Interventions to decrease both physical inactivity and sedentary behavior are much needed. They should be affordable and accessible across population groups, which is particularly pertinent, given the higher prevalence of physical inactivity and sedentary behavior documented in those with less education and lower socioeconomic status [17,18]. Behavioral interventions that include self-management strategies to promote physical activity are emerging. Systematic reviews and meta-analyses have found that behavioral change techniques related to self-management strategies were effective in increasing physical activity in young and middle-age adults [19-22], older adults [23], and overweight and obese adults [24,25]. Furthermore, techniques related to self-management were found to be linked to maintenance of physical activity behavior [19], and these were also effective in reducing sedentary behavior [26]. The use of wearable fitness trackers as tools for self-management in physical activity interventions [27-30] is growing.
Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity
A growing body of evidence suggests that DBCIs are effective in promoting PA, reducing SB, and improving overall health outcomes10. By providing a variety of behavioral techniques such as goal setting, self-monitoring, or social support, these interventions help sustain motivation for behavior change. Notably, DBCIs have shown promising potential for populations with limited accessibility, such as older adults and individuals with chronic diseases5,6,10.
These results indicate a need for further investigation on the sustainability of the health behavior change effectiveness of disease-specific nutrition apps. The findings of this study help fitness platform managers to develop more social features and realize the commercial value of fitness apps. The results facilitate users’ continuous use of fitness apps, promote positive fitness activities, and thus help users realize their health value. Meanwhile, the continuous use of fitness apps helps to create a positive fitness atmosphere throughout society.
Table A2.
It is important to consider the population being measured, as devices may vary in utility depending on personal characteristics. For instance, the Fitbit had estimation errors above 60% within a population of older adults with reduced mobility (71). It remains important to consider and report device placement when comparing step counts or energy estimates between studies and individuals (16, 71, 77, 80). Some have shown that the more expensive health and fitness applications tend to incorporate more behavior change techniques (61). Consequently, the free apps that are widely used may not offer the most effective approaches (89). Others have found favorable accuracy for smartphone accelerometers in both lab and free-living settings (90–93).
2 Social support theory
Laing et al [29] demonstrated that one of the most popular commercially available weight loss apps, MyFitnessPal, which is based on social cognitive theory, was not effective in helping overweight patients lose weight in a clinical setting over a 6-month period. One case-control study [26] identified significantly decreased weight, fat mass, and body mass index (BMI) in the intervention group compared to controls. Carter et al [43] compared an app intervention group (created on an evidence-based behavioral approach) to two other control groups, one using a paper-based food diary and the other using an online food diary.
Downstream Consequence of Behavioral Intentions of Using Fitness Apps
We review recent studies that have examined the extent to which fitness technology, including trackers, smartphones, and apps, provides accurate measurement. Technology that provides valid and reliable data shows a great deal of promise for their application to both research and personal settings. We examine the effectiveness of fitness technology for promoting behavior change and evaluate whether these changes are long lasting. Indeed, recent reports have suggested that activity trackers are used only for short periods of time (17, 18). Some have even suggested that more than half of fitness tracker owners abandon their devices within the first month (18).
- Therefore, a prior search protocol was not established and all aspects were marked directly in the methodology of this study.
- Wang (2023) conducted a meta-analysis of 58 studies examining the significance and impact effects of factors influencing the continuance intention of mobile health applications.
- Smartphones allow for the transfer of physical activity information to applications that measure and/or encourage physical activity.
- In recent years, the number of smartphone users has steadily increased throughout the world, with nearly half of the population now owning a device (Newzoo, 2021).
- In general, some studies [35,36,40,41] concluded that mHealth is effective in promoting exercise among inactive people, the results of the meta-analysis reported in this article also confirmed the observable utility of mHealth intervention.
- Health interventions based on the behavior analysis present the potentials to increase daily physical activity levels [8,9].
Affective and Behavioral Processes
Does it require daily tracking, or are you trying to automate something until it no longer needs tracking at all? When people rely on conscious effort to use an app, remembering to log meals, forcing themselves to open a meditation session, they’re drawing on the same cognitive reserves they use for every other decision in their day. The datasets generated and/or analyzed during the current study, including the extracted study characteristics and outcome data used for meta-analyses, are available from the corresponding author upon reasonable request. Publication bias was assessed for significant results in the main analysis using funnel plots and Egger’s Test. Subsequently, the Trim-and-Fill technique was applied to adjust effect sizes for outcomes that exhibited significant asymmetry. Two outcomes displayed asymmetries in the funnel plots; however, their effect estimates remained significant even after Trim-and-Fill adjustments (Supplementary Figs. 1–6).
Characteristics of Included Studies
Conceptual model of how activity adequacy mindset (AAM) may influence health and well-being through affective, behavioral, and physiological processes and how these are measured in this study. Mindsets are our core assumptions regarding a domain or category (eg, intelligence, healthy eating, stress, and physical activity) [17,18]. They help us organize, simplify, and interpret information, thereby orienting us toward a particular set of expectations, attributions, and goals. Mindsets predispose us toward a particular way of experiencing and responding to situations.
Kirwan et al [41] found a freely available app supplemented with text message feedback could significantly improve glycemic control between baseline and 9-month follow-up for patients with type 1 diabetes compared to the control group. One of the first evaluation studies of a commercially available sun protection app [11] showed that only 1/7 sun protection behaviors, wearing wide-brimmed hats, was practiced more by intervention than control participants. In a study comparing an app designed for hypertension management with traditional care [42], the intervention group participants achieved a significant decrease in systolic blood pressure at 12 weeks compared to control participants.
Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review
Ate includes options such as open-ended questions, reflections and experiments, all of which help how to choose personalized workout plans increase coach/client engagement. In this method, the bootstrapping procedure can be used twice, first without the mediation effect and second with the mediation effect. According to Table 8, in the absence of the mediation effect of satisfaction, the direct effects of utilitarian value and health value on continuance intention were significant. However, the direct effect of hedonic value on continuance intention was not significant, indicating no mediation effect, so Hypothesis 4 was not supported. Before conducting model analysis, it is necessary to check whether the data collected in the study contain common method bias [53].
Moderating Effects of Individual Differences
This research suggests that it is not only our actual physical activity behavior that matters but also our mindsets about the adequacy and health consequences of our physical activity. These insights may be used to support the design of wearable trackers and other health technologies that more effectively boost users’ health and well-being. In addition, health psychology research and public health policy may design more successful public health interventions by more deliberately––and more effectively––harnessing the power of mindsets. 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). Participants were briefed with the cover story that the study aimed to develop more accurate fitness-tracking algorithms.
How do behavior change apps use psychology to help users stick to goals?
The aim was to examine the effectiveness of mobile phone apps in achieving health-related behavior change in a broader range of interventions and the quality of the reported studies. The first point to mention is maybe the shorter time restriction compared to the prior review by Angosto et al. (2020). However, this is required since the COVID-19 pandemic is still active and national governments are implementing preventative measures based on the pandemic’s progress (Ferrer, 2021; Official State Bulletin, 2021). Many nations are enacting new temporary confinements, which may encourage the usage of exercise or health applications.
Interestingly, the effect of standalone DBCIs on body metrics was more pronounced in healthy individuals than in those with underlying unhealthy conditions, possibly due to metabolic differences, dietary habits, or disease-related limitations. Indeed, previous literature has highlighted the influence of extraneous factors such as diet and mental health on weight outcomes19. Therefore, when evaluating the impact of DBCIs on body metrics in adults with unhealthy conditions, it is imperative to develop tailored intervention strategies that holistically address lifestyle factors19,20.
