Revealing Sedentary Patterns: A New Approach Using Wearable Technology
Exploring the subtle intricacies of sedentary behavior, researchers have utilized innovative wearable sensors to decipher the patterns of sitting behavior in postmenopausal women. By applying functional principal component analysis (FPCA), this study not only identified common sitting patterns but also associated these patterns with cardiovascular health indicators like blood pressure. The insights drawn emphasize the nuanced role of minimal movements in potentially moderating health risks associated with prolonged sitting.
The approach was meticulous, involving the simultaneous use of two types of wearables - activPAL and ActiGraph - which provided detailed data on posture and movement, respectively. This dual-data stream enabled a comprehensive analysis of how even slight movements during extended sitting periods can influence health outcomes. The study's findings are pivotal as they offer a more granular understanding of sedentary behavior, moving beyond traditional measures of physical activity.
Highlighting the importance of small physical activities, the study suggests that minimal movements, often overlooked in daily life, have significant health implications. This underscores the potential of targeted interventions that encourage small activities for individuals leading sedentary lifestyles, aiming to reduce the risk of high blood pressure and related cardiovascular ailments.
Article Information
Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors
International Journal of Behavioral Nutrition and Physical Activity. Rong W. Zablocki et al.
Abstract
Background: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP).
Methods: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects.
Results: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized ˆβ�^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP.
Conclusion: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health.
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