Visualizing Physical Activity Patterns among Community-Dwelling Older Adults: A Pilot Study

Abstract

Regular physical activity relates to physical and mental functioning in older people, and promoting physical activity has the potential to substantially reduce functional decline and improve well-being. Despite this, investigations of the physical activity quotient through participation in functional activities in everyday life have traditionally gained limited focus among older populations compared to leisure-time physical activity and exercise. Considering the accumulated evidence of the health benefits of low-intensity physical activity, exploring and measuring such activities in this population is highly relevant. The aim of this study was to visualize and describe older people’s physical activity patterns in daily life using a time-geographic approach in combination with the estimation of metabolic equivalents (METS). To exemplify the new method, a sample of nine retired men (65–82 years old, mean age 76.4 ± 5.8) with no homecare services from the municipality was recruited. In order to enable a visual analysis of the physical activity patterns in daily life, we developed the VISUAL-PA software, which is a visual analysis tool that includes METS to account for intensity and enables the analysis of distinct types and domains of physical activity. The VISUAL-PA software creates graphic outputs of physical activity patterns that enable the identification, visualization, and analysis of distinct types and intensities of physical activity in addition to sedentary behavior. The use of VISUAL-PA can contribute to a broader understanding of the complexity in physical activity patterns among older adults in terms of dimensions such as activity patterns and habits, domains, and intensity level. To strengthen the public health strategies that promote health and an active lifestyle, additional knowledge about physical activity patterns is necessary. Moreover, the visualization of physical activity can enable reflections on and awareness of activity habits and preferences, and thus facilitate behavior changes in older individuals.

An Approach to a Novel Device Agnostic Model Illustrating the Relative Change in Physical Behavior Over Time to Support Behavioral Change

“…Intensity is the rate of energy expenditure during the activity. Finally, the work by Haga et al (2018) presents the software VISUAL-PA, aiming to visualize the PA pattern in daily life for elderly people, which is in line with the present work other than the actual target group.…”

“…As the focus of this work relates to changing behavior, it is supported by Alberto and Troutman's (2003) work of different dimensions to study when observing behaviors that humans perform. Haga et al (2018) developed a well-functioning software, VISUAL-PA, which focuses on elderly people and PA. The PB dimensions model presented in this paper adds important aspects to that model by having a wider perspective and adopting PB behavior as a whole and targeting a larger group-the adults.…”

“…The PB dimensions model presented in this paper adds important aspects to that model by having a wider perspective and adopting PB behavior as a whole and targeting a larger group-the adults. Another unique aspect of the presented PB dimension model is its ability to present the relative change in behavior over time, which VISUAL-PA (Haga et al, 2018) is not able to do. Similar to Strath and colleagues (2013), the PB dimension model categorizes PA in different dimensions.…”

“…Regarding the components contributing to the PB dimensions model, there are several studies involving the mode dimension, which, for instance, Haga et al (2018), has been integrated into VISUAL-PA software. Additionally, Dogra et al (2010) investigated the physical activity mode in groups of different ethnicities in Canada.…”

“…To the best of our knowledge, there are only a few studies that aim to visualize or illustrate PB patterns or changes thereof over time on an individual level. One study by Haga et al (2018) aimed to visualize and describe older people's PA pattern in daily life by using VISUAL-PA software, a visual analysis tool that analyzes the intensity of PA as well as types and domains of PA, based on time-geography diaries. Today, there is no available model that can manage data from devices that measure and present PB patterns and relative changes in PB over time.…”