JMIR Publications

ABSTRACT

Background:

The theory of complexity in aging indicates that the complexity of sensor-derived physiological and behavioral signals reflects an older adult’s adaptive capacity and, in turn, their frailty. Smart homes with ambient sensors offer a unique opportunity to longitudinally explore the complexity of older adults’ indoor movement in a real-world setting. Here, we introduce a computational method to estimate behavior complexity from sensor data. We further conduct a multiple-methods case series to explore the relationship between entropy-measured smart home data complexity and older adult frailty.

Objective:

To explore the relationship between entropy-measured ambient sensor data complexity and frailty in independent community-dwelling older adults.

Methods:

The nature of older adults’ indoor movement complexity is measured by quantifying the entropy of smart home data. Eleven cases with persons aged 65+ were drawn from an ongoing smart home study to illustrate the method. We assessed weekly frailty for these cases using the Clinical Frailty Scale (CFS). For corresponding time ranges, we measured the complexity of smart home data using a fixed-width sliding window and an entropy-based complexity index (RényiCI) built on a universal sequence map (USM-Rényi). Descriptive statistics and graphical analysis were used to describe intra-individual frailty and sensor complexity change.

Results:

Complexity of sensor-observed indoor movement does change over time in older adults as quantified by the computational method. In some individuals, these changes track with health transitions and frailty progression. The trends and monotonicity of complexity trajectories varied between cases. Three of the cases demonstrated a negative association between frailty and complexity, while the association was not as clear for the other cases.

Conclusions:

The complexity of older adults’ smart home data is highly diverse. Changes in health and frailty influence indoor movement complexity. Although the findings suggest a relationship between frailty and complexity, confounding factors such as home layout, visitors, external events, and technology disruptions may influence sensor signals.