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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".