The Use of Actigraphy Indices in the Diagnosis of Dementia
Bibliographic record
Abstract
Dementia is a life-altering neurocognitive disorder that affects the independence and quality of life of over 700,000 Canadians. Early detection of dementia is crucial to improving patient outcomes. However, current gold-standard diagnostic methods such as cognitive assessments and neuroimaging can be quite expensive, invasive and inaccessible. Emerging evidence in sleep research suggests that sleep and circadian rhythm disruption is associated with neurodegeneration and B-amyloid accumulation, thus causing dementia. Given this association, this study aims to assess whether actigraphy, a wrist-based non-invasive device measuring circadian rhythm, could serve as a potential tool for diagnosing dementia in its early stages. We conducted a systematic review evaluating the effectiveness of actigraphy in identifying sleep and circadian rhythm disruptions as dementia biomarkers. A systematic review of studies assessing actigraphy-based sleep and circadian rhythm parameters were gathered using Medline, Embase and Web of Science databases. The inclusion criteria focused on populations over the age of 60 where actigraphy assessment occurred for at least a week. The exclusion criteria included studies assessing sleep using EEG and studies discussing dementia associated with Huntington’s disease, Creutzfeldt-Jakob disease, multiple sclerosis, HIV-associated dementia, normal pressure hydrocephalus and/or frontotemporal dementia. A total of 2757 studies were imported into Covidence for title and abstract screening of which 1766 studies were included in full text review. Within the full text review phase, further inclusion criteria parameters were established to narrow the scope of the review, limiting the timeframe of studies from 2020 to 2025 and solely including studies with a sample size greater than 100. After establishing these parameters, a total of 90 studies has been included in the final review. Our future work includes ultimately understanding whether actigraphy could be utilized as a viable and accessible diagnostic tool for dementia.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".