Needs and preferences of cancer survivors in an insomnia treatment smartphone app
Bibliographic record
Abstract
Background: Insomnia affects cancer survivors at a higher rate than the general population, impacting quality of life and physical health. Cognitive behavioural therapy for insomnia (CBT-I), the first-line recommended treatment for insomnia, is effective when delivered digitally through a smartphone app. However, no digital CBT-I program has been developed that addresses the unique needs of cancer survivors. The objective of this study was to understand the experiences of cancer survivors with regards to insomnia and insomnia treatment, while assessing their needs in an insomnia treatment smartphone app. Methods: Cancer survivors meeting criteria for current or past insomnia responded to a digital questionnaire assessing demographic information, and cancer diagnosis and treatment history. Survivors then participated in one-on-one semi-structured interviews about their experiences with insomnia, smartphone use, and needs in an insomnia treatment app. Interviews were recorded and transcribed, and recurrent themes were identified using a process of thematic analysis. Results: Twenty interviews were analyzed. All participants reported incidence or worsening of insomnia following cancer diagnosis or treatment. Smartphone CBT-I was considered acceptable by cancer survivors. Primary needs identified by cancer survivors for an insomnia treatment app included user friendliness, privacy and data security, and evidential basis. Conclusion: Our results suggest that smartphone CBT-I is acceptable to cancer survivors, holding potential to greatly increase access to evidence-based insomnia treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".