Group trajectory analysis helps to identify older cancer survivors who benefit from distance‐based lifestyle interventions
Bibliographic record
Abstract
BACKGROUND: The number of older cancer survivors is increasing as more adults survive to older ages. The objectives of this study were to examine trajectories of physical activity (PA) and physical function (PF) over a 2-year lifestyle counseling study and to identify characteristics of the trajectory groups. METHODS: This was a secondary analysis of Reach Out to Enhance Wellness, a randomized controlled trial of home-based lifestyle counseling. The 641 participants were older (≥65 years), overweight (body mass index [BMI], 25 to <40 kg/m(2)), long-term community-dwelling survivors (>5 years) of breast, prostate, and colorectal cancer from Canada, the United Kingdom, and the United States (21 states) who had been randomly assigned to an immediate intervention or a 12-month-wait-listed control arm. The main outcome measures were PA and PF trajectory group membership. RESULTS: Three PA groups and 5 PF trajectory groups were observed. The baseline BMI (P < .001) and self-efficacy for performing strength (P < .0001) and endurance exercises (P < .0002) were the strongest predictors of achieving the highest amount of PA and the most favorable functional trajectory over 2 years. Individuals with low baseline self-efficacy, no PA, and a Short Form 36 PF subscale score < 65 did not benefit from the intervention. CONCLUSIONS: This study identified characteristics of survivors who benefited from home-based interventions and suggested alternative approaches for survivors requiring more structured and intensive interventions to promote behavioral changes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".