When is more better? Modeling the effect of dose on the efficacy of the MAPT multidomain intervention as a function of individual characteristics
Bibliographic record
Abstract
Abstract Background Dose is a critical factor examined in pharmacological treatment. However, little is known about the optimal dosage for non‐pharmacological multidomain interventions as well as the function that relates dose to improvement. These gaps have been assessed in this present study in interaction with individual characteristics, which may moderate the effect of dose. Magnification models predict that healthier individuals will benefit more from prolonged practice, whereas reserve models predict that training will reduce cognitive disadvantage. Method The data from the Multidomain Alzheimer Preventive Trial (MAPT) was used to determine the relationship between cognitive training dose and cognition, and its interaction with sex, age, education, entry criteria, CAIDE risk score, frailty and Apoe4 status. The MAPT is a three‐year randomized controlled trial, which includes cognitive training, physical activity, nutrition and omega‐3 polyunsaturated fatty acids in at‐risk individuals. The number of hours of cognitive training attended (max.=28) was used as a measure of dose in the 749 participants randomized to the multidomain intervention. Result First, polynomial regression analysis modelled cognitive performance over time on memory, verbal fluency and composite cognitive score. Mixed model analyses measured the effect of dose on expected scores, and interaction with individual factors were assessed. A cubic function explained the relationship between dose and performance indicating benefits up to 12‐14 training hours followed by a plateau. Interactions were found with individual characteristics. Older participants and those with a higher CAIDE score reached their plateau more rapidly. Individuals with lower education and frail individuals benefited more from increased exposure. Conclusion A non‐linear dose‐response function indicates that more is not necessarily better. Here, the optimal dose was found to be 12‐14 hours of training, about half of what was potentially available to participants. A magnification effect is found for age and vascular risk factors as younger and healthier participants show larger benefit. However, a reserve effect is found in frail or less educated participants as they benefitted more from additional training experience than their more privileged counterparts.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".