Multidomain interventions for dementia risk reduction: Can we detect a change in the signal?
Bibliographic record
Abstract
Abstract Background There is increasing evidence that modifiable lifestyle factors account for around 40% of worldwide dementia cases. Given the multifactorial etiology of dementia, multidomain lifestyle interventions have generated major interest. Results from randomized controlled trials (RCTs) suggest that implementing multidomain interventions in at‐risk individuals is an effective strategy to delay disease onset. As per the Obesity‐Related Behavioral Intervention Trials (ORBIT) model, to ensure successful interventions, pre‐efficacy trials are needed to ascertain that each intervention component can yield a predefined clinically significant change (CSC). Therefore, the main objective of this Proof‐of‐Concept was to determine if each individual part of an online multidomain dementia risk reduction program can achieve a CSC in the targeted behavioural risk outcome. Method This was a 12‐week, within‐subject, treatment‐only study with three single‐domain groups (Diet, Physical Activity [PA] or Cognitive Engagement [CE]) and one multidomain group. The study enrolled cognitively healthy individuals aged 50‐70 with at least one risk factor among the targeted domains. The personalized intervention was delivered via a digital, coach‐supported application. Primary outcomes were the percentage of participants showing a CSC at study end in each domain. CSCs were defined as a change ≥ 1 point on the Canadian Mediterranean Diet Score, ≥ 300 METs‐min/week on the short version of the International Physical Activity Questionnaire, and ≥ 6 points on the Cognitive Activity Questionnaire, respectively. Additional outcomes comprised feasibility indicators. Result Thirty‐nine participants (mean age=63.1 [51‐68]; 82.1% female) were recruited, with 31 participants enrolled in the single‐domain groups (Diet, N=11; PA, N=7; CE, N=13). Within the single‐domain groups, participants received 7.9 (6‐9) coaching sessions. The number of participants achieving a CSC was 5 (45.5%) in the Diet group, 3 (42.9%) in the PA group and 6 (50.0%) in the CE group. Overall compliance and retention were 93.5% (89.6‐98.0) and 97.4% (92.3‐100), respectively. Conclusion The results of this study support moving forward to the next ORBIT framework step, which includes refinement of the Luci intervention components and the design of a pilot feasibility trial to test the program in a larger sample of participants.
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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 | 0.081 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".