Patient-Reported Perception of Exercise and Receptiveness to Mobile Technology in Cancer Survivors Living in Rural and Remote Areas
Bibliographic record
Abstract
PURPOSE: Cancer survivors in rural and underserved areas face barriers such as limited access to oncology exercise programs and limited facilities, contributing to health inequities in cancer survivorship. This study explored cancer survivors' thoughts on exercise and mobile technology for exercising with a mobile application (app) during and after treatment in rural and remote areas. METHODS: Three online focus groups were conducted in February 2024 using semi-structured interviews with 12 open-ended questions. Eligible participants were adult cancer survivors or caregivers living in medically underserved areas, English-speaking, consented to being audiotaped, and attended one 60-min group interview. The discussions were transcribed verbatim and analyzed via a content analysis approach with consensus. RESULTS: Fifteen participants attended from four States. None of the participants were advised to exercise; availability of exercise resources depended on geographic location and a cancer-specific exercise app was desired. They understood the benefits of exercise after diagnosis but expressed a need for more guidance during treatment. Geographic location shaped their activities, with most engaging in daily physical tasks rather than structured exercise. Most participants were receptive to using an exercise app to manage fatigue. Suggested key features to exercise with an app included live trainers, exercise checklists, visual benchmarks, and programs tailored to different fitness levels. CONCLUSIONS: These results emphasize the need for personalized resources, guidance, and on-demand accessibility to an exercise oncology app. A cancer-specific exercise mobile app will mitigate health inequities for cancer survivors residing in rural and remote areas.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".