Feasibility, Barriers, and Facilitators of Long-Term Physical Activity Tracking During Treatment: Interview Study Among Childhood Cancer Patients
Bibliographic record
Abstract
Background: Children with cancer are at risk of reduced physical activity. Gaining insight into physical activity using smartwatches could improve understanding of individual potential during treatment, support early recognition of aberrant physical activity, and enable tailored support. Objective: This study aimed to explore the feasibility, barriers, facilitators, and considerations of long-term physical activity tracking using a smartwatch during childhood cancer treatment. Methods: In this prospective study, 30 children (age 8-18 years) under active cancer treatment were included in 2 phases. During phase 1, 15 children wore a smartwatch daily for 12 consecutive weeks, and in-depth interviews were conducted to identify principal considerations used to optimize wearability and the methods for phase 2. In phase 2, another 15 children wore the smartwatch, and semistructured interviews were conducted at weeks 1, 3, 6, and 12. These interviews were thematically analyzed to identify barriers and facilitators. An iterative process of alternating data collection and analysis allowed for ongoing method refinement and deepening thematic analysis during the study period. Results: Key considerations for improvement identified in phase 1 led to refinements in phase 2, including enhanced engagement, regular prompts, customized plans, personalized setup, and improved aesthetics and comfort. The interviews conducted during phase 2 identified barriers and facilitators. The 4 most prominent themes were burden and resilience, motivational drivers and perception, insight and evaluation, and user experience and functionality. Feasibility was influenced by the child's physical state and perceived burden. Motivation, perceived value, and expectations played crucial roles in sustaining adherence, while also the balance between positive reinforcement and potential confrontation affected long-term use. User experience, including attractiveness, comfort, and usability, impacted acceptance. Conclusions: Real-time and long-term physical activity tracking using a smartwatch in children during cancer treatment was not feasible in our cohort. A personalized approach, incorporating individual preferences and physical condition, is essential to support adherence.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".