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

Anouk Vroegindeweij*, Lisa Levelt, Jan Houtveen, Elise M van de Putte, Nico M Wulffraat, Joost F Swart, Sanne L Nijhof

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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)

Original languageEnglish
Article number111195
Number of pages7
JournalJournal of Psychosomatic Research
Volume167
DOIs
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
The QFS-study was supported by the Netherlands Organization for Health Research and Development (ID ZonMW 50–53000–98-566 ) and received additional funding through crowdfunding. The funding parties had no role in the study design or data collection, analyses, and interpretation.

Funding Information:
The QFS-study was funded by the Netherlands Organization for Health Research and Development (ID ZonMW 50-53000-98-566 ) and received additional funding through crowdfunding, for which we thank all contributors. We would like to thank all participants who took part of the QFS-study, Sterre van Halen for assisting the ESM data collection, and Q-support, C-support, Q-uestion, and health care providers for informing potential participants of the QFS-study. We thank Nicole van Woerden for the language check in this article.

Publisher Copyright:
© 2023 The Authors

Keywords

  • Adolescents
  • Biopsychosocial model
  • Chronic illness
  • Dynamic structural equation modeling
  • Experiencing sampling methodology
  • Persistent fatigue

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this