The underlying model most apps follow, even implicitly, is that behavior is a function of motivation, ability, and a prompt, all three arriving at the same moment. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by Ministry of Education (no. 2021R1I1A ) and Soonchunhyang University Research Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Some studies have also assessed the effects of exogenous or endogenous variables on attitudes as a moderator with ITU. Some variables that had a significant influence were PU/PE (García-Fernández et al., 2020, Pérez-Aranda et al., 2021; Yu et al., 2021), PEOU/EE (Pérez-Aranda et al., 2021; Yu et al., 2021), PEN, Gamification and Satisfaction (Pérez-Aranda et al., 2021).
How Do Behavior Change Apps Use Psychology to Help Users Stick to Goals?
The findings thus contribute to previous research into whether, and when, mobile health and fitness apps may help individuals become physically active [64,65]. Thus, adding these factors and incorporating measurements of actual physical activity may be warranted in the future. Further, the findings from this study have implications for the fitness app platform, this study further distinguishes situations in which fitness app use predicting users’ wellbeing as mediated by upward social comparison with different self-control capacities. Irrational behaviors such as giving up fitness can help people regain self-worth in a short time, but in the long term, users will feel guilty or regretful and will become unhappy. Fitness apps can use big data technology to collect fitness data to create user portraits and accordingly provide personalized marketing information for users with different levels of self-control.
2 Social comparison, social presence, and fitness interest
The BCTTv1 contains 93 distinct BCTs, and it can be used across behaviors and disciplines [47]. As individual BCTs are seldom applied in isolation, the combination of BCTs has been a topic of focus in recent studies. This combination of self-management–related BCTs has also been posited to contribute to maintenance of behavior change [49,50]. Another combination of BCTs that supports maintenance of change in physical activity includes BCTs that address capability, such as instruction or demonstration of behavior, and provide information about the significance of behavior change [25]. With the increasing use of fitness trackers as self-management tools to promote physical activity and reduce sedentary behavior, the lack of studies investigating their use of evidence-based techniques for behavioral change is surprising.
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It is biased to analyze the impact of upward social comparison on user behavior from a single negative or positive perspective. Self-control can improve people’s happiness levels, and people who are good at self-control are more likely to experience happiness and be more satisfied with their lives, self-control has emphatically corresponded with happiness (8). Self-control and social comparison have an interaction in predicting older person’s wellbeing (9). But whether the wellbeing of fitness app users with different self-control capacities varies in the upward social comparison process is still worthy of being explored. In the present study, based on the social comparison theory and self-control theory, we discussed the impact of fitness app use on users’ wellbeing in the Chinese context and the influence mechanism. It is of reference significance to improve users’ wellbeing and promote the long-term development of fitness apps.
All seven fitness trackers analyzed provided the opportunities for social support and comparison, allowing users to connect with others who have a similar device and create teams and/or compete with others (31). Some have suggested that both competition and cooperation are successful in increasing step counts (52). These social components, however, may only be effective if the user has friends, family, or coworkers with the same brand of device. Social support via social media has been shown effective in increasing healthy behaviors such as walking, and weight loss, and in an intervention setting can reduce attrition rates (53). People may experience fear, embarrassment, or guilt if they did not reach a goal that was shared with an audience (45). If fitness technology encouraged individuals to share their activity levels on social media, this could increase engagement and goal attainment.
Determinants of the Behavioral Intentions of Using Fitness Apps
Systematic literature searches were conducted in iTunes and Google Play stores between May–November 2016. Apps were included if they targeted children or adolescents, focused on improving diet, physical activity and/or sedentary behaviour, had a user rating of at least 4+ based on at least 20 ratings, and were available in English. App inclusion, downloading and user-testing for quality assessment and content analysis were conducted independently by two reviewers. Spearman correlations were used to examine relationships between app quality, and number of technical app features and BCTs included. The primary outcome was PA, captured via objective measures (e.g., pedometers, accelerometers) or subjective measures (e.g., self-report tools).
Second, this study would be strengthened by collecting additional respondent data and information. For example, collecting information regarding respondents’ body mass index, health status, or motivations for using a physical activity app may have been useful to explore the extent to which such characteristics determine apps’ impact. Furthermore, additional validation of the survey is required before using it in new studies.
- Teng and Bao (30) discovered that interactions between individuals and information, as well as interactions among individuals, served as environmental stimuli that affected individuals’ internal states, thereby influencing their stickiness to fitness apps.
- This was achieved through setting a reminder to move after an hour of inactivity, which can also be considered as a form of goal setting and prompting.
- Through a variety of specifications and functions, these mobile apps support behavior change.
- The results showed that habit and performance expectancy were the two strongest predictors of intentions of individuals to use fitness apps.
- To answer the research questions, we applied and extended the UTAUT2 model in the context of smartphone fitness apps.
- Positive relationships have also been identified for effort expectancy [18-20], facilitating conditions [18,20,21], and price value [19,21,30].
Main Findings and Comparison With Prior Work
Less frequently measured outcomes included app adherence in 44% (4/9) of the studies, depression in 33% (3/9) of the studies, and fatigue in 22% (2/9) of the studies. Fitness app practitioners and professionals should pay special attention to the female user group due to their substantial proportion. In May 2023, researchers distributed questionnaires to 600 female fitness app users in core commercial areas of Guangzhou, including Guangzhou University Town, Huacheng Plaza, and Tianhuan Plaza.
3. Data Extraction and Risk of Bias of Included Studies
Multiple researchers participated in this review to ensure the accuracy of the data and the credibility of the results. We also conducted a meta-analysis of all studies, along with a summary of the risk of bias for all studies. However, there is still a risk that bias could be diluted in the discussion and conclusions of this review. This risk can be reduced by assessing the quality of evidence for each outcome, for example, using the GRADE system. The gender differences cannot be ignored in the analysis since the characteristics of males and females vary, which could result in diverse behavior habit, schedule flexibility, and long-term mental status. Future research should control variables and make the male–female ratio more balanced to ensure the results reflect the impact of the intervention on general individuals.
Quality of Selected Studies
To summarise, the results of this review and the previous review by Angosto et al. (2020) will be compared. Concerning countries, there is an exponential increase in the number of studies conducted by authors in Chinese universities and, when compared to the previous review, there is a majority of studies from South Korea. The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. And W.W.; writing—review and editing, M.L.; visualization, W.W.; supervision, Y.Z.; project administration, M.L.
Table 6.
Self-determination theory was used in 11% (2/18) of the studies, followed by the socioecological model, control theory, and social support theory in 6% (1/18) of the studies. Some of the analyzed studies (7/13, 54%) seemed not to be based on behavioral theories or models. Some research has shown that Social Cognitive Theory has been used in feasibility studies among populations with diabetes as it can increase confidence and promote greater sustained effort to change, making it a guide for digital technology interventions. This theory also includes skill training, which can benefit diabetes management and education programs [67,68]. In total, 17% (1/6) of the analyzed studies were based on behavioral theories or models.
While using the app, users can interact with others, such as sharing fitness data and status, liking and commenting on other users’ fitness posts, and receiving advice and emotional support. Furthermore, users can view their fitness performance rankings and compare themselves workout personalization technology with others to evaluate their current fitness levels better. The extensive social features of fitness apps facilitate a more efficient and enjoyable engagement in fitness activities, thereby encouraging users to continue their usage.
New Behaviors
However, the apps that did not include self-monitoring or tracking features showed significant improvements as well. In addition, there was variability in other features combined with tracking features (ie, knowledge, goal setting, coach feedback, and clinician portals), making it difficult to attribute behavior change to self-monitoring features alone. Anthropometric measurements (ie, weight, BMI, and waist-to-hip ratio) were the most targeted health outcomes, with behavior change in 78% (14/18) of the reviewed studies, followed by diet quality and physical activity in 61% (11/18) of the studies. In addition, engagement, user acceptability, or motivation levels and body composition were measured in 39% (7/18) and 28% (5/18) of the studies, respectively. QOL, behaviors (ie, goal setting and self-efficacy), and CVD measures (ie, blood pressure, heart rate, glucose, and lipids) were included as outcomes in 22% (4/18) of the studies. Less frequent measures included stress parameters (ie, chronic stress and cortisol levels), diet knowledge, reduced screen time, and behavior and health maintenance, observed in 6% (1/18) of the studies.
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