The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study
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Abstract
BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: The 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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In a study by Nicholas et al, participants were more comfortable with health information (sleep, mood) being recorded than personal data (social activity, communication logs) being recorded.
“and in a study by Nicholas et al [234], participants were more comfortable with health information being recorded such as sleep and mood than personal data being recorded such as social activity and communication logs.”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [234]
The Discussion (Principal Findings) says participants were more comfortable sharing sleep and mood data than communication logs and social activity data. The Methods specify that the questionnaire measured comfort with sharing sensed data with doctors, electronic health record systems, or family members—not comfort with the data being recorded. The claimed preference between data types is supported, but its framing as recording is not. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn 2 studies, participants were uncomfortable about their physical location being recorded.
“In 2 studies, participants were uncomfortable about their physical location being recorded [180,234]”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [234]
The Results (Sensor Data Sharing) and Discussion (Principal Findings) show that location was among the data types participants were least comfortable sharing, with comfort varying by recipient. The Methods (Measures) specify that the questionnaire asked about sharing sensed location data, not whether participants were uncomfortable with their location being recorded. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIndividuals with mental health difficulties have varied feelings about sharing data, depending on the recipient and type of data shared.
“Research has shown that individuals with mental health difficulties have varied feelings about sharing data, depending on the recipient and type of data shared (Nicholas et al. 2019).”
From Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management (2022) · cites this paper as Nicholas et al. 2019
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- Authors
- Nicholas, J., Shilton, K., Schueller, S. M., Gray, E. L., Kwasny, M. J., & Mohr, D. C.
- Affiliations
- Northwestern University (US)
- University of Maryland, College Park (US)
- Corresponding author
- Nicholas, Jennifer
- ORCID
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-04-05
- Volume
- 7
- Issue
- 4
- Pages
- e12578
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6473465
- OpenAlex
- W2911992314
- Cited by
- 77
- References
- 37
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 9 · 2023: 13 · 2022: 20 · 2021: 16 · 2020: 5 · 2019: 5
- FWCI
- 15.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR001422)
- National Institute of Mental Health (P20MH090318, R01MH111610)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- smartphone sensor data, digital phenotyping, data sharing, mental health, data types, trust in physicians, electronic health records, family members, physical activity, sleep, mood, location data, communication logs, social activity, digital mental health, data privacy
- MeSH terms
- Smartphone, Adolescent, Adult, Aged, Confidentiality, Cross-Sectional Studies, Female, Humans, Male, Mental Health Services, Middle Aged, Psychometrics, Surveys and Questionnaires, Information Storage and Retrieval, Privacy
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