ArticleJMIR mHealth and uHealth2021
App Designs and Interactive Features to Increase mHealth Adoption: User Expectation Survey and Experiment.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Using App Store Reviews to Explore User Experiences and Perceptions of Mental Health Mobile Apps: Qualitative Study.JMIR formative research · 2026Article
- The role of behavioral nudges in sustaining public health engagement through the "Tawakkalna" app: insights from healthcare professionals.Frontiers in public health · 2026Article
- Beyond accessibility: co-designing mHealth to bridge the physical activity gap for people with disabilities.mHealth · 2026Article
- The M.A.G.I.C. framework for mHealth development: applying game design principles from 'Magic: The Gathering'.Frontiers in psychology · 2026Article
- From Assessment to Intervention: Leveraging Ecological Momentary Assessment (EMA) to Develop a Personalized mobile-health (mHealth) Ecological Momentary Intervention (EMI) for Young Adults With ADHD and High-Risk Alcohol Use.Journal of studies on alcohol and drugs · 2026Article
- Designing an App to Facilitate Self-Management in Young Adult Survivors of Childhood Cancer: Development and Usability Study.JMIR cancer · 2025Article
- What's in an App? Scoping Review and Quality Assessment of Clinically Available Hearing-Aid-Connected Apps.Audiology research · 2025Review
- User-needs based app for patients with inflammatory bowel disease: development and usability study.BMC health services research · 2025Article
- Evaluating user perceptions and usability of an AI-powered smartphone application for at-home dental plaque screening.British dental journal · 2025Article
- Participant evaluation of MOMitor™-a smartphone-based application that monitors postpartum mental and physical health status.mHealth · 2025Article
- Article
- Developing a Mood and Menstrual Tracking App for People With Premenstrual Dysphoric Disorder: User-Centered Design Study.JMIR formative research · 2024Article
- A feature-based qualitative assessment of smoking cessation mobile applications.PLOS digital health · 2024Article
- The WeThrive App and Its Impact on Adolescents Who Menstruate: Qualitative Study.JMIR formative research · 2024Article
- Acceptance and use of mobile health technology in post-abortion care.BMC health services research · 2024Article
- SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment.JMIR mHealth and uHealth · 2024Article
- Exploring the digital divide: results of a survey informing mobile application development.Frontiers in digital health · 2024Article
- Outcomes of End-User Testing of a Care Coordination Mobile App With Families of Children With Special Health Care Needs: Simulation Study.JMIR formative research · 2023Article
- Navigating the complexities of mobile medical app development from idea to launch, a guide for clinicians and biomedical researchers.BMC medicine · 2023Article
- Changes in Mobile Health Apps Usage Before and After the COVID-19 Outbreak in China: Semilongitudinal Survey.JMIR public health and surveillance · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the ubiquity of smartphones, there is little guidance for how to design mobile health apps to increase use. Specifically, knowing what features users expect, grab their attention, encourage use (via predicted use or through positive app evaluations), and signal beneficial action possibilities can guide and focus app development efforts.
objectiveWe investigated what features users expect and how the design (prototypicality) impacts app adoption.
methodsIn a web-based survey, we elicited expectations, including presence and placement, for 12 app features. Thereafter, participants (n=462) viewed 2 health apps (high prototypicality similar to top downloaded apps vs low prototypicality similar to research interventions) and reported willingness to download, attention, and predicted use of app features. Participants rated both apps (high and low) for aesthetics, ease of use, usefulness, perceived affordances, and intentions to use.
resultsMost participants (425/462, 92%) expected features for navigation or personal settings (eg, menu) in specific regions (eg, top corners). Features with summary graphs or statics were also expected by many (395-396 of 462, 86%), with a center placement expectation. A feature to "share with friends" was least expected among participants (203/462, 44%). Features fell into 4 unique categories based on attention and predicted use, including essential features with high (>50% or >231 of 462) predicted use and attention (eg, calorie trackers), flashy features with high attention but lower predicted use (eg, links to specific diets), functional features with modest attention and low use (eg, settings), and mundane features with low attention and use (eg, discover tabs). When given a choice, 347 of 462 (75%) participants would download the high-prototypicality app. High prototypicality apps (vs low) led to greater aesthetics, ease of use, usefulness, and intentions, (for all, P<.001). Participants thought that high prototypicality apps had more perceived affordances.
conclusionsIntervention designs that fail to meet a threshold of mHealth expectations will be dismissed as less usable or beneficial. Individuals who download health apps have shared expectations for features that should be there, as well as where these features should appear. Meeting these expectations can improve app evaluations and encourage use. Our typology should guide presence and placement of expected app features to signal value and increase use to impact preventive health behaviors. Features that will likely be used and are attention-worthy-essential, flashy, and functional-should be prioritized during app development.
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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.