Barriers to physical activity in women from ethnic minority groups: a systematic review
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is essential for preventing chronic diseases and improving mental health, yet global PA levels remain suboptimal, with women generally engaging in less PA than men. This gender disparity is especially concerning for women from ethnic minority groups, who face a higher risk of chronic diseases and encounter unique barriers to PA participation. Understanding these obstacles is crucial for developing targeted interventions to promote PA and reduce health disparities among ethnic minority women. This systematic review aimed to identify, categorize, and synthesize existing research on the barriers to PA among these women. METHODS: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six databases were searched (from inception to December 2024) to identify primary studies of any methodological design (quantitative, qualitative, or mixed methods) that examined barriers to PA among adult women from ethnic minority groups, excluding African American and Indigenous populations. Studies were included regardless of country, as long as they met the eligibility criteria. Thematic analysis, guided by the social-ecological model, was used to synthesize the findings. RESULTS: Sixty-four studies involving 5,555 women and conducted in 12 countries met the inclusion criteria. Thematic analysis identified 16 barriers categorized within the social-ecological framework. At the individual level, common barriers included time constraints, lack of motivation, poor physical health, and disinterest in PA. Interpersonal barriers, such as family responsibilities, cultural expectations, language barriers, and lack of social support, were prevalent. Environmental barriers included unsafe neighborhoods, limited access to PA resources, and inadequate infrastructure. Regional differences were observed, with cultural barriers and family obligations most common in the Americas, and misconceptions about PA and environmental factors more prevalent in the Eastern Mediterranean. Socioeconomic status and immigration status exacerbated these challenges. CONCLUSIONS: This review highlights the complex, multi-level barriers that ethnic minority women face in engaging in PA. Addressing these challenges requires multifaceted interventions that consider individual, interpersonal, and environmental factors. Future research should explore strategies that reduce these barriers and promote equitable access to PA opportunities for women from ethnic minority groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".