PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Abel, L.* ; Richer, R.* ; Burkhardt, F.* ; Kurz, M.* ; Ringgold, V.* ; Schindler-Gmelch, L.* ; Eskofier, B.M. ; Rohleder, N.*

Body movements as biomarkers: Machine Learning-based prediction of HPA axis reactivity to stress.

Psychoneuroendocrinology 179:107528 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Body movements and posture provide valuable insights into stress responses, yet their relationship with endocrine biomarkers of the stress response remains underexplored. This study investigates whether movement patterns during the Trier Social Stress Test (TSST) and the friendly-TSST (f-TSST) can predict cortisol reactivity. Using motion capturing, movement data from 41 participants were analyzed alongside salivary cortisol responses. Machine learning models achieved a classification accuracy of 65.2 % for distinguishing cortisol responders from non-responders and a regression mean absolute error of 2.94 nmol/l for predicting cortisol increase. Findings suggest that movement dynamics can serve as proxies of endocrine stress responses, contributing to objective, non-invasive stress assessment methods.
Impact Factor
Scopus SNIP
Altmetric
3.600
0.000
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Cortisol ; Machine Learning ; Movement ; Posture ; Stress ; Tsst; Psychosocial Stress; Salivary Cortisol; Responses; Law; Perception; Expression; Shame; Life
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 0306-4530
e-ISSN 1873-3360
Journal Psychoneuroendocrinology
Quellenangaben Volume: 179, Issue: , Pages: , Article Number: 107528 Supplement: ,
Publisher Elsevier
Publishing Place The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540008-001
Grants Deutsche Forschungsgemeinschaft (DFG, German Research foundation)
DOI 10.1016/j.psyneuen.2025.107528
WOS ID 001531305400001
Scopus ID 105009325184
PubMed ID 40592116
Erfassungsdatum 2025-07-03
[➜Report correction]