COVID‐19 and preclinical Alzheimer disease: Driving, mobility, activity and experiences of older adults in the United States
Bibliographic record
Abstract
Abstract Background As the world grapples with the COVID‐19 pandemic, there have been widespread disruptions to everyday life due to social distancing. Older adults with Alzheimer disease (AD) are at increased risk of morbidity and mortality from COVID‐19. It is unknown how COVID‐19 affects the mobility patterns of older adults with preclinical AD. Since before the pandemic, we have been monitoring the driving behaviors of older adults, enabling us to evaluate the impact of the pandemic on individuals with and without preclinical AD. Method We used in‐vehicle Global Positioning System (GPS) devices to study driving behaviors of 115 older adults enrolled in the DRIVES study (aged 65+) from 1/1/2019 to 31/12/2020. The cohort included 62 individuals with preclinical AD (PreAD) and 53 without preclinical AD (CTL), as determined by cerebrospinal fluid biomarkers. All participants completed an online survey about their overall experiences during the pandemic. Using the GPS data, we determined the average monthly distance travelled, and the number of visitations to destinations categorized as food shopping, place of worship, restaurant, leisure, or health. All measures were computed monthly. Result oth groups experienced an approximate 40% decline in average monthly distance travelled overall after the start of the pandemic (PreAD: 1287.92 to 783.38 km vs. CTL: 1751.26 to 1053.29 km). Visits to places of worship, restaurants, leisure and health places declined by 70%, 46%, 23%, and 23% for the PreAD group, and by 48%, 31%, 48%, and 22% for the CTL group, respectively. However, the pandemic did not result in a significant decline in Food Shopping among either of the groups. Overall, compared to the CTL group, the PreAD group experienced a higher level of stress in response to the recommendations for socially distancing (p<0.01), more uncertainty about their risk of COVID‐19 (p<0.05), more decline in trips for worship (p<0.05) and less decline in trips for leisure (p<0.01). Conclusion Our findings indicate decreased mobility in all older adults during the pandemic, with the preclinical AD group exhibiting more decline in trips to places of worship, less decline in leisure activities, and increased stress and uncertainty in response to COVID‐19.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".