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Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood
A. Ortiz, R. Halabi, M. Alda, A. DeShaw, MI. Husain, A. Nunes, C. O'Donovan, R. Patterson, BH. Mulsant, A. Hintze
Status neindexováno Jazyk angličtina Země Německo
Typ dokumentu časopisecké články
Grantová podpora
R21 MH123849
NIMH NIH HHS - United States
NU23-04-00534
Ministerstvo Zdravotnictví Ceské Republiky
02010PJT-450770-BSB-CEAH-188794
CIHR - Canada
1R21MH123849-01A1
NIH HHS - United States
NLK
BioMedCentral Open Access
od 2013
Directory of Open Access Journals
od 2013
Free Medical Journals
od 2013
PubMed Central
od 2013
Europe PubMed Central
od 2013
ProQuest Central
od 2013-04-01
Open Access Digital Library
od 2012-01-01
Open Access Digital Library
od 2013-01-01
Open Access Digital Library
od 2013-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2013
Springer Journals Complete - Open Access
od 2013-12-01
Springer Nature OA/Free Journals
od 2013-12-01
- Publikační typ
- časopisecké články MeSH
Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD2). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions.
Campbell Family Research Institute Centre for Addiction and Mental Health Toronto ON Canada
Department of MicroData Analytics Dalarna University Dalarna Sweden
Department of Psychiatry Dalhousie University Halifax NS Canada
Department of Psychiatry Temerty Faculty of Medicine University of Toronto Toronto ON Canada
Citace poskytuje Crossref.org
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- $a Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD2). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions.
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