Temporal dynamics of heart rate variability reveal stressor-specific autonomic patterns: a multi-stressor study

Introduction Laboratory stress paradigms provide controlled conditions to study autonomic stress responses and their relevance to health and disease. The autonomic nervous system adapts dynamically during stress, yet traditional heart rate variability (HRV) averages overlook rapid allodynamic adjustments. Few studies have characterized within-task HRV dynamics across multiple stressors or considered individual stress-related traits. This study examined fine-grained HRV responses to mental, noise, and pain stressors and assessed how perceived chronic stress and noise sensitivity contribute to these autonomic patterns. Method Thirty healthy adults completed mental arithmetic, noise exposure, and cold pressor tasks while electrocardiogram (ECG) was recorded. HRV (MeanNN, RMSSD) was computed in 30-s windows, and perceived stress and noise sensitivity were assessed with validated questionnaires. Results Mental stress elicited the largest global HRV reductions, pain affected only MeanNN, and noise produced no global changes. However, time-resolved analyses revealed distinct HRV dynamics across all stressors that were not captured by global averages. Chronic stress and noise sensitivity had little effects on global HRV but significantly moderated fine-grained responses to mental and noise stress during anticipation, stress, or recovery phases. Discussion Autonomic stress reactivity is best understood through its temporal dynamics, as global HRV metrics obscure stressor-specific responses and the influence of individual vulnerability. Temporal analyses revealed distinct autonomic signatures for mental, noise, and pain stressors, indicating that they may engage partially distinct autonomic pathways and should not be treated as interchangeable experimental proxies of “stress.” The modulation by chronic stress and noise sensitivity highlights the importance of integrating trait-level factors when modeling autonomic adaptation. In line with the allodynamic framework, these findings suggest that high-resolution temporal analyses may provide a relevant approach for studying autonomic stress responses and their links to environmental stress exposure and health.