parts of the research methodology chapter 3

Methods Section: Chapter Three

The methods section , or chapter three, of the dissertation or thesis is often the most challenging for graduate students.  The methodology section, chapter three should reiterate the research questions and hypotheses, present the research design, discuss the participants, the instruments to be used, the procedure, the data analysis plan , and the sample size justification.

Research Questions and Null Hypotheses

Chapter three should begin with a portion that discusses the research questions and null hypotheses.  In the research questions and null hypotheses portion of the methodology chapter, the research questions should be restated in statistical language.  For example, “Is there a difference in GPA by gender?” is a t-test type of question, whereas “Is there a relationship between GPA and income level?” is a correlation type of question.  The important thing to remember is to use the language that foreshadows the data analysis plan .  The null hypotheses are just the research questions stated in the null; for example, “There is no difference in GPA by gender,” or “There is no relationship between GPA and income level.”

Research Design

The next portion of the methods section, chapter three is focused on developing the research design.  The research design has several possibilities. First, you must decide if you are doing quantitative, qualitative, or mixed methods research. In a quantitative study, you are assessing participants’ responses on a measure.  For example, participants can endorse their level of agreement on some scale.  A qualitative design is a typically a semi-structured interview which gets transcribed, and the themes among the participants are derived.  A mixed methods project is a mixture of both a quantitative and qualitative study.

Participants

In the research methodology, the participants are typically a sample of the population you want to study.  You are probably not going to study all school children, but you may sample from the population of school children.  You need to include information about the characteristics of the population in your study (Are you sampling all males? teachers with under five years of experience?).  This represents the participants portion of your methods section, chapter three.

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Instruments

The instruments section is a critical part of the methodology section, chapter three.  The instruments section should include the name of the instruments, the scales or subscales, how the scales are computed, and the reliability and validity of the scales.  The instruments portion should have references to the researchers who created the instruments.

The procedure section of the methods chapter is simply how you are going to administer the instruments that you just described to the participants you are going to select.  You should walk the reader through the procedure in detail so that they can replicate your steps and your study.

Data Analysis Plan

The data analysis plan is just that — how you are going to analyze the data when you get the data from your participants.   It includes the statistical tests you are going to use, the statistical assumptions of these tests, and the justification for the statistical tests.

Sample Size Justification

Another important portion of your methods chapter three, is the sample size justification.  Sample size justification (or power analysis) is selecting how many participants you need to have in your study.  The sample size is based on several criteria:  the power you select (which is typically .80), the alpha level selected (which is typically .05), and the effect size (typically, a large or medium effect size is selected).  Importantly, once these criteria are selected, the sample size is going to be based on the type of statistic: an ANOVA is going to have a different sample size calculation than a multiple regression.

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How To Write Chapter Three Of Your Research Project (Research Methodology)

Methodology In Research Paper

Chapter three of the research project or the research methodology is another significant part of the research project writing. In developing the chapter three of the research project, you state the purpose of research, research method you wish to adopt, the instruments to be used, where you will collect your data, types of data collection, and how you collected it.

This chapter explains the different methods to be used in the research project. Here you mention the procedures and strategies you will employ in the study such as research design, study design in research, research area (area of the study), the population of the study, etc.

You also tell the reader your research design methods, why you chose a particular method, method of analysis, how you planned to analyze your data. Your methodology should be written in a simple language such that other researchers can follow the method and arrive at the same conclusion or findings.

You can choose a survey design when you want to survey a particular location or behavior by administering instruments such as structured questionnaires, interviews, or experimental; if you intend manipulating some variables.

The purpose of chapter three (research methodology) is to give an experienced investigator enough information to replicate the study. Some supervisors do not understand this and require students to write what is in effect, a textbook.

A research design is used to structure the research and to show how all of the major parts of the research project, including the sample, measures, and methods of assignment, work together to address the central research questions in the study. The chapter three should begin with a paragraph reiterating the purpose of research.

It is very important that before choosing design methods, try and ask yourself the following questions:

Will I generate enough information that will help me to solve the research problem by adopting this method?

Method vs Methodology

I think the most appropriate in methods versus methodology is to think in terms of their inter-connectedness and relationship between both. You should not beging thinking so much about research methods without thinking of developing a research methodology.

Metodologia or methodology is the consideration of your research objectives and the most effective method  and approach to meet those objectives. That is to say that methodology in research paper is the first step in planning a research project work. 

Design Methodology: Methodological Approach                

Example of methodology in research paper, you are attempting to identify the influence of personality on a road accident, you may wish to look at different personality types, you may also look at accident records from the FRSC, you may also wish to look at the personality of drivers that are accident victims, once you adopt this method, you are already doing a survey, and that becomes your  metodologia or methodology .

Your methodology should aim to provide you with the information to allow you to come to some conclusions about the personalities that are susceptible to a road accident or those personality types that are likely to have a road accident. The following subjects may or may not be in the order required by a particular institution of higher education, but all of the subjects constitute a defensible in metodologia or methodology chapter.

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Methodology

A  methodology  is the rationale for the research approach, and the lens through which the analysis occurs. Said another way, a methodology describes the “general research strategy that outlines the way in which research is to be undertaken” The methodology should impact which method(s) for a research endeavor are selected in order to generate the compelling data.

Example Of Methodology In Research Paper :

• Phenomenology: describes the “lived experience” of a particular phenomenon
• Ethnography: explores the social world or culture, shared beliefs and behaviors
• Participatory: views the participants as active researchers
• Ethno methodology: examines how people use dialogue and body language to construct a world view
• Grounding theory*: assumes a blank slate and uses an inductive approach to develop a new theory

A  method  is simply the tool used to answer your research questions — how, in short, you will go about collecting your data.

Methods Section Of Research Paper Example :

• Contextual inquiry
• Usability study
• Diary study

If you are choosing among these, you might say “what method should I use?” and settle on one or more methods to answer your research question.

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Research Design Definition: WRITING A RESEARCH DESIGN

A qualitative study does not have variables. A scientific study has variables, which are sometimes mentioned in Chapter 1 and defined in more depth in Chapter 3. Spell out the independent and dependent, variables. An unfortunate trend in some institutions is to repeat the research questions and/or hypotheses in both Chapter 1 and Chapter 3. Sometimes an operational statement of the research hypotheses in the null form is given to set the stage for later statistical inferences. In a quantitative study, state the level of significance that will be used to accept or reject the hypotheses.

Pilot Study

In a quantitative study, a survey instrument that the researcher designed needs a pilot study to validate the effectiveness of the instrument, and the value of the questions to elicit the right information to answer the primary research questions in. In a scientific study, a pilot study may precede the main observation to correct any problems with the instrumentation or other elements in the data collection technique. Describe the pilot study as it relates to the research design, development of the instrument, data collection procedures, or characteristics of the sample.

Instruments

In a research study, the instrument used to collect data may be created by the researcher or based on an existing instrument. If the instrument is the researcher created, the process used to select the questions should be described and justified. If an existing instrument is used, the background of the instrument is described including who originated it, and what measures were used to validate it.

If a Likert scale is used, the scale should be described. If the study involves interviews, an interview protocol should be developed that will result in a consistent process of data collection across all interviews. Two types of questions are found in an interview protocol: the primary research questions, which are not asked of the participants, and the interview questions that are based on the primary research questions and are asked of the participants.

In a qualitative study, this is the section where most of the appendices are itemized, starting with letters of permission to conduct the study and letters of invitation to participate with the attached consent forms. Sample: this has to do with the number of your participants or subjects as the case may be. Analysis (how are you planning to analyze the results?)

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EFFECTIVE GUIDE AND METHODOLOGY SAMPLES

This chapter deals effectively with the research methods to be adopted in conducting the research, and it is organized under the following sub-headings:

• Research Design
• Area of Study

The population of the Study

• Sample and Sampling Techniques
• Instruments for Data Collection

The validity of the Instrument

Reliability of the Instrument

• Administration of the instruments
• Scoring the instruments

Method of Data Collection

Method of Data Analysis

Research Design:

This has to do with the structure of the research instrument to be used in collecting data. It could be in sections depending on different variables that form the construct for the entire topic of the research problems. A reliable instrument with a wrong research design will adversely affect the reliability and generalization of the research. The choice of design suitable for each research is determined by many factors among which are: kind of research, research hypothesis, the scope of the research, and the sensitive nature of the research.

Area of Study:

Research Area; this has to do with the geographical environment of the study area where the places are located, the historical background when necessary and commercial activities of that geographical area. For example, the area of the study is Ebonyi State University. At the creation of Ebonyi State in 1996, the Abakaliki campus of the then ESUT was upgraded to Ebonyi State University College by Edict no. 5 of Ebonyi State, 1998 still affiliated to ESUT with Prof. Fidelis Ogah, former ESUT Deputy Vice-Chancellor as the first Rector. In 1997, the Faculty of Applied and Natural Sciences with 8 departments was added to the fledging University, and later in 1998 when the ESUT Pre-Science Programme was relocated to Nsukka, the EBSUC Pre-Degree School commenced lectures in both Science and Arts in replacement of the former. This study focused on the students of the Business Education department in Ebonyi state university.

The population is regarded in research work as the type of people and the group of people under investigation. It has to be specific or specified. For example educational study teachers in Lagos state. Once the population is chosen, the next thing is to choose the samples from the population.

According to Uma (2007), the population is referred to as the totality of items or object which the researcher is interested in. It can also be the total number of people in an area of study. Hence, the population of this study comprised of all the students in the department of Business Education, Ebonyi State University which is made up of year one to four totaling 482. The actual number for the study was ascertained using Yaro-Yamane's formula which stated thus:

n   =        N

N is the Population

1 is constant

e is the error margin

Then, n   =         482

1+482(0.05)2

= 214.35 approximately 214

Sample and sampling technique:

It may not be possible to reach out to the number of people that form the entire population for the study to either interview, observe, or serve them with copies of the questionnaire. To be realistic, the sample should be up to 20% of the total population. Two sampling techniques are popular among all the sampling techniques. These are random and stratified random sampling techniques. (A). in Random Sampling, the writers select any specific number from a place like a school, village, etc. (B). In Stratified Random Sampling, one has to indicate a specific number from a stratum which could be a group of people according to age, qualification, etc. or different groups from different locations and different considerations attached.

Instruments for Data Collection:

This is a device or different devices used in collecting data. Example: interview, questionnaire, checklist, etc. instrument is prepared in sets or subsections, each set should be an entity thus asking questions about a particular variable to be tested after collecting data. The type of instrument used will determine the responses expected. All questions should be well set so as to determine the reliability of the instrument.

This has to do with different measures in order to determine the validity and reliability of the research instrument. For example, presenting the drafted questionnaire to the supervisor for scrutiny. Giving the questionnaire to the supervisor for useful comments and corrections would help to validate the instrument.

The test-retest reliability method is one of the simplest ways of testing the stability and reliability of an instrument over time. The test-retest approach was adopted by the researcher in establishing the reliability of the instrument. In doing this 25 copies of the questionnaire were administered on twenty-five selected respondents. After two weeks another 25 copies of the same questionnaire were re-administered on the same group. Their responses on the two occasions were correlated using Parsons Product Moment Correlation. A co-efficient of 0.81 was gotten and this was high enough to consider the instrument reliable.

Administration of the instruments:

Here, the writer states whether he or she administers the test personally or through an assistant. He also indicates the rate of return of the copies of the questionnaire administered.

Scoring the instruments:

Here items on the questionnaire or any other device used must be assigned numerical values. For example, 4 points to strongly agree, 3 points to agree, 2 points to disagree, and 1 point to strongly disagree.

Table of Analysis

The researcher collected data using the questionnaire. Copies of the questionnaire were administered by the researcher on the respondents. All the respondents were expected to give maximum co-operation, as the information on the questionnaire is all on things that revolve around their study. Hence, enough time was taken to explain how to tick or indicate their opinion on the items stated in the research questionnaire.

In this study, the mean was used to analyze the data collected. A four (4) point Likert scale was used to analyze each of the questionnaire items.

The weighing was as follows:

VGE—————- Very Great Extent (4 points)

GE—————– Great Extent (3 points)

LE—————– Little Extent (2 points)

VLE—————- Very Little Extent (1 point)

SA—————– Strongly Agree (4 points)

A——————- Agree (3 points)

D—————— Disagree (2 points)

SD—————- Strongly Disagree (1 point)

The mean of the scale will then be determined by summing up the points and dividing their number as follows with the formula:

Where; x= mean

f= frequency

X= Nominal value of the option

∑= summation

N= Total Number

Therefore, the mean of the scale is 2.5.

This means that any item statement with a mean of 2.50 and above is considered agreed by the respondents and any item statement below 2.5 is considered disagreed.

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Research Methodology

• First Online: 29 June 2019

Cite this chapter

• Vaneet Kaur 3  

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

1090 Accesses

The chapter presents methodology employed for examining framework developed, during the literature review, for the purpose of present study. In light of the research objectives, the chapter works upon the ontology, epistemology as well as the methodology adopted for the present study. The research is based on positivist philosophy which postulates that phenomena of interest in the social world, can be studied as concrete cause and effect relationships, following a quantitative research design and a deductive approach. Consequently, the present study has used the existing body of literature to deduce relationships between constructs and develops a strategy to test the proposed theory with the ultimate objective of confirming and building upon the existing knowledge in the field. Further, the chapter presents a roadmap for the study which showcases the journey towards achieving research objectives in a series of well-defined logical steps. The process followed for building survey instrument as well as sampling design has been laid down in a similar manner. While the survey design enumerates various methods adopted along with justifications, the sampling design sets forth target population, sampling frame, sampling units, sampling method and suitable sample size for the study. The chapter also spells out the operational definitions of the key variables before exhibiting the three-stage research process followed in the present study. In the first stage, questionnaire has been developed based upon key constructs from various theories/researchers in the field. Thereafter, the draft questionnaire has been refined with the help of a pilot study and its reliability and validity has been tested. Finally, in light of the results of the pilot study, the questionnaire has been finalized and final data has been collected. In doing so, the step-by-step process of gathering data from various sources has been presented. Towards end, the chapter throws spotlight on various statistical methods employed for analysis of data, along with the presentation of rationale for the selection of specific techniques used for the purpose of presentation of outcomes of the present research.

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Pet Ownership Statistics 2024

Fact Checked

Updated: Jan 25, 2024, 11:09am

Pet ownership in the U.S. has jumped significantly over the past three decades. As of 2024, 66% of U.S. households (86.9 million homes) own a pet. [1] That’s up from 56% in 1988, pet ownership statistics show. From companionship to emotional support, pets are a vital part of their owners’ lives. In fact, 97% of pet owners consider their pets to be a part of their family. [12]

Forbes Advisor conducted a deep dive into the latest available pet owner statistics to determine which pets are most popular, how pet ownership and spending habits differ by generation, the cost of pet ownership and the most common lifestyle sacrifices made by pet owners.

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Pet Ownership in the U.S. at a Glance

• 66% of U.S. households (86.9 million homes) own a pet. [1]
• Dogs are the most popular pet in the U.S. (65.1 million U.S. households own a dog), followed by cats (46.5 million households) and freshwater fish (11.1 million households). [1]
• Millennials make up the largest percentage of current pet owners (33%), followed by Gen X (25%) and baby boomers (24%). [1]
• In 2022, Americans spent $136.8 billion on their pets, up nearly 11% from 2021 ($123.6 billion). [1]
• Essential dog expenses cost an average of $1,533 annually. [10]
• 42% of dog owners and 43% of cat owners got their pets from a store, while 38% of dog owners and 40% of cat owners got their pets from an animal shelter or rescue. [2]

Pet Owner Statistics

The percentage of pet owners in the U.S. spiked over the past 35 years, according to pet owner statistics analyzed by Forbes Advisor.

The steady rise in pet ownership in the U.S. comes at a time when pet insurance is also rapidly gaining in popularity. As of 2022, more than 4.8 million pets are insured, a 124.9% increase from 2018. [8]

Pet insurance can prevent you from paying the full cost of unexpected vet bills if your pet is injured or gets sick. It’s a smart way to add a layer of financial security to your budget. Here are more interesting facts related to pet ownership:

• As of 2024, 66% of U.S. households (86.9 million homes) own a pet. [1]
• Pet ownership has increased significantly over the past three decades. In 1988, only 56% of U.S. households owned a pet. [1]
• More than half of pet owners (51%) consider their pets to be as much a part of their family as a human family member. [12]
• 78% of pet owners surveyed by Forbes Advisor acquired pets during the pandemic. [3]
• Households with annual incomes of $100,000 and over are most likely to own pets: 63% of households in this income bracket own dogs and 40% own cats. [2]
• Homeowners are more likely to own pets than renters: 58% of homeowners have a dog and 36% have a cat versus 39% and 29% of renters respectively. [2]
• Americans in rural areas are more likely to own pets than Americans in suburban and urban areas. 71% of adults living in rural areas have a pet. [12]
• Residents of rural areas are also more likely to have multiple pets: 47% of adults in rural areas have more than one pet, compared with 32% in the suburbs and 26% in urban areas. [12]
• 42% of dog owners and 43% of cat owners got their pets from a store, while 38% of dog owners and 40% of cat owners got their pet from an animal shelter or rescue. [2]
• 23% of dog owners report getting their dog from a breeder compared to 7% of cat owners. [2]
• Over one third of Americans (35%) have more than one pet. [12]

Pet ownership by generation

Pet ownership statistics reveal that millennials comprise the highest percentage of pet owners in the U.S:

• Gen Z pet owners (ages 18 to 25) are far more likely than other age groups to have a variety of pets. [3]
• Gen X pet owners (ages 42 to 57) are the least likely to own pets that aren’t cats and dogs, such as hamsters, birds and fish. [3]

Most Popular Pets in the U.S.

The most common pets in the U.S. are dogs and cats, but there’s still a lot of love for other animals and species. Millions of households include fish, birds and small animals like hamsters and rabbits.

The most popular pets in the U.S. are [1] :

• Dogs (65.1 million households)
• Cats (46.5 million households)
• Freshwater fish (11.1 million households)
• Small animals such as hamsters, gerbils, rabbits, guinea pigs, chinchillas, mice and ferrets (6.7 million households)
• Birds (6.1 million households)

Are cats or dogs more popular?

Whether cats or dogs make better companions has long been a bone of contention among many pet parents. Popularity certainly has nothing to do with how much a pet is loved by its human family. But in terms of ownership, more households include dogs than cats.

• Dogs are more popular than cats in the U.S. As of 2022, 44.5% of U.S. households own dogs and 29% of households own cats. [2]
• Between 2016 and 2022, the percentage of U.S. households who own dogs increased by 6.1 percentage points, from 38.4% to 44.5%, while the percentage of households that own cats increased by 4 percentage points, from 25% to 29%. [2]

Cost of Pet Ownership

While the love of a pet is priceless, the cost of owning one is not. Veterinary care, grooming, food, treats and other outlays can add up quickly. A Forbes Advisor analysis found that essential dog expenses cost an average of $1,533 annually. [10]

This includes the cost of:

• Dog boarding for a seven day vacation: $253
• Veterinary care: $679.50
• Pet insurance: $601.01

Dog owners who rely on doggy day care twice per week can expect to spend an additional $2,980 per year on average. [10]

And if an unexpected vet bill pops up for a major incident, you can be on the hook for thousands of dollars—42% of pet owners say they can’t cover a surprise vet bill of $999 or less without going into debt. [6]

Pet insurance can partially reimburse you when you pay the bill for your pet’s unexpected accidents and illnesses. While you may not want to add another expense to your pet care costs, you might be surprised at how affordable pet insurance can be.

The average pet insurance cost for dogs is $44 a month, and the average pet insurance cost for cats is $30 a month, based on Forbes Advisor’s analysis. Having pet insurance is like putting a leash on your potential vet costs so they don’t run out of control. That can leave you with more money to spend on spoiling your furry companion.

Here’s a closer look at the cost of pet ownership:

• Dog owners spend the most on veterinary care ($367 per year), food ($339 per year) and grooming ($99 per year). [2]
• Cat owners spend the most on food ($310 per year), veterinary care ($253 per year) and toys ($50 per year). [2]
• Gen Z pet owners (ages 18 to 25) are the most likely to spoil their pets with birthday cakes (34%), birthday presents (39%) and clothing or costumes (32%). [3]
• Gen Z pet owners are also the most likely to spend money on behavioral training (41%), doggy daycare (35%), specialized pet food (44%) and dog walking services (31%). [3]

The Most Expensive Cities to Own a Dog

A Forbes Advisor analysis found that Winston-Salem, North Carolina , tops the list of most expensive cities for dog owners. [10]

Greensboro, North Carolina; Bakersfield, California; El Paso, Texas ; and Memphis, Tennessee round out the top five most expensive cities for dog owners. [10]

Regional trends for the most expensive cities to own a dog:

• Three of the top 15 most expensive cities for dog owners are located in Nevada, including North Las Vegas, Las Vegas and Henderson. [10]
• California cities appeared at both the top and bottom of the list. Two of the 15 most expensive cities for owning a dog are located in California, including Bakersfield and Fresno. Two of the 10 most affordable cities for dog owners are also located in California, including the city that ranked cheapest overall, San Jose. [10]

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Total pet industry expenditures.

Spending on pets is up, mirroring increases in pet ownership and pet insurance sales:

• Americans spent $136.8 billion on their pets in 2022, up 10.68% from 2021 ($123.6 billion). [1]
• This includes $58.1 billion spent on pet food and treats, $31.5 billion spent on supplies, live animals and over-the-counter medications, $35.9 billion billion spent on vet care and $11.4 billion spent on other services (all services outside of veterinary care such as boarding, grooming, pet insurance, and training). [1]
• Between 2018 and 2022, the amount spent on pets in the U.S. increased by 51.16% from $90.5 billion to $136.8 billion. [1]

Dog Owner Regrets

There’s no denying the benefits of dog ownership, but being responsible for a dog comes with challenges. These obligations can cause some to have regrets about owning a dog, a Forbes Advisor survey of 2,000 dog owners found. Messes, challenges in finding care for the dog and dog training are among the top reasons they regret getting a dog. The cost of vet bills is also a burden for some dog owners, and can affect how often dogs go to the vet .

Here’s more about dog owner regrets and concerns about vet bills:

• 54% of dog owners have regrets about getting a dog. [5]
• Dog owners cited cleaning up after a dog as the biggest challenge associated with dog ownership (27%), followed by finding care for the dog when traveling or going to work (26%), training the dog (25%), cost (24%) and barking or whining (24%). [5]
• A vet bill of $999 or less would cause 42% of pet owners to go into debt, while a vet bill of $499 or less would cause 28% of pet owners to go into debt. [6]
• 3% of pet owners gave their pet away between 2021 and 2022. [6] The top reasons cited for selling or giving a pet away were inflation (12%), the rising cost of rent (10%), inability to afford a pet’s medical bills (7%) and the cost of pet deposits for apartments (5%). [6]

Top Sacrifices Made by Dog Owners

Nearly all pet owners (97%) consider their pets to be a part of their family. [12] And a Forbes Advisor survey of 10,000 dog owners found that dog owners make professional, financial and lifestyle sacrifices for their canine companions. [7]

The top lifestyle sacrifices made by dog owners include:

• 39.29% lived on a tighter budget to afford their dogs’ expenses.
• 13.96% moved from an apartment to a house so their dog would have a yard.
• 7.47% stayed at a job they disliked because it allowed them to work remotely or had a dog-friendly office.
• 6.78% broke up with a significant other who didn’t like their dog.
• 5.25% took a pay cut or accepted a position with fewer benefits to work remotely or have access to a dog-friendly office.
• 4.57% left a job they liked because another company let them work from home or had a dog-friendly office.
• 36% of dog owners reported that they would spend $4,000 or more out-of-pocket on life-saving medical care for their dogs.

States with the most devoted dog owners

Colorado tops the list of states with the most devoted dog owners, followed by Virginia , Georgia , Alaska and Nevada. [7] These aren’t the only states home to devoted dog owners:

• Five of the top 10 states with the most devoted dog owners are located in the Pacific and West, including Colorado , Alaska, Nevada, Washington and Oregon . [7]
• Ohio dog owners were most likely to report living on a tighter budget to afford their dogs’ expenses (51%), followed by Wisconsin (44.5%) and Montana dog owners (44%). [7]
• Nevada dog owners were most likely to report that they moved from an apartment to a house so their dogs would have a yard (20%), followed by Colorado (19.5%) and Kansas dog owners (19%). [7]
• Nevada dog owners were most likely to leave a job they liked because another company let them work from home or had a dog-friendly office (8.5%). [7]
• Dog owners from Rhode Island were most likely to stay at a job they disliked because it allowed them to work remotely or had a dog-friendly office (15.5%). [7]

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States with the most spoiled dogs.

A Forbes Advisor survey of 10,000 dog owners found that Florida tops the list of states with the most spoiled dogs. Alaska, Washington , Colorado and California round out the top five states with the most spoiled dogs. [4]

Most Pet-Friendly Cities

Features like hardwood floors, stainless steel appliances and skyline views are nice, but that’s not the top priority for many pet owners. Almost three-quarters (72%) of prospective home buyers with pets would pass on their dream home if it didn’t accommodate their pets. [11]

If you’re a pet owner on the hunt for pet-friendly apartments with access to nearby dog parks, pet stores and affordable veterinary costs, we compared the 91 most populated cities to find the best cities for pet owners. We analyzed data across four key categories: cat and dog veterinary costs, veterinary access and pet-friendly spaces.

We found that Tucson, Arizona , tops the list of best cities for pet owners, followed by Raleigh, North Carolin a; Nashville, Tennessee; Wichita, Kansas; and Cincinnati, Ohio . [9]

Regional trends for the most pet-friendly cities:

• Four of the top 10 cities for pet owners are located in the South, including Raleigh, North Carolina; Nashville, Tennessee; Plano, Texas; and Louisville, Kentucky. [9]
• Pet owners in Memphis, Tennessee, pay the lowest vet prices for both dogs and cats. San Francisco is the most expensive city for dog veterinary care and ties with San Jose, California, as the most expensive city for cat veterinary care. [9]

What’s the Primary Reason Why You Are Unlikely to Buy Pet Insurance in the Next Three Months?

Among dog owners who do not have pet insurance and are unlikely to buy pet insurance in the next three months, 42% said the primary reason is because they think it’s too expensive, according to a Forbes Advisor survey.

Yet 89% of dog owners estimate that the cost of pet insurance is higher than it actually is. Only 11% of dog owners correctly estimated an average cost below $50 a month. And 76% of dog owners overestimate the cost of pet insurance by at least three times the average price, our survey has found.

How Much Would You Estimate Pet Insurance Costs per Month for a 3-Month Old Puppy, for $5,000 of Annual Coverage?

Pet insurance plans typically have a choice of maximum annual coverage amounts, such as $5,000, but some plans offer unlimited annual coverage. This financial safety net can help dog owners reduce the potential cost of a dog.

Fears of Big Veterinarian Bills

Half (50%) of dog owners say they are very concerned or somewhat concerned about their ability to pay for an unexpected vet bill in the next three months.

How Concerned Are You About Your Ability To Pay for an Unexpected Veterinary Bill in the Next Three Months?

Pet insurance is a good way to help offset expensive vet bills for problems like ACL ruptures, broken bones, cancer, heart disease, swallowed objects and common illnesses, such as ear infections and digestive issues.

Majority Say Vet Bill of $2,000 or Less Is “Unaffordable”

An unexpected accident or illness such as a dog’s broken bone, cancer, torn knee ligament or toxic ingestion can cost thousands of dollars in vet bills, according to a Forbes Advisor analysis of the cost of vet visits . Most dog owners (77%) consider various amounts of vet bills of $2,000 or below to be “unaffordable.”

Dog owners without pet insurance are vulnerable to the prospect of racking up debt to pay a surprise vet bill.

How Much Money Would an Unexpected Vet Bill Cost for You To Consider It “Unaffordable”?

Vet-Related Expenses Among Cuts for Dog Owners on a Tight Budget

Dog owners on a tight budget may have to face some tough financial decisions when it comes to their pups. While 51% of dog owners say they’d cut spending on items such as dog treats, outfits and toys, some dog owners say they’d cut veterinary-related expenses to save money.

More than one-quarter (27%) of dog owners say they would not pay for surgery for their dogs (elective or emergency) and 17% say they would reduce veterinary checkups.

Which Dog-Related Expenses Would You Cut if Your Budget Were Tight? (Select up to Three)

Pet insurance can help cover costs such as surgeries and medication. In addition, you can typically add a wellness plan to cover the cost of routine vet checkups and vaccinations.

Many Dog Owners Unlikely To Buy Pet Insurance in the Next Three Months

Despite fears of big vet bills, 47% of dog owners say they are unlikely to buy pet insurance in the next three months.

How Likely or Unlikely Are You To Buy a New Pet Insurance Policy in the Next Three Months?

Nearly two-thirds (63%) of pet owners said they would have difficulty paying a surprise vet bill amid inflation, according to a Forbes Advisor survey on pet costs and inflation. More than a quarter of pet owners (28%) said a vet bill of $499 or less would cause them to go into debt, while a bill of $999 or less would cause 42% to go into debt.

Even with so many pet owners concerned that a hefty vet bill would wreak financial havoc, more than three-quarters (79%) of pet owners said they do not have pet insurance, according to a Forbes Advisor survey. Many pet owners are unlikely to purchase pet insurance amid inflation:

• Nearly one-third (30%) of survey respondents said they are much less likely or somewhat less likely to buy pet insurance amid inflation.
• And 22% said they were much more likely or somewhat more likely to buy pet insurance amid inflation.

Find The Best Pet Insurance Companies Of 2024

Which of the following vet bill amounts would cause you to go into debt.

Survey methodology

Online surveys of 1,000 U.S. adults who own at least one dog were commissioned by Forbes Advisor and conducted by market research company OnePoll, in accordance with the Market Research Society’s code of conduct. Data was collected May 8-22, 2023. The margin of error is +/- 3.1 points with 95% confidence. This survey was overseen by the OnePoll research team, which is a member of the MRS and has a corporate membership with the American Association for Public Opinion Research (AAPOR). For a complete survey methodology, including geographic and demographic sample sizes, contact [email protected] .

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Michelle is a lead editor at Forbes Advisor. She has been a journalist for over 35 years, writing about insurance for consumers for the last decade. Prior to covering insurance, Michelle was a lifestyle reporter at the New York Daily News, a magazine editor covering consumer technology, a foreign correspondent for Time and various newswires and local newspaper reporter.

This paper is in the following e-collection/theme issue:

Published on 12.8.2024 in Vol 26 (2024)

The Impact of a Gamified Intervention on Daily Steps in Real-Life Conditions: Retrospective Analysis of 4800 Individuals

Authors of this article:

Original Paper

• Alexandre Mazéas 1, 2, 3 , PhD   ; 
• Cyril Forestier 4 , PhD   ; 
• Guillaume Harel 3 , MSc   ; 
• Martine Duclos 2, 5 , MD, PhD   ; 
• Aïna Chalabaev 1 , PhD  

1 Laboratoire Sport et Environnement Social (SENS), Université Grenoble Alpes, Grenoble, France

2 National Research Institute for Agriculture, Food and Environment (INRAE), Clermont-Ferrand, France

3 Kiplin, Nantes, France

4 Laboratoire Motricité, Interactions, Performance (MIP - UR4334), Nantes Université, Nantes, France

5 Department of Sport Medicine and Functional Exploration, University Hospital Clermont-Ferrand, Hospital G. Montpied, Clermont-Ferrand, France

Corresponding Author:

Alexandre Mazéas, PhD

Laboratoire Sport et Environnement Social (SENS)

Université Grenoble Alpes

1741 Rue de la Piscine

Grenoble, 38000

Phone: 33 476635081

Email: [email protected]

Background: Digital interventions integrating gamification features hold promise to promote daily steps. However, results regarding the effectiveness of this type of intervention are heterogeneous and not yet confirmed in real-life contexts.

Objective: This study aims to examine the effectiveness of a gamified intervention and its potential moderators in a large sample using real-world data. Specifically, we tested (1) whether a gamified intervention enhanced daily steps during the intervention and follow-up periods compared to baseline, (2) whether this enhancement was higher in participants in the intervention than in nonparticipants, and (3) what participant characteristics or intervention parameters moderated the effect of the program.

Methods: Data from 4819 individuals who registered for a mobile health Kiplin program between 2019 and 2022 were retrospectively analyzed. In this intervention, participants could take part in one or several games in which their daily step count was tracked, allowing individuals to play with their overall activity. Nonparticipants were people who registered for the program but did not take part in the intervention and were considered as a control group. Daily step counts were measured via accelerometers embedded in either commercial wearables or smartphones of the participants. Exposure to the intervention, the intervention content, and participants’ characteristics were included in multilevel models to test the study objectives.

Results: Participants in the intervention group demonstrated a significantly greater increase in mean daily steps from baseline than nonparticipants ( P <.001). However, intervention effectiveness depended on participants’ initial physical activity. The daily steps of participants with <7500 baseline daily steps significantly improved from baseline both during the Kiplin intervention (+3291 daily steps) and the follow-up period (+945 daily steps), whereas participants with a higher baseline had no improvement or significant decreases in daily steps after the intervention. Age ( P <.001) and exposure ( P <.001) positively moderated the intervention effect.

Conclusions: In real-world settings and among a large sample, the Kiplin intervention was significantly effective in increasing the daily steps of participants from baseline during intervention and follow-up periods compared to nonparticipants. Interestingly, responses to the intervention differed based on participants’ initial steps, with the existence of a plateau effect. Drawing on the insights of self-determination theory, we can assume that the effect of gamification could depend of the initial motivation and activity of participants.

Introduction

Physically inactive individuals are at higher risk of developing noncommunicable diseases—such as cardiovascular diseases, cancers, type 2 diabetes mellitus, or obesity—and mental health issues than those who are most active [ 1 ]. However, one-third of the world’s population is insufficiently active [ 2 , 3 ], and the trend is downward, with adults performing on average 1000 fewer steps per day than 2 decades ago [ 4 ]. In addition, it has recently been reported that the global population step count did not return to prepandemic levels in the 2 years following the onset of the COVID-19 outbreak [ 5 ]. The number of steps per day is a simple and convenient measure of physical activity (PA). Recent research suggests that an increase in the daily step count is associated with a progressively lower risk of all-cause mortality. Walking an additional 1000 steps per day can help reduce the risk of all-cause mortality [ 6 ]. For adults aged ≥60 years, this reduction in mortality rates is observed with up to approximately 6000 to 8000 steps per day, whereas for adults aged <60 years, the threshold is approximately 8000 to 10,000 steps per day [ 7 ]. However, sustaining this increase over time is crucial to achieve tangible health benefits [ 8 ]. Despite the efficacy of current programs in eliciting initial changes in individuals’ PA, they often struggle with inducing long-term behavioral shifts [ 9 ]. In this context, there is an urgent need to sustainably increase the number of daily steps of individuals in primary, secondary, and tertiary prevention.

Digital behavior change interventions are promising avenues to promote daily steps. Smartphones and digital tools, ubiquitous in our daily lives, offer several advantages, including their widespread availability, relatively low cost, and ability to access content quickly from anywhere [ 10 - 12 ]. Moreover, these technologies can collect real-time data in natural contexts (ie, daily step counts can be measured via accelerometers embedded in either commercial wearables such as Fitbit or smartphones) and present them in quantified formats, providing opportunities for exploration and reflection. This facilitates the implementation of powerful behavior change techniques such as goal setting and self-monitoring, potentially influencing behaviors [ 11 ]. However, there are concerns about the ability of digital programs to engage participants once the novelty wears off or to be effective on any type of audience regardless of their age, sociodemographic characteristics, or health status. In this context, gamification strategies introduce an exciting road map for addressing these challenges.

Gamification refers to the use of game elements in nongame contexts [ 13 ] and allows for the transformation of a routine activity into a more engaging one. Self-determination theory (SDT) [ 14 ] is a commonly used theoretical framework for understanding the motivational impact of gamification on behavior. SDT suggests the existence of different types of motivation that can be pictured on a continuum ranging from lack of motivation to completely autonomous motivation in which the behavior comes from the individual’s will. By contrast, controlled motivation will lead the individual to practice for the consequences that the activity can bring and not for the activity itself. SDT holds that people will be more likely to perform the behavior in the long term when their motivation is autonomous rather than controlled. Thus, autonomous forms of motivation represent more sustainable drivers of engagement and are an important predictor of the long-term maintenance of physical practice [ 15 , 16 ]. Autonomous motivation occurs when people perform an activity for their own satisfaction, inherent interest, and enjoyment. Moreover, 3 basic psychological needs are presumed to achieve self-determination: the need for autonomy (ie, need to feel responsible for one’s own actions), competence (ie, need to feel effective in one’s interactions with the environment), and relatedness (ie, need to feel connected to other people).

In addition to providing fun and playful experiences to users, gamification can effectively address basic psychological needs [ 17 ]. First, gamification strategies such as point scoring, badges, levels, and competitions serve to sustain the need for competence by offering feedback on users’ behaviors. Second, customizable game environments or user choices can support autonomy. Finally, features such as leaderboards, team structures, groups, or communication functions can foster a sense of relatedness. From this perspective, a gamified intervention would feed the autonomous motivation of participants and would be more correlated with the long-term adherence to PA. However, from another perspective, several criticisms have been leveled at gamification, including the fact that these mechanisms are reward oriented and that, still in line with SDT, the use of external rewards can reduce autonomous motivation [ 18 , 19 ].

A recent meta-analysis [ 20 ] revealed that digital gamified interventions lasting on average 12 weeks improved daily steps by 1600 steps on average. Importantly, the results showed that gamified interventions (1) appear more effective than digital nongamified interventions, (2) seem appropriate for any type of user regardless of their age or health status, and (3) lead to a persistent PA improvement after follow-up periods lasting on average 14 weeks with a very small to small effect size. As a result, gamified interventions are emerging as interesting behavior change tools to tackle the physical inactivity pandemic. However, these findings obtained from randomized controlled trials do not always reflect what happens in real-life settings [ 21 ]. In addition, the effect sizes reported in this meta-analysis were heterogeneous, and the authors found high between-study heterogeneity (eg, I 2 =82%).

If this heterogeneity can be explained by differences in study quality or diversity of designs in the included studies, the behavior change intervention ontology proposed by Michie et al [ 22 ] argues that heterogeneity in behavioral interventions could also be explained by different variables such as intervention characteristics (eg, content and delivery), the context (eg, characteristics of the population targeted, such as demographics, and setting, such as the policy environment or physical location), exposure of participants to the program (eg, engagement and reach), and the mechanisms of action (the processes through which interventions influence the target behavior). Considering these variables within gamification contexts could provide a useful means to better understand the conditions under which interventions are successful. Furthermore, based on SDT, we can envisage that gamification techniques will not have the same impact on all users depending on their initial motivation and the way they perceive games.

This study investigated these questions based on a retrospective analysis of real-world data collected from a large sample of adult participants who were proposed a mobile health gamified intervention developed by the company Kiplin in France from 2019 to 2022. In this intervention, participants could take part in one or several collective games in which their daily step count was tracked, allowing individuals to play with their overall activity. In addition to offering the possibility of direct intervention on people’s activity habits in a natural context, the capacity of this mobile app to collect a large amount of objective real-world data in real time can be useful for understanding the processes and outcomes of behavioral health interventions [ 23 ]. More specifically, these data can help make explicit when, where, for whom, and in what state for the participant the intervention will produce the expected effect, notably owing to continuous data collection over time. The within-person evolution in daily steps obtained via the app combined with between-person individual factors and intervention parameters is of great interest in this perspective.

Thus, the objectives of this study were to analyze the data collected to (1) examine within-individual evolutions of daily steps before, during, and after the intervention; (2) test the effectiveness of a gamified program in real-life conditions on daily steps among participants versus nonparticipants; and (3) explore the variables that could explain heterogeneity in responses to the intervention. On the basis of previous results on gamification [ 20 ], we first hypothesized that daily steps would increase during and after the gamified program compared to baseline (hypothesis 1). Second, we hypothesized that this improvement will be greater for participants than for nonparticipants (ie, participants who registered on the app but did not complete any games; hypothesis 2). Finally, we expected that the intervention’s characteristics (ie, type and number of games), the context within which the intervention was performed (ie, population and setting), and the exposure to the intervention (ie, engagement of participants with the app) will moderate the intervention effect (hypothesis 3).

Study Design and Participants

This study retrospectively analyzed data from adult participants who had registered for a Kiplin program and had given consent for their data to be collected. To be included, participants must be aged ≥18 years; have registered on the app between January 1, 2019, and January 2, 2022; and logged daily steps (measured via their smartphone or an activity monitor) on a time frame of at least 90 days with <20% of missing daily observations. Of the 134,040 individuals who registered on the Kiplin app during this time span, 4819 (3.6%) met the eligibility criteria. Figure 1 shows the study flowchart.

Nonwear days were defined as days with <1000 steps and considered as missing observations—as previous research has suggested that daily step values of <1000 may not represent full data capture [ 24 , 25 ]. Days before the first day of the first game were considered as baseline (median 14, SD 42.9 days), the period between the first day of the first game and the last day of the last game was considered as the intervention period (median 19, SD 31.2 days), and the days after the last day of the last game were considered as the follow-up (median 90, SD 22.8 days). We restricted the follow-up periods to 90 days after the intervention (ie, 3 months).

Participants could receive the Kiplin intervention (1) in the context of their work (ie, primary prevention with employees), (2) in an older adult program (ie, primary prevention with volunteer retirees), or (3) as part of their chronic disease care (ie, patients mainly treated for obesity or cancer). In all the aforementioned conditions, the program was paid not by the participant but by their employer or health care center.

Some participants registered for the program and created an account but did not take part in the intervention (ie, did not complete any games). These individuals were considered nonparticipants and were used as a control group (as proposed in previous research [ 26 ]). Similarly, the baseline period for these nonparticipants corresponded to the days before the date in which they were supposed to start the intervention period.

Ethical Considerations

This study was approved by the local ethics committee (IRB00013412; CHU de Clermont-Ferrand institutional review board 1; institutional review board number 2022-CF063) with compliance with the French policy of individual data protection.

The Kiplin Intervention

The Kiplin intervention proposes time-efficient collective games accessible through an Android or iOS app. In all games, participants’ daily step counts are converted into points, allowing for progression within the games. The Kiplin app retrieves participants’ daily step counts by integrating with the application programming interfaces (APIs) of the apps used by participants to track their activity (such as Apple Health for iPhone users, Google Health for Android users, and Garmin Health). In this way, participants could connect a wearable if they already owned one. In addition, participants had access to a visual tool to monitor their daily and weekly step counts and to a chat for communication with other participants. Depending on the program, participants were offered one or several games lasting approximately 14 days each. If several games were proposed, these games followed each other in an interval of <60 days.

Participants could take part in 4 different games with no option for selection. In The Adventure, the objective was to reach step goals collectively to progress toward a final destination. Players could track their progress on a map, with checkpoints representing distances between different cities of a digital world tour ( Figure 2 A). In The Mission, participants engaged in PA and collective challenges to unlock clues and attempt to solve missions ( Figure 2 B). In The Board Game, participants took on the role of forest rangers tasked with extinguishing fires. Achieving step goals allowed for progress on the board and advancement to higher levels, ultimately aiming to extinguish all fires and save forest residents ( Figure 2 C). Finally, in The Challenge, players aimed to achieve the highest number of steps and complete challenges to earn trophies for their team. Team and individual rankings were available ( Figure 2 D).

These games included a multitude of gamification mechanisms such as points, trophies, leaderboards, a chat, challenges, and narratives—mechanics that are closely linked to proven behavior change techniques [ 27 ]. Table 1 gives an overview of the gamification strategies included in the Kiplin games following the taxonomy proposed by Schmidt-Kraepelin et al [ 28 ] and the associated behavior change techniques. While the games share common characteristics (eg, collective gameplay and in-game challenges), it is important to note that The Adventure and The Challenge emphasize competition more than the others.

a PA: physical activity.

b No correspondence with the Behavior Change Technique Taxonomy.

The variables of interest were selected based on the behavior change intervention ontology by Michie et al [ 22 ] and included (1) the longitudinal evolution of daily steps, (2) the exposure of each participant to the intervention, (3) the intervention parameters, and (4) the context (participants’ characteristics and setting) as these variables are likely to influence the intervention effect. Table 2 specifies the measures of interest and their operationalization.

Statistical Analyses

We calculated the step count increase by subtracting the baseline average daily step count from the average daily step count during the intervention or follow-up periods for each participant and then computed the relative change (in percentage).

Mixed-effects models were used to (1) analyze within-person evolution across time (ie, changes in daily steps throughout the baseline, intervention, and follow-up periods) and across participants and nonparticipants and (2) examine the associations among intervention parameters, exposure to the intervention, participants’ characteristics and settings, and daily step evolution. This statistical approach controls for the nested structure of the data (ie, multiple observations nested within participants); does not require an equal number of observations from all participants [ 34 ]; and separates between-person from within-person variance, providing unbiased estimates of the parameters [ 35 , 36 ].

First, an unconditional model (ie, with no predictor) was estimated for each variable to calculate intraclass correlation coefficients and estimate the amount of variance at the between- and within-individual levels, which allowed us to determine whether conducting multilevel models was relevant or not. Then, a model that allowed for random slope over time (ie, model with random intercept and random slope) was compared to the null model (ie, with only random intercept) using an ANOVA to evaluate whether the less parsimonious model explained a significantly higher proportion of the variance of the outcome than the unconditional model [ 37 , 38 ]. Third, between-level predictors and confounding variables were added to another model (model 1; the equation for the model was as follows: Y ij = [β 0 + γ 0i + θ 0j ] + [β 1 + θ 1j ] time j + β 2 phase j + β 3 age j + β 4 sex j + β 5 population j + β 6 season j + β 7 captor j + β 8 baseline PA j + β 9 lockdown j + β 10 condition j × phase j + ε ij , where β 0 to β 10 are the fixed-effects coefficients, θ 0j and θ 1j are the random effect for participant j (1 random intercept and 1 random slope), γ 0i is the random effect for time i [random intercept], and ε ij is the error term) and compared to the previous models. Finally, intervention characteristics, as well as their interactions with the phases (ie, baseline, intervention, or follow-up) of the study, were added in a final model excluding nonparticipants (model 2; the equation for the model was as follows: Y ij = [β 0 + γ 0i + θ 0j ] + [β 1 + θ 1j ] time j + β 2 phase j + β 3 age j × phase j + β 4 sex j + β 5 population j × phase j + β 6 season j + β 7 captor j × phase j + β 8 baseline PA j × phase j + β 9 lockdown j + β 10 compliance ratio j × phase j + β 11 number of games played j × phase j + β 12 type of game j + ε ij , where β 0 to β 12 are the fixed-effects coefficients, θ 0j and θ 1j are the random effect for participant j (1 random intercept and 1 random slope), γ 0i is the random effect for time i [random intercept], and ε ij is the error term). Model fit was assessed via the Bayesian information criterion and –2 log-likelihood [ 39 ]. All models were performed using the lmerTest package in the R software (R Foundation for Statistical Computing) [ 40 ]. An estimate of the effect size was reported using the marginal and conditional pseudo- R 2 . When the interaction terms turned significant, contrast analyses were computed using the emmeans package [ 41 ]. The models’ reliability (estimated using residual analyses) and outlier detection were performed using the Performance package [ 42 ]. In addition to subtracting nonwear days (defined previously), we removed outliers via the check_outliers function [ 42 ] that checks for influential observations via several distance and clustering methods (ie, Z scores, IQR, and equal-tailed interval). Sensitivity analyses were conducted using all data (including data before outlier imputation) and are available in Multimedia Appendix 1 .

The data and code for the statistical analyses used in this study are available on the Open Science Framework [ 43 ].

Descriptive Results

Descriptive results are presented in Table 3 . The final sample included 4819 adults (mean age 42.7, SD 11.5 y; 2823/4819, 58.58% women). Participants wore an activity monitor measuring their daily step count for an average of 113 (SD 58.01; range 90-686) days. A total of 34,922 daily step observations were missing (ie, daily data missing or considered as a nonwear day), which is equivalent to 6.4% of missing data for the full data set.

We tested for statistical differences in sociodemographic variables and baseline daily steps between participants and nonparticipants using 2-tailed t tests and chi-square tests. Results revealed significant differences for age ( t 82,500 =–6.9149; P <.001), gender ( χ 2 2 =4028.3; P <.001), and baseline daily steps ( t 22,721 =–19.75; P <.001). However, in large samples, P values may drop below the α level despite effect sizes that are not practically meaningful [ 44 ]. Therefore, we mainly examined the magnitude of the effect sizes of these differences and observed very small to small effects ( d =–0.03 for age, d =–0.17 for baseline daily steps, and w =0.09 for gender). According to Magnusson [ 45 ], the interpretation of these effect sizes suggests that, for age and baseline daily steps, approximately 98.8% and 93.2% of individuals in both groups overlapped, respectively. In addition, there is approximately a 50.8% and 54.8% chance that a randomly selected individual from the nonparticipant group would have a higher score than a randomly selected individual from the participant group. Therefore, we considered that the differences were minor between the 2 groups. Finally, these variables were controlled in our mixed-effects models as they were included as fixed effects.

a Not applicable.

Hypothesis 1: Is the Gamified Program Effective to Promote PA?

During the intervention period, participants increased their daily steps by 2619 steps per day on average (+55.6%) compared to the baseline period and by 317 steps per day on average during the follow-up period (+13.8%) compared to the baseline. In comparison, the daily step count of the control group remained more or less stable throughout the same time frame, with a mean increase of 151 daily steps compared to baseline (+7.5%).

Overall, contrast analyses of the model for the intervention participants (model 2; Table S1 in Multimedia Appendix 1 ) revealed a negative effect of the intervention on the daily step count during the intervention phase compared to baseline activity ( b =–0.09, 95% CI –0.14 to –0.05; P <.001) and no significant effect ( b =0.01, 95% CI –0.05 to 0.06; P =.79) during the follow-up periods compared to baseline. However, the patterns were different when participants were stratified by baseline PA. Participants with lower baseline daily steps (<5000 steps per day or 5001-7500 steps per day) showed a significant increase in their daily steps during the intervention ( b =0.25, 95% CI 0.22-0.28; P <.001) and follow-up ( b =0.12, 95% CI 0.09-0.15; P <.001) periods both compared to the baseline. Participants with initial values between 7501 and 10,000 steps did not have a significant increase in their daily steps during the intervention ( b =0.00, 95% CI –0.05 to 0.05; P =.99) or during the follow-up period ( b =–0.01, 95% CI –0.04 to 0.02; P =.44) compared to baseline. Participants who performed >10,000 baseline steps had significant deteriorations during the intervention ( b =–0.13, 95% CI –0.19 to –0.08; P <.001) and follow-up ( b =–0.06, 95% CI –0.10 to –0.03; P <.001) periods. These trends are depicted in Figure 3 and Table 4 . Results were similar in sensitivity analyses that used data without outlier imputation except for participants with initial daily step counts between 7501 and 10,000, who showed significant improvements during and after the intervention (Tables S2 and S3 in Multimedia Appendix 1 ).

In parallel, contrast analyses comparing the effectiveness of the Kiplin intervention on participants who used smartphones to collect their daily steps in comparison to participants who used a wearable showed a significantly greater effect among smartphone users during both the intervention phase ( b =0.09, 95% CI 0.07-0.11; P <.001) and the follow-up period ( b =0.04, 95% CI 0.01-0.06; P =.001). These results are illustrated in Figure S1 in Multimedia Appendix 1 .

Hypothesis 2: Is the Intervention Effect Greater for Participants Than for Nonparticipants?

In model 1 (Table S1 in Multimedia Appendix 1 ), participants who received the Kiplin intervention had a significantly greater increase in mean daily steps between baseline and the intervention period compared with nonparticipants ( b =0.54, 95% CI 0.52-0.58; P <.001). The results were similar in sensitivity analyses (Table S3 in Multimedia Appendix 1 ). The comparison of the means, changes, and relative changes from baseline for participants and nonparticipants are available in Table S4 of Multimedia Appendix 1 .

Hypothesis 3: What Are the Moderators of the Intervention Effect?

The model 2 estimates are shown in Table S1 in Multimedia Appendix 1 . The variables under consideration explained 39% of the variance in daily steps. In this model, we tested the hypothesized interactions to investigate predictors associated with the efficiency of the intervention (Table S5 in Multimedia Appendix 1 ). Contrast analyses were conducted on significant interactions and revealed that the age ( b =0.05; P <.001) and compliance ratio ( b =0.37; P <.001) were positively associated with the change in daily steps between baseline and the intervention period. Specifically, the older the age, the more regularly the individuals played and the more effective the intervention was. On the other hand, the number of games played by participants was negatively associated with this change ( b =–0.02; P =.02). In other words, the longer the intervention and the higher the number of games, the less effective the intervention. For categorical outcomes, contrast analyses revealed differences in the intervention effect among the different populations ( Figure 4 ). Compared to employees, patients treated for cancer ( b =–0.18; P <.001) and older adults ( b =–0.19; P <.001) showed a significantly weaker effect of the intervention in comparison to baseline PA. There was no significant difference between employees and patients treated for obesity ( b =–0.07; P =.13). All the results of these analyses are available in Multimedia Appendix 1 .

Finally, model 2 estimates revealed that participants were significantly more active in The Adventure and The Challenge compared to The Board Game and The Mission (Table S1 in Multimedia Appendix 1 ).

Principal Findings

This study demonstrated a significant increase in daily steps among participants engaging with the Kiplin intervention compared to nonparticipants over the same period. Interestingly, the intervention effect varied according to the baseline daily step count of individuals. Participants with lower baseline steps (<7500 steps per day) significantly improved their PA during both the intervention (between +34% and +76%) and follow-up (between +10% and +33%) periods, whereas participants with >7500 steps had no significant change or significant decreases.

These results suggest that a gamified program is more efficient for inactive individuals compared to active ones, with the existence of a plateau effect. They also support recent findings [ 20 , 46 ] and the ability of gamified interventions to improve daily steps both during and after the end of the program and in real-life settings [ 47 ]—at least for the more inactive individuals. This efficacy is noteworthy given the challenges faced by current behavioral interventions in promoting PA in the long haul [ 9 ].

SDT offers a valuable framework for elucidating the disparate outcomes observed among initially active and inactive participants. Gamification strategies could enhance the autonomous motivation of inactive participants, as suggested by a previous study [ 48 ], whereas the use of rewards on already motivated people could undermine this motivation. Known as the overjustification effect [ 18 ], this phenomenon suggests that, if people receive rewards for doing an activity that they used to enjoy, they are likely to discount the internal reason and, thus, become less intrinsically motivated than before receiving the rewards. This could explain why the same intervention had positive effects on inactive participants, who performed more daily steps after the end of the intervention (ie, during follow-up periods), compared to its effects on already active ones, who observed significant decreases after the intervention compared to their baseline daily steps.

Moreover, results indicating that the intervention was more effective among users who used their smartphones to track their step counts through the Kiplin app compared to those who already owned and used a wearable device—and were significantly more active at baseline—further reinforce this argument. Individuals who already possess an activity monitor are likely motivated to monitor their daily steps, potentially diminishing the additional impact of gamification rewards. Consequently, the introduction of gamification may have less influence or even produce counterproductive effects on their behavior, particularly when compared to those who solely rely on their smartphones for activity tracking in the context of the intervention.

The results of this study also stressed that older age may not be incompatible with gamified interventions. Indeed, intervention effectiveness was moderated by the age of the individual, and gamification was more efficient among older individuals compared to younger ones. These findings are in line with those of a previous study [ 49 ] that reported higher use of gamification features among older users. The authors postulated that older adults pay generally more attention to their health and, thus, have a stronger intention to engage in a health program. From another perspective, and in light of the gamification strategies embedded in the Kiplin intervention, these results could also be explained by the fact that these strategies are accessible—inspired by traditional board game rules and mechanics widely known in the general population—and, thus, may be more attractive for older populations. Previous research has suggested that the most engaging game mechanics may diverge between youths and other populations [ 50 ], and we can expect that younger populations may prefer more complex game mechanics and need more novelty during the intervention to stay interested in the service.

Regarding the effects of the gamified intervention according to the characteristics of the population, a stronger effect was found for programs among employees and patients treated for obesity. While these results warrant caution due to the variability observed in patients or older adult participants, these findings suggest that gamified interventions are suitable for both primary and tertiary prevention, as suggested by previous work [ 20 ].

Practical Implications

The findings of this study also offer valuable insights that could help improve future intervention design. First, exposure to the content is essential for the gamified intervention to be effective. It is interesting, as gamification has often been assimilated into a self-fulfilling process permitting automatic engagement of participants. These results are consistent with previous findings demonstrating that higher use of gamification features was associated with greater intervention effectiveness [ 49 , 51 ]. If gamification can ultimately increase program engagement, developers need first to design their apps to be as attractive as possible and optimize retention.

Second, the results revealed that the total number of games played was negatively associated with the intervention effect, suggesting that shorter interventions could be more beneficial for behavior change. These results are in line with those of previous research [ 20 , 52 ] suggesting that digital interventions of <3 months tend to yield greater benefits. It also suggests a “dose-response” relationship in an inverted U shape, with an optimal “middle” to find. Nevertheless, it is important to consider that Kiplin programs incorporating multiple games are built in such a way as to administer several doses at regular intervals. Therefore, periods without games were considered in the intervention phases and could explain why, overall, the shorter games were more efficient. More refined analyses of the intervention effect over time will be necessary in the future.

Third, the daily step count of participants was significantly higher in The Adventure and The Challenge. These 2 games are characterized by their competitive nature, placing a stronger emphasis on leaderboards than the other 2 games, which are more centered on collaboration. In this vein, Patel et al [ 53 ] observed that the competitive version of their gamified intervention outperformed the collaborative and supportive arms. Moreover, various studies have highlighted that leaderboards are a particularly successful gamification mechanic [ 49 , 54 ].

Strengths and Limitations

This study has several strengths, including its large sample size, the intensive objective PA measurement in real-life conditions through daily steps, and the longer baseline and follow-up duration compared with most trials on gamification that typically incorporate measurement bursts dispersed across time [ 20 ]. However, several limitations should be considered. First, this study was observational and not a randomized controlled trial. Thus, we cannot establish the causality of the intervention’s effect on outcome improvement. The nonparticipants are not a true control group. If they did not receive the intervention, it may be because they were unable to join or for underlying motivational reasons that could impact their PA. Second, intervention lengths differed between participants. Third, although mixed-effects models are useful for describing trends in PA behavior change over time, they are limited in their capacity to assess precise fluctuation patterns of nonstationary behavior, such as daily step counts [ 55 ] across time. Future longitudinal studies could benefit from using time-series analyses to more accurately describe these patterns of change. Finally, the compliance ratio used in this study as a proxy for engagement tends to oversimplify the exposure of participants to the service. Complementary measures of behavioral engagement (eg, using the number of log-ins, time spent per log-in, and the number of components accessed) and affective engagement (eg, emotions and pleasure) should be considered to draw the longitudinal impact of the engagement of the participants on the intervention effect.

Conclusions

In this study, we conducted a comprehensive analysis of real-world data from >4800 individuals, suggesting the impact of a gamified intervention in real-life settings. Our findings indicate that the Kiplin intervention led to a significantly greater increase in mean daily steps from baseline among users than among nonparticipants. Interestingly, responses to the intervention were significantly different as a function of individuals’ initial daily step counts. Participants with <7500 baseline daily steps had significant improvements during both the intervention and follow-up periods with +3291 daily steps during the program and +945 after the intervention on average, whereas the intervention had no effect on participants with initial values of >7500. Therefore, the motivational effect of gamification could depend on the initial PA and motivational profile of the participants. This result can also be interpreted in light of our observation that participants who already owned a wearable and, thus, were likely already motivated to engage in PA exhibited significantly lower effects compared to less experienced participants who used their smartphones to track their step counts. This study also revealed that the age of participants and their engagement with the app were positively and significantly associated with the intervention effect, whereas the number of games played was negatively associated with it.

Overall, the results of this study suggest that gamification holds promise in promoting the daily steps of inactive populations, with demonstrated short- and medium-term effects. Importantly, this study represents a pioneering effort as one of the first to examine the longitudinal effect of a gamified program outside the context of a trial using intensive real-world data. As such, the findings are quite generalizable to similar settings and reaffirm the value of gamification in both primary and tertiary prevention efforts across a diverse range of age groups.

Acknowledgments

The work of AM is supported by the French National Association of Research and Technology (Cifre PhD thesis grant) and by the company Kiplin. The funders had no input in the design of the study and no influence on the interpretation or publication of the study results.

Data Availability

The anonymized data used in this study and the R code are available on the Open Science Framework [ 43 ].

Authors' Contributions

AM conceptualized the study. AM and GH performed data curation. Investigation was led by AM, while AM and CF contributed to methodology and statistics. AM conducted the formal analysis using the R software. AM was responsible for writing the original draft. All authors contributed to writing (review and editing).

Conflicts of Interest

AC, CF, and MD declare that they have no competing interests. GH is employed by Kiplin. The results of this study could be beneficial to Kiplin from a marketing point of view. The Kiplin company had no input in the design of the study and no influence on the interpretation or publication of the study results.

Supplementary Figure S1 and Table S1-S5.

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Abbreviations

Edited by G Greco; submitted 25.04.23; peer-reviewed by S Payne, A Bucher; comments to author 26.01.24; revised version received 10.03.24; accepted 01.05.24; published 12.08.24.

©Alexandre Mazéas, Cyril Forestier, Guillaume Harel, Martine Duclos, Aïna Chalabaev. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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