ArticleJMIR mHealth and uHealth2019
The Role of Data Type and Recipient in Individuals' Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 6 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
39 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.JMIR mental health · 2026Pooled it
- Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Using Passive Sensing to Predict Psychosis Relapse: An In-Depth Qualitative Study Exploring Perspectives of People With Psychosis.Schizophrenia bulletin · 2026Article
- Service Users' Views on Digital Remote Monitoring for Psychosis: Survey Study.JMIR human factors · 2026Article
- Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis.Schizophrenia bulletin · 2026Article
- Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.Journal of medical Internet research · 2025Observational
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions.JMIR mental health · 2024Article
- Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial.Frontiers in digital health · 2024Article
- Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial.Frontiers in digital health · 2024Article
- Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study.JMIR mHealth and uHealth · 2023Observational
- People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design.PLOS digital health · 2023Article
- Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- Acceptance of smart sensing: a barrier to implementation-results from a randomized controlled trial.Frontiers in digital health · 2023Article
- University students' opinions towards mobile sensing data collection: A qualitative analysis.Frontiers in digital health · 2023Article
- Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.Proceedings of the ACM on human-computer interaction · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe growing field of personal sensing harnesses sensor data collected from individuals' smartphones to understand their behaviors and experiences. Such data could be a powerful tool within mental health care. However, it is important to note that the nature of these data differs from the information usually available to, or discussed with, health care professionals. To design digital mental health tools that are acceptable to users, understanding how personal sensing data can be used and shared is critical.
objectiveThis study aimed to investigate individuals' perspectives about sharing different types of sensor data beyond the research context, specifically with doctors, electronic health record (EHR) systems, and family members.
methodsA questionnaire assessed participants' comfort with sharing six types of sensed data: physical activity, mood, sleep, communication logs, location, and social activity. Participants were asked about their comfort with sharing these data with three different recipients: doctors, EHR systems, and family members. A series of principal component analyses (one for each data recipient) was performed to identify clusters of sensor data types according to participants' comfort with sharing them. Relationships between recipients and sensor clusters were then explored using generalized estimating equation logistic regression models.
resultsA total of 211 participants completed the questionnaire. The majority were female (171/211, 81.0%), and the mean age was 38 years (SD 10.32). Principal component analyses consistently identified two clusters of sensed data across the three data recipients: "health information," including sleep, mood, and physical activity, and "personal data," including communication logs, location, and social activity. Overall, participants were significantly more comfortable sharing any type of sensed data with their doctor than with the EHR system or family members (P<.001) and more comfortable sharing "health information" than "personal data" (P<.001). Participant characteristics such as age or presence of depression or anxiety did not influence participants' comfort with sharing sensed data.
conclusionsThe comfort level in sharing sensed data was dependent on both data type and recipient, but not individual characteristics. Given the identified differences in comfort with sensed data sharing, contextual factors of data type and recipient appear to be critically important as we design systems that harness sensor data for mental health treatment and support.
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