Towards clinically actionable digital phenotyping targets in schizophrenia

Philip Henson,Matcheri Keshavan,Ian Barnett,John Torous

doi:10.1038/s41537-020-0100-1

Philip Henson, Matcheri Keshavan + Show 2 more

Open Access

https://doi.org/10.1038/s41537-020-0100-1

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Abstract

Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, “Circadian Routine” and “Weekend Day Routine,” and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from −0.23 to −0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.

Highlights

Schizophrenia affects nearly 20 million people worldwide and presents substantial health, social, and economic burdens due to the presence of comorbidities, long duration of illness, everpresent risk of relapse, and excess early mortality[1,2,3]
A focus on the social rhythm features “Circadian Routine” and “Weekend Day Routine” in 3c and 3d shows an area of statistically significant correlation for both SZ and healthy controls (HC) with Spearman ρ values ranging from −0.23 to −0.30 in SZ (p < 0.001) and from 0.20 to 0.44 in HC (p < 0.05)
Smartphone digital phenotyping is able to detect changes in social rhythms that separate those with schizophrenia from controls

Summary

Introduction

Schizophrenia affects nearly 20 million people worldwide and presents substantial health, social, and economic burdens due to the presence of comorbidities, long duration of illness, everpresent risk of relapse, and excess early mortality[1,2,3]. Sensors on phones are capable of collecting, storing, and processing vast amounts of health data, with new tools constantly emerging to help track, monitor, and augment clinical interventions These innovations bring with them the opportunity for real-time assessment of behavior and cognition, which are important among individuals with schizophrenia where the risk of relapse is ever-present yet remains challenging to identify[6]. Using such data to inform just-intime adaptive interventions for mental health[7] may increase access to evidence-based care, to date, research and products have focused primarily on mood disorders[8]. The same principles of real-time active monitoring of symptoms conducted through ecological momentary assessment and passive monitoring via automatically collected phone data (e.g. daily distance traveled) may offer potential for similar adaptive interventions for psychotic spectrum illnesses like schizophrenia

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