Dynamics of Self-Reported Symptoms Around Bipolar Disorder Relapse

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388131" target="_blank" >RIV/68407700:21230/25:00388131 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Dynamics of Self-Reported Symptoms Around Bipolar Disorder Relapse

Original language description

Introduction: Early detection of bipolar disorder (BD) relapses is crucial for better outcomes. We aimed to explore the dynamics of individual symptoms captured by mobile app self-assessments around episode onset and their potential for early relapse detection. Method: We analyzed data from the longitudinal observational study of 229 patients with BD, monitored on average for 1.7 (SD = 1.1) years. We captured 304 clinician-observed episodes (251 using monthly scales with thresholds YMRS > 12 or MADRS >= 15; 53 identified by the need for psychiatric hospitalization). We further analyzed weekly mobile self-assessment ASERT scale (previously validated against MADRS/YMRS). We explored the dynamics of ASERT scale individual symptoms (4 depressive, 4 manic) 8 weeks before each clinician-observed episode onset. Symptom deviations from individual long-term averages were assessed using non-parametric tests using step-down multiple comparisons corrections, with α = 0.01. Results: Significant deviations in self-observed symptoms were detected 2 weeks earlier for unusual optimism and accelerated thinking and 1 week earlier for decreased need for sleep and excess of energy, before the clinician-observed onset of manic episodes. Significant deviations in self-observed symptoms were detected 3 weeks earlier for loss of energy, and 2 weeks earlier for sadness, hypohedonia and pessimism, before the clinician observed onset of depressive episodes. Conclusion: We described the dynamics of the individual symptoms of the episodes of BD. Our findings suggest that digital mobile self-assessments could enable early relapse detection. While the frequency of self/clinician-observed symptoms limits interpretation, it also highlights the potential for self-assessments within the constraints of human healthcare resources.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/EH22_008%2F0004643" target="_blank" >EH22_008/0004643: Brain dynamics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů