Wearable Tech for Health Monitoring of Environmental Events: Case Studies in Experimental Medicine
Bibliographic record
Abstract
Abstract The COVID-19 pandemic underscored the need for adaptability in both daily life and scientific research. Using data from the Einstein Aging Study, we present case studies demonstrating the value of mobile and wearable technologies for behavioral monitoring in rapidly changing environmental conditions. First, we examine data availability during the COVID-19 pandemic, highlighting how smartphone-based assessments enabled real-time tracking of cognitive and psychosocial health measures—such as stress, affect, and social relationships—alongside the impact of different viral strains on cognition and behavior. Second, we explore the feasibility of low-cost wearable air quality monitors in capturing environmental exposures during two distinct events—Canada wildfires of March 2023 and holiday fireworks. Third, we examine how seasonal variations impact cognitive function measurement, highlighting the importance of considering temporal context in behavioral assessments. Our findings suggest that mobile monitoring methods offer a reliable approach to data collection in unpredictable and rapidly changing environmental conditions. Future research should identify the minimum data required for accurate outcome prediction, determining the threshold at which data remains meaningful.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".