Evidence map›Paper›PMID 35612886›Full record

SynthesisJournal of medical Internet research2022

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review.

Robert Jakob, Samira Harperink, Aaron Maria Rudolf, Elgar Fleisch, Severin Haug, Jacqueline Louise Mair, Alicia Salamanca-Sanabria, Tobias Kowatsch

2 registry-linked trialsAbstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 276 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
276citing papers in PubMed, 13 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06410755 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

Research on Evaluation of Home-based Rehabilitation Monitoring System With Wearable Devices and Self-Report Application

TypeinterventionalSponsorYonsei UniversityRan2024 to 2026Enrolled120ConditionsGait Disorders, NeurologicArmsIntegrated Wearable devices Monitoring sys-Assisted Home Rehabilitation Program
NCT07612852 narecruitingnot on this mapstarted 2026, after this paper: background citation

Digital Engagement for Lifelong Prevention and Health Improvement

TypeinterventionalSponsorIstituto per la Ricerca e l'Innovazione BiomedicaRan2026 to 2027Enrolled200ConditionsLife Style, Healthy, Risk Reduction, Health PromotionArmsDELPHI Personalized Digital Prevention Platform, Passive Digital Monitoring Control
3 · Its place in the literature

Who cites it

276 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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  6. Smartphone application-based interventions for cardiometabolic risk factor management: A systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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  13. Smartphone application-based intervention to lower blood pressure: a systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2025
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216 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Robert JakobCentre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0003-4793-1366
Samira HarperinkCentre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.ORCID 0000-0003-0583-8948
Aaron Maria RudolfCentre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.ORCID 0000-0001-8204-2885
Elgar FleischCentre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-4842-1117
Severin HaugSwiss Research Institute for Public Health and Addiction, Zurich University, Zurich, Switzerland.ORCID 0000-0002-6539-5045
Jacqueline Louise MairFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise, Singapore, Singapore.ORCID 0000-0002-1466-8680
Alicia Salamanca-SanabriaFuture Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise, Singapore, Singapore.ORCID 0000-0002-2756-5592
Tobias KowatschCentre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland.ORCID 0000-0001-5939-4145

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile 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.

objectiveThis 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.

methodsA 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.

resultsThe 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%).

conclusionsThis 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.

Indexed as

Mobile ApplicationsNoncommunicable DiseasesSelf-ManagementTelemedicineHumansMental Healthadherenceattritiondigital health interventioneHealthengagementintended usemHealthmobile phoneNCDnoncommunicable diseaseretention

Identifiers

PMID35612886
PMCPMC9178451

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.