Cross-sectional associations between mental health indicators and social vulnerability, with physical activity, sedentary behaviour and sleep in urban African young women
Bibliographic record
Abstract
Abstract Background Relationships between mental health and multiple health behaviours have not been explored in young South African women experiencing social constraints. The aim of this study was to identify associations between mental health indicators and risk factors with physical activity, sedentary behaviour, and sleep, amongst young women living in Soweto, a predominantly low-income, urban South African setting. Methods For this cross-sectional study, baseline measurements for participants (n = 1719, 18.0–25.9 years old) recruited for the Healthy Life Trajectories Initiative were used including: physical activity, sedentary behaviour (sitting, screen and television time), sleep (duration and quality), depression and anxiety indicators, emotional health, adverse childhood experiences, alcohol-use risk; social vulnerability, self-efficacy, and social support. Results Multiple regression analyses showed that depression (β = 0.161, p < 0.001), anxiety (β = 0.126, p = 0.001), adverse childhood experiences (β = 0.076, p = 0.014), and alcohol-use risk (β = 0.089, p = 0.002) were associated with poor quality sleep. Alcohol-use risk was associated with more screen time (β = 0.105, p < 0.001) and television time (β = 0.075, p < 0.016). Social vulnerability was associated with lower sitting time (β = − 0.187, p < 0001) and screen time (β = − 0.014, p < 0.001). Higher self-efficacy was associated with more moderate- to vigorous-intensity physical activity (β = 0.07, p = 0.036), better-quality sleep (β = − 0.069, p = 0.020) and less television time (β = − 0.079, p = 0.012). Having no family support was associated with more sitting time (β = 0.075, p = 0.022). Binomial logistic regression analyses supported these findings regarding sleep quality, with anxiety and depression risk doubling the risk of poor-quality sleep (OR = 2.425, p < 0.001, OR = 2.036, p = 0.003 respectively). Conclusions These findings contribute to our understanding of how mental health indicators and risk factors can be barriers to health behaviours of young women in Soweto, and that self-efficacy and social support can be protective for certain of these behaviours for these women. Our results highlight the uniqueness of this setting regarding associations between mental health and behaviours associated with non-communicable diseases risk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 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".