An investigation of the potential clinical utility of critical slowing down as an early warning sign for recurrence of depression.
Complex dynamic systems
Critical slowing down
Depression
Early warning signs
Ecological momentary assessment
Relapse
Journal
Journal of behavior therapy and experimental psychiatry
ISSN: 1873-7943
Titre abrégé: J Behav Ther Exp Psychiatry
Pays: Netherlands
ID NLM: 0245075
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
06
09
2022
revised:
06
10
2023
accepted:
25
10
2023
medline:
4
12
2023
pubmed:
13
11
2023
entrez:
13
11
2023
Statut:
ppublish
Résumé
Much of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse. In the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis. We found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement. Limitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms. CSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
Much of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse.
METHODS
METHODS
In the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis.
RESULTS
RESULTS
We found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement.
LIMITATIONS
CONCLUSIONS
Limitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms.
CONCLUSIONS
CONCLUSIONS
CSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.
Identifiants
pubmed: 37956479
pii: S0005-7916(23)00089-7
doi: 10.1016/j.jbtep.2023.101922
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101922Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest None.