Testing the iMplementation Framework fOr behavioral and LIfestyLe interventions in AlZheimer’s DiseasE (MOBILIZE) via the ACT randomized controlled trial
Bibliographic record
Abstract
Implementing multi-site behavioral intervention trials to study 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), which was developed to guide the design and implementation of behavioral interventions in AD. Building on the person-centered principle, MOBILIZE includes three implementation outcomes with corresponding team processes: (1) screening (processes), (2) intervention adherence (processes), and (3) safety (processes). This study systematically evaluated MOBILIZE implementation outcomes of the 3-site aerobic exercise and cognitive training (ACT) Trial (recruitment started on 4/1/2018 and last follow-up on 7/17/2024). Outcomes included time in screening phases, intervention adherence (attendance and intervention dose adherence, and safety [adverse events]). 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. The median days of screening-to-enrollment averaged 98 days. Attendance was 76.7 ± 28.6%. Adherence to 100% exercise session dose and 100% cognitive session dose was 71.7 ± 30.8% and 51.5 ± 26.2%, respectively. There were 10 study-related adverse events. MOBILIZE helped the ACT Trial achieve high intervention attendance and safety and may be important for early-stage trials in AD.Trial registration The ACT Trial is registered at clinicaltrials.gov (NCT03313895). Registered 15 July 2017, https://clinicaltrials.gov/study/NCT03313895 .
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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 | 0.146 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".