Feasibility and Acceptability of Objective Pain Assessment Using Multimodal Sensing Signals in Older Adults with Alzheimer's Disease: Preliminary Results of a Pilot Study
Bibliographic record
Abstract
Abstract Background Chronic pain remains mostly untreated in those with Alzheimer’s disease and related dementias (ADRD), mainly due to limited capacity to verbalize pain. Development of reliable objective biomarkers of chronic pain could improve pain assessment and treatment. We explored feasibility and acceptability of using a wearable electroencephalograph (EEG) and a screen‐based eye tracker system to identify neural signatures of chronic pain in this population. Method Inclusion criteria were ADRD in an early to moderate stage defined by Mini‐Montreal Cognitive Assessment (Mini‐Moca) and Quick Dementia Rating System (QDRS) scores and chronic pain (3 months or longer). Participants underwent a single‐session test where participant‐reported outcome (PRO) measures were obtained with 32‐channel EEG. Neural signals were recorded at resting and during a cognitive task with visual stimuli. Attention to the stimuli was captured using an eye‐tracking system. Result Five of 29 applicants met all criteria and were included. The majority were excluded due to dementia severity or lack of chronic pain. Four subjects (3 females) concluded the study. Participants' ages ranged from 76 to 93 years. They were diagnosed with Alzheimer’s disease, dementia of unspecified type, or dementia related to Parkinson’s disease. Mini‐Moca scores ranged from 2‐8 of 15 and QRDS ranged from 6‐20 of 30. On Visual Analog Scale for pain, three participants rated mild to moderate pain (2 to 5 out of 10) and one rated severe pain (9 out of 10). Three reported that pain significantly affected their ability to walk, relationships with people, and enjoyment of life. All participants reported feeling comfortable with the EEG cap and eye tracker, were satisfied with the study, and would recommend the study to others. In terms of feasibility, the major challenge was to calibrate the eye tracker system and the duration of the cognitive task which was 30 minutes. Conclusion While using a table‐top eye tracker was comfortable for all participants, the calibration phase was difficult. In one case, the image‐based task was not feasible which might be related to participant having moderate dementia. Our preliminary work suggests that wearable modalities may work better in people with ADRD and carry the potential to assess pain objectively.
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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 | 0.005 | 0.006 |
| 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) | 0.002 | 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".