Novel Approach to Characterize Heterogeneity in an Aerobic Exercise Intervention
Bibliographic record
Abstract
PURPOSE: Exercise intervention studies for brain health can be difficult to interpret due to heterogeneity in exercise intensity, exercise duration, and in adherence to the exercise intervention. This study aimed to characterize heterogeneity in these components in a cohort of healthy middle-age and older adults who participated in a prescribed 6-month supervised aerobic exercise intervention as part of the Brain in Motion study. METHODS AND RESULTS: Group-based multitrajectory analysis (GBMTA) was used to characterize variation in the trajectory of exercise intensity and duration for male and female participants in the first 3 months of the exercise program. The GBMTA for males and females revealed two distinct trajectory subgroups, namely, "high-increasing" (HI) and "low-increasing" (LI). Logistic regression was used to assess the association between the identified latent subgroups and (i) demographic characteristics; (ii) physiological characteristics, including cardiovascular and cerebrovascular function; (iii) genetic characteristics; and (iv) adherence with American College of Sports Medicine guidelines on exercise for older adults. Of the 196 participants, 54.1% met the American College of Sports Medicine aerobic exercise targets for intensity and duration during the intervention. Aerobic fitness (maximal oxygen uptake; odds ratio, 1.27; P < 0.01) was significantly different between these trajectory subgroups in males, and cerebrovascular function (cerebrovascular resistance; odds ratio, 0.14; P < 0.01) was significantly different between these trajectory subgroups in females. CONCLUSION: This novel approach to tracking a prespecified exercise program highlights that there are individual and group-specific variations within a prescribed exercise intervention. Characterizing exercise adherence in this way holds promise in developing optimized exercise prescriptions tailored to individual baseline characteristics, and additionally highlighting those participants at greatest risk of not meeting minimum dosage requirements for physiological and/or cognitive health.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".