Can accelerometry identify/monitor neuropsychiatric symptoms correlated with motor activity in persons living with dementia? Results from a diagnostic test accuracy systematic review
Bibliographic record
Abstract
Abstract Background Wearable sensor technology shows promise for neuropsychiatric symptoms (NPS) identification/monitoring in persons living with dementia (PLWD). Accelerometry measures acceleration of body segments and physical activity. This study explores the diagnostic test accuracy (DTA) of accelerometry to identify the following 6 NPS associated with motor activity: aberrant motor behavior (AMB), aggression, agitation, anxiety, apathy, and wandering. Methods As part of a larger systematic review, we conducted an extensive literature search in nine health science and engineering databases. Meta‐analysis involved converting correlations to Fisher's Z scores, calculating 95% confidence intervals using the R Studio Metacor package, and assessing fixed effects, DerSimonian and Laird's random effects models, and heterogeneity. Results Out of 12,853 identified records, 84 reports were retained for analysis. Eight reports that provided 12 datasets assessed the DTA of accelerometry data for identifying/monitoring apathy (n=4), AMB (n=1), anxiety (n=2) agitation (n=5), aggression (n=2), and wandering (n=2). Studies included one single‐blinded trial, one non‐randomized trial, and ten observational studies. A random‐effect meta‐analysis yielded a pooled correlation across studies of r=0.61 (0.50; 0.70), heterogeneity I2=68%. When the wandering NPS subgroup was eliminated from analysis, heterogeneity fell considerably to I2=36%, with a pooled correlation of r=0.55 (0.47; 0.62). Conclusions These findings suggest that accelerometry consistently provides good to moderate DTA for identifying/ monitoring NPS associated with motor behavior in PLWD. Future research should prioritize standardizing the reporting of measurement procedures, signal thresholds, outcome measures, devices, and reference standards.
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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 | 0.040 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".