A pilot study of device-assessed physical activity and ecological momentary assessment among adolescent and young adult survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Adolescent and young adult survivors of childhood cancer (AYA) are at risk for treatment-related late effects (eg, heart and lung problems) which may be mitigated by physical activity (PA). To design effective, tailored PA interventions for this population, predictors and benefits of PA behavior need to be measured in real-time. PURPOSE: To examine the feasibility and acceptability of ecological momentary assessment (EMA) combined with accelerometry and explore the dynamic associations between PA and real-time physical and psychosocial factors among AYA. METHODS: AYA (N = 20, mean age = 18.9 years) recently off cancer treatment participated in a 2-week intensive monitoring protocol in which they completed up to 4 EMA surveys/day assessing current mood, pain, fatigue, arousal, PA intentions and motivation, and social-environmental context, while PA levels were passively monitored using a wrist-worn ActiGraph GT9X accelerometer. Acceptability was measured via self-report. RESULTS: EMA and accelerometry were feasible and acceptable (≥70% compliance and study endorsement) for AYA. Multilevel models showed that AYA engaged in more PA when they were away from home, with others, in a better mood, less fatigued, more energetic, and more motivated than their own average levels. Further, when AYA engaged in more PA than their usual levels in the hour before completing an EMA survey, they subsequently reported less fatigue, less pain, more energy, and a more positive mood. CONCLUSIONS: EMA and accelerometry are acceptable and feasible among AYA survivors of childhood cancer. This methodology can be utilized for understanding the real-time barriers, facilitators, and benefits of PA behaviors in this at-risk population to design effective, dynamic PA interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".