ArticleJournal of medical Internet research2017
The Reviews Are in: A Qualitative Content Analysis of Consumer Perspectives on Apps for Bipolar Disorder.
Article in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 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
64 citing papers in PubMed, 2 syntheses or guidelines pooled it, 175 citations in OpenAlex.
- Evaluating Characteristics and Quality of Mental Health Apps Available in App Stores for Indian Users: Systematic App Search and Review.JMIR mHealth and uHealth · 2025Pooled it
- Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Exploring Components and Feasibility of Health Coaching Interventions for Self-Management of Type 2 Diabetes: Protocol for a Systematic Review.JMIR research protocols · 2026Article
- The Association Between Exercise and the Well-Being of Dementia Patients' Caregivers: An Observational Study.Health science reports · 2025Article
- Bibliometric Analysis on of the Impact of Screening to Minimize Maternal Mental Health on Neonatal Outcomes: A Systematic Review.Journal of clinical medicine · 2024Review
- Assessing the Effectiveness of an mHealth Intervention to Support Men Who Have Sex With Men Engaging in Chemsex (Budd): Single-Case and Pre-Post Experimental Design Study.JMIR formative research · 2024Article
- Mobile apps to reduce depressive symptoms and alcohol use in youth: A systematic review and meta-analysis: A systematic review.Campbell systematic reviews · 2024Review
- Exploring User Perspectives of and Ethical Experiences With Teletherapy Apps: Qualitative Analysis of User Reviews.JMIR mental health · 2023Article
- The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements.Journal of healthcare informatics research · 2023Article
- An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews.JMIR mHealth and uHealth · 2023Article
- Menstrual Tracking Mobile App Review by Consumers and Health Care Providers: Quality Evaluations Study.JMIR mHealth and uHealth · 2023Review
- Developing culturally-adapted mobile mental health interventions: a mixed methods approach.mHealth · 2023Article
- Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.JMIR human factors · 2022Article
- A review of smartphone applications designed to improve occupational health, safety, and well-being at workplaces.BMC public health · 2022Review
- Supporting Self-management and Quality of Life in Bipolar Disorder With the PolarUs App (Alpha): Protocol for a Mixed Methods Study.JMIR research protocols · 2022Article
- A systematic quality rating of available mobile health apps for borderline personality disorder.Borderline personality disorder and emotion dysregulation · 2022Article
- Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder.JMIR formative research · 2022Article
- Evaluating User Feedback for an Artificial Intelligence-Enabled, Cognitive Behavioral Therapy-Based Mental Health App (Wysa): Qualitative Thematic Analysis.JMIR human factors · 2022Article
- Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management.International journal of bipolar disorders · 2022Article
- PROTOCOL: Mobile apps to reduce depressive symptoms and alcohol use in youth: A systematic review and meta-analysis.Campbell systematic reviews · 2022Article
4 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe delivery of mobile health (mHealth) services is acceptable to mental health consumers. However, despite the benefits of accessibility, cost-effectiveness, anonymity, and ability to tailor content to individual needs, consumer engagement remains a hurdle for uptake and continued use. This may be unsurprising as few studies have examined app content from the consumer perspective or assessed consumer preferences for the content of apps for mental health management. An opportunity to examine consumer perspectives exists in using naturally generated data that is publically available in the Google Play and Apple app stores. Whereas commercial developers routinely use this data, to date there has been no in-depth evaluation within scientific research.
objectiveThe aim of our study was to explore what consumers consider useful content for mental health management apps, identify unmet needs, and understand user expectations of mental health apps within the context of apps for bipolar disorder.
methodsPublically available English language consumer reviews of 48 apps for bipolar disorder were used as data, providing a total of 2173 reviews. Review text was coded and analyzed using a team approach to qualitative content analysis. Results were presented in 2 forms: (1) a quantitative summary of the 9 major and minor themes and (2) a qualitative synthesis of key thematic findings.
resultsThe majority of reviews were for symptom monitoring apps (87.94%, 1911/2173). The qualitative content analysis revealed 5 main themes: (1) laudatory talk, comments regarding the app's benefits including helpfulness and successful design features (74.00% of reviews, 1608/2173); (2) unfavorable feedback, negative reviews largely concerning unmet needs, privacy and technical issues, and potential dangers of app use (25.54%, 555/2173); (3) conceptions of community, referring to both communities of users with mental ill-health accessed via the app and a community created among app users and developers (24.25%, 527/2173); (4) wishlist features, app features requested by users (17.53%, 381/2173); and (5) apps and therapy, app use within clinical care (10.58%, 230/2173). Four minor themes were also identified: (1) app cost, (2) privacy and data security, (3) comparisons with traditional monitoring, and (4) evidence-based mHealth.
conclusionsAlthough mostly positive, the proportion of reviews containing wishlist requests indicates consumer needs are not adequately addressed by currently available disorder management apps. Consumers value content that is helpful, supportive, and easy to use, and they are integrating apps into their health management and clinical care without necessarily considering the evidence-base or clinical effectiveness of the tool. User expectations regarding developer responsiveness to their needs has implications for community-based participatory research and integrated knowledge translation. However, this expectation is incompatible with current mHealth funding structures.
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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.