Adherence to a lifestyle intervention – just a question of self-efficacy? Analysis of the AgeWell.de-intervention against cognitive decline
Bibliographic record
Abstract
BACKGROUND: Aim of this study was to detect predictors of better adherence to the AgeWell.de-intervention, a two-year randomized multi-domain lifestyle intervention against cognitive decline. METHODS: Data of 317 intervention group-participants comprising a risk group for dementia (Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) score of ≥ 9; mean age 68.9 years, 49.5% women) from the AgeWell.de intervention study were analysed. Regression models with four blocks of predictors (sociodemographic, cognitive and psychosocial, lifestyle factors and chronic conditions) were run on adherence to the components of nutrition, enhancement of social and physical activity and cognitive training. Adherence to each component was operationalised by assessing the degree of goal achievement per component at up to seven time points during the intervention period, measured using a 5-point Likert scale (mean score of goal achievement). RESULTS: Increasing age was negatively associated with adherence, while higher education positively predicted adherence. Participants with better mental state (Montreal Cognitive Assessment (MoCA)-score > 25) at baseline and higher self-efficacy adhered better. Diabetes and cardiovascular conditions were not associated with adherence, whereas smoking negatively affected adherence. Highest education and quitting smoking in the past were the only predictors associated with all four intervention components. CONCLUSION: Results identified predictors for better and worse adherence. Particularly self-efficacy seems to be of considerable influence on adherence. This should be considered when designing future intervention trials. TRIAL REGISTRATION: German Clinical Trials Register (ref. number: DRKS00013555).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".