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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
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Cortisol ; Machine Learning ; Movement ; Posture ; Stress ; Tsst
ISSN (print) / ISBN
0306-4530
e-ISSN
1873-3360
Journal
Psychoneuroendocrinology
Quellenangaben
Volume: 179,
Article Number: 107528
Publisher
Elsevier
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)