Background Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions. Objective This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks. Methods A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings. Results The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%). Conclusions This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app’s intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

BACKGROUND Mobile health applications show vast potential in supporting patients and health care systems with the globally increasing prevalence and economic costs of non-communicable diseases. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users does not adhere to them as intended and may consequently not receive treatment. Therefore, understanding factors that act as barriers or facilitators to adherence is a fundamental concern to prevent intervention dropouts and increase the effectiveness of digital health interventions. OBJECTIVE This review aims to identify intervention- and patient-related factors influencing the continued use of mHealth applications targeting non-communicable diseases (NCDs). We further derive quantified adherence scores for different health domains, which may help stakeholders plan, develop, and evaluate mHealth apps. METHODS A comprehensive systematic literature search (January 2007- December 2020) was conducted in MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains NCD-Self-Management, Mental Health, Substance Use, Nutrition, Physical Activity, Weight Loss, Multicomponent Lifestyle Interventions, Mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between estimated intended and actual use, were derived for each study and compared with qualitative findings. RESULTS The literature search yielded 2862 potentially relevant articles, of which 99 were included as part of the inclusion criteria. Four intervention-related factors indicated positive effects on adherence across 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 and technically stable app design, and (4) personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors like user characteristics or user recruitment channels further affects adherence. Derived adherence scores of included mHealth apps averaged 56.0%. CONCLUSIONS This study contributes to the scarce scientific evidence on factors positively or negatively influencing adherence to mHealth apps and is the first to compare adherence relative to the intended use of various health domains quantitatively. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use, report objective data on actual use relative to the intended use, and ideally, provide long-term usage and retention data.

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