The Luci digital program for reducing dementia risk: efficacy study protocol
Bibliographic record
Abstract
BACKGROUND: Current evidence suggests that a large proportion of dementia cases is associated with lifestyle habits and could thus be prevented. The absence of disease-modifying treatments highlights the need for widespread access to effective preventive strategies. Luci is a tailored, coach-supported, fully remote, web-based, behavioral intervention encouraging healthy lifestyle changes in three domains: diet, physical activity, and cognitive engagement. In prior phases, proof-of-concept was established and protocol feasibility demonstrated. Here we describe the protocol of the efficacy trial, which aims to determine if the Luci 24-week program can reduce the behavioural risk for dementia in middle-aged to older adults by improving their lifestyle habits. METHOD: This 52-week study will employ a two-arm, randomized, single-blind, parallel-group design. The target population will be cognitively healthy adults aged 50-75 at risk on at least one of the three intervention domains. 370 participants will be recruited across Canada and randomized to either the Luci intervention or a Waitlist comparison group in a 1:1 ratio. Primary outcomes will be measured with self-reported behavioural risk questionnaires. Secondary outcomes will comprise cognitive performance and quality of life. Intervention effects will be measured at weeks 12 and 24, with maintenance assessed at week 52. RESULT: Based on our prior studies, we defined the primary endpoint as the proportion of participants achieving a clinically significant change in at least one lifestyle domain at week 24. We hypothesize that this proportion will be higher in the Luci group compared to the Waitlist condition, suggesting that Luci can help reduce behavioural risks associated with dementia. Data will be analyzed using logistic regression for the primary endpoint, linear mixed models for secondary outcomes, and structural equation modelling to assess moderators and mediators effects. CONCLUSION: A digital program such as Luci has numerous advantages, including reduced barriers to participation (e.g., geographical constraints), increased user convenience (e.g., in-home participation, flexible scheduling), and lower deployment costs than face-to-face delivery. Once validated, it confers the potential for large-scale implementation as a prevention tool to mitigate the public health burden of dementia.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.015 |
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".