Nocturnal heart rate variability in patients treated with cognitive–behavioral therapy for insomnia.
Bibliographic record
Abstract
OBJECTIVE: Insomnia and reduced heart rate variability (HRV) increase the risk of cardiovascular disease and its precursors; thus, it is important to evaluate whether treatment for insomnia provides cardiovascular safeguards. The present study aimed to evaluate potential cardiovascular benefits of cognitive behavioral therapy for insomnia (CBT-I). METHOD: The present study included 65 patients treated for chronic insomnia (M = 51.8 years, SD = 10.0; 66.2% female) at a university hospital. Patients received CBT-I over a 6-week period, and change scores from pre- to posttreatment derived from the Insomnia Severity Index, sleep diary, and polysomnography (PSG) were used as indices of sleep improvement. HRV variables (i.e., low frequency [LF], high frequency [HF], and the ratio of low to high frequency [LF:HF ratio]) were derived for Stage 2 (S2) and rapid-eye movement (REM) sleep at pre- and posttreatment. High HF (i.e., parasympathetic activity) and/or low LF:HF ratio (i.e., sympathovagal balance) were used as indices of HRV improvement. RESULTS: Following therapy, sleep improvements, particularly for sleep onset latency, were related with reduced HF in S2 (r = .30, p < .05) and in REM (r = .36, p < .01). A trend was also observed between reduced insomnia symptoms and increased HF in REM (r = -.21, p < .10). CONCLUSIONS: Findings suggest that contrary to expectations, sleep improvements following CBT-I were associated with reduced parasympathetic activation and increased sympathovagal balance. Although preliminary, these results raise the question as to whether insomnia treatment might play a role in physiological changes associated with cardiovascular anomalies. Future research is needed to examine the long-term impact of treatment as a preventative tool against insomnia-related morbidity. (PsycINFO Database Record
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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 | 0.001 | 0.000 |
| 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 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".