Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques International Journal of Behavioral Nutrition and Physical Activity Springer Nature Link
Prompting specific goal setting was the second most common, and providing instruction was the third most commonly used BCT. These findings suggest that app developers make an effort to integrate health behavior theories to some extent. First, they can be advised to focus on habit formation and performance (eg, goal setting) when designing fitness apps and tailoring them to potential users. Meeting users’ expectations concerning facilitating conditions, price value, and effort expectancy will also increase the likelihood of the app being accepted.
Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review
Over the 6-month study period, adherence to the trial was statistically significantly higher in the mobile phone app group compared with the online website group and the paper diary group. Further, the mean weight change, BMI change, and body fat change were highest in the app intervention group. First of all, this study fills the gap in the theoretical basis of users’ wellbeing in the field of fitness app research.
All the articles identified from the database searches will be stored in the citation management software Mendeley, which will be used to eliminate any duplicates. Studies that fail to meet the eligibility criteria will be excluded, with any disagreements being discussed until there is consensus. The full text of the remaining studies will then be examined by one reviewer and validated by the other to determine final eligibility, with any disagreements being resolved by a third reviewer.
Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis
The results for 10 respondents were excluded from the final sample because they reported not having used a health-related physical activity app in the past 6 months. Additionally, only respondents who completed all of the survey items were included in the final sample, which included a total of 207 respondents. Lee Jordan, MS, NBC-HWC, SHRM-SCP, a certified health coach and behavior change specialist in Jacksonville Beach, Florida, and adjunct professor at Point Loma Nazarene University in San Diego, reviews research about the effectiveness of behavior change apps. The most commonly used BCT was motivation or encouragement, which was used in all reviewed studies (6/6, 100%). Both prompts or cues and feedback were used in 50% (3/6) and 33% (2/6) of the studies, respectively.
Statistical Analysis
The research on long-term effectiveness is genuinely mixed, and many studies suffer from small samples and high dropout rates. This study also found that performance expectancy had a greater effect on usage intentions when education-related features were rated as unimportant. In this case, individuals might be less interested in being educated—an aspect that might distract them from achieving their goals. In addition, the effect of habit on usage intention was stronger when education-related features were rated as important. This may be explained by the fact that habits of individuals are formed best when they are exposed to education-related cues when using an app (eg, how and when to exercise best) [73]. Regarding the interaction between hedonic motivation and gamification-related features, no final conclusions can be drawn.
Information Sources and Search Strategies
Smartphone apps have certain features, that is, the set of operational functions that an app can perform (eg, gaming). The essence of fitness app features may be summarized within behavior change techniques (eg, goal setting, monitoring, and acquisition of knowledge) [42]. In addition, various frameworks of features implemented in fitness apps have been proposed.
1 Reliability and validity analysis
The transtheoretical model, Social Cognitive Theory, the theory of planned behavior, the Health Belief Model, the precaution adoption model, and goal-setting theories were used as the basis for 17% (1/6) of the interventions in this review section. Evidence for informing digital technology interventions reveals that the Health Belief Model has been widely used for goal setting and lifestyle changes for reducing cardiovascular risk as it focuses on confidence in one’s ability to take action [36]. First, since the study used convenience sampling with data collected from female fitness app users in Guangzhou, China, the generalizability of the findings may be limited.
In this study, only the most popular apps were assessed, which limits the generalizability of results to all available apps. However, app quality was assessed by two independent reviewers using a standardized instrument, which minimized the potential errors. In addition, the apps were tested by the researchers only and not by any potential users. The only reported theories were Social Cognitive Theory, Goal-Setting Theory, and the comprehensive intervention model.
Associated Data
The app content quality, particularly the use of international guidelines on physical activity, could be improved. Physical inactivity is considered as the biggest public health problem of the 21st century [1]. It is the fourth leading risk factor for global mortality, contributing to 6% of deaths globally, and is one of the main risk factors for many major noncommunicable diseases such as cardiovascular disease, diabetes, and cancer [2]. The World Health Organization (WHO) has defined the levels of physical activity per age group with an impact on health. For example, WHO recommends that adults perform at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic physical activity every week [2]. However, 23% of adults and 55% of older adults are not meeting these recommended physical activity levels and are, thus, insufficiently active [3].
Behavioral Processes
Concerning race and ethnicity, 82.1% (170/207) of respondents reported being white and 94.2% (195/207) of respondents reported being of a non-Hispanic/Latino ethnicity. Whereas the levels of education varied from less than a high school education to a professional degree, 63.8% (132/207) of respondents had either a 4-year degree or some college (not graduated) education. When asked about the number of physical activity apps used in the past 6 months, 60.9% (126/207) of respondents reported using only 1 physical activity app, whereas 29.0% (60/207) reported using 2 physical activity apps. Regarding frequency of physical activity app use in the past 6 months, 41.0% (85/207) of respondents reported using the apps daily, whereas 48.3% (100/207) of respondents reported using the apps multiple times a week. The most commonly used apps as reported by study respondents were Fitbit and MyFitnessPal, with 22.2% (46/207) of respondents reporting using Fitbit and 17.4 % (36/207) of respondents reporting using MyFitnessPal.
Features such as performance rankings create a psychological environment for social comparison, aiding users in assessing their fitness levels accurately. As one of the unique social features of fitness apps, social comparison is significant and should be studied as a behavior change mechanism within the fitness environment (23). In the process of using fitness apps, whether upward comparison and downward comparison have a positive or negative impact on users also requires further research. It has been suggested that small goals are more effective for long-term engagement compared to large goals. When people are successful in meeting smaller goals, they build momentum and over time are more likely to reach larger goals (40). The CALO-RE taxonomy describes small goals as graded tasks, and according to Mercer et al., none of the popular fitness trackers include these graded tasks (31).
Appendix. The Frequency of BCTs in the Studies
They considered self-efficacy to be a predictor of effort expectancy and innovativeness as a predictor of habit; both relationships were significant. First, the downstream effects on intentions of being physically active were not assessed in any of the studies. The linkage of fitness app usage intentions and intentions of being physically active is important, because health benefits can only be realized if intended app usage motivates people to become or remain physically active. However, the authors did not include these variables in the model because of nonsignificant findings [30].
The cost, number of BCT clusters as identified by the BCTTv1 classification, and the number of self-management and nonself-management BCTs incorporated in each fitness tracker are outlined in Table 1. These 39 fitness trackers were assessed by reviewing their specifications on their official product website, and 12 of them were considered eligible for inclusion. Of the eligible 12 fitness trackers, 6 fitness trackers were variants of others from similar brands. flexible workout routines As such, only the latest model of each brand of fitness tracker was retained, leaving a total of 6 fitness trackers for BCT analysis.
- Their search strategy was focused on literature published until September 2014, and the state of mobile health app interventions is likely to have changed since then.
- Each participant attended a personal onboarding and offboarding session in a laboratory of the Computer Science department at Stanford University at the start and end of their 5-week study participation (Figure 2).
- For example, the 30-day retention rates of newly registered users for the top three fitness apps in China—Keep, YueDong Zone, and Codoon—are only 53.2%, 54%, and 44.1%, respectively.
- Distance, which is a more traditional metric, such as number of miles or kilometers walked may be another useful way to classify activity.
- Many female users hope to achieve physical and mental health by using fitness apps, which is very significant for them.
- Community and social accountability features are among the strongest retention mechanisms.
Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity
In addition, SEM has no measurement error in analyzing the relationship between mental structures, and can also analyze the dependence between latent variables (46). The Bootstrap method was used to test the mediating mechanism of upward social comparison and the moderating effect of self-control. One study used a wait-list control [34], two studies used a non-intervention control group [21,35] and three studies provided their control group with basic health information and instructions [33,36,41]. One study compared the intervention group providing the physical promotion app and diet app with the control group providing a diet app alone [40].
Tracking specific behaviors for children, for instance, works best when tied to concrete, meaningful outcomes rather than abstract scores. For apps that work, apps where users form genuine habits, continued use of the app often becomes unnecessary. The whole point of habit formation is that the behavior becomes automatic, no longer requiring a digital nudge. Understanding the theory behind an app helps you use it more effectively, and helps you spot when an app is borrowing the vocabulary of behavioral science without actually applying it. Once the novelty fades and the initial motivation dip hits, usually around week two, users who haven’t developed any internal reasons to continue will stop. Industry data consistently shows that most apps lose the majority of their users within the first two to four weeks.