Lifestyle Risk Factors and Cognitive Outcomes from the Multidomain Dementia Risk Reduction Randomized Controlled Trial, Body Brain Life for Cognitive Decline ( <scp>BBL‐CD</scp> )
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES To evaluate the efficacy of a multidomain intervention to reduce lifestyle risk factors for Alzheimer's disease (AD) and improve cognition in individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). DESIGN The study was an 8‐week two‐arm single‐blind proof‐of‐concept randomized controlled trial. SETTING Community‐dwelling individuals living in Canberra, Australia, and surrounding areas. PARTICIPANTS Participants were 119 individuals (intervention n = 57; control n = 62) experiencing SCD or MCI. INTERVENTION The control condition involved four educational modules covering dementia and lifestyle risk factors, Mediterranean diet, physical activity, and cognitive engagement. Participants were instructed to implement this information into their own lifestyle. The intervention condition included the same educational modules and additional active components to assist with the implementation of this information into participants’ lifestyles: dietitian sessions, an exercise physiologist session, and online brain training. MEASUREMENTS Lifestyle risk factors for AD were assessed using the Australian National University‐Alzheimer's Disease Risk Index (ANU‐ADRI), and cognition was assessed using Alzheimer's Disease Assessment Scale‐Cognitive subscale, Pfeffer Functional Activities Questionnaire, Symbol Digit Modalities Test (SDMT), Trail Making Test‐B, and Category Fluency. RESULTS The primary analysis showed that the intervention group had a significantly lower ANU‐ADRI score (χ 2 = 10.84; df = 3; P = .013) and a significantly higher cognition score (χ 2 = 7.28; df = 2; P = .026) than the control group. A secondary analysis demonstrated that the changes in lifestyle were driven by increases in protective lifestyle factors (χ 2 = 12.02; df = 3; P = .007), rather than a reduction in risk factors (χ 2 = 2.93; df = 3; P = .403), and cognitive changes were only apparent for the SDMT (χ 2 = 6.46; df = 2; P = .040). Results were robust to intention‐to‐treat analysis controlling for missing data. CONCLUSION Results support the hypothesis that improvements in lifestyle risk factors for dementia can lead to improvements in cognition over a short time frame with a population experiencing cognitive decline. Outcomes from this trial support the conduct of a larger and longer trial with this participant group.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".