Behavioral patterns in elderly single-person households

Abstract

The global rise in the aging population and the increase in older adults living alone have raised concerns about health-related behaviors, particularly sedentary lifestyles and reduced daily activities. These behaviors are linked to higher risks of physical and cognitive conditions. While many global studies have explored these patterns, research within the Chilean context remains limited. This work presents a analysis of behavioral patterns in elderly individuals living alone in Chile, offering valuable insights into this population. Using clustering techniques, we identified two distinct activity patterns among the participants. The first pattern is characterized by a gradual increase in activity during the day, peaking around midday and followed by a decline, likely associated with meal preparation and rest. The second pattern demonstrates a more dynamic lifestyle, with a rapid surge in activity after waking and sustained levels throughout the day, suggesting a potentially healthier approach to aging. These findings align with previous studies indicating high levels of sedentary behavior in older adults, reinforcing the need for interventions tailored to diversify daily routines and promote physical activity. This study is the first to explore these patterns in the Chilean context, contributing to a more comprehensive understanding of elderly care and informing future strategies for improving the well-being of older adults living alone.

Keywords: Ambient Assisted Living (AAL); Behavioral pattern identification; Elderly activity monitoring.

Graphical abstract

Figure 1

Schematic representation of the behavior recognition system. It illustrates the PIR sensors distributed in the rooms, the connection to the server, and the data analysis for identifying activity patterns.

Figure 2

Activities of the persons depending on the time of the day (black line is the mean, orange lines are first and third quartiles (left) and Use of the rooms for the different persons of the dataset (right).

Figure 3

Comparative Daily Activity Curves of two Houses (subjects).

Figure 4

Level of activity for each hour for the first cluster (left) and second cluster (right) of persons.

Figure 5

Wake-up time.