Predicting response to stepped-care cognitive behavioral therapy for insomnia using pre-treatment heart rate variability in cancer patients
Bibliographic record
Abstract
OBJECTIVE: This study examined whether high frequency heart-rate variability (HF-HRV) and HF-HRV reactivity to worry moderate response to cognitive behavioural therapy for insomnia (CBT-I) within both a standard and stepped-care framework among cancer patients with comorbid insomnia. Biomarkers such as HF-HRV may predict response to CBT-I, a finding which could potentially inform patient allocation to different treatment intensities within a stepped-care framework. METHODS: = 55.3, SD = 10.4) were randomized to receive either stepped-care or standard CBT-I. 145 participants had their HRV assessed at pre-treatment during a rest and worry period. Insomnia symptoms were assessed using the Insomnia Severity Index (ISI) and daily sleep diary across five timepoints from pre-treatment to a 12-month post-treatment follow-up. RESULTS: Resting HF-HRV was significantly associated with pre-treatment sleep efficiency and sleep onset latency but not ISI score. However, resting HF-HRV did not predict overall changes in insomnia across treatment and follow-up. Similarly, resting HF-HRV did not differentially predict changes in sleep diary parameters across standard or stepped-care groups. HRV reactivity was not related to any of the assessed outcome measures in both cross-sectional and longitudinal analyses. CONCLUSION: Although resting HF-HRV was related to initial daily sleep parameters, HF-HRV measures did not significantly predict longitudinal responses to CBT-I. These findings suggest that HF-HRV does not predict treatment responsiveness to CBT-I interventions of different intensity in cancer patients.
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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) | 0.000 | 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".