Implementation Intentions for Physical Activity Behavior in Older Adult Women: An Examination of Executive Function as a Moderator of Treatment Effects
Bibliographic record
Abstract
BACKGROUND: Implementation intentions are effective for enhancing physical activity, but it is unknown how well these effects extend to older adults and/or are modified by cognitive variables. PURPOSE: Our objective is to examine (1) the efficacy of an implementation intentions intervention for physical activity in older adult women and (2) to examine the moderating effects of executive function. METHODS: Participants (N = 75, M age = 73.72) completed measures of executive function and were randomly assigned to weekly implementation intentions for physical activity (experimental condition), implementation intentions for an unrelated behavior (control condition), or no treatment. Baseline activity was measured by accelerometer and self-report; follow-up activity was measured by weekly self-report. RESULTS: Findings indicated a significant treatment effect for the experimental condition and a treatment by executive function interaction. Specifically, participants with relatively stronger executive function benefited most from the experimental intervention. CONCLUSIONS: Implementation intentions are effective for enhancing physical activity among older adult women, and the effects may be especially pronounced for those with relatively stronger executive function.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".