ArticleDigital health
Development of the mHealth App Trustworthiness checklist.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
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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
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Landscape of Mobile Apps for Healthy Eating: Case Study for a Systematic Review and Quality Assessment.JMIR mHealth and uHealth · 2026Pooled it
- Assessing Patient Adherence to and Engagement With Digital Interventions for Depression in Clinical Trials: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 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
- Systematic and Collaborative Approach to Learning and Educational Content Development (SCALED) for Health Apps: An Experience-Informed Conceptual Framework.JMIR mHealth and uHealth · 2026Article
- Trust Analysis Canvas for Teaching in the Field of Digital Public Health and Medicine: Tutorial.JMIR medical education · 2026Article
- Development of an mHealth App Prototype for LGBTQIA+ Individuals' Sexual and Reproductive Health in Gauteng Province, South Africa: Design Science Research Study.JMIR formative research · 2025Article
- Trust in Medical AI: The Case of mHealth Diabetes Apps.Journal of evaluation in clinical practice · 2025Article
- Mobile Apps and Wearable Devices for Cardiovascular Health: Narrative Review.JMIR mHealth and uHealth · 2025Review
- Trustworthiness of the electronic health record in Germany: an exploratory, user-centered analysis.Frontiers in digital health · 2025Article
- Usability Testing of a Digitized Interventional Prehabilitation Tool for Health Care Professionals and Patients Before Major Surgeries: Formative and Summative Evaluation.JMIR formative research · 2024Article
- Bringing Trust Building to Life Within Health Policy-Making Comment on "Placing Trust at the Heart of Health Policy and Systems".International journal of health policy and management · 2024Article
- Extending the Privacy Calculus to the mHealth Domain: Survey Study on the Intention to Use mHealth Apps in Germany.JMIR human factors · 2023Article
- Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study.JMIR mHealth and uHealth · 2023Review
- Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.Journal of medical systems · 2023Article
- Exploring the Needs and Expectations of Expectant and New Parents for an mHealth Application to Support the First 1000 Days of Life: Steps toward a Co-Design Approach.International journal of environmental research and public health · 2023Article
- User experience of a self-management WeChat applet for patients with neurogenic bladder: A qualitative approach.International journal of nursing sciences · 2023Article
- The Application and Prospect of Mobile Health (mHealth) in Health Service for Older People Living Alone in Community: A Narrative Review.Iranian journal of public health · 2022Review
- Digital Contact Tracing Apps for COVID-19: Development of a Citizen-Centered Evaluation Framework.JMIR mHealth and uHealth · 2022Review
- Improving Pelvic Floor Muscle Training Adherence Among Pregnant Women: Validation Study.JMIR human factors · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMobile health applications (mHealth apps) currently lack a consensus on substantial quality and safety standards. As such, the number of individuals engaging with untrustworthy mHealth apps continues to grow at a steady pace.
objectiveThe purpose of this study was to investigate end-users' opinions on the features or actions necessary for trustworthy mHealth apps; and to convey this information to app developers via a succinct but informative checklist: the mHealth app trustworthiness checklist.
methodsThe checklist was formulated in three stages: (a) a literature review of studies identified the desirable features of the most prolific mHealth apps (health and fitness apps); (b) four focus group sessions with past or current users of these apps (
resultsFive major themes emerged from the focus group discussions: informational content, organizational attributes, societal influence, technology-related features, and user control factors. The mHealth app trustworthiness checklist was developed to incorporate these five themes and subsequently modified following expert consultation. In addition to the trustworthiness themes, we identified features that lie between trust and mistrust (limited digital literacy and indifference) as well as 10 features and actions that cause end-users to mistrust mHealth apps.
conclusionThis study contributes to the evidence base on the attributes of trustworthy mHealth apps. The mHealth app trustworthiness checklist is a useful tool in advancing continued efforts to ensure that health technologies are trustworthy.
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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.