Cited by 103Mental Health Research TopicsEcosystem dynamics and resilienceBipolar Disorder and Treatment

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Early Warning Signals Based on Momentary Affect Dynamics can Expose Nearby Transitions in Depression: A Confirmatory Single-Subject Time-Series Study

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Wichers, Marieke, Smit, Arnout C., Snippe, Evelien·2020·Journal for Person-Oriented Research

DOI 10.17505/jpor.2020.22042PMC PMC7842626

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Abstract

Background: In complex systems early warning signals such as rising autocorrelation, variance and network connectivity are hypothesized to anticipate relevant shifts in a system. For direct evidence hereof in depression, designs are needed in which early warning signals and symptom transitions are prospectively assessed within an individual. Therefore, this study aimed to detect personalized early warning signals preceding the occurrence of a major symptom transition. Methods: Six single- subject time-series studies were conducted, collecting frequent observations of momentary affective states during a time-period when participants were at increased risk of a symptom transition. Momentary affect states were reported three times a day over three to six months (95-183 days). Depressive symptoms were measured weekly using the Symptom CheckList-90. Presence of sudden symptom transitions was assessed using change point analysis. Early warning signals were analysed using moving window techniques. Results: As change point analysis revealed a significant and sudden symptom transition in one participant in the studied period, early warning signals were examined in this person. Autocorrelation (r=0·51; p<2.2e-16), and variance (r=0·53; p<2.2e-16) in ‘feeling down’, and network connectivity (r=0·42; p<2.2e-16) significantly increased a month before this transition occurred. These early warnings also preceded the rise in absolute levels of ‘feeling down’ and the participant’s personal indication of risk for transition. Conclusions: This study replicated the findings of a previous study and confirmed the presence of rising early warning signals a month before the symptom transition occurred. Results show the potential of early warning signals to improve personalized risk assessment in the field of psychiatry.

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Bos, F. M., Schreuder, M. J., George, S. V., Doornbos, B., Bruggeman, R., van der Krieke, L., Haarman, B. C. M., Wichers, M., & Snippe, E. (2022) · Figshare · doi:10.6084/m9.figshare.19568752
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Authors: Wichers, M., Smit, A. C., & Snippe, E.
Affiliations: University Medical Center Groningen (NL)
Corresponding author: Wichers, Marieke
ORCID: Wichers, Marieke
Smit, Arnout C.
Snippe, Evelien
Venue: Journal for Person-Oriented Research
Publisher: Journal for Person-Oriented Research
Published: 2020-09-10
Volume: 6
Issue: 1
Pages: 1-15
ISSN: 2002-0244 (ISSN-L)
2003-0177
DOI: 10.17505/jpor.2020.22042
PMC: PMC7842626
OpenAlex: W3085042055
Cited by: 103
References: 30
Citations by year: 2026: 5 · 2025: 11 · 2024: 21 · 2023: 23 · 2022: 23 · 2021: 18 · 2020: 2
FWCI: 15.90
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by
Locations: Journal for Person-Oriented Research (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Funders: European Commission (HORIZON2020)
Topics: Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Physical Sciences › Environmental Science › Global and Planetary Change › Ecosystem dynamics and resilience
Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
Keywords: early warning indicators, depression, momentary affect, emotional dynamics, autocorrelation, variance, network connectivity, change-point analysis, individual risk assessment

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Early Warning Signals Based on Momentary Affect Dynamics can Expose Nearby Transitions in Depression: A Confirmatory Single-Subject Time-Series Study

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