Abstract and Introduction
Purpose: Information about mobility and physical function may be encoded in the complexity of daily activity pattern. Therefore, daily activity pattern complexity metrics could provide novel insight into the relationship between daily activity behavior and health. The purpose of the present study was to examine the association between the complexity of daily activity behavior and the mobility and physical function among community-dwelling older adults 75, 80, and 85 yr of age.
Methods: A total of 309 participants wore accelerometers concurrently on the thigh and the trunk for at least three consecutive days. Five activity states (lying, sitting, standing, walking, or activity other than walking) were defined in three different temporal grains (5 s, 1 min, and 5 min), and Lempel–Ziv complexity was evaluated. We assessed complexity of daily activity behavior using the life-space mobility and physical function with distance in preferred pace 6-min walk and the Short Physical Performance Battery.
Results: Weak positive associations were observed between the complexity of daily activity and the mobility and physical function at the finest temporal grains in both sexes (Spearman rho = 0.19 to 0.27, P < 0.05). No significant associations were observed in the coarsest temporal grain in either sex.
Conclusions: Lempel–Ziv estimates of daily activity complexity with a fine temporal grain seem to be associated with community-dwelling older adults' physical function. The coarsest 5-min temporal grain may have smoothed out physiologically meaningful short activity bouts. Because complexity encodes information related to timing, intensity, and patterning of behavior, complexity of activity could be an informative indicator of future physical function and mobility.
Mobility (ambulatory and transportation)[1,2] and physical activity decline with age. It is thought that this age-associated decline in mobility is at least partially driven by the age-associated decline in physical function.[4–6] Mobility and physical activity are rather inextricably linked because getting from point A to B requires at least some ambulation. This link between mobility and physical activity has been leveraged in gerontological research by designing research tools to quantify mobility. One such tool probes the extent of the geographical area that a particular individual covers in going through their daily activities and the frequency of travel (life-space assessment). Life-space assessment has subsequently been shown to be associated with physical activity in that those with larger life space also had more physical activity, and this association has been further corroborated by prospective findings, where a decreasing physical activity was associated with a diminishing life space among community-dwelling older adults. Moreover, mobility is strongly linked with quality of life and has strong prognostic value for disability and survival among older adults.
In addition to higher physical function, a larger life-space mobility may also mean a greater richness and freedom of movement, which might be reflected in the temporal pattern of different activities. It has been suggested that the structures of movement patterns include information that may not be captured with traditional measures of activity. More complex patterns in physiological or behavioral time series may characterize system's integrity and better ability to adapt flexibly to internal and external perturbations. At the functional level, this may be observed, for example, as capability for altering motor behavior to adapt to different task demands and, consequently, larger life space.
However, mobility patterns are challenging to quantify because of the multidimensional nature of daily activity behavior. Mobility patterning may be influenced by, for example, the activity type, intensity, duration, and frequency. Recognizing activities based on body-worn sensors is not a trivial task, and age-related decrease in vigor of movements[15,16] can further increase the difficulty of activity classification. Furthermore, the number and placement of wearable sensors affects the precision of recognizing activity classes. These challenges have been tackled with concurrent trunk and thigh-worn accelerometers, which enables robust classification of postures throughout the day,[13,18] while still retaining a reasonable participant burden.[13,17] Posture assessments may be particularly informative in daily activity behavior containing a large volume of stationary behavior where the distinction between sitting and standing may be of interest.
Having captured the daily activity behavior, the pattern needs to be quantified and described numerically from the recorded activity states (e.g., lying, sitting, standing, and ambulation) to enable statistical analyses. A new approach to describe patterns of daily activity behavior assessed with concurrently worn devices is to use complexity metrics (such as Lempel–Ziv complexity), which could add value to activity volume quantifications. Complexity metrics enable encoding information regarding activity patterning without requiring a known underlying structure of the patterning. Indeed, Paraschiv-Ionescu and colleagues have explored the use of complexity metrics as a way to quantify daily activity behavior without considering the volume of physical activity, and they found that people with chronic pain and fear of falling can be discriminated from nonaffected referents based on daily activity complexity assessed with prolonged accelerometry samples. Moreover, the same group has reported that changes in balance and physical function were associated with daily activity complexity after a 4-wk exercise intervention in 60–70 yr old, whereas no such associations were observed based on volume of physical activity indicated by conventional activity minute-based metrics. Recently, Rector and colleagues reported that greater complexity of daily activity behavior was associated with lower frailty index scores and higher ADL function among geriatric inpatients 65 yr or older. However, daily activity behavior complexity remains sparingly explored, and it is currently unclear whether it is associated with mobility or physical function among older community-dwelling adults.
As alluded to above, it has been recognized that metrics indicating patterning of daily activity in addition to quantifying volume of activity (e.g., minutes per day spent in moderate to vigorous activity) could be useful[11,23,24] particularly among older populations. As body-worn sensor technology is becoming more prevalent, they could be used to continually track and monitor changes in mobility and physical function in older adult population and, hence, enable the detection or prediction of adverse and deleterious decline. Metrics based on pattern analysis have already been demonstrated as more sensitive than volume of activity to changes in clinical and functional characteristics.[10,14,21,25,26] Physical task fatigability, for example, seems to be more sensitively indicated by fragmentation of daily activity behavior pattern compared with activity minutes.[25,26] Moreover, it is thought that it suffices to break a continuous bout of sedentary behavior with a brief bout of low-intensity activity to gain positive health effects. Such brief bouts may not register in the activity minutes but would increase the complexity of daily activity behavior because of the changes between activity states (e.g., an activity to rest transition).[10,14,20]
Building on such findings, we hypothesize that daily activity behavior pattern complexity could capture meaningful characteristics of daily activity and thus warrants further exploration. Therefore, the purpose of the present study was to explore the association between the complexity of daily activity behavior pattern and the mobility and physical function among community-dwelling older adults 75, 80, or 85 yr of age. We hypothesized that a positive association exists between the complexity of habitual activity pattern and the mobility and physical function.
Med Sci Sports Exerc. 2022;54(7):1210-1217. © 2022 American College of Sports Medicine