ArticleProceedings of the ACM on human-computer interaction2019
Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care.
Article in Proceedings of the ACM on human-computer interaction, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.JMIR mental health · 2024Pooled it
- Barriers and Enablers to Integrating Patient-Generated Health Data in Shared Decision-Making From Health Care Professional and Patient Perspectives: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Investigating the Role of Situational Disruptors in Engagement with Digital Mental Health Tools.CSCW : proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work · 2025Article
- Article
- Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Opportunities to design better computer vison-assisted food diaries to support individuals and experts in dietary assessment: An observation and interview study with nutrition experts.PLOS digital health · 2024Article
- Approaches to Tailoring Between-Session Mental Health Therapy Activities.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Testing a Digital Health App for Patients With Alcohol-Associated Liver Disease: Mixed Methods Usability Study.JMIR formative research · 2023Article
- Mental Health Self-Tracking Preferences of Young Adults With Depression and Anxiety Not Engaged in Treatment: Qualitative Analysis.JMIR formative research · 2023Article
- "Our Job is to be so Temporary": Designing Digital Tools that Meet the Needs of Care Managers and their Patients with Mental Health Concerns.Proceedings of the ACM on human-computer interaction · 2023Article
- Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study.JMIR formative research · 2023Article
- Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- Meeting Young Adults' Social Support Needs across the Health Behavior Change Journey: Implications for Digital Mental Health Tools.Proceedings of the ACM on human-computer interaction · 2022Article
- Veteran and Staff Experience from a Pilot Program of Health Care System-Distributed Wearable Devices and Data Sharing.Applied clinical informatics · 2022Article
- Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2022Article
- Using Real-world Data for Decision Support: Recommendations from a Primary Care Provider Survey.The Permanente journal · 2021Article
- "Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2020Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.
Indexed as
Identifiers
What OpenQuestion holds
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.