OCTOBER 1, 2021 | BUNDESAMT FÜR GESUNDHEIT (BAG)
Background: mHealth applications open up a wide range of possibilities for individualized prevention, the promotion of protective behaviors, and the self-management of non-communicable diseases. At the same time, their development and maintenance is significantly more complex compared to browser-based eHealth applications, and users often find it difficult to select suitable apps. While evaluation frameworks already exist for general quality criteria such as data protection, design, usability, or security, the conditions necessary to achieve behavioral change among users through mHealth applications have not yet been systematically researched and summarized. Within two separate literature studies, the present work investigated and identified (1) usage enhancement techniques and (2) behavior change techniques that should be considered in the design and development of mHealth applications and on the basis of which it is also possible to develop a set of criteria for evaluating health apps for users.
Objective: The first sub-study investigated which techniques used in mHealth apps on NCDs, mental health, and addiction influence user adherence. The second substudy examined the effect of behavior change techniques on intended behavior change.
Methods: In sub-study 1, a systematic literature review of existing primary studies was conducted to identify relevant techniques for increasing usage. In a first step, techniques were identified that improved usage adherence within the primary studies. In a second step, the influence of additional factors on usage adherence was investigated, such as the characteristics of the target population or the way the application was delivered. In a third step, for each primary study, usage adherence was calculated as the quotient of intended and actual usage to provide a reference value within the different health domains and to identify mHealth applications with high usage adherence. In sub-study 2, to identify relevant behavior change techniques, a systematic overview (English: Overview or Umbrella Review) of existing systematic reviews was conducted. Relevant scientific articles for both substudies were identified through systematic searches of electronic literature databases. Relevant information from each article was extracted and analyzed.
Results: The literature search for substudy 1 yielded a total of 2862 potentially relevant articles, of which 99 were relevant to this review and were analyzed in more detail. Techniques with a positive impact on app use were presented separately for the 7 health domains, with the following 3 techniques identified as relevant to all health domains: (1) personalization or tailoring the content of the mHealth app to the individual needs of the user, (2) reminders in the form of individualized push notifications, (3) a user-friendly app design, and technical stability. 5 Usage adherence derived from the primary studies averaged 56.0% and was highest for lifestyle interventions aimed at changing multiple behaviors simultaneously (60.1%) and lowest for mHealth apps aimed at reducing substance use (46.1%). Further, quantitative analysis revealed a positive correlation between adherence to use and level of personalized care during the intervention. For the NCD self-management domain, there was a significant positive correlation between adherence to use and the average age of study participants. The literature search for substudy 2 yielded a total of 615 potentially relevant articles, of which 66 were relevant to this review and were analyzed in more detail. For the area of NCD self-management, the effectiveness of exclusively app-based programs is predominantly mixed or still unclear, with the exception of diabetes management apps. Key behavior change techniques in NCD self-management are goals that can be individualized as much as possible regarding the targeted behavior (e.g., taking medications), self-monitoring of behavior (e.g., via diary function in the app), and feedback on behavior (e.g., graphical representation regarding the achievement or non-achievement of the behavioral goal). Guidance from a real-life professional appears to be an important component of effective digital programs to support chronic disease management. Evidence on the effectiveness of app-based programs for dietary behavior change is also still mixed, with dietary changes, such as increasing fruit and vegetable consumption, being achieved more frequently than reductions in the amount of energy consumed. The mHealth applications used to date predominantly use behavior change techniques that have also proven effective in traditional individual and group counseling sessions to change dietary behavior: Individual goal setting, behavioral monitoring and feedback, and social support. The extent to which other techniques, such as self-image change or social comparison, are effective cannot be answered based on the data to date. The effectiveness of apps to increase physical activity is now well established scientifically, with sicker and vulnerable populations benefiting particularly. Again, setting individual activity goals, monitoring them, and providing feedback on their achievement play a key role. The involvement of a real professional does not seem necessary in these programs. In contrast, programs that use data automatically collected by the system (e.g., via motion sensor) for individualization are more effective. In the case of apps for weight reduction and for changing several behaviors at the same time (so-called lifestyle interventions), which usually also aim at weight reduction by promoting physical activity and healthy eating, the effectiveness is mixed. Key components include the use of multiple and interactive behavior change techniques, particularly for goal setting, as well as behavioral monitoring and feedback. In mental health improvement programs, elements of cognitive behavioral therapy have proven effective in reducing anxiety and depression via the Internet or app. Similar to self-management programs for NCDs, in-person guidance from a professional seems to help with effectiveness. In addition to self-monitoring of behavior, changes in cognitive processes (e.g., increasing positive thoughts, cognitive flexibility, perceived control) and skills (e.g., using mindfulness skills or cognitive-behavioral techniques) represent key mechanisms of action. Evidence on the effectiveness of app programs to reduce alcohol use in the general population has been mixed, with some positive studies but also many without significant results. Successful programs are characterized in particular by offering users practical, easy-to-implement advice on how to replace alcohol consumption and solve problems; this should come from a source perceived as credible. Evidence on the effectiveness of tobacco cessation apps has also been quite heterogeneous. In reviews of primarily Internet-based programs, various techniques were associated with effectiveness: setting specific behavioral goals and action planning, advice on problem solving and health consequences of smoking, weighing the pros and cons of quitting smoking, and also social and medication support.
Conclusions: Technologies for personalization and individualization of content are central to high app usage and effectiveness. Users should be able to set personally relevant behavioral goals and monitor their degree of realization over time through the app. Interactive functions that take into account the degree of goal achievement as well as characteristics of the person and the context are particularly suitable. Regular reminders by the app that take into account the individual's availability and need for interaction represent an essential prerequisite for using these central techniques for goal setting, behavioral observation and feedback over a longer period of time. In addition to these automated features, opportunities for personal guidance and social support, especially for apps used in clinical groups, form an essential element for their use and effectiveness. Technical stability and user-friendly app design are also relevant for regular use. Overall, research on promising usage enhancement techniques as well as behavior change techniques for mHealth apps is still poorly advanced. The underlying studies often have a pilot character, and the implementation of the techniques and operationalization of the results is very inconsistent. Since mHealth apps usually use multiple techniques to increase usage and change behavior, causal statements about individual techniques are hardly possible. This will require more controlled and experimental studies in the future. The recommended techniques for individualized goal setting, behavioral monitoring, feedback, reminders, and social support also represent basic elements of current health behavior models and proven cognitive-behavioral therapy interventions. Their integration into mHealth applications provides a solid foundation. In order to optimize them, however, new techniques whose full potential can only be exploited through digital technologies should also be tested and reviewed in the future.