A Self-Administered Sleep Intervention for Patients With Cancer Experiencing Insomnia
Bibliographic record
Abstract
BACKGROUND: Sleep-wake disturbances are experienced by as many as 75% of patients with cancer and are associated with poor symptom management, lower functionality, and decreased quality of life. Although promising sleep interventions exist, they require extensive resources and time. OBJECTIVES: The objectives of this study were to develop a brief, self-administered sleep intervention and to evaluate the feasibility and potential efficacy of its implementation with adult patients with cancer who were about to receive, were receiving, or had received radiation therapy in an ambulatory cancer care setting. METHODS: Pre- and postintervention surveys and qualitative interviews were conducted with patients with cancer experiencing insomnia (N = 28) and receiving radiation treatment within the past six months. Patients received instruction on breathing, visualization, and intonation. Adherence and sleep quality were primary study outcomes. Analyses included descriptive statistics and repeated measure regression analysis. Thematic analysis was conducted on qualitative data. FINDINGS: Adherence to the sleep intervention was high (75%), and significant improvement was found in global sleep quality (p < 0.0001) regardless of level of adherence. Sleep onset latency (p = 0.0005), sleep duration (p = 0.0016), and sleep quality (p < 0.0001) were significantly improved. Age was significantly correlated with sleep quality (p = 0.0094), with older participants reporting greater benefit from the intervention. Participants reported that the intervention was easy to learn and implement and that it "calmed the mind."
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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) | 0.001 | 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".