ArticleJMIR mHealth and uHealth2022
Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study.
Article in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 4 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The transformative power of mHealth apps: empowering patients with obesity and diabetes - a narrative review.Journal of medicine and life · 2024Pooled it
- Middle-aged and older adults' acceptance of mobile nutrition and fitness apps: A systematic mixed studies review.PloS one · 2022Pooled it
- Privacy Fact Sheets for Mitigating Disease-Related Privacy Concerns and Facilitating Equal Access to the Electronic Health Record: Randomized Controlled Trial.JMIR human factors · 2026Trial
- Influence of Disease-Related Stigma on Patients' Decisions to Upload Medical Reports to the German Electronic Health Record: Randomized Controlled Trial.JMIR human factors · 2024Trial
- Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study.JMIR AI · 2026Article
- Who chooses telehealth? A cross-sectional cohort study of diabetic patients in outpatient clinic follow-up.PLOS digital health · 2026Article
- Mobile Health App Acceptance in Japan's Aging Society: Multigroup Structural Equation Modeling Based on the Extended Unified Theory of Acceptance and Use of Technology and eHealth Literacy Frameworks.JMIR mHealth and uHealth · 2026Article
- Adoption and Use of Social Media in Health Care Among Medical Residents: Cross-Sectional Study.JMIR medical education · 2026Article
- Measuring eHealth Literacy in the European Economic Area, Switzerland, and the United Kingdom: Scoping Review.Journal of medical Internet research · 2026Article
- Role of Technology Acceptance in the Telerehabilitation of Patients With Metabolic Syndrome: Longitudinal Study.JMIR rehabilitation and assistive technologies · 2026Article
- Evaluating a Digital Chronic Condition Prevention Intervention (THRIVE) in Australian General Practice: Protocol for a Mixed Methods Feasibility Study (ePREVENT-360).JMIR research protocols · 2026Article
- Physician-Patient Communication and Physicians' Acceptance of a Tailored Digital Health Information Service: Quantitative Online Survey.JMIR human factors · 2026Article
- Navigation, Adoption, and Use of Digital Health Technologies for Irritable Bowel Syndrome Self-Management: Focus Group Study of Patient Experience and Decision-Making.JMIR human factors · 2026Article
- Predicting the Intention to Use Generative Artificial Intelligence for Health Information: Comparative Survey Study.Journal of medical Internet research · 2026Article
- Article
- User acceptance of telerehabilitation in Germany: a structural equation modeling approach based on the UTAUT2 model.Frontiers in digital health · 2026Article
- Beyond immersion: disentangling the technological and social drivers of visitor satisfaction in XR art exhibitions.Frontiers in psychology · 2026Article
- Why health apps fail: the role of smartphone proficiency in mHealth resistance.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMobile health (mHealth) care apps are a promising technology to monitor and control health individually and cost-effectively with a technology that is widely used, affordable, and ubiquitous in many people's lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened health care systems worldwide may directly profit from the use of therapy apps by those patients already in need of medical treatment and monitoring.
objectiveWe aimed to compare the factors influencing the acceptance of lifestyle and therapy apps to better understand what drives and hinders the use of mHealth apps.
methodsWe applied the established unified theory of acceptance and use of technology 2 (UTAUT2) technology acceptance model to evaluate mHealth apps via an online questionnaire with 707 German participants. Moreover, trust and privacy concerns were added to the model and, in a between-subject study design, the influence of these predictors on behavioral intention to use apps was compared between lifestyle and therapy apps.
resultsThe results show that the model only weakly predicted the intention to use mHealth apps (R
conclusionsThe results indicate that, rather than by utilitarian factors like usefulness, mHealth app acceptance is influenced by emotional factors like hedonic motivation and partly by habit, social influence, and trust. Overall, the findings give evidence that for the health care context, new and extended acceptance models need to be developed with an integration of user diversity, especially individuals' prior experience with apps and mHealth.
Indexed as
Identifiers
What OpenQuestion holds
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.