Monitoring mobility in older adults using a Global Positioning System (GPS) smartwatch and accelerometer: A validation study
Bibliographic record
Abstract
There is growing interest in identifying valid and reliable methods for detecting early mobility limitations in aging populations. A multi-sensor approach that combines accelerometry with Global Positioning System (GPS) devices could provide valuable insights into late-life mobility decline; however, this innovative approach requires more investigation. We conducted a series of two experiments with 25 older participants (66.2±8.5 years) to determine the validity of a GPS enabled smartwatch (TicWatch S2 and Pro 3 Ultra GPS) and separate accelerometer (ActiGraph wGT3X-BT) to collect movement, navigation and body posture data relevant to mobility. In experiment 1, participants wore the TicWatchS2 and ActiGraph simultaneously on the wrist for 3 days. In experiment 2, participants wore the TicWatch Pro 2 Ultra GPS on the wrist and ActiGraph on the thigh for 3 days. In both experiments participants also carried a Qstarz data logger for trips outside the home. The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the gold standard Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under controlled conditions, the TicWatch Pro 3 Ultra GPS consistently measured step count in line with the participants' self-reported data, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models were valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-life mobility in older adults.
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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 | 0.001 |
| 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".