Mobility limitations and emotional dysfunction in old age: The moderating effects of physical activity and social ties
Bibliographic record
Abstract
This study aims to examine the association between mobility limitations and emotional dysfunction among older Ghanaians and evaluate the buffering effect of physical activity (PA) and social ties in this association.The analysis included 1201 adults aged ≥50 from the 2016-17 Aging, Health, Psychological Well-being, and Health-seeking Behavior study. The Medical Outcomes Study Short Form-36 (MOS SF-36) assessed mobility limitations and emotional dysfunction. We measured PA using the International Physical Activity Questionnaire Short Form (IPAQ-SF). Hierarchical OLS regressions were performed to evaluate the hypothesized direct and interactive relationships.The mean age of the sample was 66.2 (SD = 11.9), and 63.3% were women. After full adjustment for potential confounders, OLS regressions found that mobility limitations increased the risk of emotional dysfunction (β = 0.113, p = 0.004). Moreover, social ties (β = -0.157, p < 0.001) and PA (β = -0.096, p < 0.001) were independently and negatively associated with emotional dysfunction. We finally found a significant effect modification of the association of mobility limitations with emotional dysfunction by PA (β = -0.040, p < 0.002) and social ties (β = -0.013, p = 0.013).Mobility-enhancing strategies such as engagement in positive behavioral choices, focusing on regular PA, and maintaining resourceful interpersonal social networks can mitigate the impact of mobility limitations on emotional dysfunction in later life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".