Designing iCanFit: A Mobile-Enabled Web Application to Promote Physical Activity for Older Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Most older cancer survivors (OCS) do not engage in regular physical activity (PA) despite well-known health benefits. With the increased use of mobile technologies among older adults, mobile tools may be an effective method to deliver PA promotion programs for OCS. OBJECTIVE: To document the process of designing an OCS-friendly mobile-enabled Web application of PA promotion program. METHODS: Mixed methods encompassing group discussions, individual interviews, and brief surveys with community leaders, OCS, cancer care providers, and software professionals were used in this formative research. RESULTS: The varied stakeholders welcomed the idea of developing an online tool to promote PA in OCS. Our formative research revealed several major barriers to regular PA including limited access to senior-friendly PA resources, lack of motivation and social support, and insufficient knowledge and skills on building safe and appropriate workout plans. This feedback was incorporated into the development of iCanFit, a mobile-enabled Web application, designed specifically for OCS. The iCanFit online tools allow users to locate PA resources, set and track goals for PA, network with peer OCS in a secure online space, and receive practical and evidence-informed healthy tips. CONCLUSIONS: Our mixed-method formative research led to the design of iCanFit protocol to promote PA and well-being of OCS. The involvement of stakeholders is critical in the planning and design of the mobile application in order to enhance program relevance, appeal, and match with the needs of target users.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".