The role of reserve in cognitive and motor function and responsivity to multi‐domain interventions for Mild Cognitive Impairment – Results from the SYNERGIC Study
Bibliographic record
Abstract
BACKGROUND: Cognitive and motor deficits have been found to be important markers of Mild Cognitive Impairment (MCI), a pre-dementia risk state. Recently, aerobic exercise (AE) and cognitive training (CT) interventions significantly improved cognitive and motor function in older adults with MCI. Recently, it has been shown that nearly 50% of dementia cases could be mitigated by the elimination of 12 modifiable risk factors, such as low education and physical inactivity, as early as midlife. Cognitive and Motor Reserve (CR, MR) describe the compensation for cognitive and motor loss through lifelong cognitively and physically enriching experiences, respectively. Our objectives are to examine (1) the life historical profiles that contribute to CR and MR, which are ordinarily eliminated in RCTs,(2), if CR and MR predict better cognition and mobility at baseline, and (3) whether they affect responsivity to multi-domain lifestyle interventions for MCI. METHOD: Performing secondary data analysis, participants (n = 71) were older adults with MCI randomized to intervention arms: CT+AE, AE only, and control arm. Baseline and post-intervention assessments of cognition (e.g., executive function, memory) and mobility (simple gait and cognitive-motor dual tasking) were performed. They also provided historical data on CR and MR factors. We first used principal component analysis (PCA) of the relevant life history variables to calculate weighted CR and MR factors. Second, we ran linear regressions to assess baseline outcomes. Third, we will use linear mixed-effects (LME) models to examine if CR/MR influence responsivity to the intervention arms. RESULT: A larger sample (n = 266) revealed two principal components capturing 75.6% of the variance: MR (lifelong physical activity), and CR (education and occupational complexity). Regressions revealed that MR was associated with greater baseline dual-task walking velocity, and trail-making task (TMT) performance. CR did not predict baseline scores. CONCLUSION: This far, MR predicted baseline cognitive and motor performance. LME will reveal whether MR and CR will impact responsivity to intervention. This far, we conclude that MR may be more sensitive in detecting baseline cognitive and motor benefits. The completed study will illuminate the distinct benefits of CR and MR on intervention efficacy and the relevance of personalized interventions for MCI.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".