Identifying pre-agitation biometric signature in patients with dementia: A feasibility study
Bibliographic record
Abstract
Background:Agitation and aggression (AA) occur frequently in patients with dementia (PwD), and cause distress to PwD and caregivers. This study will investigate whether physiological parameters, such as actigraphy, heart rate variability, temperature, and electrodermal activity, measured via wearable sensors, correlate with AA in PwD. It will also explore whether these parameters could be compiled to create a pre-agitation biometric marker capable of predicting episodes of AA in PwD.Methods:This study will take place at Ontario Shores Centre for Mental Health Sciences. Thirty inpatient participants who are inpatients, males, and females, aged 60 or older, with clinically significant AA, and diagnosis of Major Neurocognitive Disorder will be recruited. Participants will wear the device for 48 to 72 hours on three occasions during an 8-week study period. Participant demographics and clinical measures used to assess behavior will be collected at specific time intervals during the study period.Ceiling mounted cameras and clinical data are collected to annotate episodes of AA, which will allow identification of peripheral physiological markers “signature” unique to the patientResults:the algorithm connecting wearable devices, cloud and cameras was tested on healthy volunteers and demonstrated feasibility and reliability. The feasibility of implementation in PwD has been demonstrated in our sample of PwD previously in a sample of 6 participants. Feasibility in this larger sample will be assessed. Correlation analysis between physiological measures, camera capture of agitation onset and clinical measures will be calculated to identify agitation events and pre-agitation triggers. Various machine learning and features extraction/exploration techniques will be used to test whether physiological measures can detect exact time of agitation and predict pre-agitation triggers. This study will provide a reasonable estimation of sample size needed to detect a meaningful effect size, which will be determined from the prediction model.Conclusion:Early detection of AA in PwD will allow caregivers to offer timely and personalized interventions which will help avoid crises and critical incidents and improve quality of life in PwD and their caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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".