Predicting Response to Stepped-Care Cognitive Behavioral Therapy for Insomnia (CBT-I) Using Pre-Treatment Heart Rate Variability (HRV) in Cancer Patients
Bibliographic record
Abstract
Objective: This longitudinal study examined whether high frequency heart-rate variability (HF-HRV) and HF-HRV reactivity to stress moderates response to cognitive behavioural therapy for insomnia (CBT-I) within a stepped-care framework in cancer patients with comorbid insomnia. \nMethods: 177 participants (86.3% female; Mage=55.3, SD=10.4) were randomized to receive either stepped-care or standard CBT-I and were followed for 12 months following treatment. HRV measures were 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. \nResults: Resting HF-HRV significantly predicted pre-treatment sleep efficiency but not ISI score. No significant time x HF-HRV or CBT-I group x time x HF-HRV interactions were found, indicating that HF-HRV does not predict differential responses to the different CBT-I group. HRV reactivity was not cross-sectionally or longitudinally related to any outcome variables. In exploratory analyses, significant insomnia severity x time x HF-HRV interactions were observed, suggesting that HF-HRV may predict treatment responses differently based on initial insomnia severity. \nConclusion: Although resting HF-HRV was related to initial sleep efficiency, HF-HRV measures did not significantly predict response to either form of CBT-I. Resting HF-HRV may predict certain treatment outcomes when initial insomnia severity is considered, however these results are exploratory and of unclear clinical significance.
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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".