Heart Rate Variability, Sleep Quality, and Depression in the Context of Chronic Stress
Bibliographic record
Abstract
BACKGROUND: Disrupted sleep quality is one of the proposed mechanisms through which chronic stress may lead to depression. However, there exist significant individual differences in sleep reactivity, which is the extent to which one experiences sleep disturbances in response to stress. PURPOSE: The aim of the current study was to investigate whether low high-frequency heart rate variability (HRV), as a psychophysiological marker of poor emotional and physiological arousal regulation, predicts stress-related sleep disturbances associated with greater risk of depression symptoms. METHODS: Using a chronic caregiving stress model, 125 mothers of adolescents with developmental disorders and 97 mothers of typically developing adolescents had their resting HRV and HRV reactivity recorded and completed a measure of depressive symptoms, as well as a 7 day sleep diary to assess their sleep quality. A moderated mediation model tested whether sleep quality mediated the association between chronic stress exposure and depressive symptoms and whether HRV moderated this mediation. RESULTS: After controlling for participant age, body mass index, ethnicity, socioeconomic status, and employment status, poor sleep quality mediated the association between chronic stress and depressive symptoms. Resting HRV moderated this indirect effect such that individuals with lower HRV were more likely to report poorer sleep quality in the context of chronic stressor exposure, which, in turn, was related to greater depressive symptoms. CONCLUSIONS: Lower HRV, a potential biomarker of increased sleep reactivity to stress, is associated with greater vulnerability to stress-related sleep disturbances, which, in turn, increases the risk for elevated depressive symptoms in response to chronic stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".