Recruitment and Baseline Characteristics of Participants in the AgeWell.de Study—A Pragmatic Cluster-Randomized Controlled Lifestyle Trial against Cognitive Decline
Bibliographic record
Abstract
Targeting dementia prevention, first trials addressing multiple modifiable risk factors showed promising results in at-risk populations. In Germany, AgeWell.de is the first large-scale initiative investigating the effectiveness of a multi-component lifestyle intervention against cognitive decline. We aimed to investigate the recruitment process and baseline characteristics of the AgeWell.de participants to gain an understanding of the at-risk population and who engages in the intervention. General practitioners across five study sites recruited participants (aged 60-77 years, Cardiovascular Risk Factors, Aging, and Incidence of Dementia/CAIDE dementia risk score ≥ 9). Structured face-to-face interviews were conducted with eligible participants, including neuropsychological assessments. We analyzed group differences between (1) eligible vs. non-eligible participants, (2) participants vs. non-participants, and (3) between intervention groups. Of 1176 eligible participants, 146 (12.5%) dropped out before baseline; the study population was thus 1030 individuals. Non-participants did not differ from participants in key sociodemographic factors and dementia risk. Study participants were M = 69.0 (SD = 4.9) years old, and 52.1% were women. The average Montreal Cognitive Assessment/MoCA score was 24.5 (SD = 3.1), indicating a rather mildly cognitively impaired study population; however, 39.4% scored ≥ 26, thus being cognitively unimpaired. The bandwidth of cognitive states bears the interesting potential for differential trial outcome analyses. However, trial conduction is impacted by the COVID-19 pandemic, requiring adjustments to the study protocol with yet unclear methodological consequences.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".