Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Emmanouil Kalisperakis; Thomas Karantinos; Marina Lazaridi; Vasiliki Garyfalli; Panagiotis P Filntisis; Athanasia Zlatintsi; Niki Efthymiou; Asimakis Mantas; Leonidas Mantonakis; Theodoros Mougiakos; Ilias Maglogiannis; Panayotis Tsanakas; Petros Maragos; Nikolaos Smyrnis

doi:10.3389/fpsyt.2023.1024965

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Front Psychiatry. 2023 Mar 13:14:1024965. doi: 10.3389/fpsyt.2023.1024965. eCollection 2023.

Authors

Emmanouil Kalisperakis^{1

2}, Thomas Karantinos¹, Marina Lazaridi^{1

2}, Vasiliki Garyfalli^{1

2}, Panagiotis P Filntisis³, Athanasia Zlatintsi³, Niki Efthymiou³, Asimakis Mantas¹, Leonidas Mantonakis^{1

2}, Theodoros Mougiakos⁴, Ilias Maglogiannis⁵, Panayotis Tsanakas³, Petros Maragos³, Nikolaos Smyrnis^{1

6}

Affiliations

¹ Laboratory of Cognitive Neuroscience and Sensorimotor Control, University Mental Health, Neurosciences and Precision Medicine Research Institute "COSTAS STEFANIS", Athens, Greece.
² 1st Department of Psychiatry, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
³ School of Electrical and Computer Engineering (ECE), National Technical University of Athens, Athens, Greece.
⁴ Psychiatric Clinic, 414 Military Hospital of Athens, Athens, Greece.
⁵ Department of Digital Systems, University of Piraeus, Piraeus, Greece.
⁶ 2nd Department of Psychiatry, Medical School, University General Hospital "ATTIKON", National and Kapodistrian University of Athens, Athens, Greece.

Abstract

Introduction: Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.

Methods: We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly.

Results: Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose.

Discussion: Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

Keywords: bipolar disorder; heart rate; heart rate variability; motion activity; relapse prevention; schizophrenia; sleep/wake ratio.

Grants and funding

This research has been financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project acronym: e-Prevention, code: T1EDK-02890/MIS: 5032797).