Non‐pharmacological interventions for older adults with early cognitive impairment
Bibliographic record
Abstract
BACKGROUND: As the population ages, mild cognitive impairment (MCI) and dementia are increasingly prevalent. Patients, families, and healthcare systems will benefit from delaying progression of cognitive decline. Non-pharmacological interventions (NPIs) are safe, well-tolerated, and preferred by patients and have been studied in a broad body of literature. Despite this, few comprehensive guidelines exist on NPIs for early cognitive decline. METHODS: We conducted a systematic review of the literature focused on NPI supported by the FINGER model and a review conducted by the AAIC Non-Pharmacological Interventions Professional Interest Area (Sikkes et al 2021). Inclusion criteria included meta-analysis or systematic review of randomized controlled trials enrolling patients ≥ 60 with MCI or dementia (MMSE >20 when provided). Included studies were published between 2014 - 2024, and outcomes assessed included cognition and/or function. RESULTS: Of 2,870 studies screened, 26 met inclusion criteria: exercise (n = 16), cognitive (n = 4), multicomponent (n = 5), and mindfulness (n = 1) interventions. Exercise, including dance, exergames, and mind-body showed improvement in global and cognitive sub-domains but benefit was not seen with walking alone. Cognitive interventions, including cognitive stimulation, mindfulness, and multicomponent interventions showed cognitive benefits. Only one study of virtual reality cognitive training improved functional status. Considerable heterogeneity including variable trial duration, multiple interventions studied, and inability to fully blind participants and researchers limit comparability and conclusions. CONCLUSIONS: NPIs benefit cognition in individuals with MCI and mild dementia. The most benefit was demonstrated with multi-domain interventions incorporating cognitive and exercise interventions, mindfulness, and some cognitive interventions. These interventions can be accessed through community or senior centres or integrated into clinic settings with multidisciplinary care teams. We recommend an individualized approach for each patient, which incorporates their interests, frailty, mobility, medical comorbidity, and level of function. Further research is needed to inform specific recommendations about how much cognitive or physical activity is optimal for improving cognition or function.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".