Multi-level Correlates of Women's Physical Activity Behavior Following Participation in a Primary Prevention Program
Bibliographic record
Abstract
BACKGROUND: Regular physical activity (PA) reduces the risk of premature death and disability from a variety of health conditions, including cardiovascular diseases (CVD). In response to the growing burden of CVD in women, prevention initiatives are fundamentally important for this population. Physical activity is a particularly important health behavior and warrants an increased effort to identify variables that predict a person's likelihood of engaging in and maintaining regular PA. Despite the known benefits of being active, population wide surveillance data show a high prevalence of physical inactivity, particularly among women, worsening with increased age groups. PUPOSE: The purpose of this study was to identify patterns of PA in women at risk for CVD, and factors that are predictive of maintenance 6 to 36 months following participation in a primary prevention (PP) program. METHODS: A convenience sample of women enrolled in a PP program between May 2002 and February 2005 were targeted for recruitment. Data were collected through a self-administered questionnaire. The outcome variable for this study was self-reported PA levels, assessed by the International Physical Activity Questionnaire, the Kaiser Physical Activity Survey, and the Duke Activity Status Index. Independent samples t-tests, Chi-square and Fisher's exact tests were performed to compare variables between the active and inactive groups. Bivariate analysis was done using each of the PA measures as separate dependent variables and only those found to be significant at p < 0.05 were then used in the multiple regression models. RESULTS: A total of 105 women were eligible for this study. A total of 73 completed surveys were received for a 70% response rate. Approximately 82 % of respondents were categorized as being "sufficiently active" with a median expenditure of 1680 METmin/week. Greater functional status was associated with younger age (t=−2.13, p=0.04) and greater physical quality of life (t=6.22, p<0.0001). Higher PA levels were shown in women who were employed (t= −3.95, p=0.0002) and those who made plans to be physically active (t=−1.97, p=0.05). CONCLUSION: This study provides important information on the assessment of PA among women and the results can be used to help develop more effective interventions for women at risk for CVD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".