0884 User Needs and Preferences for a Smartphone App to Treat Insomnia in Cancer Survivors
Bibliographic record
Abstract
Abstract Introduction Cancer patients with insomnia have unique needs and require tailored healthcare interventions. iCANSleep is a mobile health intervention aimed at providing evidence-based insomnia treatment via a smartphone app. As part of a user-centered development process, virtual needs assessment interviews were conducted with cancer survivors who report insomnia to determine the needs and preferences of this patient group for insomnia treatment. Methods 22 cancer survivors from 5 Canadian provinces completed an online survey and participated in a needs assessment interview. Surveys were analyzed using descriptive statistics, while interviews were transcribed and analyzed using thematic analysis. Results 81.8% (18/22) of cancer survivors report that their insomnia started or was exacerbated during the time of their cancer diagnosis and treatment. Of the participants who discussed insomnia with a medical provider, hypnotic medications were prescribed in 64.3% (9/14) of cases. With some exceptions, participants reported strong familiarity with smartphone technology, had experience using apps for health management, and found an insomnia treatment app highly acceptable. Participant-identified facilitators of app use included ease of access, lack of cost, anonymity, empirical basis, and recommendation by local care teams. Smartphone ownership, cumbersome user interface, and limited access to internet were raised as potential challenges of implementation. Additional functionality identified by participants included options to link consumer wearable devices, additional modules to address comorbid conditions such as pain and anxiety, and an anonymous peer support forum. Conclusion Mobile apps hold promise as an avenue for the effective delivery of insomnia treatment; however, treatments must be evidence-based, and apps must be designed for maximum ease of use. Findings provide novel insight into how to best promote uptake and sustained use of mobile health interventions in cancer survivors and will be used to develop functional guidelines. Support (if any) Samlau Kutana is a trainee in the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute, with funds provided by the Canadian Cancer Society’s JD Irving, Limited – Excellence in Cancer Research Fund. Dr. Sheila Garland is supported by a Canadian Cancer Society Emerging Scholar Award (Survivorship) (grant #707146).
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".