A Digital Educational Intervention With Wearable Activity Trackers to Support Health Behaviors Among Childhood Cancer Survivors: Pilot Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors are at increased risk of cardiometabolic complications that are exacerbated by poor health behaviors. Critically, many survivors do not meet physical activity guidelines. OBJECTIVE: The primary aim was to evaluate the feasibility and acceptability of iBounce, a digital health intervention for educating and engaging survivors in physical activity. Our secondary aims were to assess the change in survivors' physical activity levels and behaviors, aerobic fitness, and health-related quality of life (HRQoL) after participating in the iBounce program. METHODS: We recruited survivors aged 8 to 13 years who were ≥12 months post cancer treatment completion. The app-based program involved 10 educational modules, goal setting, and home-based physical activities monitored using an activity tracker. We assessed objective physical activity levels and behaviors using cluster analysis, aerobic fitness, and HRQoL at baseline and after the intervention (week 12). Parents were trained to reassess aerobic fitness at home at follow-up (week 24). RESULTS: In total, 30 participants opted in, of whom 27 (90%) completed baseline assessments, and 23 (77%) commenced iBounce. Our opt-in rate was 59% (30/51), and most (19/23, 83%) of the survivors completed the intervention. More than half (13/23, 57%) of the survivors completed all 10 modules (median 10, IQR 4-10). We achieved a high retention rate (19/27, 70%) and activity tracker compliance (15/19, 79%), and there were no intervention-related adverse events. Survivors reported high satisfaction with iBounce (median enjoyment score 75%; ease-of-use score 86%), but lower satisfaction with the activity tracker (median enjoyment score 60%). Parents reported the program activities to be acceptable (median score 70%), and their overall satisfaction was 60%, potentially because of technological difficulties that resulted in the program becoming disjointed. We did not observe any significant changes in physical activity levels or HRQoL at week 12. Our subgroup analysis for changes in physical activity behaviors in participants (n=11) revealed five cluster groups: most active, active, moderately active, occasionally active, and least active. Of these 11 survivors, 3 (27%) moved to a more active cluster group, highlighting their engagement in more frequent and sustained bouts of moderate-to-vigorous physical activity; 6 (56%) stayed in the same cluster; and 2 (18%) moved to a less active cluster. The survivors' mean aerobic fitness percentiles increased after completing iBounce (change +17, 95% CI 1.7-32.1; P=.03) but not at follow-up (P=.39). CONCLUSIONS: We demonstrated iBounce to be feasible for delivery and acceptable among survivors, despite some technical difficulties. The distance-delivered format provides an opportunity to engage survivors in physical activity at home and may address barriers to care, particularly for regional or remote families. We will use these pilot findings to evaluate an updated version of iBounce. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000259842; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=ACTRN12621000259842.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".