A comparison of passive and active estimates of sleep in a cohort with schizophrenia

Patrick Staples,Jukka-Pekka Onnela,Matcheri Keshavan,John Torous,Luis Sandoval,Ian Barnett,Kenzie Carlson

doi:10.1038/s41537-017-0038-0

Patrick Staples, Jukka-Pekka Onnela + Show 5 more

Open Access

https://doi.org/10.1038/s41537-017-0038-0

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Abstract

Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones. Subjects were given tri-weekly ecological momentary assessments (EMAs) on their own smartphones, and passive data including accelerometer, GPS, screen use, and anonymized call and text message logs was continuously collected. We compare the in-clinic assessment of sleep quality, assessed with the Pittsburgh Sleep Questionnaire Inventory (PSQI), to EMAs, as well as sleep estimates based on passively collected accelerometer data. EMAs and passive data classified 85% (11/13) of subjects as exhibiting high or low sleep quality compared to the in-clinic assessments among subjects who completed at least one in-person PSQI. Phone-based accelerometer data used to infer sleep duration was moderately correlated with subject self-assessment of sleep duration (r = 0.69, 95% CI 0.23–0.90). Active and passive phone data predicts concurrent PSQI scores for all subjects with mean average error of 0.75 and future PSQI scores with a mean average error of 1.9, with scores ranging from 0–14. These results suggest sleep monitoring via personal smartphones is feasible for subjects with schizophrenia in a scalable and affordable manner.

Highlights

Sleep abnormalities are considered an important feature of schizophrenia[1] and can be found in up to 80% of subjects.[2]
While several kinds of passive data are available for estimation of sleep duration, we focus our attention to the use of accelerometer data
We investigated the ability of smartphone data to predict Pittsburgh Sleep Questionnaire Inventory (PSQI) scores

Summary

Introduction

Sleep abnormalities are considered an important feature of schizophrenia[1] and can be found in up to 80% of subjects.[2] These sleep disturbances are of clinical importance given their relationship to symptom severity,[3] psychotic relapse,[4] premature mortality,[5] and even suicide.[6] Sleep disturbances may represent an early marker of psychosis,[7] and abnormal sleep architecture may serve as an intermediate phenotype of schizophrenia.[8] Despite the clinical and research importance of sleep in schizophrenia, sleep monitoring remains a challenge. Actigraphy is the non-invasive monitoring of subjects, often via wearable sensors. Actigraphy has a rich history in psychiatric research,[12] and it offers a partial solution to sleep monitoring

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