Actigraphy as a proxy biomarker for motoric agitation in Alzheimer disease
Bibliographic record
Abstract
Abstract Background Agitation is an important symptom of dementia and is commonly assessed using clinician and/or caregiver rated scales. These scales are useful but they rely on a mixture of direct and indirect observations and therefore are subject to limited reliability and validity depending of factors such as staffing. Actigraphy is a tool to assess motoric activity, a core feature of agitation. There is a growing interest in using Actigraphy as a tool to diagnose and monitor agitation of dementia. We previously published a report demonstrating feasibility and a correlation between Actigraphy and informant‐based rating of physical agitation in a sample of hospital and nursing home patients with Alzheimer in moderate level of cognitive impairment from Kingston Ontario, Canada. Method This is an extension to our previous observational, cohort study now involving a sample of older adults with Alzheimer disease and agitation from London Ontario, Canada in hospital and nursing home with severe level of cognitive impairment. Measurements: Baseline characteristics included demographics, severity of cognitive impairment, medical comorbidity, and agitation symptoms assessed using CMAI and NPI. Actigraphy was measured over several continuous days. Results Twenty participants were enrolled (mean age=81.3/years, SD=7.97; Male=12; mean MMSE=6.3, SD=5.7; mean CMAI score=66.83, SD=10.79). Pearson correlation coefficient was explored between agitation and Actigraphy measures. We found that total CMAI scores correlated with 24‐hours Actigraphy data (r(18)= 51, p= .02), further analysis showed that only physical non‐aggressive agitation correlated with 24‐hours actigraphy measures (r(18)= 0.49, p=.03), this was mainly related to day‐time and evening‐time Actigraphy but not overnight. Conclusions Actigraphy was correlated significantly and moderately with informant‐based methods for measuring physical agitation in individuals with dementia and showed a temporal pattern of activity. Further studies are required to understand the application of Actigraphy as a biomarker for motoric agitation in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".