Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review
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Abstract
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.
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The adherence score for the experimental condition was comparable with adherence scores for mobile mental health apps targeting the clinical population (56.6%) and mindfulness apps targeting the general public (59%).
“the adherence score for the experimental condition was comparable with the adherence scores for mobile mental health apps targeting the clinical population (ie, 56.6%) and mindfulness apps targeting the general public (ie, 59%), as reported by Jakob et al [37].”
From A Gamified Mobile App That Helps People Develop the Metacognitive Skills to Cope With Stressful Situations and Difficult Emotions: Formative Assessment of the InsightApp (2023) · cites this paper as [37]
The Results sections on Mental Health and Mindfulness report mean adherence scores of 56.6% and 59.0%, respectively. The Mindfulness section says most included apps aimed to improve well-being or reduce work-related stress rather than treat a chronic mental condition, but the review does not characterize the entire mindfulness group as targeting the general public or the entire mental-health group as targeting a clinical population. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTo increase treatment uptake among students with depression or anxiety symptoms, there is a need for bCBT approaches that fit therapists' as well as patients' needs and preferences.
“Thus, to increase treatment uptake among students with depression or anxiety symptoms, there is a need for bCBT approaches that fit therapists’ as well as patients’ needs and preferences [39].”
From Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study (2023) · cites this paper as [39]
The Discussion sections on personalization and professional support recommend tailoring mHealth apps to users’ needs and suggest that hybrid app-and-human support may improve adherence. The paper does not establish that bCBT fitted to both therapists’ and patients’ preferences increases treatment uptake among students with depression or anxiety symptoms. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSome participants proposed including additional notifications to remind users to complete tasks before the next therapy session, which they thought could increase overall therapy adherence.
“In addition, some participants proposed including additional notifications in the app that would remind users to complete tasks before the next therapy session. In their opinion, this could have increased overall therapy adherence:”
From Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study (2023) · cites this paper as [39]
The Discussion section on individualized reminders says push notifications can improve adherence to mHealth app interventions, and the Results section on mental health reports an association between customizable reminders and better adherence (pages unavailable). The paper does not report participants proposing notifications for tasks before the next therapy session or expressing that opinion about overall therapy adherence. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPersonalization options that allow users to skip specific exercises or time-consuming tasks are perceived as crucial for engagement with DTx among students.
“Previous research has shown that personalization options that would, for example, allow users to skip specific exercises or time-consuming tasks, are perceived as crucial for the engagement with DTx among students [37,39], and this feature was not provided in this bCBT program.”
From Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study (2023) · cites this paper as [39]
The Discussion section on personalization reports that personalized mHealth content can improve adherence and that individualized apps can improve engagement. The Results section on physical activity also links customizable exercise plans to adherence. Neither passage establishes that students using DTx perceived the ability to skip specific exercises or time-consuming tasks as crucial. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe onboarding function of the app was evaluated as positive but not essential for the therapy process.
“In contrast to the findings of Jakob et al [39], who found that skipping the tutorial significantly increased the chance of dropping out, the onboarding function of the app was evaluated as positive but not essential for the therapy process.”
From Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study (2023) · cites this paper as [39]
The Results (Nutrition and Mindfulness; page unavailable) associate initial tutorials or in-app tutorials with better adherence, and the Discussion lists an integrated tutorial as a positive factor. The review does not report that skipping a tutorial significantly increased the chance of dropping out. The assessment that onboarding was positive but not essential is the citing authors’ contrasting finding, not a claim attributed to Jakob et al. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Low adherence reduces the meaningfulness of the results in relation to the intervention under evaluation.
“While not uncommon in digital intervention trials [71], low adherence reduces the meaningfulness of the results in relation to the intervention under evaluation.”
From Effects of a self-guided digital mental health self-help intervention for Syrian refugees in Egypt: A pragmatic randomized controlled trial (2024) · cites this paper as [71]
Reminders are an effective way of improving engagement, especially when they address specific needs.
“Reminders (Figure 1C) are an effective way of improving engagement, especially when they address specific needs [76,77]”
Inclusion of personal contact options in case of emergencies and frequent guidance, especially for patients with severe symptoms, seems essential for effective bCBT programs.
“As more pronounced depressive symptoms have been shown to be associated with lower adherence to self-management apps [39], the inclusion of personal contact options in case of emergencies and frequent guidance, especially for patients with severe symptoms, seems to be essential for effective bCBT programs.”
More pronounced depressive symptoms are associated with lower adherence to self-management apps.
“As more pronounced depressive symptoms have been shown to be associated with lower adherence to self-management apps [39], the inclusion of personal contact options in case of emergencies and frequent guidance, especially for patients with severe symptoms, seems to be essential for effective bCBT programs.”
Reminders in the form of customizable push notifications can increase user engagement.
“To further increase the adherence, some students suggested including additional reminders in the app, and this is in line with the conclusions of the systematic review by Jakob et al [39], who mentioned that reminders in the form of customizable push notifications can increase user engagement.”
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Smartphone Technology and Text Messaging for Weight Loss in Young Adults
Stephens, J. D., Yager, A. M., & Allen, J. K. (2015) · The Journal of Cardiovascular Nursing · doi:10.1097/jcn.0000000000000307 · Cited by 70 · see citing works
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A Social Networking and Gamified App to Increase Physical Activity: Cluster RCT
Edney, S. M., Olds, T., Ryan, J. C., Vandelanotte, C., Plotnikoff, R. C., Curtis, R., & Maher, C. A. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2019.09.009 · Cited by 67 · see citing works
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González, C. S. G., del Río, N. G., & Adelantado, V. N. (2018) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2018.03.005 · Cited by 60 · see citing works
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A New mHealth application to support treatment of sleep apnoea patients
Isetta, V., Torres, M., González, K., Ruı́z, C., Dalmases, M., Embid, C., Navajas, D., Farré, R., & Montserrat, J. M. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15621848 · Cited by 47 · see citing works
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Harris, M. A. (2018) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2018.11.009 · Cited by 45 · see citing works
Reminders make people adhere better to a self-help sleep intervention
Horsch, C., Spruit, S., Lancee, J., van Eijk, R., Beun, R. J., Neerincx, M., & Brinkman, W.-P. (2017) · Health and Technology · doi:10.1007/s12553-016-0167-x · Cited by 44 · cited 7× · see citing works
“The following information was extracted for each included study: general study characteristics, study population characteristics, intervention characteristics, factors influencing adherence relative to intended use, and information on app use (Multimedia Appendix 3 [20,26-123]).”
BackgroundMethodInfluentialGamification of Medication Adherence in Epilepsy
Rahim, M. I. A., & Thomas, R. H. (2017) · Seizure · doi:10.1016/j.seizure.2017.09.008 · Cited by 42 · see citing works
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Adhärenz digitaler Interventionen im Gesundheitswesen: Definitionen, Methoden und offene Fragen
Kernebeck, S., Busse, T. S., Ehlers, J. P., & Vollmar, H. C. (2021) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-021-03415-9 · Cited by 41 · see citing works
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Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study
Su, J., DUGAS, M. A., Guo, X., & Gao, G. G. (2020) · JMIR mhealth and uhealth · doi:10.2196/17709 · Cited by 38 · see citing works
A pilot evaluation of a smartphone application for workplace depression
Collins, D. A. J., Harvey, S. B., Lavender, I., Glozier, N., Christensen, H., & Deady, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186753 · Cited by 37 · cited 9× · see citing works
“The following information was extracted for each included study: general study characteristics, study population characteristics, intervention characteristics, factors influencing adherence relative to intended use, and information on app use (Multimedia Appendix 3 [20,26-123]).”
BackgroundMethodInfluentialAlcohol Avoidance Training as a Mobile App for Problem Drinkers: Longitudinal Feasibility Study
Laurens, M. C., Pieterse, M. E., Brusse-Keizer, M. G. J., Salemink, E., Allouch, S. B., Bohlmeijer, E. T., & Postel, M. G. (2019) · JMIR mhealth and uhealth · doi:10.2196/16217 · Cited by 37 · see citing works
Hermosa, J. L. R., Gomila, A. F., Puente‐Maestu, L., Diago, C. A. A., González, F. J. C., de Molina, R. M., Fuentes, M. E., Álvarez-Sala, J. L., & Rubio, M. C. (2020) · JMIR mhealth and uhealth · doi:10.2196/15699 · Cited by 36 · see citing works
Deng, N., Chen, J., Liu, Y., Wei, S., Sheng, L., Lü, R., Wang, Z., Zhu, J., An, J., Wang, B., et al. (2020) · JMIR mhealth and uhealth · doi:10.2196/15978 · Cited by 36 · see citing works
Vilardaga, R., Rizo, J. L., Ries, R. K., Kientz, J. A., Ziedonis, D. M., Hernandez, K., & McClernon, F. J. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/iby097 · Cited by 35 · see citing works
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A Systematic Review of the Effect of Gamification on Adherence Across Disciplines
Croon, R. D., Geuens, J., Verbert, K., & Abeele, V. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77277-2_14 · Cited by 35 · see citing works
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HabitWorks: Development of a CBM-I Smartphone App to Augment and Extend Acute Treatment
Beard, C., Ramadurai, R., McHugh, R. K., Pollak, J. P., & Björgvinsson, T. (2021) · Behavior Therapy · doi:10.1016/j.beth.2020.04.013 · Cited by 29 · see citing works
Identifying and Evaluating User Requirements for Smartphone Group Fitness Applications
Luhanga, E. T., Hippocrate, A. A. E., Suwa, H., Arakawa, Y., & Yasumoto, K. (2018) · IEEE Access · doi:10.1109/access.2018.2793844 · Cited by 28 · see citing works
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Estimating causal effects of internet interventions in the context of nonadherence
Hesser, H. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100346 · Cited by 26 · see citing works
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ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study
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A feasibility study of a mobile app to treat insomnia
Aji, M., Glozier, N., Bartlett, D. J., Peters, D., Calvo, R. A., Zheng, Y., Grunstein, R. R., & Gordon, C. J. (2020) · Translational Behavioral Medicine · doi:10.1093/tbm/ibaa019 · Cited by 22 · see citing works
Predicting User Engagement in Longitudinal Interventions with Virtual Agents
Trinh, H., Shamekhi, A., Kimani, E., & Bickmore, T. (2018) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3267851.3267909 · Cited by 21 · see citing works
Gamification to Motivate the Unmotivated Smoker: The “Take a Break” Digital Health Intervention
Blok, A. C., Sadasivam, R. S., Amante, D. J., Kamberi, A., Flahive, J., Morley, J., Conigliaro, J. C., & Houston, T. K. (2019) · Games for Health Journal · doi:10.1089/g4h.2018.0076 · Cited by 21 · see citing works
A Smartphone App to Monitor Mood Symptoms in Bipolar Disorder: Development and Usability Study
Ryan, K. A., Babu, P., Easter, R. E., Saunders, E. F. H., Lee, J. S., Klasnja, P., Verchinina, L., Micol, V. J., Doil, B., McInnis, M. G., et al. (2020) · JMIR Mental Health · doi:10.2196/19476 · Cited by 21 · see citing works
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Certified Peer Specialists’ Perspective of the Barriers and Facilitators to Mobile Health Engagement
Fortuna, K. L., Muralidharan, A., Goldstein, C. M., Venegas, M. D. R. A., Glass, J. E., & Brooks, J. (2020) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-020-00138-7 · Cited by 7 · see citing works
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Engagement and retention in digital mental health interventions: a narrative review
Boucher, E. M., & Raiker, J. S. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00105-9 · Cited by 201 · see citing works
Kupila, S. K. E., Joki, A., Suojanen, L.-U., & Pietiläinen, K. H. (2023) · Current Obesity Reports · doi:10.1007/s13679-023-00515-2 · Cited by 95 · see citing works
Lee, S., Oh, J. W., Park, K., Lee, E. M., & Lee, E. M. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00800-3 · Cited by 94 · see citing works
Sylla, B., Ouédraogo, I., & Diallo, G. (2024) · Journal of Medical Internet Research · doi:10.2196/59042 · Cited by 74 · see citing works
Jabir, A. I., Lin, X., Martinengo, L., Sharp, G., Theng, Y. L., & Car, L. T. (2023) · Journal of Medical Internet Research · doi:10.2196/48168 · Cited by 70 · see citing works
Daniore, P., Nittas, V., & von Wyl, V. (2022) · Journal of Medical Internet Research · doi:10.2196/39910 · Cited by 60 · see citing works
Chen, E., Prakash, S., Reddi, V. J., Kim, D. A., & Rajpurkar, P. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01115-0 · Cited by 51 · see citing works
Designing personalised mHealth solutions: An overview
Rivera-Romero, O., Gabarrón, E., Ropero, J., & Denecke, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104500 · Cited by 48 · see citing works
Mair, J. L., Hashim, J., Thai, L., Tai, E. S., Ryan, J. C., Kowatsch, T., Müller‐Riemenschneider, F., & Edney, S. M. (2025) · Translational Behavioral Medicine · doi:10.1093/tbm/ibaf010 · Cited by 47 · see citing works
Martins, A., Londral, A. R. M., Nunes, I. L., & Lapão, L. V. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105385 · Cited by 48 · see citing works
Yin, R., Martinengo, L., Smith, H. E., Subramaniam, M., Griva, K., & Car, L. T. (2024) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2024.08.007 · Cited by 44 · see citing works
Jat, A. S., & Grønli, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39764-6_4 · Cited by 49 · see citing works
Castro, Ó., Mair, J. L., Salamanca-Sanabria, A., Alattas, A., Keller, R., Zheng, S., Jabir, A. I., Lin, X., Frese, B. F., Lim, C. S., et al. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1039171 · Cited by 42 · see citing works
Li, Y., Wu, X., Liu, M., Deng, K., Tullini, A., Zhang, X., Shi, J., Lai, H., & Tonetti, M. S. (2024) · Journal of Clinical Periodontology · doi:10.1111/jcpe.13987 · Cited by 40 · see citing works
“…(i) personalization or tailoring of the content to the individual needs of the user; (ii) reminders in the form of individualized push notifications; (iii) user-friendly and technically stable app design; and (iv) personal support complementary to the digital intervention (Jakob et al., 2022).”
Woodley, S. J., Moller, B., Clark, A. R., Bussey, M. D., Sangelaji, B., Perry, M., & Kruger, J. (2023) · JMIR mhealth and uhealth · doi:10.2196/44929 · Cited by 39 · see citing works
Zhou, X., Wei, X., Cheng, A., Liu, Z., Su, Z., Li, J., Qin, R., Zhao, L., Xie, Y., Huang, Z., et al. (2023) · JMIR mhealth and uhealth · doi:10.2196/48253 · Cited by 38 · see citing works
Ulrich, S., Gantenbein, A. R., Zuber, V., Von Wyl, A., Kowatsch, T., & Künzli, H. (2024) · Journal of Medical Internet Research · doi:10.2196/50132 · Cited by 36 · see citing works
“Reminders (Figure 1C) are an effective way of improving engagement, especially when they address specific needs [76,77]; refer to Multimedia Appendix 4 [49,56,68,78-81] for the detailed aspects of the engagements implemented.”
When and Why Adults Abandon Lifestyle Behavior and Mental Health Mobile Apps: Scoping Review
Kidman, P. G., Curtis, R., Watson, A., & Maher, C. A. (2024) · Journal of Medical Internet Research · doi:10.2196/56897 · Cited by 38 · see citing works
Wong, A. K. C., Bayuo, J., Wong, F. K. Y., Chow, K. K. S., Wong, S. M., & Lau, A. C. K. (2023) · Journal of Medical Internet Research · doi:10.2196/43678 · Cited by 36 · see citing works
Dissemination Strategies for mHealth Apps: Systematic Review
Moungui, H. C., Nana-Djeunga, H. C., Anyiang, C. F., Cano, M., Ruiz-Postigo, J. A., & Carrión, C. (2023) · JMIR mhealth and uhealth · doi:10.2196/50293 · Cited by 36 · see citing works
Mobile applications, physical activity, and health promotion
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Digitalising mental health care: Practical recommendations from the European Psychiatric Association
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- Authors
- Jakob, R., Harperink, S., Rudolf, A. M., Fleisch, E., Haug, S., Mair, J. L., Salamanca-Sanabria, A., & Kowatsch, T.
- Affiliations
- ETH Zurich (CH)
- University of St.Gallen (CH)
- University of Zurich (CH)
- National University of Singapore (SG)
- Singapore-ETH Centre (SG)
- Corresponding author
- Jakob, Robert
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-05-25
- Volume
- 24
- Issue
- 5
- Pages
- e35371
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9178451
- OpenAlex
- W4225577560
- Cited by
- 446
- References
- 149
- Citations by year
- 2026: 121 · 2025: 164 · 2024: 94 · 2023: 59 · 2022: 8
- FWCI
- 15.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Research Foundation
- National Research Foundation Singapore
- Bundesamt für Gesundheit
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- non-communicable diseases, digital health interventions, barriers and facilitators, content personalization, push notifications, gamification, social support, mobile application design, lifestyle intervention, mental health, substance use, nutrition, physical activity, weight loss, mindfulness, user characteristics, recruitment channels
- MeSH terms
- Self-Management, Noncommunicable Diseases, Humans, Mental Health, Telemedicine, Mobile Applications
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