Dynamic modeling of experience sampling methodology data reveals large heterogeneity in biopsychosocial factors associated with persistent fatigue in young people living with a chronic condition

Abstract

Objective: To evaluate associations between self-reported biopsychosocial factors and persistent fatigue with dynamic single-case networks. Methods: 31 persistently fatigued adolescents and young adults with various chronic conditions (aged 12 to 29 years) completed 28 days of Experience Sampling Methodology (ESM) with five prompts per day. ESM surveys consisted of eight generic and up to seven personalized biopsychosocial factors. Residual Dynamic Structural Equation Modeling (RDSEM) was used to analyze the data and derive dynamic single-case networks, controlling for circadian cycle effects, weekend effects, and low-frequency trends. Networks included contemporaneous and cross-lagged associations between biopsychosocial factors and fatigue. Network associations were selected for evaluation if both significant (α < 0.025) and relevant (β ≥ 0.20). Results: Participants chose 42 different biopsychosocial factors as personalized ESM items. In total, 154 fatigue associations with biopsychosocial factors were found. Most associations were contemporaneous (67.5%). Between chronic condition groups, no significant differences were observed in the associations. There were large inter-individual differences in which biopsychosocial factors were associated with fatigue. Contemporaneous and cross-lagged associations with fatigue varied widely in direction and strength. Conclusions: The heterogeneity found in biopsychosocial factors associated with fatigue underlines that persistent fatigue stems from a complex interplay between biopsychosocial factors. The present findings support the need for personalized treatment of persistent fatigue. Discussing the dynamic networks with the participant can be a promising step towards tailored treatment. Trial registration: No. NL8789 (http://www.trialregister.nl)