Can a Lifestyle Intervention Increase Active Transportation in Women Aged 55–70 years? Secondary Outcomes From a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Physical activity confers many health benefits to older adults, and adopting activity into daily life routines may lead to better uptake. The purpose of this study was to test the effect of a lifestyle intervention to increase daily physical activity in older women through utilitarian walking and use of public transportation. METHODS: In total, 25 inactive women with mean age (SD) of 64.1 (4.6) years participated in this pilot randomized controlled trial [intervention (n = 13) and control (n = 12)]. Seven-day travel diaries (trips per week) and the International Physical Activity Questionnaire (minutes per week) were collected at baseline, 3, and 6 months. RESULTS: At 3 months, intervention participants reported 9 walking trips per week and 643.5 minutes per week of active transportation, whereas control participants reported 4 walking trips per week and 49.5 minutes per week of active transportation. Adjusting for baseline values, there were significant group differences favoring Everyday Activity Supports You for walking trips per week [4.6 (0.5 to 9.4); P = .04] and active transportation minutes per week [692.2 (36.1 to 1323.5); P = .05]. At 6 months, significant group differences were observed in walking trips per week [6.1 (1.9 to 11.4); P = .03] favoring the intervention (9 vs 2 trips per week). CONCLUSION: Given these promising findings, the next step is to test Everyday Activity Supports You model's effectiveness to promote physical activity in older women within a larger study.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".