Feasibility of community‐based remote, biometric data collection in persons with Alzheimer’s disease and behavioral symptoms
Bibliographic record
Abstract
Abstract Background Aging at home with Alzheimer’s disease (AD) is progressively difficult due to cognitive dysfunction interfering with persons’ functional activity and behavioral regulation. Community‐based collection of biometric data via wearable devices provide an innovative approach as a measure of behavioral symptomology. This study analyzes feasibility of remote, biometric data collection during a non‐pharmacological, home‐based randomized controlled trial of older adults with AD. Methods Individuals, aged 65 or older, with diagnosis of AD as primary dementia type and behavioral symptoms who resided in the community were included in this prospective study. ActiGraphs were mailed to care partners who implemented device use and management. Feasibility was assessed via accepting and wearing of the device by the person with AD through care partner facilitation at baseline and post‐intervention (continuous wear for 6 days at both time points). Baseline actigraphy data (mean motor activity; MMA) were assessed to determine utility of acceptance/wearing the device as means to collect biometric data. Secondary analyses assessed Pearson correlation of demographic information including cognition measured by the Montreal Cognitive Assessment and the Neuropsychiatric Inventory‐Questionnaire (NPI‐Q). Results Twenty‐one participants with AD engaged in the study. Eighteen participants completed baseline data collection (three caregivers refused the device on behalf of person with AD). Fourteen participants or 66.7% (95% CI: 46.6% to 86.8%) also completed post‐intervention data collection (one dropout in mid‐study and three care partners refused the device at second time point). Baseline MMA data were measured: 24‐hour MMA = 608,913; daytime MMA = 253,534; evening MMA = 308,113; nighttime MMA = 47,079. Age (x̄ = 77.8±1.7; r = ‐.56, p‐value = 0.038) was negatively correlated and cognitive impairment (x̄ = 11.13±2.3; r = .789, p‐value = 0.002) was positively correlated with 24‐hour MMA. NPI‐Q total and emotional items (agitation, anxiety, liability) were not correlated in this sample. Conclusion Community‐based biometric data collection for older adults with AD and behavioral symptoms is feasible. Behavioral disturbance and care burden likely lowered acceptability of the device. Future power calculations anticipating 66% compliance maybe sufficient for primary outcome analyses in community‐based data collection. Additional studies are needed to validate wearable devices as a primary outcome measure for behavioral symptoms in community‐based participants as this study was not sufficiently powered for such analyses.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".