Abstract
Objective
This study aims to investigate the intrinsic motivations driving continued usage of health and fitness apps, addressing a gap in the literature where user motivation has received limited attention. The study focuses on the role of entertainment as a mediator between intrinsic motivations and continuance intention, using the Uses and Gratifications Theory as a conceptual framework.
Methods
Data were collected through an online survey of 2869 health and fitness app users in Vietnam. Intrinsic motivations were categorized into challenge, curiosity, fantasy, and social interaction. Hypotheses were tested using SEM, and subgroup analyses were conducted to examine variations across demographic groups.
Results
The findings reveal that entertainment mediates the relationship between intrinsic motivations and continuance intention, with challenge and curiosity being the strongest predictors of sustained app usage. Challenge also demonstrated a direct effect on continuance intention, independent of entertainment. Demographic analysis indicated that males are primarily motivated by challenge and curiosity, while females and higher-income users are driven more by curiosity.
Conclusion
This study highlights the importance of intrinsic motivations in shaping user engagement with health and fitness apps. Practical implications include the need for app developers to design features that align with user motivations, such as gamified challenges, personalized content, and interactive elements. Future research should explore extrinsic motivations and validate the framework across other digital platforms to expand its applicability.
Keywords: Health and fitness, apps, continuance intention, intrinsic, entertainment
Introduction
With the growing popularity of the internet and personal devices, health and fitness apps have become mainstream in the wellness technology space. 1 As of recent reports, a significant percentage of users cite fitness apps as one of their primary methods of maintaining health and wellness. 2 Health and fitness apps now account for a substantial portion of the fitness market, and this share is expected to continue growing in the coming years. 3 However, despite the rapid growth in the value of the health and fitness app industry in recent years, growth rates appear to be slowing down. 4 According to market estimates, the health and fitness app market reached approximately $9.25 billion in 2023, with an annual growth rate projected at 14.08%. 5 Forecasts suggest that the market size will see only moderate growth, reaching around $10.06 billion by 2029, reflecting the industry's gradual deceleration. 6
As demand growth slows, competition in the health and fitness app industry has intensified. The primary revenue sources for these apps include subscription fees and the sale of premium features or in-app services. 7 Unlike other industries where piracy or replication of services might be a concern, this model provides fitness app developers with a significant safeguard for profitability. As a result, many developers and companies have entered this competitive market. However, engagement with health and fitness apps requires a significant investment of time and effort, and users are typically limited in the number of apps they can actively use.
Evidence suggests that in many markets, more than 80% of users concentrate their time on less than 20% of the available fitness apps. 8 This indicates that only a small percentage of app developers are likely to be profitable, while the rest may struggle to attract and retain users. As developers continue to release new apps in an attempt to capture a larger market share, slowing demand growth means companies must focus on retaining their existing user base to maximize profitability.
The question is, why do users continue using the same health and fitness app for a long time? How can developers successfully keep users engaged and committed to their app? This is a key challenge for health and fitness app developers. Chiu, and Cho 2 suggested that during the first month of using an app, users are often in a phase of confusion, which is when they are most likely to stop using the app. Once they move past this confusion phase, they enter a longer excitement and engagement phase. To remain profitable, developers must find ways to prevent users from entering the boredom phase too early and instead keep them engaged for the long term by maintaining the excitement and engagement phases.
In recent years, researchers have begun to explore the factors influencing users’ continued participation intentions in digital platforms.9,10 Past research on the continued usage intentions of digital behaviors frequently applies models such as the Theory of Planned Behavior (TPB),11,12 the Theory of Reasoned Action (TRA),13,14 the Technology Acceptance Model (TAM),15,16 the Expectancy Confirmation Theory (ECT), and Flow theory.17–19 For health and fitness apps, the research often follows similar models. For example, Hyun, Thavisay 20 explored the impact of social influence and flow on users’ intentions to continue engaging with online platforms, which can similarly be applied to health and fitness apps.
The current literature on continuance intentions to use health and fitness apps is relatively well-established.21,22 However, much of the existing research focuses primarily on factors like perceived usefulness, ease of use, or habit formation. 23 Fewer studies have explored how specific entertainment-related factors—such as challenge, curiosity, control, fantasy, and social interaction—contribute to the entertainment value of these apps and, subsequently, their impact on continuance intentions. While previous research has predominantly examined these factors in contexts such as online learning and online repurchase behavior,24,25 there is a lack of comprehensive analysis of how entertainment-driven elements influence user engagement and sustained app usage in the health and fitness domain. This highlights a gap in understanding the role of entertainment as a mediator in the relationship between app features and continuance intention, presenting an opportunity for further investigation.
Interestingly, while motivation significantly influences user behavior, there is relatively little research that explores the factors influencing the continued use of health and fitness apps from a motivational perspective. 21 Instead, motivation has often been examined in the context of initial acceptance intentions, 26 user satisfaction with the app, 27 and overuse or addiction. 28 Furthermore, existing research lacks consensus on the types of motivations, with each scholar proposing different classification systems, making it difficult to compare, analyze, and accumulate findings across studies.
Moreover, evidence suggests that the motivations driving different types of users to engage with health and fitness apps vary.26,29 However, there has been limited in-depth exploration of these differences across user groups. Based on this, the current study has three primary objectives: (1) to review the existing literature and propose a preliminary framework for classifying user motivations for engaging with health and fitness apps; (2) to develop a theoretical model for continued usage intentions from a motivational perspective; and (3) to test this theoretical model and explore the moderating effects of different user groups, to contribute to both academic research and practical applications.
Theoretical background
Characteristics and related research on health and fitness apps
An activity can be considered an effective health and fitness experience if it meets several key characteristics. From a design perspective, users of health and fitness apps must follow certain rules or guidelines, which establish challenges, goals, and a sense of achievement. 30 Users derive enjoyment and engagement (playfulness) by overcoming these challenges or reaching their fitness goals. These rules must be set within an appropriate context, interaction model, and perspective to be meaningful.
The context refers to the environment where the fitness activities take place, such as a virtual running track or a simulated workout studio. The interaction model represents how users engage with the app, such as selecting workout plans, tracking progress, or participating in virtual challenges with friends. 31 The perspective relates to the way users view and experience fitness activities, such as through a first-person view in a virtual race or following a third-person guided workout session.
Health and fitness apps often feature background narratives or virtual simulations that evolve with user progress, creating a sense of realism or immersion. 32 For example, users may participate in globally renowned fitness challenges, where detailed, realistic environments and the excitement of competing with others create an immersive experience that makes users feel as though they are truly part of a live event.
Similarly, health and fitness apps incorporate elements of virtuality and simulation. 33 In other words, these apps create an immersive, seemingly realistic psychological environment that helps users relax and fully engage in challenging and rewarding fitness activities. This immersive experience stimulates users’ motivation by allowing them to focus on self-improvement and achievement within a virtual context. 34 Additionally, intrinsic motivation plays a key role in encouraging users to engage with health and fitness apps. 35 Users are unlikely to enjoy or sustain their fitness activities if they feel forced to participate. Third, using these apps provides enjoyable experiences. 36 During their fitness journey, users often engage in high levels of mental activity, leading to various psychological and emotional experiences. Through these experiences, users can achieve optimal life experiences, helping them to build a sense of self-worth and accomplishment.
In addition, health and fitness apps possess a community feature. These apps often involve social interactions, where users can connect with others and form fitness groups or teams. 37 Features like gamification and community-building elements have been shown to enhance user engagement in the apps. 38 Through interactions within these groups, a set of unique rules for thriving in the virtual fitness world gradually emerges, and users internalize these as behavioral guidelines and norms. 39 This community aspect can attract more users to join the same app; conversely, there may also be instances where users leave the app en masse.
Furthermore, health and fitness apps exhibit an exclusivity aspect. Users must remain engaged over an extended period to develop strong fitness habits and fully enjoy the app's benefits. 40 This creates a sense of exclusivity in terms of commitment, time, and financial investment. Once users become deeply familiar with a particular app, they are likely to stick with it. 41
Most studies reference specific theories, such as the Uses and Gratifications Theory, 42 the Technology Acceptance Model, 43 app design principles, 44 and migration theory. 45 Perhaps due to the immersive nature of health and fitness apps, which transport users into a virtual fitness environment, flow theory has been a popular choice.21,46
Finally, some studies believe that the entertainment value of an activity is a key antecedent to users’ acceptance or continued usage intentions. For example, Hung and Chang 47 suggested that entertainment effects influence acceptance intentions, while Zhou and Mou 48 argued that once users experience a sense of immersion, including enjoyment or entertainment, they are more likely to continue using the app. The strength of the entertainment effect is closely related to whether users can experience immersion during their engagement with the app. Since motivation is one of the key factors that facilitate and shape immersion experiences, 49 and using health and fitness apps is typically a voluntary activity driven by personal motivation, user motivation has become a popular explanatory variable. However, relatively few studies have explored the influence of user motivation on the intention to continue using health and fitness apps.
Motivation of health and fitness app users
Motivation is one of the factors that initiates and sustains specific behaviors, driving individuals from within to work toward satisfying personal goals or needs. 50 Generally, people engage in behaviors to gain some form of benefit. When individuals feel that a certain need or goal is unmet or unattainable, a sense of tension or stress arises, prompting them to take action to alleviate this discomfort. 51 For instance, when someone experiences stress at work and feels the need for a break or relaxation, they may take action, such as going on a trip, to relieve this tension.
Goals drive and guide people's actions. 52 Hommel 53 described motivation as a psychological process related to the arousal, direction, intensity, and persistence of goal-directed voluntary behavior. Arousal refers to the process of becoming interested in a particular goal (e.g., a student with poor academic performance seeking extracurricular activities to satisfy their need for achievement). Direction involves setting a goal and deciding to pursue it (e.g., the student joins a sports team to become a team member within a short period). Intensity refers to the amount of effort an individual puts into achieving a goal, and persistence is the continued effort to pursue the goal despite encountering difficulties. Thus, motivation is closely related to what we strive for (arousal and direction) and how we pursue these goals (intensity and persistence). It determines why, when, and how much time and energy we allocate to specific activities. 52
Therefore, behavior typically arises from motivation. For example, some consumers engage in online shopping to stay ahead of trends, 54 while others may download illegal music files to keep up with rapidly changing music trends. 55 Similarly, if developers aim to create a health and fitness app that provides users with an optimal experience and encourages them to continue using it, identifying and understanding users’ motivations for engaging with such apps becomes a critical issue.
Several past studies have attempted to categorize and summarize various behavioral motivations. These frameworks aim to enrich the understanding of different types of motivations, seeking to encompass the wide range of motivations that arise in everyday life.56,57 However, research focused on specific behaviors often develops its own set of motivations based on the unique requirements of that context. For instance, Imtiyaz, Soni 58 explored how the motivations of individuals influenced their intention to purchase convenience food. To ensure research findings can be compared and accumulated, many fields develop classification frameworks or models of motivation. For example, tourism research commonly categorizes motivations into “push” and “pull” factors, 59 while shopping motivations are often divided into utilitarian and hedonic types. 60
Online behavior is often seen as an action carried out through the use of information systems. Motivation plays a crucial role in determining whether users will continue using a system. 61 People engage with information systems because they anticipate future internal or external benefits. Morris and Grehl 50 categorized motivation into two types: intrinsic and extrinsic. Intrinsic motivation refers to the satisfaction derived from performing the behavior itself and the internal fulfillment users experience while using the system. Any other factors beyond this do not fall under intrinsic motivation. In contrast, extrinsic motivation refers to the various valuable outcomes an individual expects to gain from performing the behavior, such as rewards, status, or career advancement. Thus, the behavior is seen as having instrumental meaning and value.
Since engaging with health and fitness apps can provide enjoyment and is a voluntary, internally driven behavior, 46 intrinsic motivations such as passion and immersion should naturally be important factors influencing users to engage with these apps. 34 Molina and Myrick 40 also discovered that some individuals use apps as a means to fulfill their inner desires for success and happiness. Therefore, it can be inferred that for the behavior of playing health and fitness apps, intrinsic motivation may be a more important driving factor than extrinsic motivation. Of course, people may engage with apps for extrinsic reasons, such as winning competitions, gaining rewards, or earning recognition. However, since using fitness apps is a voluntary activity driven by intrinsic motivation, it is unlikely that someone with no internal desire to exercise will engage with the app solely for external rewards. In other words, while external incentives may encourage users to be more committed, a person who has no interest in fitness is unlikely to start using a health app just for the sake of rewards. Therefore, intrinsic motivation should be seen as a more critical yet often overlooked factor in driving engagement with health and fitness apps. Based on this, the current study aims to focus on constructing and validating a framework for understanding intrinsic motivations in the context of health and fitness app usage.
A preliminary classification framework of health and fitness app users’ motivation
Studies exploring how users develop sustained engagement with health and fitness apps certainly do not overlook motivation as a key variable. However, two key points emerge from the literature review. First, motivation is mostly used to examine the initial acceptance of apps, meaning the users’ intent to begin using a health and fitness app, 3 their satisfaction with the app, or their immersion in the activity. 27 Although some studies have explored users’ continuance intention or loyalty, most of the explanatory variables are not focused on motivation. For example, many studies used the Technology Acceptance Model, the Theory of Planned Behavior, and Flow Theory to examine the effects of immersion, social norms, and community influence.3,62 Molina and Sundar 63 investigated this issue from the perspective of app design or features, while Gupta, Dhiman 64 used the Expectation Confirmation Theory in their analysis.
Secondly, existing research on motivation lacks consensus on the types of motivations. This makes it difficult to compare, analyze, synthesize, and accumulate research findings. For instance, studies categorized participation motivations into self-affirmation, anonymous companionship, social learning, and escape to belonging.29,65 Other studies classified them as self-affirmation, anonymous companionship, escape to belonging, social activities, and leisure entertainment.66,67 This divergence illustrates that each researcher has their own classification system, leading to considerable differences and little consensus.
Currently, there is very limited research specifically focused on classifying motivations for continuance intention to health and fitness apps. After a review of the literature, this study proposes using the classification framework suggested by Malone and Lepper 68 as the basis for categorizing health and fitness app motivations. This framework divides motivations into personal and interpersonal categories, providing high comprehensiveness and mutual exclusivity, allowing most motivations mentioned in existing research to be incorporated.
For personal motivations, this study follows their original classification, which includes challenge, curiosity, control, and fantasy. For interpersonal motivations, the study adopts social interaction motivation as proposed by Sherry, Greenberg. 69 Below is an explanation of personal motivation categories 68 :
Challenge: This is the motivation to engage in competitions or difficult tasks. In the context of health and fitness apps, challenges such as achieving fitness goals or competing in virtual races could motivate users to continue using the app.
Curiosity: Curiosity arises from a lack of knowledge. Users are intrigued by the evolving scenarios, new levels, or challenges within the app that they cannot predict in advance, sparking a desire to explore and solve these mysteries.
Control: Users have control over the progression of activities, which satisfies the desire to manage or control outcomes. As the app progresses, users face increasingly difficult fitness challenges. If users feel they lose control over the activities, they may experience frustration or boredom. In a health and fitness app, the ability to customize workouts or control the pace of progress can maintain user engagement.
Fantasy: Fantasy refers to the mental creation of scenarios that do not exist in the real world. In health and fitness apps, the app's background, storylines, characters, and evolving information (such as tracking fitness stats or virtual challenges) allow users to imagine themselves in near-real environments, motivating them to engage with the app.
Some studies suggest that entertainment or passing time are significant motivations for app usage. 70 In the context of health and fitness apps, which are inherently designed to provide a balance between functionality and engagement, entertainment becomes a crucial value. 71 The goal of enjoying the app's features, such as challenges or interactive social elements, serves to motivate users, helping them stay engaged and committed to their fitness goals. Therefore, entertainment motivation and continuance intention are intrinsically linked, and entertainment should not be viewed as an isolated motivational factor but rather as a core driver within the framework, influencing user retention and sustained app usage.
This study does not adopt the interpersonal relationship motivations proposed by Malone and Lepper 68 due to their emphasis on competitive and cooperative dynamics, which are less relevant in the context of health and fitness apps.2,72 Unlike games where competition and cooperation gradually develop, fitness apps offer more immediate real-life alternatives like team sports. 73 Additionally, Malone and Lepper's focus on competition overlooks the broader social interactions and community support that are key in fitness apps. Lastly, cognitive motivation, which involves achievement and peer recognition, overlaps with personal motivations such as challenge or self-affirmation, making it difficult to classify motivations as entirely distinct. As a result, this study proposes adopting social interaction 69 to represent the concept of interpersonal motivation. This approach better reflects the growing emphasis on the social aspects of health and fitness apps, where users not only compete but also build connections, find belonging, and engage in meaningful social exchanges. 74
Although the literature identifies various motivations, not all motivations have the same impact on users’ intentions to continue using health and fitness apps. Research has shown that the primary motivations driving different types of users to engage with these apps can vary. 75 For example, women are less likely to use apps to fulfill social interaction needs. 76 People who face less pressure and have more leisure time, tend to use fitness apps to achieve personal goals and a sense of accomplishment. 42 Similarly, adults may use fitness apps to relieve work-related stress. 77 Therefore, for different user groups, the impact of various motivations on their intention to continue using health and fitness apps is not always consistent. Different motivations can influence users’ intentions based on their demographics, lifestyles, and personal goals.
Hypotheses development and conceptual model
The Uses and Gratifications Theory suggests that individuals use media to satisfy their motivations, and once these motivations are fulfilled, they are more likely to continue using that media. This is because people can self-regulate and respond to their inner needs. 78 The effects that media usage has on an individual are largely determined by the user's purpose for engaging with the media. 69 One of the most important goals of media use is to satisfy urgent, non-monetary intrinsic needs, such as the pursuit of enjoyment. 79 Therefore, the more entertainment or intrinsic satisfaction the media provides, the greater the impact on users, which leads them to continue engaging with it. For example, the entertainment value of TV shows is often proportional to the time spent watching TV, and this effect is amplified when individuals feel the need for relaxation or enjoyment.
The entertainment value of online media is highly correlated with audience attitudes and usage intentions. 80 Similarly, health and fitness apps can be viewed as a leisure tool, providing stress relief and offering entertainment value. 81 One of the most basic goals of users is to find enjoyment and distraction through these apps. Health and fitness apps can fulfill fundamental user needs, such as escaping stress, enjoying their time, and gaining entertainment. 33 When these needs are satisfied, users are likely to continue using the app. 69 Several studies have also shown that the entertainment value of a platform is a key antecedent of users’ continued usage intentions. 47
H1: The more significant the entertainment value perceived by users of health and fitness apps, the stronger their intention to continue using the app in the future.
The Uses and Gratifications Theory posits that the entertainment value of media comes from its ability to satisfy people's needs. 82 Personal motivations are divided into four categories: challenge, curiosity, control, and fantasy. 68 Below is a discussion of the relationship between these motivations and entertainment value.
First, facing challenges in the pursuit of personal growth is one of the main motivations for engaging in activities. 83 Generally, people have a natural desire to face challenges and solve puzzles. 84 In the context of health and fitness apps, users may complete workout tasks, accumulate fitness progress, and gradually improve their physical abilities—similar to the experience of leveling up a character in a game. An app that is too easy may become boring and unengaging, whereas one that provides adequate challenges can keep users entertained and motivated. The stronger the challenge motivation, the more users will actively try to overcome obstacles and solve fitness challenges, resulting in longer engagement with the app and increased entertainment value.
H2: The more users of health and fitness apps perceive that the app satisfies their challenge motivation, the more significant the entertainment value they will experience.
When someone perceives a gap in their knowledge, it triggers curiosity. 68 Humans are often driven by curiosity to engage in exploratory behavior, such as browsing product catalogs to gain new information. This exploratory behavior satisfies the desire to know more. Similarly, some users of health and fitness apps are inclined to explore new features and workouts to satisfy their curiosity and enjoyment.
In multiplayer scenarios, the progression of challenges and tasks in health and fitness apps might depend on the activities of many users, making future updates or challenges unpredictable. 85 This unpredictability can spark curiosity and motivate users to explore the app further. 86 The stronger the curiosity, the more frequent the exploratory behavior, leading to longer engagement with the app and a greater sense of entertainment. Therefore, the more an app can satisfy this curiosity, the more entertainment users will experience.
H3: The more users of health and fitness apps perceive the app satisfies their curiosity, the more significant the entertainment value they will experience.
From a health and fitness app design perspective, using the app can be viewed as a goal-oriented, problem-solving process. The user, acting as the problem solver, utilizes tools (e.g., workout plans, tracking features) to overcome challenges and achieve fitness goals (e.g., completing a workout or reaching a milestone). The app responds to the user's progress (e.g., providing feedback or unlocking new features as goals are met), creating a dynamic interaction. 87 By allowing users to control various aspects such as workout routines, tracking progress, and adjusting challenges, the app empowers them with a sense of control over their fitness journey. This active participation fosters a feeling of accomplishment and enhances enjoyment, as users manipulate their fitness path, thereby increasing their engagement and entertainment value. 31 If designed effectively, this sense of control can lead to more frequent interaction with the app and greater user satisfaction.
H4: The extent to which users of health and fitness apps perceive the app satisfies their control motivation is positively related to the entertainment value they experience.
One of the key reasons for using media, such as health and fitness apps, is to satisfy the desire for personal achievement and transformation. 32 Just as games allow players to immerse themselves in fantasy worlds, health and fitness apps offer users the opportunity to envision and work toward a better version of themselves. Through features such as progress tracking, personalized workouts, and virtual challenges, users can break free from the limitations of their daily routines and pursue goals they might find difficult to achieve in real life, such as attaining an ideal physique, surpassing fitness milestones, or building endurance. 88 This sense of accomplishing personal transformations can be deeply compelling, enhancing users’ immersion and enjoyment. If users feel that the app helps them fulfill these aspirational goals, their engagement and satisfaction with the app will likely increase, leading to continued use and higher enjoyment.
H5: The extent to which users of health and fitness apps perceive the app satisfies their fantasy motivation is positively related to the entertainment value they experience.
Regarding interpersonal motivation, Sherry and Greenberg 69 noted that many users, particularly adolescents, engage in activities like gaming to fulfill their social interaction needs, such as making friends and building relationships. Similarly, social interaction is a fundamental aspect of using health and fitness apps, where users can connect with others, share progress, and participate in group challenges. 89 In the context of health and fitness apps, features like leaderboards, group challenges, and community forums provide spaces for users to interact, exchange fitness tips, and encourage one another. With the rise of the stay-at-home economy and increasing social isolation, the community aspect of these apps has become an essential platform for social interaction. 90 Engaging with the app's community and participating in group activities not only helps users stay focused on their fitness goals but also enhances the entertainment value, as the sense of belonging and shared achievement motivates continued use.
H6: The more users of health and fitness apps perceive the app satisfies their social interaction motivation, the more significant the entertainment value they will experience.
Based on the hypotheses above, the conceptual mode is proposed (Figure 1):
Figure 1.
Conceptual model.
Method
Measures
In this study, data were collected through a questionnaire survey and the hypotheses were tested using SEM. The measurement items used in this study were adapted from previously validated scales in the literature. The adapted items were modified to fit the context of health and fitness apps while maintaining their original theoretical constructs. All scales used in this study fall under fair academic use for research purposes. Where required, permission to use or adapt specific items was obtained from the respective copyright holders. The following constructs were measured:
Challenge (CH): This construct reflects the intrinsic motivation of users to seek out or tackle difficult challenges. The scale was adapted from Novak and Hoffman 91 and consisted of 4 items.
Curiosity (CU) refers to users’ desire to explore new or uncommon elements within the app. The three-item scale was based on the work of Agarwal and Karahanna. 92
Control (CO): This construct captures users’ desire for autonomy and the ability to control elements within the app. It was measured using a three-item scale from Agarwal and Karahanna. 92
Fantasy (FA) reflects users’ motivation to immerse themselves in scenarios and experiences unattainable in real life. The four-item scale was derived from Sherry, Greenberg. 69
Social Interaction (SO) represents users’ motivation to interact with others, form friendships, and engage in social exchange within the app. This was measured using a two-item scale from Sherry, Greenberg. 69
Entertainment (EN) refers to the degree to which users perceive the app as a source of recreational enjoyment. This construct was adapted from Davis, Bagozzi 93 using three items.
Behavioral Intention (BI): This construct measures users’ intention to continue using the health and fitness app in the future. The three-item scale was adapted from Hsu and Lu. 94
These measures were designed to capture the various motivations and behavioral outcomes associated with the sustained use of health and fitness apps.
In addition to the key variables, this study also collected basic demographic information from users of health and fitness apps, including gender, age, occupation, educational level, monthly income, frequency of app usage, app usage time per session, and years of experience using health and fitness apps. Furthermore, the study explored the primary types of fitness activities users engage with through the app, referencing the classification used by prominent fitness platforms. The categories included: Cardio workouts: Focus on improving cardiovascular endurance through exercises like running, cycling, and aerobics. Strength training: Emphasizes building muscle strength through weightlifting and resistance exercises. Yoga and flexibility: Focus on enhancing flexibility, balance, and relaxation through yoga and stretching routines. Other activities: Include activities such as HIIT (High-Intensity Interval Training), pilates, and meditation.
To ensure the face validity and content validity of the questionnaire, all items were translated into Vietnamese. The questionnaire was then pre-tested by representative users of health and fitness apps. Based on the feedback regarding the content, clarity of items, and appropriateness of constructs, the questionnaire was revised to improve its overall effectiveness.
Participants and procedures
This study employed a cross-sectional survey design to collect data from users of health and fitness apps in Vietnam. The target population consisted of users with prior experience using health and fitness apps in Vietnam. Due to budget constraints and challenges in identifying the full population, purposive sampling was utilized. This method enabled the recruitment of participants who were most likely to provide relevant insights into the research objectives. While purposive sampling is effective for targeted recruitment, it may introduce potential biases, such as self-selection bias, which are acknowledged in the limitations section.
A structured questionnaire was developed to gather data, including sections on demographic information, app usage behaviors, and intrinsic motivations such as challenge, curiosity, control, fantasy, and social interaction. A 5-point Likert scale was used to measure key constructs, adapting well-established scales to the context of health and fitness apps. To ensure quality and reliability, the survey included three reverse-coded items to identify and filter out inconsistent or invalid responses. The questionnaire was pre-tested with a pilot sample of 30 participants to confirm clarity and relevance. Based on feedback from the pilot study, minor revisions were made to enhance the comprehensibility of the survey items.
Ethical considerations were central to this study. The study received approval from the Ethics Committee of Ho Chi Minh City University of Economics and Finance (UEF). The ethics approval number for this study is 720/QD-UEF. All procedures were conducted in full compliance with the ethical guidelines outlined in the Declaration of Helsinki to uphold the highest ethical standards. Prior to participation, written informed consent was obtained from all participants. For minor participants under the age of 18, written informed consent was obtained from their legally authorized representatives (parents or guardians). Participants were informed about the study's purpose, their right to withdraw at any time, and the confidentiality of their responses.
Data collection was conducted over two weeks (January 15 to January 29, 2024). The questionnaire was distributed through popular fitness forums and websites in Vietnam, where health and fitness app users frequently gather. Links to the survey were shared on these platforms, accompanied by an invitation to participate. To boost response rates, incentives were offered, including 300 coupons valued at $3 each for participants who completed the survey. Despite the voluntary nature of participation, which may introduce self-selection bias, efforts were made to ensure a diverse and representative sample by targeting platforms with a high density of fitness app users.
A total of 3188 questionnaires were collected during the data collection period. After filtering for invalid or inconsistent responses, 2869 valid questionnaires were retained, yielding a valid response rate of 89.99%. The online survey platform's built-in validation features ensured no missing responses, and reverse-coded items helped identify and exclude inconsistent responses.
The data analysis was conducted using Structural Equation Modeling (SEM) to examine the hypothesized relationships between intrinsic motivations, entertainment value, and continuance intentions. LISREL software was employed for SEM analysis, while SPSS was used for preliminary data checks. Confirmatory Factor Analysis (CFA) was performed to assess the reliability and validity of the measurement model. Path analysis was utilized to explore the mediating role of entertainment and the influence of intrinsic motivations on continuance intentions. Additionally, subgroup analyses were conducted to examine variations in these relationships based on demographic factors such as gender, income, engagement levels, and activity types.
Results
Characteristics of the sample
The results indicate that younger users dominate the sample, with age groups distributed as follows: 16–18 years (19.0%), 19–22 years (30.0%), and 23–25 years (18.8%), collectively accounting for 67.8% of the total sample. Among the app users, 71.4% were male and 28.6% were female, with 55.8% of users aged 15–24 years. The main composition of the collected sample is as follows: 69.8% of respondents reported an average monthly income of less than 10 million VND, primarily comprising students (62.1%). Regarding the educational level, most of the respondents hold a Bachelor's degree (70.5%).
The most popular type of health and fitness activity among respondents was strength training (77.6%). Additionally, 73.3% of the total sample reported using the app at least once per day, and 68.5% indicated that they used the app for less than three hours per session. In terms of app usage experience, 53.7% of users had one to four years of experience, while 29.9% had less than a year of experience, and 16.4% had more than four years. These data suggest that the majority of respondents are highly active users, indicating a strong level of engagement. Therefore, the sample appears to be well-suited for data analysis in this study on health and fitness app usage (Table 1).
Table 1.
Characteristics of the participants.
| Variables | Categories | Number | % |
|---|---|---|---|
| Gender | Male | 2048 | 71.40 |
| Female | 821 | 28.60 | |
| Age | 16–18 years | 544 | 19.0 |
| 19–22 years | 860 | 30.0 | |
| 23–25 years | 538 | 18.8 | |
| 26–30 years | 653 | 22.7 | |
| 31–40 years | 217 | 7.5 | |
| 41 years and above | 57 | 1.9 | |
| Income | Less than 5 million VND | 1200 | 41.8 |
| 5–10 million VND | 804 | 28.0 | |
| 10–15 million VND | 412 | 14.4 | |
| 15–20 million VND | 208 | 7.2 | |
| 20–25 million VND | 145 | 5.0 | |
| 25–30 million VND | 65 | 2.3 | |
| 30–35 million VND | 25 | 0.9 | |
| Over 35 million VND | 10 | 0.4 | |
| Educational level | High school or below | 732 | 25.5 |
| Bachelor | 2023 | 70.5 | |
| Master or above | 114 | 4.0 | |
| Occupation | Employees | 970 | 33.8 |
| Students | 1780 | 62.10 | |
| Others | 119 | 4.10 | |
| App usage experience | <1 year | 856 | 29.9 |
| 1–4 years | 1539 | 53.7 | |
| >4 years | 474 | 16.4 | |
| App usage frequency | At least once per day | 2102 | 73.3 |
| Several times a week | 577 | 20.1 | |
| Once a week | 134 | 4.6 | |
| Once for several weeks | 56 | 1.9 | |
| App usage time per session | Less than 1 h | 572 | 19.90 |
| 1–2 h | 698 | 24.30 | |
| 2–3 h | 694 | 24.20 | |
| 3–4 h | 510 | 17.80 | |
| More than 4 h | 395 | 13.80 | |
| Activity types | Strength training | 2226 | 77.6 |
| Cardio workouts | 328 | 11.4 | |
| Yoga and flexibility | 229 | 8.0 | |
| Other activities | 86 | 3.0 |
Validity and reliability
The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy for this study's sample was 0.899, and Bartlett's test of sphericity yielded a p-value of 0, indicating that the sample is suitable for factor analysis, as common factors are present in the population. Factor analysis was conducted using Principal Component Analysis (PCA) with Varimax rotation.
As shown in Table 2, all items loaded as expected onto their respective constructs, and all achieved high-reliability levels. The factor loadings for all items were above 0.5, except for CO3, which had a loading of 0.493. Additionally, the reliability of the control motivation construct was only 0.384, which is below acceptable levels. Therefore, this construct was excluded from further analysis, and H4 could not be tested.
Table 2.
Results of exploratory factor analysis.
| Component | 1 | 2 | 3 | 4 | 5 | 6 | α |
|---|---|---|---|---|---|---|---|
| CH1 | 0.646 | 0.829 | |||||
| CH2 | 0.750 | ||||||
| CH3 | 0.764 | ||||||
| CH4 | 0.765 | ||||||
| CU1 | 0.555 | 0.707 | |||||
| CU2 | 0.616 | ||||||
| CU3 | 0.421 | ||||||
| CO1 | 0.627 | 0.384 | |||||
| CO2 | 0.732 | ||||||
| CO3 | 0.493 | ||||||
| FA1 | 0.782 | 0.832 | |||||
| FA2 | 0.812 | ||||||
| FA3 | 0.813 | ||||||
| FA4 | 0.689 | ||||||
| SO1 | 0.898 | 0.740 | |||||
| SO2 | 0.846 | ||||||
| EN1 | 0.755 | 0.887 | |||||
| EN2 | 0.734 | ||||||
| EN3 | 0.720 | ||||||
| BI1 | 0.794 | 0.833 | |||||
| BI2 | 0.835 | ||||||
| BI3 | 0.803 |
Measurement model
This study employed CFA to assess the convergent and discriminant validity of the measurement model. The results indicated a good fit between the data and the measurement model (RMSEA=0.046; NFI=0.98; NNFI=0.98; CFI=0.99; SRMR=0.035; GFI=0.97; and AGFI=0.95). The summary of the CFA results is shown in Table 3.
Table 3.
Convergent validity analysis for each construct.
| Construct | Measurement Item | t-value | Loading | AVE |
|---|---|---|---|---|
| Challenge | CH1 | 44.59* | 0.62 | 0.56 |
| CH2 | 49.01* | 0.72 | ||
| CH3 | 40.17* | 0.67 | ||
| CH4 | 40.68* | 0.67 | ||
| Fantasy | FA1 | 46.97* | 0.84 | 0.62 |
| FA2 | 38.04* | 0.75 | ||
| FA3 | 50.75* | 0.87 | ||
| FA4 | 39.31* | 0.66 | ||
| Social interaction | SO1 | 17.94* | 0.59 | 0.76 |
| SO2 | 21.61* | 1.24 | ||
| Curiosity | CU1 | 41.14* | 0.64 | 0.46 |
| CU2 | 38.13* | 0.59 | ||
| CU3 | 30.05* | 0.54 | ||
| Entertainment value | EN1 | 58.12* | 0.69 | 0.73 |
| EN2 | 58.31* | 0.74 | ||
| EN3 | 49.05* | 0.66 | ||
| Continuance intention | BI1 | 51.23* | 0.80 | 0.63 |
| BI2 | 46.19* | 0.77 | ||
| BI3 | 43.02* | 0.71 |
p < .05.
The results show that, except for curiosity, which had an Average Variance Extracted (AVE) of 0.46 (slightly below the threshold of 0.5), all other constructs had AVE values greater than 0.5, meeting the criteria for convergent validity. 95 Since the t-values for all items in the curiosity construct were significant, 96 this variable was retained for subsequent analysis.
This study followed the method by Igbaria and Iivari 97 to test for discriminant validity. The test involves comparing the squared correlation coefficients between two constructs with the AVE values of each construct. If the squared correlation between two constructs is smaller than the AVE of each construct, it indicates that the constructs have discriminant validity.
As shown in Table 4, all constructs in this study demonstrated good discriminant validity, as the squared correlations were smaller than the corresponding AVE values.
Table 4.
Discriminant validity.
| Construct | EN | BI | CH | CU | FA | SO |
|---|---|---|---|---|---|---|
| EN | 0.73 | |||||
| BI | 0.37 | 0.63 | ||||
| CH | 0.38 | 0.25 | 0.56 | |||
| CU | 0.41 | 0.19 | 0.43 | 0.46 | ||
| FA | 0.21 | 0.11 | 0.19 | 0.29 | 0.62 | |
| SO | 0.09 | 0.04 | 0.06 | 0.06 | 0.03 | 0.76 |
Hypothesis testing
This study used the LISREL software and SEM to test the proposed hypotheses, with path parameters estimated using the maximum likelihood method. The overall model fit indices 98 indicated a good fit for the model (χ²=1067.41, p = 0.00; df = 141, χ²/df = 7.6; GFI=0.96; RMSEA=0.048; NFI=0.98; and CFI=0.98). The Chi-square value is known to be highly sensitive to large sample sizes, which, while improving the stability of the observed data, often leads to inflated Chi-square values. This sensitivity may explain why the chi-square to degrees of freedom ratio (7.6) exceeded the recommended threshold of less than 5. 98
The results of the structural model verification are presented in Figure 2. As expected, entertainment was positively associated with continuance intention to play the game (H1). For intrinsic motivations, challenge (H2), curiosity (H3), fantasy (H5), and social interaction (H6) all showed significant positive relationships with entertainment. However, due to the low reliability of the control motivation construct, H4 could not be tested.
Figure 2.
Structural model. ***p < .001.
The path analysis of the structural model and the results of hypothesis testing are presented in Table 5.
Table 5.
Hypotheses testing results.
| Hypotheses | Structural Path | β value | t-value | Result |
|---|---|---|---|---|
| H1 | Entertainment → Continuance intention | 0.59 | 29.27*** | Accepted |
| H2 | Challenge → Entertainment | 0.34 | 12.93*** | Accepted |
| H3 | Curiosity → Entertainment | 0.33 | 10.73*** | Accepted |
| H4 | Control → Entertainment | Not tested | ||
| H5 | Fantasy → Entertainment | 0.11 | 5.19*** | Accepted |
| H6 | Social interaction → Entertainment | 0.12 | 8.35*** | Accepted |
p < .001.
This study further explores whether entertainment has a mediating effect. Previous research 69 suggested that motivations directly influence behavioral intentions. However, this study proposes that motivations influence continuance intention to engage in health and fitness apps through the mediating effect of entertainment.
To test this hypothesis, two additional theoretical models were developed. Model 2 includes direct paths from the four motivations to continuance intention without considering entertainment as a mediator. Model 3 adds direct paths from the four motivations to continuance intention in addition to the mediating paths from Model 1 that involve entertainment. The test results are presented in Table 6.
Table 6.
Mediation effects test results.
| Structural Path | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| Challenge → Entertainment | 0.34 (12.93***) | 0.32 (12.33***) | |
| Curiosity → Entertainment | 0.33 (10.73***) | 0.33 (10.72***) | |
| Fantasy → Entertainment | 0.11 (5.19***) | 0.11 (5.07***) | |
| Social interaction → Entertainment | 0.12 (8.35***) | 0.12 (8.35***) | |
| Entertainment → Continuance intention | 0.59 (29.27***) | 0.42 (14.63***) | |
| Challenge → Continuance intention | 0.36 (11.45***) | 0.22 (7.23***) | |
| Curiosity → Continuance intention | 0.14 (3.99***) | 0.00 (0.06) | |
| Fantasy → Continuance intention | 0.08 (3.30***) | 0.04 (1.46) | |
| Social interaction → Continuance intention | 0.05 (3.21**) | 0.00 (0.03) |
**p < .01; ***p < .001.
Comparing Model 1 and Model 3, it is evident that after adding direct paths from motivations to continuance intention, the effects of the four motivations on entertainment and the impact of entertainment on continuance intention remain largely unchanged. In Model 2, all four motivations significantly influence continuance intention directly. However, when comparing Model 2 and Model 3, it becomes clear that once the mediating paths through entertainment are introduced, the direct effects of the motivations on continuance intention—except for challenge—become insignificant.
This result indicates that entertainment plays a full mediating role between curiosity, fantasy, social interaction, and continuance intention in the context of health and fitness apps. However, challenge still retains a partial direct effect on continuance intention, meaning that entertainment only partially mediates the relationship between challenge and continuance intention. This partial mediation could be attributed to the nature of health and fitness activities, where the inherent challenge of achieving personal fitness goals or overcoming physical obstacles is often enough to drive users to continue engaging with the app, independent of the entertainment value.
Preliminary depiction of how different motivations affect various user groups
Wang and Qi 75 argued that different motivations influence the behavior of various user groups differently. While his research supports this argument, it has not yet been fully confirmed, as many researchers have either overlooked or avoided this topic. Existing studies 75 typically used a limited set of variables to categorize users into a few groups.
This section attempts to take a broader look at how different motivations may impact various potential user groups of health and fitness apps. The goal is to provide a rough depiction of these differences, offering insights into the academic and practical implications of such analysis. This exploration also highlights the potential value and feasibility of conducting further research in this area to better understand how motivation impacts different types of users in the health and fitness context.
In this study, users were first categorized based on eight grouping variables, and then LISREL was used to calculate the path coefficients for different groups. The results are shown in Table 7. To further verify the moderating effects, the study adopted the Chi-square difference test method. 99 The process is illustrated using gender as an example.
Table 7.
Motivational effects for different user groups.
| Hypotheses | Male | Female | High income | Low income | Heavy user | Light user | High engagement | Low engagement |
|---|---|---|---|---|---|---|---|---|
| CH→EN | 0.34 (11.49***) | 0.24 (4.39***) | 0.26 (5.08***) | 0.36 (10.88***) | 0.30 (9.67***) | 0.39 (7.60**) | 0.30 (9.10***) | 0.36 (8.16***) |
| CU→EN | 0.30 (8.51***) | 0.45 (6.64***) | 0.40 (7.08***) | 0.31 (8.15***) | 0.36 (9.64***) | 0.28 (4.75***) | 0.33 (8.39***) | 0.34 (6.62***) |
| FA→EN | 0.13 (5.27***) | 0.04 (0.88) | 0.09 (2.35*) | 0.12 (4.50***) | 0.11 (4.33***) | 0.11 (2.60*) | 0.12 (4.50***) | 0.08 (2.22*) |
| SO→EN | 0.13 (7.50***) | 0.09 (3.56***) | 0.14 (5.07***) | 0.12 (6.66***) | 0.11 (6.50***) | 0.14 (5.03***) | 0.12 (6.66***) | 0.13 (4.97***) |
| EN→BI | 0.45 (13.28***) | 0.38 (6.46***) | 0.48 (9.41***) | 0.40 (11.21***) | 0.41 (12.24***) | 0.46 (8.12***) | 0.44 (12.66***) | 0.38 (7.47***) |
*p < .05; **p < .01; ***p < .001.
First, the path coefficients for the male sample were calculated using LISREL, yielding a degree of freedom (df) of 137 and a Chi-square value of 817.48. Next, we standardized the path coefficients, including the γ and β values, in the male model to match those of the female model, and recalculated the df and Chi-square values for the male model, which resulted in values of 291 and 1296.47, respectively. We then calculated the Chi-square difference (1296.47‒817.48 = 421.64) and the df difference (291‒137 = 158). Consulting the Chi-square table, we found the critical value for df = 158 at the 0.05 significance level to be 188.33.
Since the chi-square difference (421.64) was greater than the critical value (188.33), this indicates a significant difference between the male and female models. The same procedure was followed for the other group classifications, and the results showed that moderating effects were present across all eight classification variables.
From the results in Table 6, it can be observed that, with a few exceptions, all four motivations significantly influence entertainment, with challenge and curiosity having a stronger impact than the other two motivations. Additionally, entertainment significantly affects continuance intention. These findings are consistent with the overall model results shown in Table 4, indicating that the motivation classifications and the proposed model in this study are highly stable. However, different groups exhibit moderating effects, leading to variations in how motivations influence entertainment across different user groups. A deeper explanation follows.
This study first divided the sample by gender, with 2151 males and 718 females. According to the LISREL analysis in Table 7, the entertainment effect was stronger for males than for females. For males, all four motivations enhanced entertainment, with challenge and curiosity being the most influential factors, and challenge having the greatest impact. This could be attributed to males’ tendency to seek challenges to demonstrate competence and achieve fitness goals.
For the female group, curiosity was the primary driver of entertainment. This suggests that women with higher curiosity are more likely to explore different aspects of the health and fitness app and derive enjoyment from discovering new features or workouts. While females also enjoy challenges, the effect is not as strong as it is for males. The fantasy motivation did not have a significant impact on entertainment for females. Fantasy-related enjoyment, such as imagining achieving ideal fitness goals or escaping daily stress, may not be as prominent in the context of fitness apps as it is in other leisure activities.
Finally, social interaction had an effect, though not a very strong one. This is in line with findings from previous studies, 76 which noted that women may also prioritize individual goals over social interaction when using the apps.
The study also divided the sample based on disposable income into two groups: those with an income of below 10 million VND (1843 respondents) and those with an income of above 10 million VND (1026 respondents). LISREL was used to analyze the two groups, and the results are presented in Table 7. From the analysis, it was observed that participants with higher incomes were slightly more influenced by entertainment in their continuance intention to use the app compared to those with lower incomes, though the difference between the two groups was not substantial. For the higher-income group, curiosity was the main driver of entertainment, suggesting that wealthier individuals might be more engaged with discovering new features or workouts within the health and fitness app. In contrast, for the lower-income group, challenge was the primary source of entertainment. This could be because lower-income individuals may seek a sense of achievement and competence through overcoming fitness challenges in the app, which they may feel is lacking in their real-life circumstances. This finding suggests that the motivations for using health and fitness apps differ slightly based on income levels, with higher-income users seeking novelty and exploration, and lower-income users focusing on overcoming challenges to gain a sense of accomplishment.
The study then divided participants into heavy users (those who use the health and fitness app at least once per day) and light users (those who use the app less than once per day). There were 2102 heavy users and 767 light users. According to the analysis results in Table 7, entertainment had a slightly greater influence on the continuance intention of light users compared to heavy users. This could be because light users, with less frequent engagement, find entertainment to be more impactful. Both challenge and curiosity were the main motivational drivers for both groups. However, light users, who may encounter fewer challenges in the app, place greater emphasis on the enjoyment they derive from overcoming these obstacles. In contrast, heavy users, who have more experience in completing challenges, are more motivated by curiosity for their entertainment.
Additionally, the study divided participants based on time spent per session into low-engagement users (those who use the app for less than three hours per session) and high-engagement users (those who use it for more than three hours per session). There were 905 low-engagement users and 1964 high-engagement users. As seen in Table 7, low-engagement users experienced less entertainment, which explains why the impact of entertainment on their continuance intention was weaker compared to high-engagement users. For both groups, challenge, and curiosity were the primary motivations, with curiosity having a similar effect across both. However, low-engagement users were more influenced by the challenge. Since challenges can directly affect continuance intention without necessarily being mediated by entertainment, this explains why low-engagement users are less affected by the entertainment factor.
The fifth analysis was based on the participants’ experience with health and fitness apps. Those with less than two years of experience were classified as novice users (1500 participants), while those with more than two years of experience were classified as experienced users (1369 participants). According to Table 7, the influence of entertainment on continuance intention was generally similar for both groups. Although all four motivations had an impact, the primary drivers remained challenge and curiosity. However, the effect of these two motivations differed between the groups. Novice users were more influenced by curiosity, likely because their shorter usage time meant there were still many new features and activities to discover within the app. In contrast, experienced users were more driven by the challenge motivation. Having used the app for a longer period, they were already familiar with its content and features and therefore placed greater importance on achieving fitness milestones or improving their performance, seeking a sense of accomplishment through overcoming challenges.
In the context of health and fitness apps, the type of activity is often regarded as a significant moderating variable. 88 Therefore, this study also conducted an exploratory analysis of the moderating effects of different fitness activities. Among all participants, those engaging in strength training dominated the sample, totaling 2226 users (77.6%), while other types of activities had fewer users. Since the sample size can affect the stability of path analysis results and the suitability of various indicators, 98 the following results should be interpreted with caution. The findings may change if more data are collected.
First, the study divided users into two groups: those engaged in strength training (2226 participants) and those participating in non-strength training activities (643 participants). According to the analysis in Table 7, the impact of entertainment on continuance intention was generally similar across both groups, with all four motivations showing significant influence. Challenge and curiosity remained the most important factors, but curiosity had a slightly greater influence on users engaged in non-strength training activities. Interestingly, in the strength training group, the impact of the fantasy motivation was more pronounced. Conversely, in the non-strength training group, social interaction had a stronger effect. This may be because, in non-strength training activities, it is more challenging for users to envision themselves as achieving idealized versions of their fitness goals solely through the activity, and thus they may rely more on social interaction for motivation and entertainment. In contrast, strength training may offer users a greater sense of fantasy, as they envision themselves transforming physically and reaching their fitness aspirations, which enhances the entertainment value of the app for this group.
Next, the study divided users into two groups: those engaged in high-intensity, fast-paced activities (203 participants) and those participating in other types of activities (2666 participants). According to the analysis in Table 7, users engaged in fast-paced activities, such as HIIT or cardio-based workouts, were more influenced by curiosity and also tended to use these activities for social interaction with friends. In contrast, the fantasy motivation did not have a significant impact on the entertainment value for this group (t = 0.14). This may be because fast-paced workouts focus heavily on quick reactions, intensity, and rapid progress, leaving little room for users to engage in a fantasy mindset. Users are more focused on keeping up with the pace and reacting to the workout's demands. However, these activities do provide opportunities for social interaction through shared participation with others, offering users the chance to bond over similar fitness goals, discuss workouts, and satisfy their need for social connection.
Finally, the study divided users into two groups: those engaged in adventure-type activities (158 participants) and those involved in non-adventure activities (2711 participants). Each group was analyzed using LISREL, and the results are shown in Table 7. Adventure-type activities typically involve users acting as protagonists in their fitness journey, where the process mimics real life with a series of events such as building relationships, developing skills, and accumulating progress over time. These activities often feature larger variations in challenges and progress, catering more to users’ curiosity but may not offer rapidly changing or highly difficult challenges.
When comparing the two groups, it was found that users in the adventure-type group likely seek to experience excitement and thrill that they may not get from real life, which is why their continuance intention is more influenced by entertainment. For adventure-type users, curiosity and social interaction were the primary factors influencing the entertainment value. This could be because users driven by curiosity tend to stay engaged to explore how the fitness journey will unfold. In the process, instead of focusing on competition, these users are more inclined to seek interaction and friendship, relying on the support and help of others to complete their fitness goals.
Discussion and implications
Discussion
According to flow theory, 100 motivation is one of the key factors influencing users’ experiences and their willingness to continue engaging with online platforms. Similarly, the act of using health and fitness apps is driven by intrinsic motivation, meaning that motivation plays a crucial role in users’ ongoing engagement. Despite this, compared to other research fields such as tourism, where there is a high focus on the role of motivation, 101 studies on health and fitness apps have paid relatively little attention to the role of motivation. The literature review reveals a pressing need for a widely accepted and stable classification of motivations that can offer explanatory power, along with a comprehensive exploration of how different motivations impact various user groups. This study aimed to address that gap by conducting an in-depth investigation, providing a framework for understanding motivation's role in health and fitness apps, and exploring how these motivations affect different types of users. Through this exploration, the study contributes to closing the knowledge gap and opens up new paradigms and opportunities for future research in this area.
Through a review of the literature, this study proposed a preliminary intrinsic motivation classification framework and developed a theoretical model. Nearly 3000 data points were collected to test the explanatory power of the model. As observed from the hypothesis testing results (Table 4), all hypotheses were supported, except for H4, which could not be tested. Additionally, this study found that entertainment acts as a mediating factor between motivation and continuance intention (Table 5). The findings suggest that the stronger a user's challenge, curiosity, fantasy, and social interaction motivations are, the higher their perceived entertainment from the health and fitness app will be, ultimately increasing their intention to continue using the app. Furthermore, the data analysis yielded several interesting and novel insights.
First, the integrated motivation classification framework and the proposed theoretical model proved to be very stable. The data analysis showed that the mediating model, where the four motivations (challenge, curiosity, fantasy, and social interaction) influence continuance intention through entertainment, remained highly consistent across different user groups (Table 6). This indicates that whether a health and fitness app is enjoyable is closely tied to whether users continue to engage with it. The level of entertainment, however, is influenced more by users’ motivations than by their characteristics or the type of activity.
Second, while challenge, curiosity, fantasy, and social interaction motivations all significantly influenced entertainment in most cases, challenge and curiosity had a stronger impact than the other two. This pattern remained stable across most types of activities, except for adventure-style activities, where the influence of motivations varied slightly. Thus, in most contexts, challenge and curiosity are the primary factors driving users to continue engaging with the same fitness app.
Third, the challenge motivation had a direct effect on continuance intention, independent of entertainment. This suggests that users are intrinsically motivated to overcome obstacles and participate in challenges to satisfy their need for achievement and self-validation. In some cases, people may not engage with a fitness app solely for entertainment, but rather to gain a sense of accomplishment by meeting their fitness goals, boosting their confidence and self-esteem. The pursuit of achievement through challenges can keep users engaged in the app, similar to findings by Rehman and Abbasi. 34
Fourth, this study provides a preliminary framework for understanding why different user groups continue to engage with a health and fitness app, offering insights that can assist businesses in market segmentation and marketing strategy development. Sherry and Greenberg 69 suggested that the design of an app must align with users’ motivations to attract and retain them. Wang and Qi 75 further found that the primary motivations driving different user groups to use a product vary. Our study delved deeper into this notion and revealed several interesting findings. The results indicate that males are the most typical users of health and fitness apps, and those driven by challenge and curiosity are more likely to maintain their engagement. Conversely, females are more strongly driven by curiosity, which immerses them in the app's content. Among groups with higher income, heavy usage, high engagement, and novice users, curiosity has a greater influence than challenge. In non-strength training, adventure, and high-intensity activities, users with higher curiosity are more likely to be loyal to the app. Social interaction played a stronger role in activities like adventure and high-intensity training, likely because these activities rely less on direct competition and more on cooperation, coordination, and mutual support among users. These insights can help app developers and marketers tailor their strategies to align with users’ motivations and create more engaging experiences for specific user groups.
H4, which proposed that control influences entertainment in health and fitness apps, could not be tested due to the poor reliability of the control variable. Additionally, the AVE value for curiosity was slightly below the 0.5 threshold, indicating there is room for improvement in the measurement quality of these two variables. Further investigation with several user groups revealed that, in high-engagement fitness programs, which had the highest participation, factors such as the user's fitness level, access to equipment, workout progression strategies, and coordination within social or virtual fitness communities, continuously impacted participants’ sense of control. The concept of motivation stems from unmet needs, where behavior is motivated by the ability to satisfy those needs. In this context, the perception of whether the need for control is being met fluctuates frequently based on workout dynamics and external factors. In contrast, self-paced fitness activities like yoga or single-session guided workouts may allow users to more easily assess whether their control needs are being satisfied. Another possible reason for the poor measurement quality may stem from the variety of health and fitness apps, each with different levels of difficulty and control mechanisms. For instance, fitness apps that focus on guided programs or interactive challenges may offer users a simplified sense of control, failing to fully satisfy more advanced users’ control needs. Moreover, the large number of survey items might have impacted data quality. Armstrong and Overton 102 suggested that responses collected later in the survey process tend to be of lower quality. When reanalyzing the first 400 responses, the reliability of the control variable increased to 0.52, and curiosity improved to 0.74, with the AVE value for curiosity rising to 0.49, which is close to the 0.5 threshold. This suggests that removing lower-quality responses could improve the overall measurement quality in future studies.
Theoretical implications
In recent years, many studies have focused on identifying the factors that influence users’ specific behaviors in online environments. Some studies have emphasized the impact of business-related factors, such as exploring the influence of service quality using the SERVQUAL model. 103 Another research approach is based on the Technology Acceptance Model (TAM), which has been adapted to different contexts to develop suitable explanatory theories. 43 However, TAM has often been criticized for placing too much emphasis on extrinsic motivations, which has led some researchers to attempt to integrate intrinsic motivations to modify the model. 104 This study contributes to that line of inquiry by focusing on the role of intrinsic motivations—such as challenge, curiosity, and social interaction—in influencing user engagement and continuance intention in health and fitness apps.
Compared to research on other behaviors, studies that investigate user continuance intention from a motivation perspective are relatively rare. This has limited our understanding, both academically and practically, of the psychological processes that influence users’ decisions to continue engaging with a health and fitness app. Additionally, the lack of consensus on how to classify motivations has restricted the ability to compare, accumulate, and synthesize research findings. While some existing studies have contributed to our understanding of user psychology and behavior, the varied results have hindered the advancement of related theories.
This study offers a preliminary classification framework for intrinsic motivations in the context of health and fitness app engagement, which has been shown through empirical research to be highly stable. This framework can serve as a common foundation for future researchers to explore the role of intrinsic motivations. Furthermore, this study's exploratory examination of how different motivations function across diverse user groups provides a more detailed understanding of user behavior patterns. These insights can help fitness app developers segment users more effectively and develop targeted marketing strategies.
Of course, this model is not without its limitations. For example, due to space constraints and certain limitations, the study did not employ the most up-to-date scales or incorporate extrinsic motivations, which could enhance the theoretical foundation. However, these issues are easily addressed, and future researchers can refine this framework by incorporating the latest theories and measurement scales to systematically examine motivation in more depth.
Additionally, this study opens up many avenues for future research. For example, the same framework could be used to explore other information system-related behaviors, such as motivations for social media participation or mobile commerce usage. Researchers could also integrate this framework with other models, such as TAM, to gain a deeper understanding of users’ behavioral intentions. Moreover, employing advanced statistical techniques, such as cluster analysis, 105 could further validate whether the motivations identified in this study are suitable for use as market segmentation variables.
Practical implications
Research from various institutions predicts that the health and fitness app market will continue to grow, with user numbers increasing each year. However, competition among developers is expected to become more intense. This study's in-depth exploration of different user groups can assist companies in effectively identifying target consumers and conducting market segmentation. For example, women with high curiosity are likely to be more loyal users. Developers can tailor marketing strategies to different market segments based on these insights.
To attract more users to health and fitness apps, companies must consider the most important intrinsic motivations within their target market when designing their products. One key finding from this study is that challenge is the most important motivation. Users often seek to achieve a sense of accomplishment that they may not experience in real life, thus boosting their self-confidence and validating their abilities. Drawing from the perspectives of Hoffman and Novak 100 and Kuru 88 on challenges in digital platforms, the difficulty in fitness activities within the app must be appropriately matched to users’ skill levels. If the workouts are too easy or too difficult, users will not derive enjoyment or feel a sufficient sense of achievement. Health and fitness apps allow users to choose different levels of difficulty and intensity, so developers need to continuously understand users’ abilities to create appropriate challenges and regularly introduce new, more difficult challenges to keep users engaged.
In many cases, curiosity plays a crucial role in user engagement. The design of a fitness app's visuals, sounds, and storylines can stimulate users’ curiosity about novel or unique features. To explore these new elements and enjoy the unexpected progressions in the app's content, users are likely to remain engaged. Thus, companies must regularly update the app, introducing new workout routines, activities, and features, whether through changes in the interface, exercise plans, or audio-visual elements, to maintain users’ curiosity.
From a social interaction perspective, users are often motivated by a desire to connect with peers and share experiences, which adds to their enjoyment of the app. Therefore, apps should offer more interactive opportunities, such as group challenges, partner workouts, or community engagement, where users can collaborate, assist one another, and work toward common goals. By establishing social communities within the app, developers can facilitate more interactions among users, meeting their social needs and fostering user loyalty.
Limitations and future research
This study highlights several areas for further exploration. First, while a comprehensive motivation classification framework was proposed, other intrinsic motivations, such as escapism, remain unaddressed. Additionally, distinctions between similar motivations, like achievement, self-affirmation, and challenge, need further clarification to develop a more refined model. Secondly, the study's analysis of motivation across different user groups was data-driven and lacked a strong theoretical foundation. Future research could develop hypotheses to test differences in path coefficients between groups more rigorously. Third, further exploration of various app types could enhance understanding of user motivations. Future research should also involve validation studies to improve segmentation criteria. Fifth, using more up-to-date scales could improve the framework. The control motivation scale, in particular, may need refinement to better capture users’ dynamic experiences in health and fitness apps. Sixth, future studies could explore extrinsic motivations, such as social pressure or group dynamics, which were not addressed in this research. Lastly, future studies could employ qualitative methods, such as in-depth interviews, focus groups, or user diaries, to explore nuanced user experiences and uncover additional motivations or barriers. Combining qualitative insights with quantitative data could enrich the theoretical framework and provide a more comprehensive understanding of user engagement.
Conclusions
This study advances the understanding of intrinsic motivations in driving user engagement and continuance intention within health and fitness apps. Using flow theory and the Uses and Gratifications Theory, a robust theoretical model that highlights the mediating role of entertainment was developed and validated. With nearly 3000 responses, the findings demonstrate that intrinsic motivations—challenge, curiosity, fantasy, and social interaction—significantly influence entertainment, which in turn impacts users’ intention to continue using these apps.
A key strength of this study is its innovative focus on intrinsic motivations, addressing gaps in the literature by offering a stable classification framework. Challenge and curiosity emerged as the most influential drivers across user groups, underscoring the importance of adaptive challenges and dynamic, curiosity-driven features in app design. Subgroup analyses revealed valuable insights into user-specific behaviors, such as males prioritizing challenges and females emphasizing curiosity, providing actionable guidance for targeted app development.
The findings have practical relevance for developers aiming to enhance engagement through features that align with users’ intrinsic motivations. By fostering entertainment and tailoring experiences to user needs, health and fitness apps can drive sustained engagement and loyalty. This study not only contributes to theoretical advancement but also offers practical strategies for success in the competitive digital health market.
Supplemental Material
Supplemental material, sj-docx-1-dhj-10.1177_20552076251326151 for Intrinsic motivations in health and fitness app engagement: A mediation model of entertainment by Thuy Dung Pham Thi and Nam Tien Duong in DIGITAL HEALTH
Acknowledgments
We would like to thank all the participants for supporting this work.
Footnotes
Authors’ contributions: Thuy Dung Pham Thi performed the examination of the literature and was a major contributor to writing the manuscript. Nam Tien Duong supervised the whole process. All authors read and approved the final manuscript.
Availability of data and material: The data that support the findings of this study are available from the corresponding author upon request.
Consent for publication: All the authors declare their consent for publication.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics approval and consent to participate: This study was approved by the Ethics Committee of Ho Chi Minh City University of Economics and Finance (UEF) with approval number 720/QD-UEF. Written informed consent was obtained from all participants before they took part in the study. For participants under the age of 18, written informed consent was obtained from their legally authorized representatives (parents or guardians). This study was conducted in accordance with the ethical guidelines outlined in the Declaration of Helsinki.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Nam Tien Duong https://orcid.org/0000-0001-6757-797X
Supplemental material: Supplemental material for this article is available online.
References
- 1.Wang Y, Gong X. Optimization of data processing system for exercise and fitness process based on internet of things. Wirel Commun Mob Comput 2021; 2021. DOI: 10.1155/2021/7132301 [DOI] [Google Scholar]
- 2.Chiu W, Cho H, Christina G. Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Inf Technol People 2020; 34: 978–998. [Google Scholar]
- 3.Chiu W, Cho H. The role of technology readiness in Individuals’ intention to use health and fitness applications: a comparison between users and non-users. Asia Pac J Mark Logist 2020; 33: 807–825. [Google Scholar]
- 4.Global Market Indices. Fitness app market size & growth trends analysis, 2024–2032, 2024.
- 5.Mehrab Ashrafi D, Mone FH, Zabeen M, et al. What drives users to recommend mobile fitness apps? A three-stage analysis using PLS-SEM, machine learning, and fsQCA. Int J Hum Comput Interact 2024: 1–31. [Google Scholar]
- 6.Statista. Health & fitness app market outlook, fitness apps worldwide. 2024. Available at https://www.statista.com/outlook/hmo/digital-health/digital-fitness-well-being/health-wellness-coaching/fitnessapps/worldwide (accessed 7 January 2025).
- 7.Lemos N, Machado CS, Cardoso C. Free apps and paid apps: monetization strategies for health apps in the Portuguese market. Int J Pharm Healthcare Mark 2024; 18: 237–251. [Google Scholar]
- 8.Ilhan A, Fietkiewicz KJ. Learning for a healthier lifestyle through gamification: a case study of fitness tracker applications. Perspect Wearable Enhanced Learn (WELL) Curr Trends Res Pract 2019: 333–364. [Google Scholar]
- 9.Luo Y, Wang G, Li Y, et al. Examining protection motivation and network externality perspective regarding the continued intention to use M-health apps. Int J Environ Res Public Health 2021; 18: 5684. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Hassan L, Dias A, Hamari J. How motivational feedback increases user’s benefits and continued use: a study on gamification, quantified-self and social networking. Int J Inf Manage 2019; 46: 151–162. [Google Scholar]
- 11.Pham Thi TD, Pham VK, Duong NT. Consumers’ continuance intention toward online shopping apps: an integrated model. J Comput Inf Syst 2024: 1–21. DOI: 10.1080/08874417.2024.2413582 [DOI] [Google Scholar]
- 12.Ajzen I. From intentions to actions: a theory of planned behavior. Action control: from cognition to behavior. Springer, 1985, pp. 11–39. [Google Scholar]
- 13.Santarossa S, Kane D, Senn CY, et al. Exploring the role of in-person components for online health behavior change interventions: can a digital person-to-person component suffice? J Med Internet Res 2018; 20: e144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley, 1977. [Google Scholar]
- 15.AbdulKareem AK, Oladimeji KA. Cultivating the digital citizen: trust, digital literacy and E-government adoption. Transform Gov People Process Policy 2024; 18: 270–286. [Google Scholar]
- 16.Davis FD. Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS Inform Seeking BehavTechnol Adoption 1989; 205: 219. [Google Scholar]
- 17.Wang Y, Lin KY. Understanding continuance usage of mobile learning applications: the moderating role of habit. Front Psychol 2021; 12. DOI: 10.3389/fpsyg.2021.736051 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Oliver RL. Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation. J Appl Psychol 1977; 62: 480–486. [Google Scholar]
- 19.Csikszentmihalyi M. Flow: the psychology of optimal experience. USA: Haper and Row, 1990. [Google Scholar]
- 20.Hyun H, Thavisay T, Lee SH. Enhancing the role of flow experience in social media usage and its impact on shopping. J Retail Consum Serv 2022; 65: 102492. [Google Scholar]
- 21.Yan M, Filieri R, Raguseo E, et al. Mobile apps for healthy living: factors influencing continuance intention for health apps. Technol Forecast Soc Change 2021; 166: 120644. [Google Scholar]
- 22.Elsotouhy MM, Ghonim MA, Alasker TH, et al. Investigating health and fitness app users’ stickiness, WOM, and continuance intention using SOR model: the moderating role of health consciousness. Int J Hum Comput Interact 2024; 40: 1235–1250. [Google Scholar]
- 23.Chen C-C, Hsiao K-L, Li W-C. Exploring the determinants of usage continuance willingness for location-based apps: a case study of bicycle-based exercise apps. J Retail Consum Serv 2020; 55: 102097. [Google Scholar]
- 24.Li Z, Kuang Y, He P, et al. Research on middle-aged and elderly Users’ continuous use behavior of online leisure and entertainment platform. Acad J Bus Manage 2022; 4: 14–22. [Google Scholar]
- 25.Yin X, Li J, Si H, et al. Attention marketing in fragmented entertainment: how advertising embedding influences purchase decision in short-form video apps. J Retail Consum Serv 2024; 76: 103572. [Google Scholar]
- 26.Damberg S. Predicting future use intention of fitness apps among fitness app users in the United Kingdom: the role of health consciousness. Int J Sports Mark Sponsorship 2022; 23: 369–384. [Google Scholar]
- 27.Yin S, Cai X, Wang Z, et al. Impact of gamification elements on user satisfaction in health and fitness applications: a comprehensive approach based on the kano model. Comput Human Behav 2022; 128: 107106. [Google Scholar]
- 28.Arora C, Razavian M. Ethics of gamification in health and fitness-tracking. Int J Environ Res Public Health 2021; 18: 11052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kc B, Alrasheedy AA, Goh BH, et al. The types and pattern of use of Mobile health applications among the general population: a cross-sectional study from selangor, Malaysia. Patient Prefer Adherence 2021; 15: 1755–1762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Esmaeilzadeh P. The influence of gamification and information technology identity on postadoption behaviors of health and fitness app users: empirical study in the United States. JMIR Serious Games 2021; 9: e28282.. 20210705. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yang Y, Koenigstorfer J. Determinants of fitness app usage and moderating impacts of education-, motivation-, and gamification-related app features on physical activity intentions: cross-sectional survey study. J Med Internet Res 2021; 23: e26063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Honary M, Bell BT, Clinch S, et al. Understanding the role of healthy eating and fitness mobile apps in the formation of maladaptive eating and exercise behaviors in young people. JMIR Mhealth Uhealth 2019; 7: e14239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Menhas R, Qin L, Saqib ZA, et al. The association between COVID-19 preventive strategies, virtual reality exercise, use of fitness apps, physical, and psychological health: testing a structural equation moderation model. Front Public Health 2023; 11: 1170645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Rehman U, Abbasi AZ, Ting DH, et al. Exploring the impact of gamified experiences on user engagement in fitness apps: a gamefulquest perspective. IEEE Trans Eng Manage 2023; 71: 3613–3628. [Google Scholar]
- 35.Alqahtani F, Orji R, Riper H, et al. Motivation-based approach for tailoring persuasive mental health applications. Behav Inf Technol 2023; 42: 569–595. [Google Scholar]
- 36.Tu R, Hsieh P, Feng W. Walking for fun or for “likes”? The impacts of different gamification orientations of fitness apps on consumers’ physical activities. Sport Manage Rev 2019; 22: 682–693. [Google Scholar]
- 37.Westmattelmann D, Grotenhermen J-G, Sprenger M, et al. Apart we ride together: the motivations behind users of mixed-reality sports. J Bus Res 2021; 134: 316–328. [Google Scholar]
- 38.Ghosh P, Proffitt R, Bosworth KT, et al. Mhealth app features that facilitate adolescent use for lifestyle management, and are endorsed by caregivers and health care providers. Mhealth 2024; 10: 21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Liu Z, Zhang Y, Zhang J, et al. Run for the group: examining the effects of group-level social interaction features of fitness apps on exercise participation. Decis Support Syst 2024; 114335: 1–12. [Google Scholar]
- 40.Molina MD, Myrick JG. The ‘how’ and ‘why’ of fitness app use: investigating user motivations to gain insights into the nexus of technology and fitness. Sport in Society 2021; 24: 1233–1248. [Google Scholar]
- 41.Wei J, Vinnikova A, Lu L, et al. Understanding and predicting the adoption of fitness mobile apps: evidence from China. Health Commun 2021; 36: 950–961. [DOI] [PubMed] [Google Scholar]
- 42.Jiang L, Sun M, Huang G. Uncovering the heterogeneity in fitness app use: a latent class analysis of Chinese users. Int J Environ Res Public Health 2022; 19: 10679. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Cho H, Chi C, Chiu W. Understanding sustained usage of health and fitness apps: incorporating the technology acceptance model with the investment model. Technol Soc 2020; 63: 101429. [Google Scholar]
- 44.Cotton V, Patel MS. Gamification use and design in popular health and fitness mobile applications. Am J Health Promot 2019; 33: 448–451. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Chen JV, Ha Q-A, Widjaja AE, et al. To switch or not to switch? Investigating users’ switching behaviours of fitness wearable devices. Int J Mobile Commun 2023; 21: 95–118. [Google Scholar]
- 46.Whelan E, Clohessy T. How the social dimension of fitness apps can enhance and undermine wellbeing: a dual model of passion perspective. Inf Technol People 2021; 34: 68–92. [Google Scholar]
- 47.Hung S-W, Chang C-W, Ma Y-C. A new reality: exploring continuance intention to use mobile augmented reality for entertainment purposes. Technol Soc 2021; 67: 101757. [Google Scholar]
- 48.Zhou F, Mou J, Kim J. Toward a meaningful experience: an explanation of the drivers of the continued usage of gamified mobile app services. Online Inf Rev 2022; 46: 285–303. [Google Scholar]
- 49.Lee HE, Cho J. What motivates users to continue using diet and fitness apps? Application of the uses and gratifications approach. Health Commun 2016; 32: 1445–1453. [DOI] [PubMed] [Google Scholar]
- 50.Morris LS, Grehl MM, Rutter SB, et al. On what motivates us: a detailed review of intrinsic v: Extrinsic motivation. Psychol Med 2022; 52: 1801–1816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Salerno A, Janiszewski C, Laran J. Focus on the need or feeling good? Coping through instrumental action versus prohedonic distraction depends on the temporal efficacy of means. Motiv Emot 2023; 47: 887–907. [Google Scholar]
- 52.Frese M, Sabini J. Goal Directed Behavior: The Concept of Action in Psychology (1st ed.). London: Routledge, 1985. [Google Scholar]
- 53.Hommel B. GOALIATH: a theory of goal-directed behavior. Psychol Res 2022; 86: 1054–1077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Müller A, Joshi M, Thomas TA. Excessive shopping on the internet: recent trends in compulsive buying-shopping disorder. Curr Opin Behav Sci 2022; 44: 101116. [Google Scholar]
- 55.Savelkoul R. Superstars vs the long tail: how does music piracy affect digital song sales for different segments of the industry? Inf Econ Policy 2020; 50: 100847. [Google Scholar]
- 56.Vartanian LR, Pinkus RT, Smyth JM. Experiences of weight stigma in everyday life: implications for health motivation. Stigma Health 2018; 3: 85–92. [Google Scholar]
- 57.Ortner CNM, Chadwick L, Pennekamp P. Do motives matter? Short- and long-term motives as predictors of emotion regulation in everyday life. Emotion 2022; 22: 1625–1638. [DOI] [PubMed] [Google Scholar]
- 58.Imtiyaz H, Soni P, Yukongdi V. Assessing the Consumers’ purchase intention and consumption of convenience food in emerging economy: the role of physical determinants. Sage Open 2023; 13: 215824402211484. [Google Scholar]
- 59.Arowosafe F, Akinwotu O, Tunde-Ajayi O, et al. Push and pull motivation factors: a panacea for tourism development challenges in oluminrin waterfalls, Nigeria. J Policy Res Tourism Leisure Events 2022; 14: 63–74. [Google Scholar]
- 60.Chang Y-W, Hsu P-Y, Chen J, et al. Utilitarian and/or hedonic shopping–consumer motivation to purchase in smart stores. Ind Manag Data Syst 2023; 123: 821–842. [Google Scholar]
- 61.Al-Azawei A, Alowayr A. Predicting the intention to use and hedonic motivation for mobile learning: a comparative study in two middle eastern countries. Technol Soc 2020; 62: 101325. [Google Scholar]
- 62.Beldad A, Hegner SM. Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German Users’ willingness to continue using a fitness app: a structural equation modeling approach. Int J Hum Comput Interact 2017; 34: 882–893. [Google Scholar]
- 63.Molina MD, Sundar SS. Can mobile apps motivate fitness tracking? A study of technological affordances and workout behaviors. Health Commun 2020. doi: 10.1080/10410236.2018.1536961 [DOI] [PubMed] [Google Scholar]
- 64.Gupta A, Dhiman N, Yousaf A, et al. Social comparison and continuance intention of smart fitness wearables: an extended expectation confirmation theory perspective. Behav Inf Technol 2021; 40: 1341–1354. [Google Scholar]
- 65.Springer A, Venkatakrishnan A, Mohan S, et al. Leveraging self-affirmation to improve behavior change: a Mobile health app experiment. JMIR Mhealth Uhealth 2018; 6: e157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Zhang X, Xu X. Continuous use of fitness apps and shaping factors among college students: a mixed-method investigation. Int J Nurs Sci 2020; 7: S80–S87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Kogler A-M, Schöttl SE. Sports-related Leisure behavior in Alpine regions during the COVID-19 pandemic—a cross-sectional study in Austria, Germany and Italy. Front Public Health 2023; 11. DOI: 10.3389/fpubh.2023.1136191 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Malone TW, Lepper MR. Making learning fun: a taxonomy of intrinsic motivations for learning. Aptitude, learning, and instruction. London: Routledge, 2021, pp.223–254. [Google Scholar]
- 69.Sherry JL, Greenberg BS, Lucas K, et al. Video game uses and gratifications as predictors of use and game preference. Playing video games. New York: Routledge, 2012, pp.248–262. [Google Scholar]
- 70.Haught MJ, Wei R, Karlis JV. Apps, apps, and more apps: motivations and user behaviours. Int J Mobile Comput Multimedia Commun (IJMCMC) 2016; 7: 1–14. [Google Scholar]
- 71.Depper A, Howe PD. Are we fit yet? English adolescent girls’ experiences of health and fitness apps. Health Sociol Rev 2017; 26: 98–112. [Google Scholar]
- 72.Gay V, Leijdekkers P. Bringing health and fitness data together for connected health care: mobile apps as enablers of interoperability. J Med Internet Res 2015; 17: e260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Trukeschitz B, Eisenberg S, Schneider C, et al. Exploring the Effectiveness of a Fitness-app Prototype for Home Care Service Users in Austria and Italy. Health Soc Care Commun 2022; 30. DOI: 10.1111/hsc.13733 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Oyibo K, Vassileva J. Persuasive features that drive the adoption of a fitness application and the moderating effect of age and gender. Multimodal Technol Interact 2020; 4: 17. [Google Scholar]
- 75.Wang C, Qi H. Influencing factors of acceptance and use behavior of Mobile health application users: systematic review. Healthcare 2021; 9: 57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Wang N, Deng ZG, Wen LM, et al. Understanding the use of smartphone apps for health information among pregnant Chinese women: mixed methods study. JMIR Mhealth Uhealth 2019; 7: e12631. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Guo Y, Ma X, Chen D, et al. Factors influencing use of fitness apps by adults under influence of COVID-19. Int J Environ Res Public Health 2022; 19: 15460. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Lerner RM. A life-span perspective for early adolescence: biological-psychosocial interactions in early adolescence. London: Routledge, 2021, pp.9–34. [Google Scholar]
- 79.Oliveira T, Barbeitos I, Calado A. The role of intrinsic and extrinsic motivations in sharing economy post-adoption. Inf Technol People 2022; 35: 165–203. [Google Scholar]
- 80.Allam R, Dinana H. The future of TV and online video platforms: a study on predictors of use and interaction with content in the Egyptian evolving telecomm, media & entertainment industries. Sage Open 2021; 11: 21582440211040804. [Google Scholar]
- 81.Wang Y, Collins WB. Systematic evaluation of mobile fitness apps: apps as the tutor, recorder, game companion, and cheerleader. Telemat Inform 2021; 59: 101552. [Google Scholar]
- 82.Ma S, Zhang S, Li G, et al. Exploring information security education on social media use: perspective of uses and gratifications theory. Aslib J Inf Manage 2019; 71: 618–636. [Google Scholar]
- 83.Ryan RM, Deci EL, Vansteenkiste M, et al. Building a science of motivated persons: self-determination theory’s empirical approach to human experience and the regulation of behavior. Motiv Sci 2021; 7: 97–110. [Google Scholar]
- 84.Vahlo J, Karhulahti V-M. Challenge types in gaming validation of video game challenge inventory (CHA). Int J Hum Comput Stud 2020; 143: 102473. [Google Scholar]
- 85.Galetsi P, Katsaliaki K, Kumar S. Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: an analysis of mobile applications for health specialists. Technovation 2023; 121: 102598. [Google Scholar]
- 86.Vaghefi I, Tulu B. The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth Uhealth 2019; 7: e12983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Liu G, Yang H, Jing N. Research on the influence of online fitness user engagement on value co-creation behavior. Asia Pac J Mark Logist 2023; 35: 3138–3155. [Google Scholar]
- 88.Kuru H. Identifying behavior change techniques in an artificial intelligence-based fitness app: a content analysis. Health Educ Behav 2023; 51: 636–647. [DOI] [PubMed] [Google Scholar]
- 89.Whelan E, Clohessy T. How the social dimension of fitness apps can enhance and undermine wellbeing. Inf Technol People 2020; 34: 68–92. [Google Scholar]
- 90.Morris NJ, Orton-Johnson K. Camping at home: escapism, self-care, and social bonding during the COVID-19 pandemic. Ann Leisure Res 2024; 27: 14–35. [Google Scholar]
- 91.Novak T, Hoffman D. Measuring the flow experience among web users. Paper Presented at the Interval Research Corporation, Project 2000, Vanderbilt University, 1997.
- 92.Agarwal R, Karahanna E. Time flies when you're having fun: cognitive absorption and beliefs about information technology usage. MIS Q 2000: 665–694. doi: 10.2307/3250951 [DOI] [Google Scholar]
- 93.Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace 1. J Appl Soc Psychol 1992; 22: 1111–1132. [Google Scholar]
- 94.Hsu C-L, Lu H-P. Why do people play on-line games? An extended TAM with social influences and flow experience. Inf Manage 2004; 41: 853–868. [Google Scholar]
- 95.Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. Los Angeles, CA: Sage publications Sage CA, 1981. [Google Scholar]
- 96.Morales Espinoza M. Assessing the cross-cultural applicability of a service quality measure A comparative study between Quebec and Peru. Int J Serv Ind Manage 1999; 10: 449–468. [Google Scholar]
- 97.Igbaria M, Iivari J. The effects of self-efficacy on computer usage. Omega (Westport) 1995; 23: 587–605. [Google Scholar]
- 98.Hair JF, Black WC, Babin BJ, et al. Multivariate data analysis. 6th edn. New Jersey: Pearson Prentice Hall, 2006. [Google Scholar]
- 99.Johnson JL. Strategic integration in industrial distribution channels: managing the interfirm relationship as a strategic asset. J Acad Mark Sci 1999; 27: 4–18. [Google Scholar]
- 100.Hoffman DL, Novak TP. Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 1996; 60: 50–68. [Google Scholar]
- 101.Chi NTK, Phuong VH. Studying tourist intention on city tourism: the role of travel motivation. Int J Tourism Cities 2022; 8: 497–512. [Google Scholar]
- 102.Armstrong JS, Overton TS. Estimating nonresponse bias in mail surveys. J Mark Res 1977; 14: 396–402. [Google Scholar]
- 103.Sumi RS, Kabir G. Satisfaction of e-learners with electronic learning service quality using the servqual model. J Open Innovation Technol Market Complexity 2021; 7: 227. [Google Scholar]
- 104.Lai CY, Cheung KY, Chan CS. Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: an extension of the technology acceptance model. Comput Educ Artif Intell 2023; 5: 100178. [Google Scholar]
- 105.Beh A, Bruyere BL. Segmentation by visitor motivation in three Kenyan national reserves. Tourism Manage 2007; 28: 1464–1471. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material, sj-docx-1-dhj-10.1177_20552076251326151 for Intrinsic motivations in health and fitness app engagement: A mediation model of entertainment by Thuy Dung Pham Thi and Nam Tien Duong in DIGITAL HEALTH