ArticleJMIR mHealth and uHealth2020
Understanding and Preventing Health Concerns About Emerging Mobile Health Technologies.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: systematic review and meta-analysis.BMC health services research · 2023Pooled it
- Efficacy of an mHealth App to Support Patients' Self-Management of Hypertension: Randomized Controlled Trial.Journal of medical Internet research · 2023Trial
- Acceptability of artificial intelligence for cervical cancer screening in Dschang, Cameroon: a qualitative study on patient perspectives.Reproductive health · 2024Article
- Usage Behaviour and Adoption Criteria for Mobile Health Solutions in Patients with Chronic Diseases in Gastroenterology.Visceral medicine · 2024Article
- Acceptability and concerns about innovative wearable health sensors in persons with and without chronic disease diagnosis.Internet interventions · 2024Article
- Engagement and Resource Considerations in Developing and Implementing Mobile Health Technologies for COVID-19 Pandemic: Filipino Developers' Perspectives.Acta medica Philippina · 2024Article
- Artificial Intelligence and Public Health: Evaluating ChatGPT Responses to Vaccination Myths and Misconceptions.Vaccines · 2023Article
- An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning With an Urban African American Population of Young Adults: User-Centered Design Approach.JMIR formative research · 2022Article
- Web-based analysis on the role of Digital Media in Health Communication: the experience of VaccinarSinSardegna Website.Acta bio-medica : Atenei Parmensis · 2021Article
- Health Communication in COVID-19 Era: Experiences from the Italian VaccinarSì Network Websites.International journal of environmental research and public health · 2021Article
- Mobile applications for prematurity: a systematic review protocol.BMJ paediatrics open · 2021Article
- Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review.JMIR mHealth and uHealth · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New technologies and innovations have often improved population well-being and societal function; however, these are also often initially accompanied by worry and fear. In some cases, such worries can impede, or even prevent entirely, the adoption of the technology. Mobile health (mHealth), a discipline broadly focused on employing ambulatory technologies to improve the affordability, reach, and effectiveness of health promotion and clinical intervention approaches, offers new innovations and opportunities. Despite emerging evidence supporting mHealth efficacy (eg, for improving health outcomes), some individuals have concerns about mHealth technology that may impede scalability, efficacy, and, ultimately, the public health benefits of mHealth. We present a review and conceptual framework to examine these issues, focusing on three overarching themes: biophysiological, psychological, and societal concerns. There are features of mHealth that lead to worries about the potential negative effects on an individual's health (eg, due to exposure to electromagnetic or radio waves), despite evidence supporting the safety of these technologies. When present, such beliefs can lead to worry that gives rise to the experience of unpleasant and concerning physical symptoms-the nocebo effect. This may represent an important implementational barrier because of apprehension toward beneficial mHealth products (or features thereof, such as wireless charging, wearable or implantable sensors, etc) and may also have broader ramifications (eg, leading to economic, governmental, and legislative actions). In addition to reviewing evidence on these points, we provide a broad three-step model of implementation research in mHealth that focuses on understanding and preventing health concerns to facilitate the safe and effective scalability of mHealth (and that may be generalizable and applied to similar technologies): (1) evaluating and better discerning public perceptions and misperceptions (and how these may differ between populations), (2) developing theory-based public health communication strategies regarding the safety of mHealth, and (3) disseminating this messaging using evidence-based methods. Collectively, these steps converge on reviewing evidence regarding the potential role of worry and nocebo in mHealth and providing a model for understanding and changing attitudes and preventing unfounded negative perceptions related to mHealth technology.
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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.