Preliminary feasibility and development of a heart rate-based mobility and activity scale for hospitalized older adults
Bibliographic record
Abstract
BACKGROUND: Mobility is a key health indicator in hospitalized older adults, yet routine mobility tracking remains limited by lack of automated and standardized measurements. Advances in smartwatch technology and machine learning may enable mobility quantification using heart rate (HR) and HR variability data. METHODS: In this pilot study, we recruited 30 adults aged ≥ 65 years in a tertiary care geriatric ward to develop (n = 8) and validate (n = 30) the automated Mobility and Activity Scale (MAS). Twelve smartwatch-derived HR features were used in a random forest model to predict 5 activity levels (0 = sleep to 4 = walking with at least a moderate effort or >20 min). We examined concurrent validity with Hierarchical Assessment of Balance and Mobility (HABAM), gait speed, and functional status, as well as discriminant validity with frailty and multimorbidity. We assessed acceptability of smartwatch use. RESULTS: Participants' mean (SD) age was 86 years (8), 18 (60%) were female, and mean follow-up was 8.3 (5.2) days. Mean (SD) HABAM score was 36 (18) and gait speed was 0.53 (0.26) m/s. Across the cohort, mean (SD) MAS score was 1.2 (1.0) overall and 2.1 (0.7) for 10 most active hours. MAS scores were moderately correlated with HABAM (r = 0.43 [95% CI = 0.07,0.69]) and functional status (r = -0.31 [95% CI = -0.60,0.06]), but not with gait speed (r = 0.02 [95% CI = -0.39,0.42]). MAS scores had no association with frailty or multimorbidity. Smartwatch wearing was acceptable. CONCLUSIONS: Smartwatch-derived HR data may quantity hourly mobility and activity of hospitalized older adults, facilitating automated and real-time monitoring.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".