Risk factor profiles and changes in risk factors and cognitive function over 12 months: The CAN‐THUMBS UP Brain Health Support Program Study
Bibliographic record
Abstract
BACKGROUND: Dementia risk factors often coexist and interact in ways that create distinct profiles, which may influence the efficacy of lifestyle-based interventions aimed at reducing dementia risk. This study aimed to: 1) characterize dementia risk profiles in participants of the CAN-THUMBS-UP Brain Health Support Program (BHSP), and 2) evaluate whether these profiles predict responses to a 45-week educational intervention. METHOD: We analyzed data from 296 BHSP participants with complete baseline assessments. Principal component analysis identified risk profiles based on seven modifiable risk factors: physical activity, cognitive engagement, diet, sleep, social and psychological health, vascular health, and vision and hearing. All participants accessed Brain Health PRO, a web-based educational intervention targeting these modifiable risk factors. Risk factor changes were measured through lifestyle questionnaires administered every three months, and cognitive changes were measured via a neuropsychological test battery at baseline and 12 months. Linear mixed models with repeated measures, adjusted for age, sex, education, and cognitive status evaluated associations between risk profiles and changes in cognition and risk factors. RESULT: Three distinct profiles were identified: 1) sleep and social/psychological health, 2) cognitive engagement, and 3) diet, physical activity and vascular heath. Across all profiles, lower baseline scores indicated a higher risk in the factors that characterized each profile. Participants with lower baseline scores in the first two profiles showed greater improvements in associated risk factor domains compared to those with better baseline scores. In the third profile, participants with lower baseline scores showed greater improvements in diet and physical activity, but no change was observed in vascular health. No association was observed between any of the risk profiles and cognitive change. A longer follow-up or a more intensive intervention beyond online education may be needed to impact vascular health and cognition. CONCLUSION: Understanding dementia risk factor profiles and their influence on intervention effects can help inform more personalized prevention strategies. Further research is needed to validate and refine these profiles.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".