Predicting physical activity among cancer survivors: Meta-analytic path modeling of longitudinal studies.
Bibliographic record
Abstract
OBJECTIVE: We conducted meta-analyses and meta-analytic structural equation modeling of longitudinal studies among cancer survivors to (a) quantify associations between psychosocial predictors and physical activity, (b) test how psychosocial predictors combine to influence physical activity, and (c) identify study, demographic, and clinical characteristics that moderate associations. METHOD: Eligible studies used a longitudinal, observational design, included a sample of cancer survivors, and measured both a psychosocial predictor at baseline and physical activity at a later time-point. Of 2,431 records located through computerized searches, 25 independent tests (N = 5,897) met the inclusion criteria for the review. Random effects meta-analyses and meta-analytic structural equation modeling were conducted. RESULTS: Eight psychosocial predictors of physical activity were identified. Self-efficacy (r+ = 0.26) and intentions (r+ = 0.33) were the strongest predictors in bivariate analyses. The structural equation models included attitudes, injunctive norms, self-efficacy, intentions, and physical activity (k = 22, N = 4,385). The model with the best fit, χ2(2) = 0.11, p = .95, root mean square error of approximation = .00, comparative fit index = 1.00, Tucker-Lewis index = 1.00, indicated that all specified paths were significant. Intentions were the strongest predictor of physical activity (β = 0.27, p < .001), and attitudes and self-efficacy were strong predictors of intentions (both βs = 0.29, ps < .001). Few significant moderators were observed. CONCLUSION: This review indicates that self-efficacy and intentions are direct predictors of physical activity in cancer survivors. Further, attitudes and norms predict physical activity through intentions. Findings inform intervention development to increase physical activity engagement among cancer survivors. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.054 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.055 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".