A mobile-based multidomain lifestyle intervention using Cognitive Evergreenland for older adults with subjective cognitive decline: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Dementia is a growing public health issue. Non-drug interventions targeting individuals before the onset of overt cognitive decline may be effective. Subjective cognitive decline (SCD) is present in > 50% of older adults and associated with progression to dementia. Here, we tested the compliance and effectiveness of a Multidomain Lifestyle Intervention Program using the mini-program, Cognitive Evergreenland, (MLIP-CE), based on the Health Action Process Approach model to support home-based intervention in older adults with SCD. METHODS: Cognitive Evergreenland was designed to improve cognitive reserve and maintain brain function in people at high risk of dementia and included: cognitive stimulation, cognitive training, health education, vascular risk monitoring, social support, and functional assessment, among other features. This was an exploratory trial designed to examine participant compliance with the mobile lifestyle intervention and its effectiveness, as well as changes in health-related indicators and cognitive function of older adults with SCD from baseline to 12 and 24 weeks post-intervention. RESULTS: The retention rate for MLIP-CE was 90.2% (37/41). Mean participant age was 70.93 ± 6.91 years, 73.2% of participants were female, and mean Montreal Cognitive Assessment score was 24.51 ± 2.87. Throughout the 24 weeks of the prescribed intervention, app usage remained consistently high, with over 92% of participants using the mini-program at least once a week and successfully completing corresponding health management tasks. In terms of average usage, cognitive training emerged as the most frequently used functional module (95.73%), closely followed by health education (95.02%). The health behavior levels of older adults with SCD, measured in terms of ability, opportunity, and motivation, were significantly increased relative to baseline (p < 0.001). Regarding cognitive function, Mini-Mental State Examination scores were significantly improved post-intervention, with a moderate effect size (Hedges' g = 0.60). CONCLUSIONS: These findings suggest that MLIP-CE, which was designed based on a theoretical framework, has potential for implementation, and support ongoing research into use of MLIP-CE for individuals at high risk of SCD or other dementia conditions. TRIAL REGISTRATION: The trial was prospectively registered at the Chinese Clinical Trials Registry with the registration number ChiCTR2200058665 on 13 April 2022.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".