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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)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold (Paid Option)
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Cortisol ; Machine Learning ; Movement ; Posture ; Stress ; Tsst
ISSN (print) / ISBN 0306-4530
e-ISSN 1873-3360
Zeitschrift Psychoneuroendocrinology
Quellenangaben Band: 179, Heft: , Seiten: , Artikelnummer: 107528 Supplement: ,
Verlag Elsevier
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)