ArticleBMC medicine2015
'Trust but verify'--five approaches to ensure safe medical apps.
Article in BMC medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 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
50 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Research-Tested Mobile Apps for Breast Cancer Care: Systematic Review.JMIR mHealth and uHealth · 2019Pooled it
- Mental Health Mobile Apps for Preadolescents and Adolescents: A Systematic Review.Journal of medical Internet research · 2017Pooled it
- Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies.BMC medical informatics and decision making · 2016Pooled it
- Integrating Artificial Intelligence, Electronic Health Records, and Wearables for Predictive, Patient-Centered Decision Support in Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Understanding Device Integrations Within Diabetes Apps: Mixed Methods Analysis of App Features and User Reviews.JMIR diabetes · 2025Article
- Public Understanding and Expectations of Digital Health Evidence Generation: Focus Group Study.JMIR formative research · 2025Article
- Mobile Apps for COVID-19: A Systematic Review of Reviews.Healthcare (Basel, Switzerland) · 2024Review
- Review
- Characterization of Self-reported Improvements in Knowledge and Health Among Users of Flo Period Tracking App: Cross-sectional Survey.JMIR mHealth and uHealth · 2023Article
- Transformation of chronic disease management: Before and after the COVID-19 outbreak.Frontiers in public health · 2023Review
- The promise of digital healthcare technologies.Frontiers in public health · 2023Review
- Gamification for Machine Learning in Surgical Patient Engagement.Frontiers in surgery · 2022Review
- Seeking Inspiration: Examining the Validity and Reliability of a New Smartphone Respiratory Therapy Exergame App.Sensors (Basel, Switzerland) · 2021Article
- Person-Generated Health Data in Women's Health: Protocol for a Scoping Review.JMIR research protocols · 2021Article
- Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results.International journal of environmental research and public health · 2021Article
- Assessing the quality of mobile applications in chronic disease management: a scoping review.NPJ digital medicine · 2021Article
- Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps.JMIR diabetes · 2021Article
- Digital Health Technology to Enhance Adolescent and Young Adult Clinical Preventive Services: Affordances and Challenges.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2020Review
- The Mobile Health App Trustworthiness Checklist: Usability Assessment.JMIR mHealth and uHealth · 2020Article
- Smartphone apps for managing alcohol consumption: a literature review.Addiction science & clinical practice · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile health apps are health and wellness programs available on mobile devices such as smartphones or tablets. In three systematic assessments published in BMC Medicine, Huckvale and colleagues demonstrate that widely available health apps meant to help patients calculate their appropriate insulin dosage, educate themselves about asthma, or perform other important functions are methodologically weak. Insulin dose calculators lacked user input validation and made inappropriate dose recommendations, with a lack of documentation throughout. Since 2011, asthma apps have become more interactive, but have not improved in quality; peak flow calculators have the same issues as the insulin calculators. A review of the accredited National Health Service Health Apps Library found poor and inconsistent implementation of privacy and security, with 28% of apps lacking a privacy policy and one even transmitting personally identifying data the policy claimed would be anonymous. Ensuring patient safety might require a new approach, whether that be a consumer education program at one extreme or government regulation at the other. App store owners could ensure transparency of algorithms (whiteboxing), data sharing, and data quality. While a proper balance must be struck between innovation and caution, patient safety must be paramount.Please see related articles: http://dx.doi.org/10.1186/s12916-015-0444-y , http://www.biomedcentral.com/1741-7015/13/106 and http://www.biomedcentral.com/1741-7015/13/58.
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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.