Preliminary feasibility and development of a heart rate-based mobility and activity scale for hospitalized older...

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.

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.

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.

Smartwatch-derived HR data may quantity hourly mobility and activity of hospitalized older adults, facilitating automated and real-time monitoring.