P3‐221: POPULAR OPINION ON THE USE OF DIGITAL BIOMARKERS FOR EARLY DETECTION OF COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
We conducted a survey to gain insight into public opinion regarding passive monitoring of cognition. Monitoring cognitive function through technology may act as a tool for early detection (Dagum P, npj Digital Medicine. 2018 Mar;1:1), which would improve clinical patient care and fuel scientific research. The data obtained from the surveys, will optimize the design of smartphone applications and aim to increase the population's receptiveness to this promising technology. The study consisted of a one-time, internet-based survey (n=148). The surveys were distributed electronically with the help of the UMass Conquering Diseases program. 60.5% of respondents would be highly likely to agree to passive monitoring of cognition via a smartphone application. Among respondents over the age of 50, 87% own a smartphone (n=86). Age had a significant impact on the perception of worsening learning and memory, with the highest perceived decline in the ranges between 50 and 69 years of age (p=0.01). A higher degree of experience with technology made it significantly more likely for participants to agree to an application (p=0.005). Healthcare professionals were significantly less likely to agree to monitoring (p=0.03). There were significant concerns regarding privacy in such an application (p<0.01). Sex, age, education level, a diagnosis of dementia in the respondent or a relative, and the perception of decline in cognition, did not significantly affect the participant's likelihood of agreeing to monitoring. Most participants would be willing to have their behavior monitored on their smartphone as a tool for early detection of cognitive changes. Based on our data, the design of such applications must be particularly strict regarding patient privacy. Interestingly, health care professionals may need specific coaching to agree to use such tools. Finally, as technology becomes more widespread in our society, we would expect a larger percentage of the population to be amenable to monitoring, which would improve the sensitivity of technology-based tools.
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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".