Use of Global Positioning System (GPS) Watches and Accelerometry to Monitor Mobility in Older Adults
Bibliographic record
Abstract
Mobility is an important indicator of physical and psychosocial health in older people. Although physical activity monitors (pedometers and accelerometers) measure parameters related to walking, they do not measure other types of movement or allow for direct quantification of distances, speeds or locations traveled. PURPOSE: To determine the utility of using combined global positioning system (GPS) and accelerometry data to quantify mobility in older adults. METHODS: Twenty older adults, 74.4 ± 4.2 years of age (mean ± SD), were recruited to wear a GPS watch and an accelerometer for one day as they went about their usual routine. The following day subjects were asked to answer questions about their experience wearing the equipment. RESULTS: Subjects averaged 26.9 ± 8.4 activity bouts per day (denned as periods of walking for 3 or more consecutive minutes with a minimum of 10 steps per minute). Of these, 7.8 ± 6.0 took place away from home. In terms of total steps, subjects took 10,012 ± 5026 steps per day and walked 2.7 ± 2.9 km when away from home. The accelerometer recorded 793 ± 1205 steps erroneously while subjects were traveling in a vehicle. GPS data was successfully recorded for 18 of the subjects. For these 18 subjects, the duration of data collection varied from 43 minutes to over 12 hours (mean 7.66 ± 4.06 hours). Subjects left their homes 1.8 ± 1.3 times per day and took 3.9 ± 3.5 trips by vehicle, traveling 20.6 ± 19.1 km. They averaged 94.91 ±21.74 minutes away from home per trip. All subjects rated the GPS watch and accelerometer easy to use and the accelerometer comfortable to wear on a belt. Nineteen of twenty rated the GPS watch comfortable to wear. CONCLUSIONS: Although difficulties were encountered in using GPS watches to acquire a full day's worth of data in some subjects, use of this combined technology looks promising in terms of the comprehensive mobility data that can be measured in older adults. Supported by CIHR Institute of Aging Mobility in Aging Grant and CIHR fellowship.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".