Perceived Barriers to Leisure-time Physical Activity among Individuals at Risk of Type 2 Diabetes in India
Bibliographic record
Abstract
Abstract Background: Research on barriers to physical activity among adults in India is limited, and even less is known about this issue among individuals at high risk of developing diabetes. Objectives: We aimed to identify the perceived barriers to leisure-time physical activity among individuals at risk of developing type 2 diabetes in an Indian population. Materials and Methods: This cross-sectional study utilized baseline data (collected in 2013) from 1007 participants enrolled in the Kerala Diabetes Prevention Program. These participants were randomly selected from 60 polling areas (electoral divisions) in the Trivandrum district of Kerala state. Standardized questionnaires were employed to collect information on socio-demographics, leisure-time physical activity levels, and perceived barriers to physical activity. Statistical analyses comprised chi-square and t tests. Results: More than three-fourths (79.6%) of participants reported being physically inactive during their leisure time, with females showing a higher prevalence (84.6%) than males (75.2%; P < 0.001). The most common perceived barriers for both males (52.4%) and females (51.0%) in participating in leisure-time physical activities were “other priorities,” such as time constraints, family obligations, and work commitments. Females were significantly more likely than males to cite “lack of exercise skills,” “lack of social support,” and “exercise is not important” as barriers to leisure-time physical activity (all P < 0.05). Conclusion: Our study shows concerning levels of physical inactivity during leisure time among participants, particularly among females. Furthermore, the findings underscore the critical need for implementing interventions specifically targeted at addressing gender-specific barriers to leisure-time physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".