TESTING THE IMPLEMENTATION FRAMEWORK FOR BEHAVIORAL AND LIFESTYLE INTERVENTIONS IN ALZHEIMER’S DISEASE (MOBILIZE)
Bibliographic record
Abstract
Abstract Introduction Implementing multi-site behavioral intervention trials in Alzheimer’s disease (AD) has many unique challenges, leading to substantial variations in delivered intervention doses and cognitive findings. These issues can be addressed by the IMplementation Framework fOr Behavioral and LIfestyLe Interventions In AlZheimer’s DiseasE (MOBILIZE) that was developed to guide the design and implementation of behavioral interventions in AD. Methods This study systematically evaluated the implementation outcomes of the multi-site aerobic exercise and cognitive training (ACT) trial that employed MOBILIZE. Building on the person-centered care principle, MOBILIZE includes three implementation outcomes (screening, intervention adherence, and safety) with corresponding team processes to guide trial implementation. Screening was operationalized as the duration (days) between each screening visit, last screening visit to enrollment, and enrollment to first intervention session. Intervention adherence outcomes included: adherence (attendance), session dose adherence (calculated by dividing the number of sessions that achieved >=70% of the prescribed session dose / total number of sessions completed), and days required to complete the prescribed 72 sessions. Safety was operationalized as the type, number, and severity of study-related adverse events (AEs). RESULTS. The sample (n=146) was 73.8±5.7 years in age and 23.4±2.1 on Montreal Cognitive Assessment score, with 48.0% female, and 91.8% white. Screening-to-enrollment averaged 117.4±71.7 days. Intervention adherence was 77.4%±28.3%. There was 9-study related adverse events. Screening-to-enrollment and intervention adherence differed across sites, mainly due to COVID-19 influence. CONCLUSIONS. MOBILIZE helped the ACT Trial achieve high intervention adherence and safety and may be particularly important for early-stage and multi-site trials in AD.
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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 | 0.166 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".