A pilot feasibility randomized controlled trial on combining mind-body physical exercise, cognitive training, and nurse-led risk factor modification to reduce cognitive decline among older adults with mild cognitive impairment in primary care
Bibliographic record
Abstract
Objectives To examine the feasibility and preliminary effectiveness of (1) combining cognitive training, mind-body physical exercise, and nurse-led risk factor modification (CPR), (2) nurse-led risk factor modification (RFM), and (3) health advice (HA) on reducing cognitive decline among older adults with mild cognitive impairment (MCI). Methods It was a 3-arm open-labeled pilot randomized controlled trial in the primary care setting in Hong Kong. Nineteen older adults with MCI were randomized to either CPR ( n = 6), RFM ( n = 7), or HA ( n = 6) for 6 months. The primary outcome was the feasibility of the study. Secondary outcomes included the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the Montreal Cognitive Assessment Hong Kong version (HK-MoCA), the Clinical Dementia Rating (CDR), the Disability Assessment for Dementia (DAD), quality of life, depression, anxiety, physical activity, health service utilization, and diet. Results Nineteen out the 98 potential patients were recruited, with a recruitment rate of 19% (95% CI [12–29]%, P = 0.243). The adherence rate of risk factor modification was 89% (95% CI [65–98]%, P = 0.139) for CPR group and 86% (95% CI [63–96]%, P = 0.182) for RFM group. In the CPR group, 53% (95% CI [36–70]%, P = 0.038) of the Tai Chi exercise sessions and 54% (95% CI [37–71]%, P = 0.051) of cognitive sessions were completed. The overall dropout rate was 11% (95% CI [2–34]%, P = 0.456). Significant within group changes were observed in HK-MoCA in RFM (4.50 ± 2.59, P = 0.008), cost of health service utilization in CPR (−4000, quartiles: −6800 to −200, P = 0.043), fish and seafood in HA (−1.10 ± 1.02, P = 0.047), and sugar in HA (2.69 ± 1.80, P = 0.015). Group × time interactions were noted on HK-MoCA favoring the RFM group ( P = 0.000), DAD score favoring CPR group ( P = 0.027), GAS-20 favoring CPR group ( P = 0.026), number of servings of fish and seafood ( P = 0.004), and sugar ( P < 0.001) ate per day. Conclusions In this pilot study, RFM and the multi-domain approach CPR were feasible and had preliminary beneficial effects in older adults with MCI in primary care setting in Hong Kong. Trial registration Chinese Clinical Trial Registry (ChiCTR1800015324).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".