Digital Measurement of Subjective Experiences in Alzheimer Disease and Related Dementias (AD/ADRD)
Bibliographic record
Abstract
Unlabelled: Symptoms such as loss of pleasure, agitation, and sadness are subjective experiences that contribute significantly to caregiver burden and health care costs in Alzheimer disease and related dementias (AD/ADRD). However, traditional self-report measures of subjective experiences are limited in AD/ADRD due to cognitive impairments and awareness. Passive sensing, which collects data without active participant input, has emerged as a promising approach to quantify aspects of subjective experiences. Smartphones, wearables, and in-home sensors can quantify mobility, physiology, speech, and social interaction markers of constructs relevant to AD/ADRD. Available research indicates potential but is largely at the proof-of-concept stage. In this Commentary, we discuss several roadblocks to future translation of passive sensing in measuring subjective experiences in AD/ADRD, including technical implementation, data harmonization, validation, ethical and privacy principles. Addressing these challenges could lead to transformative applications to care for AD/ADRD, enabling precise monitoring of behavioral symptoms and related treatment targets, ultimately improving quality of life for persons with AD/ADRD and their caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".