Measuring lifespace in older adults with mild Alzheimer's disease using smartphone GPS
Bibliographic record
Abstract
Withdrawal from activity can be challenging to assess due to the nature of amnesia associated with Alzheimer's disease (AD). GPS can capture the geographical area a person covers in daily life, or lifespace, as a proxy measure of voluntary and social activity. The purpose of the study was to examine the utility of GPS to measure lifespace in older adults with mild AD. We hypothesized that lifespace parameters (time and distance away from home, area covered) would be reduced in AD compared to healthy older adults. Nineteen community-dwelling older adults with mild AD (MMSE=14-28, age=70.7±2.2y) and 35 controls (CTL, age=74.0±1.2 y) wore a GPS-enabled smartphone during the day for 3 days. Time away from home was calculated as the percentage of time spent outside a 25m radius of their home coordinates. Mean distance was measured as the average distance from home. Area covered was determined by computing the area of the convex hull of the GPS data for each participant. All measures were log 10 transformed to produce a parametric distribution and tested for group effect using 2-sample t-tests. We found that lifespace size is smaller in the AD group, indicated by lower mean distance from home and area covered. However, we did not find significant group difference in time away from home. Our preliminary findings suggest that GPS data can provide insight into spatial and temporal patterns of activity, and can potentially be used as an outcome measure of treatment intervention. Acknowledgments: We acknowledge the contributions to Alicia Capobianco and Teresa Wu in collecting data. This work was funded by a grant from the Alzheimer's Association. J. Tung was funded by the Alzheimer's Association of Canada.
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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 | 0.001 | 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 | 0.001 |
| 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".