FEASIBILITY AND VALIDITY OF WEARABLE TECHNOLOGY FOR ASSESSING MOBILITY AMONG HOSPITALIZED OLDER ADULTS
Bibliographic record
Abstract
Abstract The need for strategies to promote mobility during acute care hospital stays among older adults is increasingly recognized. Wearable technologies show great potential for this purpose. The objective of this study was to determine the most accurate wearable sensor device and location for capturing in-hospital mobility in older patients. Twenty-five older adults (79.6±8.1 years) admitted to a hospital medical care unit volunteered to test the feasibility and concurrent validity of the ActiGraph wGT3X-BT, Mox1, MetaMotionC and Fitbit Versa. All 25 enrolled patients wore the devices simultaneously on the wrist, hip, and ankle while undertaking a mobility activity protocol consisting of supine lying, sitting, and standing tasks. Participants also performed the Timed Up and Go (TUG) and 10-meter walk test (10MWT) wearing the devices. A trained physiotherapist supervised the performance of the tasks. Most participants (58%) preferred to wear the device on the ankle. Overall, the ActiGraph and Mox1 were the easiest to set up and download. The ActiGraph was the most reliable device, retrieving 100% of the collected data. Regarding body posture, the thigh-worn ActiGraph algorithm accurately classified 78% of sitting and lying postures, as well as 84% of standing postures, indicating high overall accuracy. After implementing the recommended low-frequency filter extension for slower gait speeds, the ankle-worn ActiGraph showed the narrowest limits of agreement and observations closer to zero for step count during the TUG and the 10MWT. These findings lay the foundation for subsequent work to validate wearable devices for monitoring “free-living” mobility in this population.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".