High-Frequency Heart Rate Variability Reactivity and Trait Worry Interact to Predict the Development of Sleep Disturbances in Response to a Naturalistic Stressor
Bibliographic record
Abstract
BACKGROUND: High-frequency heart rate variability (HF-HRV) reactivity was proposed as a vulnerability factor for stress-induced sleep disturbances. Its effect may be amplified among individuals with high trait worry or sleep reactivity. PURPOSE: This study evaluated whether HF-HRV reactivity to a worry induction, sleep reactivity, and trait worry predict increases in sleep disturbances in response to academic stress, a naturalistic stressor. METHOD: A longitudinal study following 102 undergraduate students during an academic semester with well-defined periods of lower and higher academic stress was conducted. HF-HRV reactivity to a worry induction, trait worry using the Penn State Worry Questionnaire, and sleep reactivity using the Ford Insomnia Stress Reactivity Test were measured during the low stress period. Sleep disturbances using the Pittsburgh Sleep Quality Index were assessed twice during the lower stress period and three times during the higher stress period. RESULTS: Greater reductions in HF-HRV in response to the worry induction predicted increases in sleep disturbances from the lower to the higher academic stress period. Trait worry moderated this association: individuals with both higher trait worry and greater HF-HRV reactivity to worry had larger increases in stress-related sleep disturbances over time, compared to participants with lower trait worry and HF-HRV reactivity. A similar, but marginally significant effect was found for sleep reactivity. CONCLUSION: This study supports the role of HF-HRV reactivity as a vulnerability factor for stress-induced sleep disturbances. The combination of high trait worry and high HF-HRV reactivity to worry might identify a subgroup of individuals most vulnerable to stress-related sleep disturbances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".