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

Frontiers in Psychiatry

Psychiatry

Ilias Maglogiannis¹  Thomas Karantinos²  Asimakis Mantas²  Emmanouil Kalisperakis³  Vasiliki Garyfalli³  Leonidas Mantonakis³  Marina Lazaridi³  Nikolaos Smyrnis⁴  Theodoros Mougiakos⁵  Panagiotis P. Filntisis⁶  Athanasia Zlatintsi⁶  Panayotis Tsanakas⁶  Niki Efthymiou⁶  Petros Maragos⁶ 

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

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

DOI  :  10.3389/fpsyt.2023.1024965

 received in 2022-08-22, accepted in 2023-02-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionMonitoring 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.MethodsWe 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.ResultsOur 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.DiscussionOur 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.

【 授权许可】

Unknown   
Copyright © 2023 Kalisperakis, Karantinos, Lazaridi, Garyfalli, Filntisis, Zlatintsi, Efthymiou, Mantas, Mantonakis, Mougiakos, Maglogiannis, Tsanakas, Maragos and Smyrnis.