ArticleJMIR mental health2021
Clinical Utility of Wearable Sensors and Patient-Reported Surveys in Patients With Schizophrenia: Noninterventional, Observational Study.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02224430 (Remote Physiological, Behavioral and Symptom Assessment Study to Identify Predictors of Symptom Exacerbation and Relapse in Schizophrenia), which is not on this map. Cited by 9 papers, 1 of them a synthesis 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.
Remote Physiological, Behavioral and Symptom Assessment Study to Identify Predictors of Symptom Exacerbation and Relapse in Schizophrenia
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review.JMIR mental health · 2025Pooled it
- Targeting Sleep to Improve Outcomes in Psychosis: Digital and Non-Pharmacological Interventions.Medicina (Kaunas, Lithuania) · 2026Review
- Assessing the Use of Wearable Mobile-Monitoring Devices Among Individuals With Serious Mental Illness: Qualitative Acceptability and Feasibility Study.Journal of medical Internet research · 2026Article
- Sleep disturbances as an early indicator of psychotic disorders in adolescents and young adults.Frontiers in synaptic neuroscience · 2026Review
- Wearables research for continuous monitoring of patient outcomes: A scoping review.PLOS digital health · 2025Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Comparing Adherence to the Experience Sampling Method Among Patients With Schizophrenia Spectrum Disorder and Unaffected Individuals: Observational Study From the Multicentric DiAPAson Project.Journal of medical Internet research · 2023Observational
- Accessibility and availability of smartphone apps for schizophrenia.Schizophrenia (Heidelberg, Germany) · 2022Article
- Wearables in Schizophrenia: Update on Current and Future Clinical Applications.JMIR mHealth and uHealth · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRelapse in schizophrenia may be preceded by early warning signs of biological, sensory, and clinical status. Early detection of warning signs may facilitate intervention and prevent relapses.
objectiveThis study aims to investigate the feasibility of using wearable devices and self-reported technologies to identify symptom exacerbation correlates and relapse in patients with schizophrenia.
methodsIn this observational study, patients with schizophrenia were provided with remote sensing devices to continuously monitor activity (Garmin vivofit) and sleep (Philips Actiwatch), and smartphones were used to record patient-reported outcomes. Clinical assessments of symptoms (Positive and Negative Syndrome Scale and Brief Psychiatric Rating Scale) were performed biweekly, and other clinical scales on symptoms (Clinical Global Impression-Schizophrenia, Calgary Depression Scale), psychosocial functioning, physical activity (Yale Physical Activity Survey), and sleep (Pittsburgh Sleep Quality Index) were assessed every 4 weeks. Patients were observed for 4 months, and correlations between clinical assessments and aggregated device metrics data were assessed using a mixed-effect model. An elastic net model was used to predict the clinical symptoms based on the device features.
resultsOf the 40 patients enrolled, 1 patient relapsed after being stable with evaluable postbaseline data. Weekly patient-reported outcomes were moderately correlated with psychiatric symptoms (Brief Psychiatric Rating Scale total score, r=0.29; Calgary Depression Scale total score, r=0.37; and Positive and Negative Syndrome Scale total score, r=0.3). In the elastic net model, sleep and activity features derived from Philips Actigraph and Garmin vivofit were predictive of the sitting index of the Yale Physical Activity Survey and sleep duration component of the Pittsburgh Sleep Quality Index. On the basis of the combined patient data, a high percentage of data coverage and compliance (>80%) was observed for each device.
conclusionsThis study demonstrated that wearable devices and smartphones could be effectively deployed and potentially used to monitor patients with schizophrenia. Furthermore, metrics-based prediction models can assist in detecting earlier signs of symptom changes. The operational learnings from this study may provide insights to conduct future studies.
trial registrationClinicalTrials.gov NCT02224430; https://www.clinicaltrials.gov/ct2/show/NCT02224430.
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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.