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
Highlights
BackgroundPsychotic symptom exacerbation and relapse are frequently observed in patients with schizophrenia and can lead to a decline in social functioning, reduced treatment response, and worsening of clinical outcomes [1]
Relapse in schizophrenia may be preceded by early warning signs, including depressed mood; social withdrawal; and changes in physical activities, feelings, emotions, and sleep disturbances [5]
We subsequently evaluated the feasibility of using wearable devices and self-reporting technologies to identify potentially predictive symptom correlates in patients with schizophrenia or schizoaffective disorder who are at increased risk of relapse
Summary
Psychotic symptom exacerbation and relapse are frequently observed in patients with schizophrenia and can lead to a decline in social functioning, reduced treatment response, and worsening of clinical outcomes [1]. A consumer wrist-worn smartwatch or fitness band can provide measurements of precise and objective activity patterns spanning sleep-rest and active-awake periods These devices have an advantage of generating continuous streaming data that are more reproducible and less obtrusive than relying on patient and caregiver reports alone. Methods: In 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.
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