survey findings from the Centers for Disease Control and Prevention (CDC). High school students who are gay, lesbian or bisexual, as well as girls, were especially likely to say their mental health has suffered during the pandemic.

This analysis explores U.S. high school students’ self-reported mental health challenges during the COVID-19 pandemic. It expands on Pew Research Center surveys that have explored U.S. adults’ mental health difficulties during this time. Not all of the survey questions asked specifically about mental health during the pandemic.

This analysis relies on the Center for Disease Control and Prevention’s Adolescent Behaviors and Experiences Survey (ABES), which was conducted from January to June 2021 to assess students’ health-related behaviors and experiences during the COVID-19 pandemic. ABES surveyed high school students in grades 9-12 attending U.S. public and private schools. More information about the survey and its methodology can be found on the CDC’s website.

The results from this one-time survey are not directly comparable to previous CDC surveys on these topics.

Overall, 37% of students at public and private high schools reported that their mental health was not good most or all of the time during the pandemic, according to the CDC’s Adolescent Behaviors and Experiences Survey , which was fielded from January to June 2021. In the survey, “poor mental health” includes stress, anxiety and depression. About three-in-ten high school students (31%) said they experienced poor mental health most or all of the time in the 30 days before the survey. In addition, 44% said that, in the previous 12 months, they felt sad or hopeless almost every day for at least two weeks in a row such that they stopped doing some usual activities. (Not all of the survey questions asked specifically about mental health during the pandemic.)

High school students who are gay, lesbian or bisexual reported higher rates of mental health stresses than their heterosexual (straight) peers. The share of LGB high schoolers who said their mental health was not good most of the time or always during the pandemic was more than double that of heterosexual students (64% vs. 30%). More than half of LGB students (55%) said they experienced poor mental health at least most of the time in the 30 days before the survey, while 26% of heterosexual teens said the same. And about three-quarters of LGB high schoolers (76%) said they felt sad or hopeless almost daily for at least two weeks such that they stopped doing some of their usual activities, compared with 37% of heterosexual students.

There were also differences by gender. About half of high school girls (49%) said their mental health was not good most of the time or always during the COVID-19 outbreak – roughly double the share of boys who said this (24%). And roughly four-in-ten girls (42%) reported feeling this way in the 30 days before the survey; 20% of boys said the same. About six-in-ten high school girls (57%) reported that at some point in the 12 months before taking the survey (in the first half of 2021) they felt sad or hopeless almost every day for at least two weeks in a row such that they stopped doing some usual activities, compared with 31% of high school boys who said this.

LGB high schoolers were also more likely than their heterosexual peers to have sought mental health care – including treatment or counseling for alcohol or drug use – via telemedicine during the COVID-19 pandemic. Around one-in-five LGB students (19%) said they received treatment this way at some point during the pandemic, compared with 6% of heterosexual students. Girls were more likely than boys to have received mental health care through telemedicine (10% vs. 7%, respectively).

Pandemic-related disruptions to schooling, socializing and family life have created a situation that the U.S. surgeon general has described as a “ youth mental health crisis ,” with high rates of teens experiencing distress. But public health experts had called attention to teen mental health even before the coronavirus outbreak. For instance, a separate CDC survey conducted in 2015 found that LGB teens were at greater risk of depression than their heterosexual peers. And a Pew Research Center analysis of pre-pandemic data from the National Survey for Drug Use and Health showed teenage girls were more likely than their male peers to report recent experiences with depression , as well as to receive treatment for it.

• Coronavirus (COVID-19)
• Generation Z
• Happiness & Life Satisfaction
• LGBTQ Attitudes & Experiences
• Teens & Youth

Katherine Schaeffer is a research analyst at Pew Research Center

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, mental health and the pandemic: what u.s. surveys have found, most popular.

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Expert Commentary

Improving college student mental health: Research on promising campus interventions

Hiring more counselors isn’t enough to improve college student mental health, scholars warn. We look at research on programs and policies schools have tried, with varying results.

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Denise-Marie Ordway, The Journalist's Resource September 13, 2023

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If you’re a journalist covering higher education in the U.S., you’ll likely be reporting this fall on what many healthcare professionals and researchers are calling a college student mental health crisis.

An estimated 49% of college students have symptoms of depression or anxiety disorder and 14% seriously considered committing suicide during the past year, according to a national survey of college students conducted during the 2022-23 school year. Nearly one-third of the 76,406 students who participated said they had intentionally injured themselves in recent months.

In December, U.S. Surgeon General Vivek Murthy issued a rare public health advisory calling attention to the rising number of youth attempting suicide , noting the COVID-19 pandemic has “exacerbated the unprecedented stresses young people already faced.”

Meanwhile, colleges and universities of all sizes are struggling to meet the need for mental health care among undergraduate and graduate students. Many schools have hired more counselors and expanded services but continue to fall short.

Hundreds of University of Houston students held a protest earlier this year , demanding the administration increase the number of counselors and make other changes after two students died by suicide during the spring semester, the online publication Chron reported.

In an essay in the student-run newspaper , The Cougar, last week, student journalist Malachi Key blasts the university for having one mental health counselor for every 2,122 students, a ratio higher than recommended by the International Accreditation of Counseling Services , which accredits higher education counseling services.

But adding staff to a campus counseling center won’t be enough to improve college student mental health and well-being, scholars and health care practitioners warn.

“Counseling centers cannot and should not be expected to solve these problems alone, given that the factors and forces affecting student well-being go well beyond the purview and resources that counseling centers can bring to bear,” a committee of the National Academies of Sciences, Engineering, and Medicine writes in a 2021 report examining the issue.

Advice from prominent scholars

The report is the culmination of an 18-month investigation the National Academies launched in 2019, at the request of the federal government, to better understand how campus culture affects college student mental health and well-being. Committee members examined data, studied research articles and met with higher education leaders, mental health practitioners, researchers and students.

The committee’s key recommendation: that schools take a more comprehensive approach to student mental health, implementing a wide range of policies and programs aimed at preventing mental health problems and improving the well-being of all students — in addition to providing services and treatment for students in distress and those with diagnosed mental illnesses.

Everyone on campus, including faculty and staff across departments, needs to pitch in to establish a new campus culture, the committee asserts.

“An ‘all hands’ approach, one that emphasizes shared responsibility and a holistic understanding of what it means in practice to support students, is needed if institutions of higher education are to intervene from anything more than a reactive standpoint,” committee members write. “Creating this systemic change requires that institutions examine the entire culture and environment of the institution and accept more responsibility for creating learning environments where a changing student population can thrive.”

In a more recent analysis , three leading scholars in the field also stress the need for a broader plan of action.

Sara Abelson , a research assistant professor at Temple University’s medical school; Sarah Lipson , an associate professor at the Boston University School of Public Health; and Daniel Eisenberg ,  a professor of health policy and management at the University of California, Los Angeles’ School of Public Health, have been studying college student mental health for years.

Lipson and Eisenberg also are principal investigators for the Healthy Minds Network , which administers the Healthy Minds Study , a national survey of U.S college students conducted annually to gather information about their mental health, whether and how they receive mental health care and related issues.

Abelson, Lipson and Eisenberg review the research to date on mental health interventions for college students in the 2022 edition of Higher Education: Handbook of Theory and Research . They note that while the evidence indicates a multi-pronged approach is best, it’s unclear which specific strategies are most effective.

Much more research needed

Abelson, Lipson and Eisenberg stress the need for more research. Many interventions in place at colleges and universities today — for instance, schoolwide initiatives aimed at reducing mental health stigma and encouraging students to seek help when in duress – should be evaluated to gauge their effectiveness, they write in their chapter, “ Mental Health in College Populations: A Multidisciplinary Review of What Works, Evidence Gaps, and Paths Forward .”

They add that researchers and higher education leaders also need to look at how campus operations, including hiring practices and budgetary decisions, affect college student mental health. It would be helpful to know, for example, how students are impacted by limits on the number of campus counseling sessions they can have during a given period, Abelson, Lipson and Eisenberg suggest.

Likewise, it would be useful to know whether students are more likely to seek counseling when they must pay for their sessions or when their school charges every member of the student body a mandatory health fee that provides free counseling for all students.

“These financially-based considerations likely influence help-seeking and treatment receipt, but they have not been evaluated within higher education,” they write.

Interventions that show promise

The report from the National Academies of Sciences, Engineering, and Medicine and the chapter by Abelson, Lipson and Eisenberg both spotlight programs and policies shown to prevent mental health problems or improve the mental health and well-being of young people. However, many intervention studies focus on high school students, specific groups of college students or specific institutions. Because of this, it can be tough to predict how well they would work across the higher education landscape.

Scientific evaluations of these types of interventions indicate they are effective:

• Building students’ behavior management skills and having them practice new skills under expert supervision . An example: A class that teaches students how to use mindfulness to improve their mental and physical health that includes instructor-led meditation exercises.
• Training some students to offer support to others , including sharing information and organizing peer counseling groups. “Peers may be ‘the single most potent source of influence’ on student affective and cognitive growth and development during college,” Abelson, Lipson and Eisenberg write.
• Reducing students’ access to things they can use to harm themselves , including guns and lethal doses of over-the-counter medication.
• Creating feelings of belonging through activities that connect students with similar interests or backgrounds.
• Making campuses more inclusive for racial and ethnic minorities, LGBTQ+ students and students who are the first in their families to go to college. One way to do that is by hiring mental health professionals trained to recognize, support and treat students from different backgrounds. “Research has shown that the presentation of [mental health] symptoms can differ based on racial and ethnic backgrounds, as can engaging in help-seeking behaviors that differ from those of cisgender, heteronormative white men,” explain members of National Academies of Sciences, Engineering, and Medicine committee.

Helping journalists sift through the evidence

We encourage journalists to read the full committee report and aforementioned chapter in Higher Education: Handbook of Theory and Research . We realize, though, that many journalists won’t have time to pour over the combined 304 pages of text to better understand this issue and the wide array of interventions colleges and universities have tried, with varying success.

To help, we’ve gathered and summarized meta-analyses that investigate some of the more common interventions. Researchers conduct meta-analyses — a top-tier form of scientific evidence — to systematically analyze all the numerical data that appear in academic studies on a given topic. The findings of a meta-analysis are statistically stronger than those reached in a single study, partly because pooling data from multiple, similar studies creates a larger sample to examine.

Keep reading to learn more. And please check back here occasionally because we’ll add to this list as new research on college student mental health is published.

Peer-led programs

Stigma and Peer-Led Interventions: A Systematic Review and Meta-Analysis Jing Sun; et al. Frontiers in Psychiatry, July 2022.

When people diagnosed with a mental illness received social or emotional support from peers with similar mental health conditions, they experienced less stress about the public stigma of mental illness, this analysis suggests.

The intervention worked for people from various age groups, including college students and middle-aged adults, researchers learned after analyzing seven studies on peer-led mental health programs written or published between 1975 and 2021.

Researchers found that participants also became less likely to identify with negative stereotypes associated with mental illness.

All seven studies they examined are randomized controlled trials conducted in the U.S., Germany or Switzerland. Together, the findings represent the experiences of a total of 763 people, 193 of whom were students at universities in the U.S.

Researchers focused on interventions designed for small groups of people, with the goal of reducing self-stigma and stress associated with the public stigma of mental illness. One or two trained peer counselors led each group for activities spanning three to 10 weeks.

Five of the seven studies tested the Honest, Open, Proud program, which features role-playing exercises, self-reflection and group discussion. It encourages participants to consider disclosing their mental health issues, instead of keeping them a secret, in hopes that will help them feel more confident and empowered. The two other programs studied are PhotoVoice , based in the United Kingdom, and

“By sharing their own experiences or recovery stories, peer moderators may bring a closer relationship, reduce stereotypes, and form a positive sense of identity and group identity, thereby reducing self-stigma,” the authors of the analysis write.

Expert-led instruction

The Effects of Meditation, Yoga, and Mindfulness on Depression, Anxiety, and Stress in Tertiary Education Students: A Meta-Analysis Josefien Breedvelt; et al. Frontiers in Psychiatry, April 2019.

Meditation-based programs help reduce symptoms of depression, anxiety and stress among college students, researchers find after analyzing the results of 24 research studies conducted in various parts of North America, Asia and Europe.

Reductions were “moderate,” researchers write. They warn, however, that the results of their meta-analysis should be interpreted with caution considering studies varied in quality.

A total of 1,373 college students participated in the 24 studies. Students practiced meditation, yoga or mindfulness an average of 153 minutes a week for about seven weeks. Most programs were provided in a group setting.

Although the researchers do not specify which types of mindfulness, yoga or meditation training students received, they note that the most commonly offered mindfulness program is Mindfulness-Based Stress Reduction and that a frequently practiced form of yoga is Hatha Yoga .

Meta-Analytic Evaluation of Stress Reduction Interventions for Undergraduate and Graduate Students Miryam Yusufov; et al. International Journal of Stress Management, May 2019.

After examining six types of stress-reduction programs common on college campuses, researchers determined all were effective at reducing stress or anxiety among students — and some helped with both stress and anxiety.

Programs focusing on cognitive-behavioral therapy , coping skills and building social support networks were more effective in reducing stress. Meanwhile, relaxation training, mindfulness-based stress reduction and psychoeducation were more effective in reducing anxiety.

The authors find that all six program types were equally effective for undergraduate and graduate students.

The findings are based on an analysis of 43 studies dated from 1980 to 2015, 30 of which were conducted in the U.S. The rest were conducted in Australia, China, India, Iran, Japan, Jordan, Kora, Malaysia or Thailand. A total of 4,400 students participated.

Building an inclusive environment

Cultural Adaptations and Therapist Multicultural Competence: Two Meta-Analytic Reviews Alberto Soto; et al. Journal of Clinical Psychology, August 2018.

If racial and ethnic minorities believe their therapist understands their background and culture, their treatment tends to be more successful, this analysis suggests.

“The more a treatment is tailored to match the precise characteristics of a client, the more likely that client will engage in treatment, remain in treatment, and experience improvement as a result of treatment,” the authors write.

Researchers analyzed the results of 15 journal articles and doctoral dissertations that examine therapists’ cultural competence . Nearly three-fourths of those studies were written or published in 2010 or later. Together, the findings represent the experiences of 2,640 therapy clients, many of whom were college students. Just over 40% of participants were African American and 32% were Hispanic or Latino.

The researchers note that they find no link between therapists’ ratings of their own level of cultural competence and client outcomes.

Internet-based interventions

Internet Interventions for Mental Health in University Students: A Systematic Review and Meta-Analysis Mathias Harrer; et al. International Journal of Methods in Psychiatric Research, June 2019.

Internet-based mental health programs can help reduce stress and symptoms of anxiety, depression and eating disorders among college students, according to an analysis of 48 research studies published or written before April 30, 2018 on the topic.

All 48 studies were randomized, controlled trials of mental health interventions that used the internet to engage with students across various platforms and devices, including mobile phones and apps. In total, 10,583 students participated in the trials.

“We found small effects on depression, anxiety, and stress symptoms, as well as moderate‐sized effects on eating disorder symptoms and students’ social and academic functioning,” write the authors, who conducted the meta-analysis as part of the World Mental Health International College Student Initiative .

The analysis indicates programs that focus on cognitive behavioral therapy “were superior to other types of interventions.” Also, programs “of moderate length” — one to two months – were more effective.

The researchers note that studies of programs targeting depression showed better results when students were not compensated for their participation, compared to studies in which no compensation was provided. The researchers do not offer possible explanations for the difference in results or details about the types of compensation offered to students.

About The Author

Denise-Marie Ordway

Stress in College Students: What to Know

Strong social connections and positive habits can help ease high levels of stress among college-age adults.

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From socializing to working out, here's how college students can better manage stress.

From paying for school and taking exams to filling out internship applications, college students can face overwhelming pressure and demands. Some stress can be healthy and even motivating under the proper circumstances, but often stress is overwhelming and can lead to other issues.

"Stress is there for a reason. It's there to help mobilize you to meet the demands of your day, but you're also supposed to have times where you do shut down and relax and repair and restore," says Emma K. Adam, professor of human development and social policy at Northwestern University in Illinois.

Chronic and unhealthy levels of stress is at its worst among college-age students and young adults, some research shows. According to the American Psychological Association's 2022 "Stress in America" report , 46% of adults ages 18 to 35 reported that "most days they are so stressed they can't function."

In a Gallup poll that surveyed more than 2,400 college students in March 2023, 66% of reported experiencing stress and 51% reported feelings of worry "during a lot of the day." And emotional stress was among the top reasons students considered dropping out of college in the fall 2022 semester, according to findings in the State of Higher Education 2023 report, based on a study conducted in 2022 by Gallup and the Lumina Foundation.

As students are navigating a new environment and often living independently for the first time, they encounter numerous opportunities, responsibilities and life changes on top of academic responsibilities. It can be sensory overload for some, experts say.

“Going to college has always been a significant time of transition developmentally with adulthood, but you add to it everything that comes along with that transition and then you put onto it a youth mental health crisis, it’s just compounded in a very different way," says Jessica Gomez, a clinical psychologist and executive director of Momentous Institute, a researched-based organization that provides mental health services and educational programming to children and families.

Experts say college students have experienced heightened stress since the COVID-19 pandemic, a trend likely to continue for the foreseeable future.

“What some of our research at Gallup has shown is that we had a rising tide of negative emotions, not just in the U.S. but globally, in the eight to 10 years leading up to the pandemic, and of course it got worse during the pandemic," says Stephanie Marken, senior partner of the education research division at Gallup who conducted the 2023 study. “For currently enrolled college students, there’s so many contributing factors.”

Adam notes that multiple factors combine to contribute to heightened stress among younger adults, including the nation’s racial and political controversies, as well as anxiety regarding their futures fueled by climate change, global unrest and economic uncertainty. Female students reported higher levels of stress than males in the Gallup poll, which Marken says could be attributed to several factors like increased internal academic pressure, caregiving responsibilities and the recent uncertainty regarding abortion rights following the reversal of Roe v. Wade.

All of this, plus the residual effects of pandemic learning, has contributed to rising stress for college students, Marken says.

"We need to give them a lot of credit," she says. "They had the most challenge in remote learning of all the learners that have come before them. Many of them had to graduate high school and study remotely, or were a first-year college student during the pandemic, and that was incredibly difficult."

The challenges that came with that learning environment will likely affect students throughout college, she says, as well as typical stressors like discrimination, harassment and academic challenges.

"Those will always be present on college campuses," she says. "The question is, how do we create a student who overcomes those challenges effectively?"

Experts suggest a range of specific actions and positive shifts that can help ease stress in college students:

• Notice the symptoms of heightened stress.
• Build and maintain social connections.
• Sleep, eat well and exercise.

Notice the Symptoms of Heightened Stress

College students can start by learning to identify when normal stress increases to become unhealthy. Stress will appear differently in each student, says Lindsey Giller, a clinical psychologist with the Child Mind Institute, a nonprofit focused on helping children and young adults with mental health and learning disorders.

"Students prone to anxiety may avoid assignments as well as skip classes due to experiencing shame for being behind or missing things," she says. "For some, they may also start sleeping in more, eating at more random times, foregoing self-care, or look to distraction or escape mechanisms, like substances, to fill time and further avoid the reality of workload assignments."

Changes in diet and sleeping are also telling, as well as increased social isolation and pulling away from activities that once brought you pleasure is also a red flag, Gomez says.

She warns students to watch for signs of irritability, a classic indicator of increased stress that can often compound issues, especially within interpersonal relationships.

"Your body speaks to you, so be in tune with your body," she says.

Build and Maintain Social Connections

Socializing can help humans release stress. Experts say having fun and finding joy in life keep stress levels manageable, and socializing is particularly important developmentally for young adults. In the 2023 Gallup poll, 76% of students reported feeling enjoyment the previous day, which Marken says was an encouraging sign.

But 39% reported experiencing feelings of loneliness and 36% reported feeling sad. “We are in the midst of an epidemic of loneliness in our country, where we are noticing people don’t have the skills to build friendships,” Gomez says.

Discover six

Talking about feelings of stress can help college students cope, which is why the amount of students feeling lonely is concerning, Marken says. If students don't feel like they belong or have a social network to call on when feeling stressed, negative emotions are compounded.

“I think we’re more connected, and yet we’re more isolated than ever," she says. "It feels counterintuitive. How can you be more connected to your network and campus than ever, yet feel this lonely? Just because they have a device to connect with each other in a transactional way doesn’t mean it’s a meaningful relationship. I think that’s what we’re missing on a lot of college campuses is students creating meaningful connections about a shared experience."

Setting boundaries on social media use is crucial, Gomez says, as is getting plugged in with people and organizations that will be enriching. For example, Gomez says she joined a Latina sorority to be in community with others who shared some of her life experiences and interests.

Sleep, Eat Well and Exercise

Maintaining healthy habits can help college students better manage stressors that arise.

"Prioritizing sleep, moving your body, getting organized, and leaning on your support network all help college students prevent or manage stress," John MacPhee, CEO of The Jed Foundation, a nonprofit that aims to protect emotional health and prevent suicide among teens and young adults, wrote in an email. "In the inevitable moments of high stress, mindful breathing, short brain breaks, and relaxation techniques can really help."

Experts suggest creating a routine and sticking to it. That includes getting between eight and 10 hours of sleep each night and avoiding staying up late, Gomez says. A nutrient-rich diet can also go a long way in maintaining good physical and mental health, she says.

Getting outdoors and being active can also help students limit their screen time and use of social media.

“Walking to campus, maybe taking that longer walk, because your body needs that to heal," Gomez says. "It’s going to help buffer you. So if that’s the only thing you do, try to do that."

Colleges typically offer mental health resources such as counseling and support groups for struggling students.

Students dealing with chronic and unhealthy stress should contact their college and reach out to friends and family for support. Reaching out to parents, friends or mentors can be beneficial for students when feelings of stress come up, especially in heightened states around midterm and final exams .

Accessing student supports and counseling early can prevent a cascading effect that results in serious mental health challenges or unhealthy coping mechanisms like problem drinking and drug abuse , experts say.

"Know there are lots of resources on campus from academic services to counseling centers to get structured, professional support to lower your workload, improve coping skills, and have a safe space to process anxiety, worry, and stress," MacPhee says.

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• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

380 Accesses

Metrics details

Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

Peer Review reports

Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Nursing Education, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Rawhia Salah Dogham & Heba Fakieh Mansy Ali

Critical Care & Emergency Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Nermine M. Elcokany

Obstetrics and Gynecology Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Asmaa Saber Ghaly

Faculty of Nursing, Beni-Suef University, Beni-Suef, Egypt

Mohamed Mahmoud Seweid

Psychiatric and Mental Health Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt

Ayman Mohamed El-Ashry

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Contributions

Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Correspondence to Ayman Mohamed El-Ashry .

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Ethics approval and consent to participate.

The research adhered to the guidelines and regulations outlined in the Declaration of Helsinki (DoH-Oct2008). The Faculty of Nursing’s Research Ethical Committee (REC) at Alexandria University approved data collection in this study (IRB00013620/95/9/2022). Participants were required to sign an informed written consent form, which included an explanation of the research and an assessment of their understanding.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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Academic Stress and Emotional Well-Being in United States College Students Following Onset of the COVID-19 Pandemic

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The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

COVID-19 has resulted in extraordinary disruptions to the higher education landscape. Here, we provide a brief report on 295 students’ academic perceptions and emotional well-being in late May 2020. Students reported the high levels of uncertainty regarding their academic futures as well as significant levels of stress and difficulty coping with COVID-19 disruptions. These outcomes were related to the higher levels of neuroticism and an external locus of control. Female students reported worse emotional well-being compared to males, and the students of color reported the significantly higher levels of stress and uncertainty regarding their academic futures compared to White students. These results suggest that some students may be at particular risk for academic stress and poor emotional well-being due to the pandemic and highlight the urgent need for intervention and prevention strategies.

Introduction

In response to the COVID-19 pandemic, over 1,000 colleges and universities in the United States closed their doors in March 2020. Millions of students were forced to finish the semester via remote learning, resulting in extraordinary disruptions to higher education in the United States ( Goldstein, 2020 ). Although COVID-19 poses a low risk to the health and mortality of college-aged students ( Centers for Disease Control and Prevention, 2020 ), the pandemic has likely resulted in stark uncertainty and distress in this population.

One particular area of concern for students in higher education is academic stress relating to their ability to succeed in this new environment. While enrollment in online courses has increased over the past several years, the majority of students remain unfamiliar with remote learning. A recent report indicates that prior to COVID-19, only 35% of United States college students had taken one or more courses online ( D’Amato, 2020 ). This concerning given that one of the best predictors of academic success in an online format is prior online course experience ( Hachey et al., 2012 ). This lack of experience may be compounded by challenging home conditions, including loss of access to academic resources (e.g., computers and internet connectivity) and distractions in the home learning environment. Indeed, the initial research shows that at-home distractions (including disruptions from other family members and additional responsibilities) are a significant challenge for college students learning from home during COVID-19 ( Son et al., 2020 ). Taken together, these factors are likely to lead to significant academic stress and uncertainty.

Aside from dealing with stressors related to a potentially unfamiliar online learning environment, students are also coping with the emotional impact of COVID-19. Much of the initial research on the mental health consequences of COVID-19 comes from areas hardest hit at the beginning of the pandemic including countries in Asia and Europe. This research shows that COVID-19 and its associated disruptions have resulted in significant increases in stress, anxiety, depression, and suicidality in college students ( Husky et al., 2020 ; Li et al., 2020 ; Luo et al., 2020 ; Patsali et al., 2020 ). More recent investigations in the United States indicate that college students show a similar pattern in mental health and well-being to those from other regions of the world coping with COVID-19 (e.g., Luo et al., 2020 ; Son et al., 2020 ). Unfortunately, studies from the United States addressing these phenomena thus far have focused on students from single institutions and have under-explored gender and ethnic differences in COVID-19 related mental health issues. These are crucial to investigate, particularly because men and ethnic minorities are more likely to experience negative health outcomes after exposure to COVID-19 ( Griffith, 2020 ), while women and ethnic minorities are more likely to suffer negative occupational and mental health consequences due to the pandemic ( Adams-Prassl et al., 2020 ; Alonzi et al., 2020 ; NAACP, 2020 ). These differences are crucial to investigate, particularly, because the initial research suggests that women and ethnic minorities are more likely to suffer adverse changes in their emotional well-being due to the pandemic ( Adams-Prassl et al., 2020 ; Alonzi et al., 2020 ; Rothman et al., 2020 ; Smith et al., 2020 ; Thibaut and van Wijngaarden-Cremers, 2020 ). For example, using a large, the geographically representative sample of United States adults, Adams-Prassl et al. (2020) documented a significant decrease in mental health as a result of initial COVID-19 stay-at-home orders. Of note, this decrease was entirely driven by worsening mental health in females. Similarly, research on ethnic minority populations suggests that the pandemic is likely to exacerbate pre-existing mental health disparities due to significant rates of COVID-19 infection in these communities as well as quarantine-related impediments to mental health care ( Rothman et al., 2020 ; Smith et al., 2020 ). Thus, many students (women and minority populations in particular) are likely facing challenges to their well-being during the pandemic.

Emotional well-being during the times of turmoil depends on factors at both the individual and societal level. Thus far, research on emotional well-being during COVID-19 has focused on societal-level factors including response to situational stressors (e.g., infection fears, constraints on physical movement, limited social contact, and sudden lifestyle changes). What remains under-explored is how the effects of these stressors may vary based on individual differences such as personality traits. Neuroticism, for example, has profound implications for mental and physical health (e.g., Lahey, 2009 ; Widiger and Oltmanns, 2017 ). Research shows that individuals who are high in neuroticism are at increased risk for negative physical health outcomes and the various forms of psychopathology including anxiety and mood disorders (see Tackett and Lahey, 2017 for a review). For example, a recent investigation in Germany found that individuals with higher neuroticism attended to and worried about the ongoing COVID-19 pandemic more than those lower on neuroticism ( Kroencke et al., 2020 ). Additionally, locus of control (LoC) has been shown to predict the ability to cope with stressful life experiences ( Zeidner, 1993 ; Lefcourt, 2013 ). During the SARS pandemic of 2003, having a more external LoC was associated with the development of PTSD following a SARS infection ( Mak et al., 2010 ). Thus, it is likely that these individual differences also influence students’ well-being during the COVID-19 pandemic.

The goals of the current study were 2-fold. First, in an effort to capture the impacts of COVID-19 on the higher-education landscape, we explored academic perceptions, emotional well-being, and individual differences among United States college students during the beginning stages of the pandemic in April and May 2020. As part of this exploration, we also assessed students’ COVID-19 perceptions and behaviors and examined relationships between all variables of interest. Second, given that female and ethnic minority students are disproportionately likely to suffer negative occupational and mental health consequences related to the pandemic, we investigated gender and ethnic differences. In light of the recency of the pandemic, this study was exploratory and descriptive in nature. Such studies are a necessary first step toward understanding pandemic-related well-being and can inform later investigations that are more targeted and theory-driven.

Materials and Methods

A Qualtrics survey was distributed to students at the Arcadia University (Glenside, PA) via an online psychology major’s community as well as through various department chairs. Outside of Arcadia, the link was distributed to psychology department chairs at institutions near Philadelphia, PA, including in OH, NJ, NY, DE, and Washington D.C. and was posted to an online teaching Listserv, so that members could distribute the survey link to their students at their own discretion. Participation was voluntary and not compensated.

The survey took ~10 min to complete. Multiple measures were administered; some of which were for another study and are not further reported here. Measures for this study were administered in the following order: demographics, questions assessing COVID-19 perceptions and behaviors, academic perceptions, locus of control, perceptions of stress within the past month, and neuroticism. The IRB at Arcadia University approved all procedures.

Participants

Three-hundred and 45 individuals started the survey. Two were removed for not meeting the inclusion criteria (full- or part-time undergraduate at least 18 years of age). Fifty participants were removed for incomplete data leaving a final sample of 295 participants (see Table 1 ). Eighty-five percent of respondents were from colleges in the Northeast United States Notably, no student indicated that they had tested positive for COVID-19 (four did not answer), and 97% reported that no one in their immediate family had tested positive (four reported yes, and five did not answer).

Student self-reported demographics.

Of note, these data were collected from mid-April to May 8, when the survey was made inactive. During this time much of the United States was undergoing extensive stay at home orders, in varying forms, generally allowing only essential businesses to remain open ( Moreland, 2020 ). Additionally, at the time of data collection, wearing masks in public was not uniformly recommended and was therefore not assessed.

Academic Perceptions

Participants responded to three questions assessing academic concerns related to COVID-19: “To what extent do you think your academic future is at risk due to COVID-19?,” “What is the likelihood that you would reduce (or withdraw) from your courses in the Fall of 2020 if classes were still completely or predominantly online due to COVID-19?,” and “To what extent is distraction an issue in your current environment?.” Additionally, participants rated their level of agreement with the statement, “Transitioning to a completely online education is the correct response for schools and universities to take in response to COVID-19.”

Emotional Well-Being

Participants responded to a four-item Perceived Stress Scale ( Cohen et al., 1983 ) to assess the degree to which individuals perceive events in their lives within the past month as stressful on a 1–5 scale, ranging from “never” to “very often” ( α = 0.78). Participants also responded to a single item, “Compared to those around you (e.g., family, friends, and co-workers), how well do you feel you are coping with disruptions in your life caused by COVID-19,” on a 1–5 scale, ranging from “not well at all” to “extremely well.”

Personality

Participants responded to a nine-item brief LoC scale ( Sapp and Harrod, 1993 ) to assess the extent to which individuals perceive control over their own lives and the events around them on a 1–5 scale, ranging from “strongly disagree” to “strongly agree.” Higher scores indicate a more internal LoC ( α = 79). Participants also responded to an five-item Neuroticism subscale of the Big Five Inventory ( John and Srivastava, 1999 ) to assess the extent to which a person is prone to worry and emotional instability on a 1–5 scale, ranging from “strongly disagree” to “strongly agree” ( α = 0.84).

COVID-19 Perceptions and Behaviors

Participants responded to three questions assessing COVID-19 perceptions and behaviors, adapted from Wise et al. (2020) : “How serious do you believe COVID-19 is?,” “How do you think you will be affected if you personally catch the virus?,” and “How often are you completing risk management behaviors?”

Descriptives

Academic perceptions and emotional well-being.

One-third of students (33%) felt their academic future was “very” or “extremely” at risk due to COVID-19 ( Figure 1A ), and 32% reported being “somewhat” or “extremely” likely to reduce or withdraw from classes in the Fall of 2020 if classes are completely or predominantly online ( Figure 1B ). Sixty percent reported that distraction was “very much” or “extremely” an issue in their current environment ( Figure 1C ). Nonetheless, 81% of students “agreed” or “strongly agreed” that transitioning to an online education in the spring of 2020 was the correct response to take ( Figure 1D ).

Percent response frequencies for COVID-19 academic perceptions (A–E) and boxplot for Perceived Stress Scale (F) . Means and standard deviations are listed at the top of each graph.

Regarding emotional well-being, 30% of students reported that they were coping “slightly well” or “not well at all” with COVID-19 disruptions ( Figure 1E ) and reported stress levels were significantly above the scale mid-point of 2.5 ( M = 3.39, SD = 0.77, t (286) = 19.46, p < 0.01, d = 1.16; Figure 1F ).

Regarding COVID-19, 86% of students characterized the virus as “very” or “extremely serious” ( Figure 2A ), and 62% reported they would be “very” or “extremely” affected if they were to catch the virus ( Figure 2B ). Nearly all students (95%) reported engaging in risk management behaviors either “most of the time” or “almost constantly” ( Figure 2C ).

Percent response frequencies for questions related to COVID-19. Means and standard deviations are listed at the top of each graph.

Aim1: Correlational Analyses

We first sought to assess the relationship between personality variables (neuroticism; M = 3.35, SD = 0.77; LoC; M = 3.46, SD = 0.64), academic perceptions, and emotional well-being (see Table 2 ). Both neuroticism and LoC showed similar relationships with academic concerns. Specifically, the higher levels of neuroticism and a more external LoC were associated with perceptions of academic future being at greater risk, higher likelihood of reducing or withdrawing from online courses in the fall, and the higher reported levels of distraction in the home learning environment. Agreement that the transition to online education was the “correct” response to the pandemic was not related to any personality variables but was significantly related to lower risk perception for academic future, lower likelihood of reducing or withdrawing from online classes in the fall, lower levels of distraction in the home learning environment, and better coping with COVID-19 disruptions. Thus, students coping well with academic concerns and COVID-19 tended to agree that transitioning online was the correct choice.

Bivariate correlations between academic perceptions, emotional well-being, personality measures, and COVID-19 perceptions and behaviors.

N = 280–295. Variables A–F and A–C are shown in Figures 1 , ​ ,2, 2 , respectively. Correlations within −0.1 and 0.1 are not shown.

Regarding emotional well-being, the higher levels of neuroticism and a more external LoC were associated with the higher levels of stress and worse coping. Academic concerns (variables 1–3) were also significantly related to poor emotional well-being (variables 5 and 6). For example, students reporting the higher levels of concern about their academic future reported higher stress and worse coping.

Lastly, we investigated whether students’ COVID-19 perceptions and behaviors were related to their academic perceptions or emotional well-being. Though the perceptions of COVID-19 severity correlated with the degree to which students believed that they would be affected if they were to catch the virus, as well as their frequency of risk management behaviors, none of these COVID-19 questions were related to academic perceptions or emotional well-being ( Table 2 ).

Aim2: Gender and Ethnic Differences

Our second aim was to investigate gender and ethnic differences in COVID-19 related academic perceptions and emotional well-being (see Table 3 ). Females reported the significantly higher levels of distraction in their home learning environment compared to males; however, there were no other significant gender differences in academic perceptions. With regard to personality and emotional well-being, females had the higher levels of neuroticism, more perceived stress, and worse coping compared to males. Females also reported the higher levels of perceived COVID-19 severity as well as greater frequency of engaging in risk management behaviors.

Exploratory gender and ethnic differences.

Only significant differences are shown.

Compared to White students, students of color (SoC: Black, Hispanic/Latinx, Asian, and Other) reported the perceptions of greater risk for their academic future and higher likelihood of reducing or withdrawing from online classes in the fall (although this difference was only marginally significant). Similarly, SoC reported that they would be more severely affected if they were to contract COVID-19 than White students. Somewhat unexpectedly, White students reported significantly more frequent engagement in risk management behaviors (e.g., washing hands) than SoC. There were no significant ethnic differences on stress, coping with COVID-19 disruptions, or either of the personality variables.

In an effort to contribute to documenting the effects of the COVID-19 crisis on the higher-education landscape, this study provides a snapshot of college student academic perceptions and emotional well-being at the end of May 2020. Roughly one-third of students perceived their academic future to be at high risk due to COVID-19. Similarly, about 30% of students indicated that they were likely to reduce or withdraw from classes in the Fall of 2020, should these classes be conducted online. Importantly, this study assessed students’ perceptions, not actual academic decisions (e.g., the decision to enroll in classes). However, the initial reports, as of January 2021, indicate that undergraduate enrollment across all the types of higher education institutions is down about 4% from the previous year; a decline that is twice the rate from the previous Fall 2019 enrollment ( National Student Clearinghouse Research Center, 2020 ).

Consistent with previous research on emotional well-being in college students during COVID-19 (e.g., Ma et al., 2020 ; Son et al., 2020 ), a significant proportion (about one-third) of students reported difficulty coping with COVID-19 related disruptions and the elevated levels of stress. Given research showing that college students are at particularly high risk for adverse mental health outcomes ( Son et al., 2020 ), this study demonstrates that these concerns likely persist and, in fact, may be exacerbated by the pandemic. Interestingly, students’ emotional well-being was significantly related to academic perceptions but was unrelated to perceptions of COVID-19. Likewise, perceptions of COVID-19 were related to each other (e.g., perceptions of disease severity correlated with frequency of engaging in risk management behaviors) but were unrelated to academic perceptions. Thus, students are experiencing the high levels of stress, difficulty coping with COVID-19 disruptions, and have academic concerns specific to COVID-19, yet these variables were unrelated to their perceptions of COVID-19 itself.

These results above suggest that emotional well-being may have a stronger relationship with variables that have a more “immediate” impact on students’ lives, rather than their overall perceptions of the disease itself. For example, academic performance or changes in the home environment (e.g., those imposed by social distancing/lockdown measures) may impact students’ well-being or academic beliefs more than perceptions of the virus. Indeed, students coping well with COVID-19 disruptions (a measure assessing the immediate impact of COVID-19) were more likely to agree that the transition to an online teaching format was the correct choice.

Of note, none of the participants in this sample reported testing positive for COVID-19, and the vast majority (97%) reported that no immediate family member tested positive. Therefore, the relationship between disease perception and emotional well-being should be tested in a sample that has more direct experience with the virus (e.g., changes in stress and coping before and after a positive diagnosis of COVID-19). Likewise, this survey investigated coping with COVID-19 disruptions via a single-item in order to understand how students’ perceptions of those disruptions impact their emotional well-being. It is important to acknowledge that students utilize different mechanisms for dealing with stress (coping strategies). For example, college women tend to use more emotion-focused coping strategies compared to college men ( Brougham et al., 2009 ). Further, students’ lifestyle habits and coping strategies can effectively mitigate stress, but not all strategies are equally effective and different races/genders utilize different strategies ( Welle and Graf, 2011 ). Thus, future investigations would benefit from a deeper investigation of which coping strategies may be particularly effective for students during the COVID-19 pandemic.

In line with previous research ( Gunthert et al., 1999 ; Mak et al., 2010 ; Roddenberry and Renk, 2010 ; Widiger and Oltmanns, 2017 ; Kroencke et al., 2020 ), higher neuroticism and a more external locus of control were related to greater academic concerns and worse emotional well-being. This suggests that some students may be particularly at risk for poor emotional well-being during the pandemic. Exploratory analyses revealed that females in our sample reported higher stress levels and worse coping with COVID-19 disruptions than males. This gender difference in emotional well-being could be partly explained by the higher levels of neuroticism seen in our female sample as is typical of research on gender differences in personality (e.g., McCrae and Terracciano, 2005 ). It is also possible that female students face unique stressors during the pandemic that contribute to poor emotional health. For example, female students may be more likely to take on additional domestic or caregiving responsibilities during quarantine compared to male students. This seems a likely possibility, as previous research shows that females are disproportionately likely to serve as caregivers for ill family members compared to males ( Bott et al., 2017 ). Balancing caregiving responsibilities with academic work may place female students at particular risk for negative mental health outcomes during COVID-19. Future research should investigate the role of both neuroticism and additional responsibilities faced by female students on their mental health.

Alarmingly, the students of color reported the perceptions of greater risk for their academic future and the higher likelihood of reducing or withdrawing from online classes in the Fall of 2020. In fact, according to recent surveys by the National Student Clearing House, Fall 2020 enrollment for minority students is down 6–10% from the previous year’s numbers ( National Student Clearinghouse Research Center, 2020 ). This is in line with data showing that ethnic minority students are disproportionately likely to suffer negative educational consequences due to the pandemic ( NAACP, 2020 ). These findings are particularly disconcerting, as they indicate that pre-existing inequalities in access to quality education are likely to continue to widen. Additionally, minority college students are more likely to rely on higher education institutions to meet basic needs, such as food and housing ( NAACP, 2020 ), thus, withdrawal from classes during the pandemic has the potential to create problems beyond the interruption of education. Institutions of higher education should be cognizant of discrepancies in both academic and basic needs for minority students and work toward the implementation of interventions to support these students.

Though these data reveal several interesting relationships between academic perceptions, emotional well-being, and personality, they do not imply causation. The diversity of our sample (majority White and female) largely reflects the institution, where the survey was created and is not representative of all United States undergraduates. Additionally, our survey did not differentiate individuals from a socioeconomic perspective. It is likely that along with ethnicity, socio-economic inequities exacerbate pre-existing achievement gaps among students in higher education ( Borman and Rachuba, 2001 ; Stephens et al., 2012 ). Indeed, it is possible that students’ perceptions and risk behaviors regarding COVID-19 do impact their academic perceptions and emotional well-being, but the relationship is moderated by factors related to socio-economic status (e.g., reliable internet access). Given the cross-sectional nature of the current study and limitations addressed above, longitudinal studies are needed to assess the long-term impact on student academic perceptions and emotional well-being.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the Arcadia University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AC, JD, and LF contributed significantly to the development, implementation, analysis, and subsequent reporting of this study. AC is the corresponding author (data available upon request). JD prepared all figures. LF assisted with editing and finalizing references. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all the professors who voluntarily distributed the survey to their students.

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• Introduction
• Conclusions
• Article Information

Standardized prevalence rates for participants, excluding nonbinary students, were calculated, creating a sample size of 68 106 students for T1; 22 205, T2; and 44 157, T3. Stress decreased 10.3% between T1 and T2 and increased 2.5% between T2 and T3. Anxiety decreased 18.4% between T1 and T2 and increased 13.9% between T2 and T3. Depression decreased 11.9% between T1 and T2 and increased 22.2% between T2 and T3. Suicidal thoughts increased 16.0% between T1 and T2 and 12.2% between T2 and T3. Posttraumatic stress disorder (PTSD) increased 61.9% between T2 and T3.

a Comparisons between T3 and T1 and between T2 and T1 were not conducted because acute distress was measured at T1 (using the Impact of Events Scale–Revised) rather than PTSD.

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Wathelet M , Horn M , Creupelandt C, et al. Mental Health Symptoms of University Students 15 Months After the Onset of the COVID-19 Pandemic in France. JAMA Netw Open. 2022;5(12):e2249342. doi:10.1001/jamanetworkopen.2022.49342

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Mental Health Symptoms of University Students 15 Months After the Onset of the COVID-19 Pandemic in France

• 1 Department of Psychiatry, Centre Hospitalo-Universitaire de Lille, Lille, France
• 2 Fédération de Recherche en Psychiatrie et Santé Mentale des Hauts-de-France, Lille, France
• 3 Centre National de Ressources et de Résilience Lille-Paris, Lille, France
• 4 University Lille, Inserm, Centre Hospitalo-Universitaire de Lille, U1172–Lille Neuroscience & Cognition, Lille, France
• 5 Assistance Publique–Hôpitaux de Paris, Avicenne Hospital, Department of Infant, Child and Adolescent Psychiatry, Sorbonne Paris Nord University, Centre de recherche en Epidémiologie et Santé des Populations, Bobigny, France
• 6 Fonds Fédération Hospitalière de France Recherche et Innovation, Paris, France

Question   Has the mental health of university students in France changed 15 months after the start of the COVID-19 pandemic?

Findings   In this cross-sectional study of 44 898 university students who participated in the third measurement time of the Conséquences de la pandémie de COVID-19 sur la santé mentale des étudiants (COSAMe) survey, high prevalence rates for stress (20.6%), anxiety (23.7%), depression (15.4%), suicidal thoughts (13.8%), and posttraumatic stress disorder (29.8%) were observed.

Meaning   These results suggest that the pandemic may have had long-lasting consequences on the mental health of students.

Importance   The Conséquences de la pandémie de COVID-19 sur la santé mentale des étudiants (COSAMe) survey was conducted among university students in France during the COVID-19 pandemic and found that although there was a slight decrease in anxiety, depression, and stress between the first lockdown (T1) and 1 month after it ended (T2), the prevalence of suicidal ideation had increased between these periods and 1 in 5 students had probable posttraumatic stress disorder (PTSD) at T2. These results emphasize the need to explore the long-term consequences of the COVID-19 pandemic.

Objectives   To measure the prevalence of mental health symptoms among university students in France 15 months after the first lockdown (T3) and to identify factors associated with outcomes.

Design, Setting, and Participants   This cross-sectional study reports data from the third measurement time of the repeated COSAMe survey, which took place from July 21 to August 31, 2021, through an online questionnaire sent to all French university students.

Main Outcomes and Measures   The prevalence of suicidal thoughts, PTSD (PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition] [PCL-5]), stress (Perceived Stress Scale), anxiety (State-Trait Anxiety Inventory), and depression (Beck Depression Inventory) at T3 were gender- and degree-standardized and compared with prevalence rates at T1 and T2. Multivariable logistic regression analyses identified risk factors.

Results   A total of 44 898 students completed the questionnaires. They were mainly women (31 728 [70.7%]), and the median (IQR) age was 19 (18-21) years. Standardized prevalence rates of stress, anxiety, depression, suicidal thoughts, and PTSD were 20.6% (95% CI, 20.2%-21.0%), 23.7% (95% CI, 23.3%-24.1%), 15.4% (95% CI, 15.1%-15.8%), 13.8% (95% CI, 13.5%-14.2%), and 29.8% (95% CI, 29.4%-30.2%), respectively. Compared with the decreased prevalence rates at T2, there was an increase at T3 for stress (2.5% increase), anxiety (13.9% increase), and depression (22.2% increase). The prevalence of suicidal ideation continued to increase from T1 (10.6%) to T3 (13.8%), and the prevalence of probable PTSD increased from 1 in 5 students to 1 in 3 students between T2 and T3. Female and nonbinary participants; participants without children and living in an urban area; and those with financial difficulties, a chronic condition, psychiatric history, COVID-19 history, social isolation, and low perceived quality of information received were at risk of all poor outcomes at T3 (eg, stress among women: adjusted OR, 2.18; 95% CI, 2.05-2.31; suicidal thoughts among nonbinary respondents: adjusted OR, 5.09; 95% CI, 4.32-5.99; anxiety among students with children: adjusted OR, 0.68; 95% CI, 0.56-0.81; depression among students living in a rural area: adjusted OR, 0.80; 95% CI, 0.75-0.85).

Conclusions and Relevance   These results suggest severe long-lasting consequences associated with the pandemic on the mental health of students. Prevention and care access should be a priority.

The COVID-19 pandemic had a major impact on mental health. Numerous studies conducted during the first months of the pandemic found high rates of mental health symptoms (stress, distress, anxiety, depression, posttraumatic stress) in the general population. 1 The student population, whose vulnerability to mental health disorders was already well known, 2 was quickly identified as particularly at risk of negative psychological repercussions from the pandemic and associated health measures. 1 , 3 , 4

In France, the repeated cross-sectional survey Conséquences de la pandémie de COVID-19 sur la santé mentale des étudiants (COSAMe), whose first measurement time (T1) took place during the first lockdown (March 17 to May 11, 2020), reported high prevalence rates of severe self-reported stress (24.7%; 95% CI, 24.4%-25.1%), anxiety (27.5%; 95% CI, 27.1%-27.8%), depression (16.1%; 95% CI, 15.8%-16.4%), and suicidal thoughts (11.4%; 95% CI, 11.2%-11.7%) among the 69 054 participants. Overall, nearly half of students were affected by at least 1 severe mental health issue. 4 During the second measurement period (T2), 1 month after the lifting of the lockdown, the prevalence of anxiety, depression, and stress had decreased without reaching prepandemic levels. In contrast, suicidal ideation increased from 11.4% to 13.2% (95% CI, 12.8%-13.6%), and symptoms of posttraumatic stress disorder (PTSD) were reported by nearly 1 in 5 students. 5

The COVID-19 pandemic has been characterized by the occurrence of multiple waves of outbreaks and multiple measures deployed to limit the consequences of these waves. 6 While there is still a growing body of research on the consequences of the pandemic on students’ mental health, studies assessing the long-term impacts are rarer. However, the direct (infections, hospitalizations, deaths) and indirect (economic crisis, difficulties in accessing care, isolation) consequences of the pandemic are likely to induce a very long-lasting mental health crisis, 7 , 8 and recommendations invite monitoring the mental health of populations over the next few years. 9 The present study used data from the third measurement time (T3) of the COSAMe survey, conducted 15 months after the beginning of the COVID-19 pandemic to (1) measure the prevalence rates of self-reported mental health symptoms (stress, anxiety, depression, PTSD, and suicidal thoughts) and (2) identify factors associated with mental health outcomes.

The study used data from the repeated cross-sectional university-based survey COSAMe, which consisted of 3 measurement times: T1, during the first lockdown (April 17 to May 4, 2020); T2, 1 month after the lift of the first lockdown (June 15 to July 15, 2020); and T3, 15 months after the start of the pandemic (July 21 to August 31, 2021). At each time, the French Ministry of Higher Education, Research, and Innovation requested the 82 universities to send an email to their students (target population, approximately 1 600 000 students) asking them to participate in the survey by completing online self-administered questionnaires. Due to the heterogeneity of sanitary measures from 1 country to another, the study only included students residing in France during the first lockdown.

The 2 first measurement times have already been analyzed, and the results have been published elsewhere. 4 , 5 , 10 For this study, they are recalled to facilitate the interpretation of the third measurement time.

This survey was reviewed by a French research ethics committee, the Comité de Protection des Personnes Ile de France VIII, before its initiation. For T3, financial compensation was offered: €100 was awarded to 100 students randomly selected from those who completed the questionnaire entirely. To maintain anonymity, at the end of the questionnaire, students were directed to a page disconnected from the questionnaire, allowing them to enter their contact details to participate. Consent is not required for this type of observational study. An information note presented before the questionnaire informed the students about the study and the possibility of refusing to participate. Completion of the questionnaire was considered consent to participate. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

The following outcomes were screened: (1) suicidal thoughts, by asking participants whether they had experienced suicidal thoughts during the preceding month (yes or no); (2) PTSD, using the French version of the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PCL-5), a 20-item scale that explores PTSD symptom severity over the past month 11 , 12 ; (3) stress, using the 10-item Perceived Stress Scale (PSS-10) to evaluate stress experiences during the preceding month 13 - 15 ; (4) depression, using the 13-item Beck Depression Inventory (BDI-13) to assess current depression symptoms 16 , 17 ; and (5) anxiety, using the 20-item State-Trait Anxiety Inventory, State subscale (STAI Y-2), to measure the intensity of current anxiety symptoms. 18 , 19 The Cronbach α of the 4 scales in the sample were all greater than 0.89.

Outcomes were the presence of severe symptoms, ie, the presence of suicidal thoughts or a score above the threshold identified in the literature on 1 of the scales (PCL-5, >32 of 80; PSS-10, >26 of 40; BDI-13, >15 of 39; STAI Y-2, >55 of 80). 13 - 21 We considered the following covariates to evaluate their association with the outcomes: (1) sociodemographic characteristics, age (in years), gender (male, female, other), academic degree (bachelor, master, doctorate), being a foreign student (yes, no), living area (urban, semiurban, rural), and having children (yes, no); (2) precariousness, financial difficulties (important for students reporting that it is difficult to make ends meet every month, moderate for participants for whom it is a bit difficult, low or no financial difficulties for others); (3) health-related data, history of psychiatric follow-up (benefiting from follow-up by a health professional for mental health reasons before the pandemic: yes, no), chronic condition (physical infirmity, handicap, or chronic disease: yes, no), COVID-19 (positive test for SARS-CoV-2 infection or suspected but without confirmatory test: yes, no); (4) social isolation, students who never physically meet or who only have very episodic contacts (sometimes in the year or less) with the members of all of their social networks (family, friends, neighbors, classmates, or members of their associative activities) were considered isolated; and (5) information data, perceived quality of information received about COVID-19 (on a scale of 10).

First, we described the sample using medians with IQRs for the scores of the measurement tools and quantitative covariates, since they were mostly not normally distributed. We used numbers and percentages for scores ranked by level and other qualitative variables.

To compare the results of T3 with those of T1 and T2, we calculated gender- and degree-standardized prevalence rates, using the university student population 2019 to 2020 published by the French Ministry of National Education 22 and excluding nonbinary students given that their proportion among students was not available. We conducted χ 2 tests to compare prevalence rates 2 by 2 (ie, T1 vs T2, T2 vs T3, T1 vs T3). PTSD was measured at T2 and T3 because according to the DSM-5, symptoms of PTSD must last for at least 1 month. 23 At T1, we measured acute distress using the Impact of Events Scale–Revised.

Bivariate analyses were conducted to test the association between outcomes and covariates using χ 2 tests, and multivariate logistic regression models identified risk factors of reporting at least 1 poor outcome (suicidal thoughts, PTSD, stress, depression, or anxiety) at T3. Then, similar models were conducted for each outcome. All explanatory variables were included except age due to collinearity with the year of study. Associations between risk factors and outcomes were presented as odds ratios (ORs) and 95% CIs.

Data analysis was performed using R version 3.6.1 (R Project for Statistical Computing). The level of significance was set at .05, and all tests were 2-sided.

A total of 55 457 students opened the online questionnaire. Among them, 44 898 completed (81.0%) it entirely and were analyzed.

The sample was mainly composed of women (31 728 [70.7%]), with 12 429 (27.7%) men and 741 students (1.6%) identifying as nonbinary. The median (IQR) age was 19 (18-21) years. Half of the respondents (22 716 [50.6%]) were in their first academic year, and 36 772 (81.9%) were bachelor students, whereas only 880 (2.0%) were in the sixth year or more. Among the participants, 3276 (7.3%) declared being foreign students, and 698 (1.5%) had children. Finally, 19 909 (44.3%) lived in an urban area, 11 808 (26.3%) in a semiurban area, and 13 181 (29.4%) in a rural area.

Nearly 1 in 8 students reported important financial difficulties (5830 [13.0%]). This proportion increased to 1 in 3 (15 844 [35.3%]) when we considered important and moderate financial difficulties together.

Regarding health information, 4002 respondents (8.9%) declared having a history of psychiatric follow-up, and 4713 (10.5%) reported having a chronic condition. More than one-quarter of the participants (12 105 [27.0%]) reported having had COVID-19 (positive test or suspicion without confirmatory test). Concerning social ties, 2013 (4.5%) were socially isolated. Finally, participants rated the quality of the information related to COVID-19 and quarantine, giving a median (IQR) score of 5 (3-7) of 10.

Crude and standardized prevalence rates are described in Table 1 . Among the 44 898 respondents, the crude prevalence rates of anxiety, depression, stress, PTSD, and suicidal thoughts were 25.0% (95% CI, 24.6%-25.4%), 16.9% (95% CI, 16.6%-17.3%), 22.5% (95% CI, 22.1%-22.9%), 31.0% (95% CI, 30.6%-31.4%), and 15.0% (14.7%-15.3%), respectively. After gender- and degree-standardization, prevalence rates were slightly lower (23.7% [95% CI, 23.3%-24.1%], 15.4% [95% CI, 15.1%-15.8%], 20.6% [95% CI, 20.2%-21.0%], 29.8% [95% CI, 29.4%-30.2%], and 13.8% [95% CI, 13.5%-14.2%], respectively). The medians (IQRs) of the scores were 45 (34-56) for the STAI Y-2 (anxiety), 8 (4-13) for the BDI-13 (depression), 20 (15-26) for the PSS-10 (stress), and 22 (10-37) for the PCL-5 (PTSD).

The Figure presents the standardized prevalence rates measured at T3 as well as those measured previously at T2 and T1. For stress, depression, and anxiety, a V-shaped pattern was identified, with a decrease in prevalence at T2 and an increase at T3. Prevalence rates at T3 were lower than those at T1 for stress (20.6% vs 22.4%) and anxiety (23.7% vs 25.5%), but higher for depression (15.4% vs 14.3%); however, these results represented a 2.5% increase for stress, a 13.8% for anxiety, and a 22.2% increase for depression compared with T2. The prevalence of PTSD was particularly high at T3, with 29.8% of students affected, compared with 18.4% at T2. Finally, the prevalence of suicidal ideation increased since the beginning of the survey, reaching 13.8% at T3, against 12.3% at T2 and 10.6% at T1. All tests performed were significant ( P  < .001), except for the comparison of stress prevalence between T2 and T3 ( P  = .13).

Bivariate analyses are presented in Table 2 . Multivariate analyses are presented in Table 3 .

After adjustment, women and nonbinary students had increased risks of poor mental health symptoms compared with men (eg, stress among women: adjusted OR, 2.18; 95% CI, 2.05-2.31; suicidal thoughts among nonbinary respondents: adjusted OR, 5.09; 95% CI, 4.32-5.99). On the contrary, having children and living in a rural area (vs urban area) were associated with less risk (eg, anxiety among students with children: adjusted OR, 0.68; 95% CI, 0.56-0.81; depression among students living in a rural area: adjusted OR, 0.80; 95% CI, 0.75-0.85). Academic degree program was associated with all outcomes, except stress, but with less clear patterns. Compared with bachelor students, PhD students had lower risk of depression (OR, 0.63; 95% CI, 0.51-0.78; P  < .001), PTSD (OR, 0.74; 95% CI, 0.63-0.87; P  < .001), and suicidal thoughts (OR, 0.64; 95% CI, 0.51-0.80; P  < .001). Master students were less at risk for depression (OR, 0.82; 95% CI, 0.76-0.88; P  < .001) and suicidal thoughts (OR, 0.91; 95% CI, 0.84-0.98; P  = .02) but at higher risk for anxiety (OR, 1.12; 95% CI, 1.05-1.19; P  < .001). Being a foreign student was associated with a higher risk of depression (OR, 1.14; 95% CI, 1.04-1.24; P  = .003), PTSD (OR, 1.18; 95% CI, 1.07-1.31; P  = .001), and suicidal thoughts (OR, 1.87; 95% CI, 1.72-2.02; P  < .001).

For all mental health outcomes, the greater the financial difficulties reported by the students, the higher the risk of a poor outcome. Compared with students with no or few difficulties, those reporting moderate difficulties had ORs ranging from 1.36 (95% CI, 1.27-1.45) for suicidal thoughts to 1.75 (95% CI, 1.67-1.84) for PTSD, and those declaring significant difficulties had ORs ranging from 2.19 (95% CI, 2.03-2.35) for suicidal thoughts to 3.44 (95% CI, 3.21-3.68) for depression.

Prevalence rates of mental health disorders were significantly higher among students with a history of COVID-19 (confirmed or suspected), with a chronic condition, and with a history of psychiatry follow-up. ORs ranged from 1.12 (95% CI, 1.05-1.19) for suicidal thoughts to 1.42 (95% CI, 1.36-1.49) for PTSD among those with a history of COVID-19, from 1.49 (95% CI, 1.39-1.59) for PTSD to 1.62 (95% CI, 1.50-1.75) for suicidal thoughts among those with a chronic condition, and from 2.06 (95% CI, 1.92-2.21) for PTSD to 2.81 (95% CI, 2.60-3.03) for suicidal thoughts among those with psychiatry history.

Socially isolated students were consistently at higher risk for mental health issues. ORs ranged from 1.28 (95% CI, 1.16-1.41; P  < .001) for PTSD to 2.07 (95% CI, 1.86-2.30; P  < .001) for depression.

The lower the quality of the information received, the more the students were at risk for severe mental health issues. ORs ranged from 1.25 (95% CI, 1.19-1.32) for PTSD to 1.36 (95% CI, 1.30-1.42) for suicidal thoughts if they rated the quality with a score between 4 and 5 compared with a score greater than 6, and from 1.57 (95% CI, 1.48-1.66) for suicidal thoughts to 1.80 (95% CI, 1.70-1.91) for PTSD if they rated the quality with a score less than 4.

This large nationwide study found high rates of stress, anxiety, depression, suicidal thoughts, and PTSD among university students in France 15 months after the start of the pandemic. By comparing these results with the 2 previous measurement times of the COSAMe survey (during the first lockdown and 1 month after it ended), a V-shaped pattern was observed for anxiety and depression, ie, an increase following a drop in the prevalence rates observed after the lifting of the first lockdown. Only the prevalence of suicidal thoughts has been steadily increasing since the first lockdown. The prevalence of PTSD has reached important levels 15 months after the beginning of the pandemic, with nearly 1 in 3 students concerned. Women and nonbinary students, those without children and living in an urban area, and those with financial difficulties, social isolation, history of psychiatric follow-up, history of COVID-19 (suspected or confirmed), a chronic condition, and low assessment of the quality of the information received had increased risk of mental health issues.

Comparisons with other studies are complex insofar as the prevalence of disorders is influenced by health restrictions (which differed from one country to another), study period (given variations in restrictions over time and seasonality of certain mental disorders), the type of population concerned (as vulnerabilities may vary), and the measuring tools (whose psychometric properties may differ). 24 However, our results are consistent with the study by Charbonnier et al, 24 who measured levels of anxiety and depression among French university students. Using the Hospital Anxiety and Depression Scale, the authors found a higher prevalence of probable depression (between 12.1% and 26.4%) and anxiety (between 26.6% and 45.0%) in 2021 compared with 2020. 24 Results are also consistent with the study by Schmits et al, 28 including 23 307 French university students 1 year after the beginning of the pandemic. The authors reported that 50.6% of participants described anxiety symptoms, 55.1% described depressive symptoms, and 20.8% described suicidal ideation. 28 However, this study was cross-sectional without a prior point of comparison. A few longitudinal studies have been conducted in the general population over a period like that of our study. A study of 1838 Belgian adults, conducted from April 2020 to June 2021, 25 showed that the prevalence of symptoms of anxiety and depression was higher in times of stricter policy measures. This study did not include student status. However, although time trends were similar for all population subgroups, higher levels of both anxiety and depression were generally found in young people. 25 Conversely, a longitudinal study conducted among 988 adults in Argentina showed a gradual increase in anxiety and depression between August 2020 and April 2021. 26 Again, young adults had higher prevalence rates of symptoms than other age groups. 26 Finally, in the study by Lu et al, 27 including 613 French adults, a continuing increase in the mean scores of anxiety and depression symptoms was observed throughout the 2 lockdown periods in France, with younger participants being more vulnerable to anxiety symptoms.

The risk factors identified in our study are similar to those identified in the previous measurement times of the COSAMe survey and consistent with those described in the literature on pandemics or lockdowns. The review by Brooks et al 3 pointed out that gender, psychiatric history, physical symptoms, social isolation, lack of information, and financial loss were all associated with mental health conditions. Schmits et al 28 also identified women and nonbinary individuals as having increased risk for poor mental health as well as a deterioration of the financial situation and reduced contact with family and friends. Of note, for most students, the academic year at T3 differed from the academic year at the time of exposure. Except for students repeating a year or returning to university after a break, they all had been confined while they were in the lower academic year, including the last high school year for first-year students at T3. Consequently, standardization on the current academic degree does not correspond to standardization on the academic degree at the time of exposure, and this may slightly bias the comparisons of the results at T3 with those at T1 and T2. Nevertheless, whatever the measurement time, we found that the prevalence of mental health disorders tended to decrease among those pursuing a higher degree, and in particular, that the risk appeared to be lower among doctoral students. This association is consistent with the literature that has established a strong link between mental health and the level of education. 29

Even if the situation were still unstable in the summer of 2021, restrictions had been reduced compared with the strict lockdown periods. This improvement in the COVID-19 health crisis was instead accompanied by a worsening of the mental health of students. Several hypotheses can explain this situation. The first explanation is that the health crisis has led to a social and economic crisis, with well-known consequences. 30 It has already been shown that, during periods of crisis (natural disasters, war, or epidemics), suicide rates may momentarily decrease before increasing thereafter, particularly under the influence of economic repercussions and social of the crisis. 8 , 31 The occurrence or increase of unemployment, poverty, or even loneliness is likely to contribute to the increase in mental health disorders, such as depression, anxiety, and PTSD, and the suicide rate. 8 However, the specific evolution in the prevalence of PTSD, which reached a particularly high level at T3, raises questions. The recent network approach to psychopathology explains the persistence of disorders over time, even after the disappearance of the initial triggering event (eg, the first lockdown). 32 It posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms 33 : according to this theory, there are causal associations between mental health symptoms, and if these associations are strong enough, the symptoms can be self-perpetuating, regardless of the event that initially triggered them. 34 The COVID-19 pandemic is an unprecedented and particularly complex event, which may be better understood as a collection of several events that are direct or indirect consequences of COVID-19. We hypothesize that the COVID-19 pandemic and its related consequences are underestimated and that prolonged and cumulative exposure to stressors and/or potentially traumatic events during this period could have led to the occurrence or decompensation of mental health disorders, self-sustaining even after the health situation has improved. Although recent and still unstable, exploring the network approach to mental disorders could help to better understand mental health symptoms interact with each other, contributing to a better understanding of how mental disorders persist over time.

This study has limitations. First, although the number of respondents is large, it represents a minority of students (2.8%). The overrepresentation of women and bachelor students was considered by standardizing on gender and academic degree, the only 2 variables common to our sample and those available at the national level via the Ministry of Education. Association measures are only marginally affected by a low response rate. Second, self-administrated tools used in this study cannot be considered diagnostic tools. Third, the present study cannot establish the direct link between the high rates of mental health disorders and the COVID-19 pandemic and its associated restriction measures, even though high rates were also observed in other studies related to the COVID-19 pandemic or previous pandemics. 3 , 35 Fourth, this survey did not include any measures prior to the pandemic. However, as discussed by Wathelet et al 4 regarding estimates obtained at T1, the prevalence rates measured were higher than the prepandemic measurements identified in the literature. Fifth, the seasonality of certain mental disorders might be a subject of concern. Indeed, in our study, T3, unlike T2 and T1, was conducted exclusively during the summer holidays. Although the phenomenon is complex and poorly understood and contrary to most mental health disorders, 36 an increased risk of suicide during late spring and early summer has been observed. 37 Among students, the differences in suicidal ideation between summer and winter were shown to be, in large part, accounted for by belongingness. 38 That being said, although seasonality cannot be strictly controlled here, all of the measurement times took place during spring or summer periods, which limits the bias in the comparisons. Additionally, some other factors could be associated with mental health disorders but have not been considered here, such as relationship, residence, or institution type.

This large nationwide study found high prevalence rates of anxiety, depression, perceived stress, PTSD, and suicidal ideation 15 months after the beginning of the COVID-19 pandemic among university students in France. If a slight decrease had been observed just after the first lockdown for anxiety and depression, evidence shows that suicidal ideation has increased throughout the survey and that PTSD has jumped from 1 in 5 to 1 in 3 students concerned. These results suggest long-lasting consequences associated with the pandemic on the mental health of students. Prevention and care access should be a priority.

Accepted for Publication: November 13, 2022.

Published: December 29, 2022. doi:10.1001/jamanetworkopen.2022.49342

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Wathelet M et al. JAMA Network Open .

Corresponding Author: Marielle Wathelet, MD, Department of Psychiatry, Centre Hospitalo-Universitaire de Lille, CS 70001, 59037 Lille Cedex, France ( [email protected] ).

Author Contributions: Drs D’Hondt and Wathelet had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Wathelet, Fovet, Habran, Vaiva, D’Hondt.

Acquisition, analysis, or interpretation of data: Wathelet, Horn, Creupelandt, Fovet, Baubet, Habran, Martignène, D’Hondt.

Drafting of the manuscript: Wathelet, D’Hondt.

Critical revision of the manuscript for important intellectual content: Horn, Creupelandt, Fovet, Baubet, Habran, Martignène, Vaiva, D’Hondt.

Statistical analysis: Wathelet, Habran, D’Hondt.

Obtained funding: Wathelet, Vaiva, D’Hondt.

Administrative, technical, or material support: Creupelandt, Habran, Vaiva, D’Hondt.

Supervision: Horn, Fovet, Baubet, Vaiva, D’Hondt.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the French National Research Agency and the Hauts-de-France region (ANR-21-HDF1-0013), the Fondation de Lille, and the Fondation de France.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See the Supplement .

Additional Contributions: We thank the French Ministry of Higher Education, Research and Innovation and the French National Center for School and University Affairs (CNOUS) for disseminating the survey. We are also grateful to university students for their participation.

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• Published: 22 April 2024

Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: evidence from students at a university in Finland

• Walid El Ansari 1 , 2 , 3 ,
• Rene Sebena 4 ,
• Kareem El-Ansari 5 &
• Sakari Suominen 6 , 7 , 8  

BMC Public Health volume  24 , Article number:  1103 ( 2024 ) Cite this article

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Metrics details

No previous research of university students in Finland assessed lifestyle behavioral risk factors (BRFs), grouped students into clusters, appraised the relationships of the clusters with their mental well-being, whilst controlling for confounders. The current study undertook this task.

Students at the University of Turku ( n  = 1177, aged 22.96 ± 5.2 years) completed an online questionnaire that tapped information on sociodemographic variables (age, sex, income sufficiency, accommodation during the semester), four BRFs [problematic alcohol consumption, smoking, food consumption habits, moderate-to-vigorous physical activity (MVPA)], as well as depressive symptoms and stress. Two-step cluster analysis of the BRFs using log-likelihood distance measure categorized students into well-defined clusters. Two regression models appraised the associations between cluster membership and depressive symptoms and stress, controlling for sex, income sufficiency and accommodation during the semester.

Slightly more than half the study participants (56.8%) had always/mostly sufficient income and 33% lived with parents/partner. Cluster analysis of BRFs identified three distinct student clusters, namely Cluster 1 (Healthy Group), Cluster 2 (Smokers), and Cluster 3 (Nonsmokers but Problematic Drinkers). Age, sex and MVPA were not different across the clusters, but Clusters 1 and 3 comprised significantly more respondents with always/mostly sufficient income and lived with their parents/partner during the semester. All members in Clusters 1 and 3 were non-smokers, while all Cluster 2 members comprised occasional/daily smokers. Problematic drinking was significantly different between clusters (Cluster 1 = 0%, Cluster 2 = 54%, Cluster 3 = 100%). Cluster 3 exhibited significantly healthier nutrition habits than both other clusters. Regression analysis showed: (1) males and those with sufficient income were significantly less likely to report depressive symptoms or stress; (2) those living with parents/partner were significantly less likely to experience depressive symptoms; (3) compared to Cluster 1, students in the two other clusters were significantly more likely to report higher depressive symptoms; and (4) only students in Cluster 2 were more likely to report higher stress.

Conclusions

BRFs cluster together, however, such clustering is not a clear-cut, all-or-none phenomenon. Students with BRFs consistently exhibited higher levels of depressive symptoms and stress. Educational and motivational interventions should target at-risk individuals including those with insufficient income or living with roommates or alone.

Peer Review reports

Transition to university corresponds with a crucial time of psychosocial stressors including separation from family home, pressures associated with academic work, and unhealthy lifestyle habits [ 1 ]. Mental well-being can be affected in early adulthood during the university years, rendering students’ mental well-being a potential concern [ 2 , 3 , 4 ]. For instance, a study in the USA found that pre-veterinary students’ mental well-being declined as they progressed in their undergraduate careers [ 5 ].

The mental well-being of university students is extremely important, as it is crucial for their academic achievement and social progress, and for the economic development and success of the country [ 6 ]. Two common conditions that are frequently encountered by students are depressive symptoms and stress [ 7 , 8 ].

In terms of depressive symptoms, a systematic review found that their prevalence among students ranged from 1.4 to 73.5% [ 9 ]. Others reported that 60.1% of the students surveyed across 32 distinct degree programs had mild to severe depressive symptoms in Italy [ 10 ], and depressive symptoms were present in 46% of female [ 11 ] and 37% male respondents [ 12 ] in North American medical schools. Presence of depressive symptoms among students is a crucial determinant of their academic and social functioning [ 13 ].

Likewise, students also experience high stress levels [ 14 ]. For nursing students, practicing in clinical settings was a major stress [ 15 ]; and in the UK, students reported stress [ 16 ]. University students’ stress levels are important as it can have negative academic, emotional and health outcomes, and students might employ different unhealthy strategies to cope with stress (e.g., alcohol, smoking, illicit drug/s use, unhealthy eating) [ 17 , 18 , 19 ].

In addition, an interplay between stress and depressive symptoms has been suggested. Students with a history of depression were more likely to experience high stress levels [ 20 ]; and while mild stress can be associated with a positive effect on students by posing alternative solutions to problems, and enhancing motivation, high stress levels are associated with depression [ 21 , 22 ].

Similarly, the interplay between mental well-being and lifestyle habits is important. For instance, starting university negatively influenced students’ well-being, physical activity (PA) levels, and diet quality [ 23 ]; and a survey of freshmen students across five European countries found that stress and depressive symptoms were associated with problem drinking [ 24 ]. Likewise, among students with high levels of depressive symptoms, moderate or vigorous PA was associated with less depressive symptoms [ 25 ].

Sociodemographic characteristics also play a role. The incidence of common mental health problems differs significantly by sociodemographic characteristics such as sex, age, and living place during university time [ 26 ]. For instance, the prevalence and levels of depressive symptoms among female students were significantly higher than among men [ 27 ]; and stress levels were higher among female students than males [ 28 ]. As for accommodation during the semester, living outside the parental home in student dormitories, on campus, or in private homes, whether with roommates or alone, brings less exposure to parental control and more frequent exposure to peer influence, and thus opportunities to engage in unhealthy behaviors such as drinking alcohol, or tobacco and other drug/s use [ 29 , 30 , 31 ].

However, the literature reveals knowledge gaps. To the best of our knowledge, we are not aware of studies of university students in Finland that assessed the relationships between harmful lifestyle behavioral risk factors (BRFs) e.g., smoking, problematic alcohol consumption, low PA, and unhealthy nutrition patterns on the one hand; and depressive symptoms and stress on the other, employing such lifestyle BRFs and using cluster analysis (CA) to categorize students into clusters, before appraising the associations of such clusters with depressive symptoms and stress.

CA is used to identify subgroups of cases based on shared characteristics in heterogeneous samples and combines them into homogeneous groups. It provides a great deal of information about the types of cases and the distributions of variables [ 32 ]. CA is viewed as a quantitative complement to traditional linear statistics that emphasizes diversity and ecological context of behavior rather than central tendencies and simple interactions and is more person-centered and of stronger methodological rationale; nevertheless, traditional approaches are more frequently used in BRF research [ 33 , 34 ]. Given that lifestyle BRFs do not occur in isolation from each other, CA is a sound method that is increasingly being employed to group together university students with similar lifestyle behaviors [ 35 , 36 ].

The current study bridges these knowledge gaps. The aim of the study was to appraise the relationships between clusters of lifestyle BRFs and depressive symptoms and stress. The specific objectives were to: (1) assess four lifestyle BRFs (tobacco, smoking, problematic alcohol use, dietary habits, PA), and group students correspondingly into clusters; (2) compare the socio-demographic features of students in the generated clusters; and, (3) appraise the relationships between the generated BRFs clusters and depressive symptoms and stress.

In this paper, we use the WHO definitions of depressive symptoms (involves a depressed mood or loss of pleasure or interest in activities for long periods of time) and of stress (a state of worry or mental tension caused by a difficult situation) [ 37 , 38 ].

Ethics, Sample, procedures

The study was approved by the Research and Ethics Committee at the University of Turku, Finland (Approval # Lausunto 10/2010) with an informed consent waiver. An email invitation with the research objectives was sent to all first, second- and third-year undergraduate students (n = 4387) enrolled at all faculties of the university, inviting them to participate. A university-wide English-language online survey was used to collect data during the academic year 2013–2014. As skills in English are generally good among young adults in Finland and particularly among university students, translation of the questionnaire into Finnish was not considered necessary. Students from all the seven faculties of the University of Turku (Humanities, Mathematics and Natural Sciences, Medicine, Law, Social Sciences, Education, and Economics) were invited. Participation was voluntary and anonymous, and data were confidential and protected (anonymous, no identifiers, strict access only to the research team, secure computer storage, password updated and regularly changed every month, no paper copies). Students were provided with information about the study as well as contact details for any questions and were informed that by completing the questionnaire, they were providing consent to participate in the study. Both the initial invitation to participate and the subsequent reminder emails fourteen days later were sent to all undergraduate students. In addition, three posters about the study were displayed in the university’s student café and the reminder was announced on the University intraweb site. Webropol platform was used for the online survey and all students received a link to the questionnaire. After completing the survey, students' answers were automatically saved and forwarded to the university’s Student Management Office. The Student Management Office gathered the completed online responses, and the data was entered electronically into an Excel spreadsheet to ensure a high level of quality assurance. Once this stage was completed, the data was forwarded to the research team.

Research tool: Survey Questionnaire

Socio-demographic information included students’ sex and age. Subjective financial situation was measured by a single item: “How sufficient is your income?”, with a 4-point response scale, subsequently dichotomized into (always /mostly sufficient vs. always/mostly insufficient) [ 24 ]. Students were also asked about the type of accommodation during the semester, and responses were dichotomized into “I live with my parents/ partner” vs “with roomates/alone” [ 29 ]. The rationale is that living with a partner/ parents may involve compromise and need to respect one another’s boundaries and preferences which may lead individuals to engage in fewer risky behaviors; while living in student dormitories, in private flats, either with roommates or alone, entails less exposure to parental control and more exposure to peer influence and therefore to opportunities to engage in problematic behavior [ 39 , 40 ].

Perceived stress was measured with the four-item version of Cohen’s perceived stress scale (PSS) that measures the degree to which situations in one’s life over the past month are appraised as stressful [ 41 ]. The questions are of a general nature and items are designed to detect how unpredictable, uncontrollable, and overloaded respondents find their lives, e.g. “How often have you felt that you were unable to control the important things in your life?”; and, “How often have you felt confident about your ability to handle your personal problems?”. Students responded on a five-point scale (0 = “never”, 1 = “almost never”, 2 = “sometimes”, 3 = “fairly often”, 4 = “very often”). The PSS score was obtained by summing up answers to individual questions. Items were recoded so that higher scores indicated more perceived stress. Cronbach’s alpha coefficient was 0.75.

Depressive symptoms were measured using a modified version of the Beck Depression Inventory (M-BDI) [ 42 ]. Participants were asked to describe how often they experienced 20 depressive feelings during the past few days with 6-point scale responses (from 0=”never” to 5 = “almost always”). Sample items include: “I feel sad”, “I feel I am being punished”, “I have thoughts of killing myself”, and “I have lost interest in other people”. The M-BDI score is obtained by summing up answers to individual questions. The scale showed good level of reliability. Cronbach’s alpha coefficient was 0.94.

Problem drinking was assessed using CAGE screening test for problem alcohol use, consisting of four questions (Have you ever felt you should C ut down on your drinking? Have people A nnoyed you by criticizing your drinking? Have you ever-felt bad or G uilty about your drinking? Have you ever had a drink in the morning to get rid of a hangover? ( E ye opener). Each item has 2 response options (“Yes,” “No”) [ 43 ]. Two or three affirmative answers suggest problem drinking. We classified the respondents as non-problematic drinkers (less than two positive answers) and problematic drinkers (two or more positive answers).

Smoking was measured with the item “Within the last 3 months, how often did you smoke (cigarettes, pipes, cigarillos, cigars)?” with response options “Daily,” “Occasionally,” and “Never” [ 44 ].

Dietary assessment (12 items): respondents self-reported their dietary habits in a food frequency questionnaire, which included 12 variables assessing their consumption of sweets, cakes/crackers, fast food and canned foods, fresh fruit, raw and cooked vegetables and salads, meat and fish, dairy products and cereals. In the initial question “How often do you eat the following foods?“, students were asked about the frequency of their usual consumption of each food group separately (5-point scale: “several times a day”, “daily”, “several times a week”, “1–4 times a month”, “never”). The question elicited information on student’s overall food consumption. The instrument was based on pre-existing food frequency questionnaires, adapted and used in previous studies [ 45 ].

Dietary guideline adherence score was calculated based on students’ responses to the food frequency questionnaire [ 45 ]. There are no specific guidelines for sweets, cakes/cookies, snacks, fast food/canned foods and sodas/soft drinks, so “1–4 times per month” and “never” were used as the recommended values. We used the above composite food intake pattern score (sweets, cakes/cookies, and snacks score) to assess sweets, cakes/cookies, and snacks combined, and healthy eating was considered present if this score was ≤ 6, corresponding to intake of these items “less often than 1–4 times a month” for each food item. Each of the fast food/canned food and lemonade/soft drink items was included as a separate item in the calculation of the objective dietary guideline adherence score. For other food groups, the WHO recommendations for the European region were used [ 46 ]. Subsequently, for the number of daily servings of fruit, raw and cooked vegetables, the cut-off value was “daily” or “several times a day”; and for meat, the cut-off was “less than daily” and for fish, it was “several times a week”. Milk and cereals were not included in the calculation of the dietary guideline adherence score because information on milk and cereals was generally too non-specific to be categorized as healthy/unhealthy. The dietary guideline adherence score (Supplementary File 1) has a maximum of 8 points calculated from the recommendations of 8 food groups: (1) sweets, cookies, snacks; (2) fast food/canned food; (3) lemonade/soft drinks; (4) fruit; (5) salad, raw vegetables; (6) cooked vegetables; (7) meat; and (8) fish [ 44 , 46 ].

Two forms of physical activity (i.e., vigorous PA, moderate PA) were assessed using the questions, “On how many of the past 7 days did you: (1) participate in vigorous exercise of ≥ 20 min; (2) participate in moderate exercise of ≥ 30 min?” For each form of PA, students reported the number of days they engaged in such activity (range 0–7 days). Moderate-to-vigorous PA (MVPA) was calculated by combining moderate-intensity PA and vigorous-intensity PA [ 47 ].

Statistical analysis

Quantitative variables were presented as mean ± standard deviation, while qualitative variables were presented as frequency and percent. Independent samples t-test compared quantitative variables, while Pearson chi-square test compared qualitative variables. Two-step cluster analysis was applied to 4 BRFs (tobacco smoking, alcohol drinking, PA, eating behavior) to identify clusters that differed in criterion variables within the dataset, and the procedure combined pre-clustering and hierarchical methods. A log-likelihood distance measure was used in the two-step cluster analysis because the BRFs comprised continuous and categorical variables. Cluster number selection was automated using the Schwarz Bayesian criterion. Within each cluster, the frequency of categories and percentages were reported for categorical BRFs, whereas mean ± standard deviation were reported for continuous BRFs. Differences in the distribution of sociodemographic characteristics and BRFs across clusters were tested using Chi-square tests for categorical variables or independent samples t-tests for continuous variables. Two separate multiple linear regression models examined the association between cluster membership and depressive symptoms and stress while adjusting for participant’s sex, income sufficiency, and accommodation during semesters. Any missing values were not imputed. The number of missing values was negligible, hence we decided to use complete case analysis which limits the analysis to respondents with complete data [ 48 ]. Statistical analyses were performed using SPSS v25.0 and statistical significance was set at p  < 0.05.

Characteristics of the sample

The total number of responses was 1179 (response rate = 27%). Mean age of the students was ≈ 23 (SD 5) years and 823 (70.4%) were female. More than half the respondents reported always/mostly sufficient income (Table  1 ). During university semesters, about a third of the students lived with parents or partners. Daily smoking was rare (about 6%), and mean MVPA was 4.27 ± 3.27 days/week. Sex differences were apparent as significantly more females exhibited non-problematic drinking and had healthier eating habits.

Clustering of four behavioral risk factors among students

The silhouette value of cohesion and separation was ≈ 0.7 indicating good cluster quality. A total of 82 participants with missing values in the items used for the cluster analysis were not included, reducing the sample size to 1097. Cluster analysis of the four BRFs resulted in three well-defined clusters, namely Cluster 1 (Healthy Group, n  = 649), Cluster 2 (Smokers, n  = 245), and Cluster 3 (Nonsmokers but Problem Drinkers, n  = 203). There were significant differences across the clusters for some of the sociodemographic characteristics (Table  2 ). Age and sex were not different across the clusters, however, Cluster 1 and Cluster 3 comprised significantly more respondents who reported always/ mostly sufficient income and lived with their parents/ partner during semester time.

The clusters exhibited significant differences across most of the BRFs. However, all students in Clusters 1 and 3 had never smoked, but all Cluster 2 students were occasional/daily smokers. Although problematic drinking increased from Cluster 1 (0%) to Cluster 3 (100%), Cluster 3 respondents had significantly healthier nutrition habits than both other clusters. MVPA was not significantly different between the 3 clusters, however an additional post-hoc test revealed a significant difference in MVPA between Cluster 1 and Cluster 2 ( p  = 0.02).

Associations of behavioral risk factor clusters with depressive symptoms and perceived stress

For the regression analysis, Table  3 depicts that two sociodemographic characteristics (female sex, income insufficiency) were significantly associated with both depressive symptoms and perceived stress. Males and those with sufficient income were significantly less likely to report depressive symptoms or stress. Accommodation status during the semester was significantly associated with depressive symptoms only, those living with parents/partner were significantly less likely to experience depressive symptoms. Compared to Cluster 1, students in the two other clusters were significantly more likely to report higher depressive symptoms. However, only Cluster 2 was significantly more likely to be associated with higher perceived stress.

University students are at a critical stage of their lives, transitioning into adulthood with its unique challenges that can impact their lifestyle and health behaviors. BRFs among students refer to behaviors that can increase their risk of developing negative health outcomes [ 49 ]. Hence, there have been calls to actively provide vulnerable students with the support required to manage their mental well-being [ 50 ]. To the best of our knowledge, this study is the first to cluster four BRFs among a large sample of university students in Finland, and to weigh up the links of the clusters with depressive symptoms and stress.

Our main findings revealed three distinct BRFs clusters, with significant differences across almost all the BRFs under examination. Cluster 1 (Healthy Group) comprised students with healthier lifestyle habits who did not smoke and had no problematic drinking. On the other hand, Cluster 2 (Smokers) included occasionally/ daily smokers and almost half were problem drinkers; and Cluster 3 (Nonsmokers but Problem Drinkers) comprised 100% problematic drinkers.

These findings confirm that lifestyle BRFs do not appear in a solitary manner and do not transpire in isolation from each other. Rather, they cluster together in constellations, where individuals engaging in one risky behavior are more likely to engage in other risky behaviors. Conversely, students with healthier lifestyles are likely to maintain healthy diets, not smoke and have no problematic drinking. Except for Cluster 1, the other two clusters represented students with 50% and 100% problematic alcohol consumption. It could be that for these young adults at this stage of life within a university setting characterized by a heightened sense of fraternity, excessive drinking patterns might be part of the student life [ 51 ].

The current study assessed the relationships between the BRFs clusters and depressive symptoms and stress after adjusting for sex, income sufficiency and accommodation during semesters. In terms of gender, the current findings demonstrate that males were significantly less likely to report depressive symptoms and stress, congruent with a body of evidence among students in several countries [ 28 , 52 ]. The so-called gender paradox in health is that women live longer than men but have more chronic and mental health problems throughout the life course [ 53 ]. A recent study assessed sex differences in mental well-being using items that included feeling unhappy or depressed, having lost confidence in oneself and being unable to overcome one’s problems [ 54 ]. This study found that sex differences in mental well-being in the Nordic countries are not particularly small and also remain when other social and lifestyle factors are considered [ 54 ]. Similarly, another recent study on loneliness, mental well-being, and self-esteem among adolescents in four Nordic countries (Denmark, Finland, Iceland, Sweden) found the prevalence of positive mental well-being among boys was higher than girls; boys had higher self-esteem compared to girls; and feelings of loneliness were more frequent among girls [ 55 ]. Such existence of poorer health outcomes and gender differences in mental well-being within Nordic countries despite their robust welfare systems and gender equality policy, has been proposed to be a reflection of complex societal influences [ 54 ].

As regards income sufficiency and accommodation during the semester, students with sufficient income were less likely to report depressive symptoms and stress, congruent with studies where perceived socioeconomic status (SES) predicted mental and general well-being [ 56 , 57 ]. Although SES is not the sole predictor of mental well-being, its impact can help to identify at-risk populations and inform policy decisions aimed at reducing health disparities. In addition, living with parents/partners was protective for depressive symptoms in the current study. This is supported by other evidence where young adults living with parents/partners reported fewer depressive symptoms compared to those who live alone/with roommates, as living with parents/partner can provide a sense of security, financial stability, social and emotional support, and a sense of belonging which positively impacts mental well-being [ 58 ].

Associations between BRF clusters and stress

Compared to Cluster 1 (Healthy Group), Cluster 2 (Smokers) who also exhibited some problem drinking and had significantly lower MVPA, were more likely to report higher stress ( p  < 0.01), even after adjusting for the three potential confounders. This is congruent with a raft of studies where the use of alcohol or other substances, as well as the presence of more substance-related problems, were associated with higher stress [ 24 , 59 ]. Tension reduction theory holds that tension-producing circumstances (i.e., stressors) might lead to increased drinking, as alcohol is perceived to reduce tension and therefore increased tension (strains or stress) may cause drinking [ 60 ]. In addition, regular PA, whether moderate or intense, helped to reduce stress [ 61 ], improve mood [ 62 ] and sleep quality [ 63 ], all important for managing stress.

Such differences that we identified between Cluster 1 and Cluster 2 in terms of the association of the latter with higher stress were not observed when comparing Cluster 1 (Healthy Group) with Cluster 3 (Nonsmokers but Problem Drinkers). Healthy eating habits among the risk-taking Cluster 3 students may serve as protective factor against perceived stress, supporting studies that found stress was associated with unhealthy eating behavior changes [ 64 , 65 ].

Associations between BRF clusters and depressive symptoms

Compared to Cluster 1 (Healthy Group), the two other less healthy clusters were significantly more likely to be associated with higher depressive symptoms after adjusting for sex, income sufficiency and accommodation during semesters. These findings are consistent with research of college students that found relationships between depressive symptoms and various BRFs such as problematic drinking [ 24 ] or sedentary behavior and physical inactivity [ 66 ].

The association between cluster membership and depressive symptoms exhibited a p value of < 0.05 when Cluster 1 was compared with Cluster 3 (Nonsmokers but Problem Drinkers, but simultaneously also physically active). However, when Cluster 1 (Healthier group) was compared with Cluster 2 (Smokers who also simultaneously exhibited the least PA), the significance level increased ( p  < 0.001). This suggests that the relationship between cluster and depressive symptoms was more pronounced among Cluster 2 students. As highlighted above, PA might have numerous mental well-being benefits, including reducing the risk of developing depressive symptoms, as regular exercise helps to improve mood, reduce stress, and increase the release of the natural mood-enhancing endorphins in the brain [ 67 , 68 , 69 ]. Therefore, a sedentary lifestyle and lack of PA can increase the risk of depressive symptoms.

This study has limitations. The survey was cross-sectional, so the direction of the association between BRFs and depressive symptoms and stress cannot be ascertained. A point to note is that BRFs such as physical inactivity or problematic drinking can be a consequence of stress or depressive symptoms [ 70 ], although the relationship has been suggested to be bi-directional, where sedentary behavior and drinking might also lead to depressive symptoms [ 71 ]. Data were self-reported, with possible recall, social desirability/ sociability biases; and the response rate was not very high which is quite common with internet-based surveys [ 72 ] and could negatively impact representativeness of a sample which in turn affects the internal validity and limits generalizability of findings [ 73 ]. As we were unable to obtain data about those who did not participate in the survey, we could not assess differences between students who participated in the survey and those who did not.

The study has many strengths, including a large sample of students from across all the university departments/faculties categorized into clusters, reporting on a wide range of BRFs pertinent to health, thus extending previous studies that focused on a single/few health behavior(s). It is the first study among university students in Finland that appraised and categorized students into BRFs clusters and explored the associations of the clusters with two mental well-being indicators, whilst controlling for several potential confounders. In the questionnaire, an item regarding problems related to completion of the survey instrument in English was included, but according to the responses, almost none of the respondents reported serious difficulties understanding any of the questions.

BRFs include problematic drinking, smoking, low PA, and unhealthy dietary patterns. These risk factors usually do not occur in isolation but rather tend to cluster together, creating congregations that are associated with depressive symptoms and stress among university students. Fortunately, BRFs are a product of lifestyle choices, and therefore can be potentially modified through effective behavioral modification interventions. Our findings are important to educators, policymakers and other stakeholders involved with these young adult populations. Prevention and intervention efforts could focus on risk groups (e.g., students with insufficient income, living with roommates or alone) and on implementing effective educational and motivational interventions to encourage regular PA, healthy eating habits and nutrition, as well as smoking cessation and responsible drinking programs.

Data availability

Data are available from the authors upon reasonable request to corresponding authors.

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Acknowledgements

The authors thank the university and students who participated in the survey.

Rene Sebena was supported by the Slovak Research and Development Agency under the contract No. APVV-19-0284.

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Conceptualization, Walid El Ansari and Sakari Suominen; methodology, Walid El Ansari, Rene Sebena, Sakari Suominen; software, Rene Sebena; investigation, Walid El Ansari, Sakari Suominen, Rene Sebena and Kareem El-Ansari; data curation, Walid El Ansari, Rene Sebena, Sakari Suominen and Kareem El-Ansari; writing original draft preparation, Walid El Ansari, Rene Sebena and Kareem El-Ansari. All authors have read and agreed to the published version of the manuscript.

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El Ansari, W., Sebena, R., El-Ansari, K. et al. Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: evidence from students at a university in Finland. BMC Public Health 24 , 1103 (2024). https://doi.org/10.1186/s12889-024-18421-0

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This cross-sectional study, conducted amid the COVID-19 pandemic, delves into the intricate connections between coping strategies and levels of anxiety and depression, presenting vital implications for medical, clinical, and broader societal contexts. As crises like the pandemic highlight the importance of adaptive coping, this investigation underscores the imperative to comprehend and address maladaptive coping strategies. The study utilized a diverse sample of 386 participants during the pandemic's peak, employing online platforms for recruitment and ensuring broad demographic representation. Data were collected through self-report measures, including the Patient Health Questionnaire-4 (PHQ-4) for depression and anxiety symptoms and the Brief Coping Orientation to Problems Experienced (COPE) inventory to assess coping skills across various domains. The coping skills assessment measured strategies such as Self-Distraction, Active Coping, Denial, Substance Use, Emotional and Instrumental Support, Behavioral Disengagement, Venting, Positive Reframing, Planning, Humor, Acceptance, Religion, and Self-Blame. The Colorado Multiple Institutional Review Board prioritized and approved ethical considerations, and participants provided informed consent. Data analysis involved rigorous cleaning, recoding, and quantitative analysis using SPSS. Descriptive statistics, regression analyses, and correlation analyses were employed to uncover nuanced relationships between coping strategies and mental health outcomes, contributing to understanding the phenomena under investigation within the context of the pandemic. The findings highlight the pivotal role of individualized approaches and the potential of humor as an essential coping mechanism, emphasizing the need for tailored interventions during crises.

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