THINKING, FEELING AND MOVING IN AGING: THE ROLE OF COGNITION AND DEPRESSION IN BALANCE CONTROL
Bibliographic record
Abstract
Emerging evidence shows that motor and balance control in older people is affected by cognitive status and by depressive symptoms. However, how static balance is affected by the presence of both, cognitive and depressive symptoms together, in the same individual is unknown. We hypothesize that balance control will differ in older individuals based on their cognitive status (MCI), presence of depressive symptoms, or both factors combined. Ninety six older participants (mean age =75 ± 6) were stratified by cognitive and depressive status as follows: No cognitive or depressive symptoms(Controls, n=25; 71 years old), Cognitive but no depressive symptoms (MCI; n=36; 75 years old); Cognitive and with depressive symptoms (MCI_ds; n=19; 76 years old); and Cognitive with major depression (MCI_D; n=16; 74 years old). Balance (area of body sway) was assessed while standing during eyes open and eyes closed conditions using an electronic rigid platform (Bertec® Inc.). Balance under eyes open condition did not significantly differ across groups (Mixed RM-ANCOVA) after controlling for age, sex, cognitive performance, physical activity, previous falls, number of medications and antidepressants. Interestingly, participants having cognitive and depressive deficits (MCI_ds and MCI_D) showed a lack of “physiological” increase in balance sway in the challenging condition of eyes closed (p=0.003). Our findings suggest that combination of depressive and cognitive symptoms may reduce flexibility of balance control in older adults, placing them at higher risk of falls. Potential mechanism of these associations including the “hyper” cautious control through cognitive resources will be discussed.
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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 | 0.002 |
| 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 | 0.001 |
| 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".