Remote Monitoring of Instrumental Activities of Daily Living Reveals Intrinsic Capacity in Older Adults

Unadopted intrinsic capacity (U-IC) may be an early indicator of loss of independence, reflecting the gap between intrinsic abilities and actual engagement in daily activities. Impairments in instrumental activities of daily living (IADLs) are central to this construct but are usually assessed subjectively. We developed and evaluated a remote patient monitoring solution that operationalizes U-IC by combining objective measures of mobility, IADL engagement, frailty, and patient-reported outcomes, and tested its ability to identify older adults with cognitive impairment and fallers, two key risk factors for loss of independence.

Community-dwelling adults aged 50-95 years were recruited from a convenience sample for this feasibility study. We examined U-IC-related risk factors including physical frailty, depression, self-reported IADL ability (Lawton Scale), remotely derived IADL inability (medication noncompliance, leisure disengagement, food-preparation inactivity), and mobility deficits (inactivity, postural transition, walking, and standing deficits). Cognitive status was classified using the Montreal Cognitive Assessment (MoCA; cutoff 25). All variables were normalized to a 1 to 10 scale (1 best, 10 worst) to support holistic visualization. Group-level comparisons by cognitive status and fall history were used to evaluate the discriminative ability of U-IC models.

Sixty-four participants completed the assessment (cognitively healthy n = 22; cognitively impaired n = 42). U-IC metrics collected over 7 days (24/7) related to food-preparation inactivity (odds ratio (OR) = 3.76, p = 0.026) and leisure disengagement (OR = 3.42, p = 0.033) significantly differentiated cognitively impaired participants. Seventeen (27%) participants reported a fall in the past year and, versus non-fallers, showed greater slowness (OR = 3.70, p = 0.018), lower self-reported IADL ability (OR = 3.50, p = 0.034), and more postural transition deficits (OR = 3.00, p = 0.042). U-IC models incorporating key variables showed good discrimination for cognitive impairment (AUC = 0.80) and fair discrimination for fall status (AUC = 0.78).

Remote, objective assessment of U-IC is a promising approach to identify older adults at high risk for loss of independence, including those with cognitive impairment and a history of falls. The remote monitoring platform and holistic visualization may serve as an efficient pre-screening tool to flag at-risk individuals and guide timely, multidimensional interventions. Longitudinal studies are needed to determine how changes in U-IC predict future loss of independence and response to targeted interventions.