Any qualitative data reported will be examined by performing a thematic analysis to provide contextual data about the potential relationships between BCTs and certain components of engagement. Several reviews have examined the evidence of effectiveness of health-related apps when targeting one specific behavior, such as physical activity, or a specific condition, such as chronic pain [15-19]. Another study reviewed behavioral functionality of apps in health interventions without assessing the quality of the included studies [20]. The aims of this review are to examine the effectiveness of mobile phone apps in achieving health-related behavior change across a broader range of health issues and to examine the quality of the reported studies. Health apps are supposed to support fighting sedentary lifestyles and, consequently, a variety of chronic diseases.

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MHealth apps present problems for typical self-affirmation exercises, including constantly changing environments, competing attentional requirements, and different affordances for inputs. This work indicated that self-affirmation exercises can be adapted in ways that make them amenable to increasing adherence in mHealth contexts. In turn, this enables these increases in adherence to benefit large groups of people, since mHealth apps have the ability to be deployed to a large number of people quickly. Rather than showing the full 10 questions to the participants during a booster affirmation, we simply showed them 2 questions for each affirmation booster. Figure 2 (right) shows an example question adapted from the kindness questionnaire. We adapted questions so participants could include both the binary answer and an example in the given text box [13].

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Both have been included in this table, but if one were excluded, there would be significant evidence for 22% (11/50) studies. Additionally, the BCT taxonomy approach used to summarize the characteristics of interventions still has some limitations, despite the inclusion of the extension proposed by Dugas et al [27]. This taxonomy allows for the coding of different BCTs but does not assess the intensity or dosage of interventions. Nevertheless, the taxonomy works perfectly well as an excellent starting point and a standard to systematically describe an mHealth intervention. The scientific databases selected for the review were Scopus, PubMed, Web of Science, and IEEE Xplore. The combination of these databases provides comprehensive coverage of publications in the context of medical informatics, high relevance, and a complete advanced search.

Quantitative Data

Approximately 46% (6/13) of studies [24-26,28,29,33] found significant results in all outcome measures assessed and targeted no more than 2 outcomes, suggesting the increased effectiveness of apps with a narrow behavior change target. Automatic data collection [24,25,32] and gamification [27-29,31,33,35,36] were the key features of apps that resulted in effective interventions. Multicomponent interventions appear to be superior compared with standalone app interventions.

Risk of Bias Assessment

• Headspace and Calm built their names on guided meditation, but newer apps like Woebot deliver cognitive behavioral therapy through a digital platform, walking users through thought records and behavioral activation exercises.
• Approximately 46% (6/13) of studies [24-26,28,29,33] found significant results in all outcome measures assessed and targeted no more than 2 outcomes, suggesting the increased effectiveness of apps with a narrow behavior change target.
• According to Fogg’s Behaviour Model (FBM), for instance, suggests that behaviour occurs when motivation, ability, and a triggers converge.
• A total of 11 studies explicitly stated that allocation was concealed (eg, using sequentially numbered opaque, sealed envelopes, central allocation) [9,27-29,31-33,41-44].
• Recent research shows that using the WW program is significantly more effective for weight loss than doing it on your own.
• On the other hand, if a user completes all the activities in an app by the time they are scheduled, the user adherence is 100% [7].

This aspect of health behavior change apps has not been assessed, with most studies being exceptionally small in scale. Apps that offer even a small health benefit could still be a valuable public health intervention if the population-level reach is high enough. But, encouragingly, we identified some registered large-scale clinical trial protocols of app-based interventions, suggesting that the current limited scientific evidence may be eased in coming years [57-60]. As shown in Multimedia Appendix 3, only a small number of studies were found under the themes of diabetes management, sun protection, hypertension management, cardiac rehabilitation smoking cessation, family planning, and pain management. 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.

Participants discussed the idea of competing with friends by sharing their behavior tracking with others; however, the group disagreed about whether this would be beneficial or detrimental for adolescent users. Other comments included ensuring that goals are easy to input and that the goals suggested by the app are achievable and easy to fit into pre-existing schedules. Participants were shown an example of how the app may display tracked health behaviors using a prototype design, before being asked to comment on the example. Participants were then asked to make suggestions for alternative ways to show this information, including by drawing their ideas. Participants were also asked for their thoughts and suggestions regarding how the goal-setting section of the app could function and be presented.

Apps Effectiveness and Persuasive Strategies Employed

However, a number of participants were unable to articulate the most useful features of their favorite health apps. The Health4Life app is a co-designed, self-monitoring smartphone app for adolescents that concurrently targets the Big 6 lifestyle behaviors. The app has the potential to efficiently and effectively modify important risk factors for chronic disease among young people and is currently being evaluated in a world-first trial of 6640 secondary school students in 71 schools across Australia. Following the manipulation check, participants were shown a document outlining the risks of not consuming enough fruit and vegetables. This is standard practice for self-affirmation interventions that attempt to improve health behaviors [10,14,19,28].

After a six-month intervention, the researchers observed a significant improvement in oxygen saturation levels among participants in the intervention group compared to the control group. This study suggests that wearables, when integrated with behavior change techniques focused on respiratory health, can lead to improved oxygen saturation levels and better management of chronic respiratory conditions. Vandelanotte et al. [19] conducted a six-week pilot study to examine the impact of wearable social nudges on improving sleep quality. Participants in the intervention group received personalized nudges via their wearable devices that encouraged them to adopt healthy sleep habits and routines.

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Then participants wrote 3 reasons that their number 1–ranked value was important to them and wrote about a past experience where they demonstrated that value. Participants in the control condition were similarly asked to write about their last (10th)-ranked value and 3 reasons it could be important to someone else and how such a person might demonstrate that value. Participants were compensated with a gift certificate for up to a maximum of US $50 for their participation. To receive full compensation, participants needed to complete the initial survey and the postsurvey, and make 20 out of 28 possible daily entries in the app.

Table 8.

Studies have highlighted instances of user disengagement and abandonment of wearable devices, indicating the need for innovative strategies to address these issues. Additionally, concerns regarding data privacy and security pose another limitation to the widespread adoption and effectiveness of these technologies. As wearables collect and transmit sensitive health-related data, ensuring robust privacy safeguards and regulatory compliance becomes imperative to mitigate potential risks and maintain user trust. Figure 14 categorizes a collection of 524 research documents into four main themes related to wearable and IoT devices. ’Personalized Feedback’ (212 documents) demonstrates how these devices offer customized feedback to users by constantly monitoring activities like exercise, sleep, and diet.

By performing a logistic regression for each feature, it will be possible to determine whether the scores on the different scales predict the selection of the functionality. For each functionality selected, participants were asked to indicate how much that functionality would motivate them to adopt healthier madmuscles vs centr behavior on a scale of 0 to 100. This study aims to validate our conceptual framework by investigating if the proposed relationships between the functionalities and profiles are reflected in the preferences of our target population in an experimental setting. Missing one day does not ruin your progress as long as you get back on track quickly.

Effectiveness of Apps and Features

These responses were qualitatively analyzed until data saturation was reached. Using an inductive approach [64], one author (LT) coded the responses and grouped them according to key themes. Research on the Medisafe app indicates that it may help improve medication adherence, though it’s important to keep in mind that these studies are often conducted by researchers with connections to Medisafe Inc., which may influence results. Similarly, another 2016 study noted that participants who used the CBT-i Coach app as a supplement to CBT-i treatment experienced significant improvements in sleep and that the app didn’t compromise the benefits of CBT-i therapy. It’s important to know, however, that participants who only received CBT-i treatment without using the app, also reported significant sleep improvements.

An alternative interpretation of our results could be that, rather than exhibiting dosing effects, different self-affirmation exercises have varying effectiveness outside of laboratory settings. The values essay that served as our initial self-affirmation was relatively unstructured; participants wrote freely in answering a prompt with a few questions. The self-affirmations that we constructed based on the kindness questionnaire were more structured; we asked participants specific targeted questions and they then answered with an example from their lives. We cannot confirm that more structured self-affirmation exercises are more effective outside of laboratory settings, but this should be explored further.

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First, what types of mobile health apps and BCTs are being used to support user engagement with their health behaviors? Second, how effective are mobile health apps in improving and maintaining positive health behavior changes? Finally, what are participant perceptions of the feasibility, functionality, and overall user experience of the mobile health apps they use?