Assessing Upper Extremity Motion: An Innovative Method to Identify Frailty
Bibliographic record
Abstract
OBJECTIVES: To objectively identify frailty using wireless sensors and an innovative upper extremity motion assessment routine that does not rely on gait. DESIGN: Validation study. SETTING: Southwestern tertiary academic medical center, Tucson, Arizona. PARTICIPANTS: Convenience subsample of the Arizona Frailty Cohort, a community-dwelling older adults (≥65; n = 117; 50 nonfrail, 51 prefrail, 16 frail). MEASUREMENTS: Wireless sensors were attached to the upper arm and forearm with bands, and subjects performed repetitive elbow flexion for 20 seconds on each side. Information was extracted on objective slowness, weakness, exhaustion, and flexibility measures, and associations between parameters and Fried frailty categories were determined. RESULTS: Speed of elbow flexion (slowness) was 29% less in prefrail and 59% less in frail than in nonfrail controls (P < .001), power of movement (weakness) was 61% less in prefrail and 89% less frail (P < .001), and speed variation (exhaustion) was 35% more in prefrail and 272% more in frail (P < .001). Using elbow flexion parameters in regression models, sensitivity and specificity of 100% were achieved in predicting frailty and sensitivity of 87% and specificity of 95% in predicting prefrailty compared to Fried frailty category. CONCLUSION: The suggested innovative upper extremity frailty assessment method integrates low-cost sensors, and the physical assessment is easily performed in less than 1 minute. The uniqueness of the proposed technology is its applicability in older nonambulatory individuals, such as those in emergency settings. Further improvement is warrant to make it suitable for routine clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".