The Role of Predictive Anxiety, Depression, Cognitive Function, and Confidence in Balance for Participation, Level of Disability, and Duration of Physical Activity in Community‐Dwelling Elderly Individuals
Bibliographic record
Abstract
Background and Objective: Aging can be described as a gradual decline in physical and mental abilities, accompanied by an increased susceptibility to illnesses and disabilities. This global phenomenon is escalating rapidly. Accurate prediction of future consequences based on identified factors is crucial for clinicians and researchers, particularly in progressive states such as aging. Thus, this study aims to investigate some of the most prevalent psycho‐cognitive factors that may influence elderly individuals’ participation levels, disability status, and duration of physical activity. Methods and Materials: In this correlational study, 150 subjects (87 males and 63 females) were selected through a simple non‐probability sampling method from community‐dwelling older adults in Tehran. Social participation, level of disability, and physical activity duration were evaluated using the CCIQ, the MHAQ, and a qualitative questionnaire, respectively. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS) and the Geriatric Depression Scale‐15 (GDS‐15), respectively. Cognitive function and balance were examined with the Montreal Cognitive Assessment (MoCA) and the Fullerton Advanced Balance (FAB) scale, respectively. Results: The multiple regression models showed that FAB alone accounted for 13.5%−61.9% of the variance in all three outcomes. MoCA, GDS‐15 and educational level were the second most significant predictorsof participation disability and physical activity time respectively. Conclusion: These results suggest that treatments that target balance, cognitive, and depression states and improve manual function may be particularly important for improving participation, disability state, and physical activity time in community‐dwelling older adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".