Recruitment and enrolment feasibility of the Luci risk reduction study in Québec
Bibliographic record
Abstract
Abstract Background Evidence suggests that lifestyle interventions can reduce the incidence of cognitive decline in older adults. Multidomain lifestyle intervention thus generates major interest. Luci is a Web‐based multidomain coach‐assisted dementia risk reduction program. Although such a technology‐based delivery mode represents a scalability opportunity, reaching the target population may be a challenge. A main goal of the Luci pilot feasibility study was to evaluate recruitment and enrolment rates. Method This is an ongoing 24‐week, Luci vs waitlist randomized, single‐blind pilot feasibility study to enrol cognitively healthy individuals aged 50‐70 with ≥1 lifestyle risk factor (physical activity, diet, cognitive engagement). Exclusion criteria comprised severe health conditions (e.g., neurocognitive, psychiatric) and logistical reasons (e.g., no device or internet). We aimed to recruit 120 participants at a rate of ≥30 participants/month over 4 months, and to achieve a screening‐to‐enrolment ratio of ≥48%. Participants were recruited in weekly waves from a list of volunteers obtained through a media campaign on the Luci project, and a research centre database. Result Of the 178 people invited to complete screening, 157 (88.2%) did so, and 124 (78.9%) met inclusion/exclusion criteria. The recruitment of the 120 participants was achieved over 1 week, and screening‐to‐enrolment ratio was 76.4%. Reasons for exclusion were not having any lifestyle risk factor (18.9%), exceeding study capacity (10.8%), logistical (24.3%) and health (45.9%) reasons. Included participants had a mean age of 60.7 (SD = 5.6) and 16.7 (SD = 3.4) years of education; 85% were female. Within enrolled individuals, 71.7% were living with someone, 37.5% were retired, and 66.7% had a household income greater than the Quebec median. 75.0% had ≥2 lifestyle risk factors. Conclusion Our recruitment and enrolment goals were met. Eligibility slightly exceeded our enrolment aim, a situation not unexpected. Almost all individuals who completed the screening had dementia risk factors suitable for Luci preventive interventions. However, some subgroups are not well represented in our sample, and in particular, men, individuals who are older (≥65 years), less educated, with a low‐income, and living alone. Results highlight the need to develop recruitment strategies to reach a more diverse population, and that specifically target individuals facing social inequities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".