Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.

Autor: Zablocki RW; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA., Hartman SJ; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA., Di C; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, Washington, USA., Zou J; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA., Carlson JA; Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Kansas City, 610 E. 22nd St., Kansas City, 64108, Missouri, USA., Hibbing PR; Department of Kinesiology and Nutrition, University of Illinois Chicago, 1919 W Taylor St, Chicago, IL, 60612, USA., Rosenberg DE; Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, 98101, Washington, USA., Greenwood-Hickman MA; Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, 98101, Washington, USA., Dillon L; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA., LaCroix AZ; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA., Natarajan L; Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA. lnatarajan@health.ucsd.edu.
Jazyk: angličtina
Zdroj: The international journal of behavioral nutrition and physical activity [Int J Behav Nutr Phys Act] 2024 Apr 26; Vol. 21 (1), pp. 48. Date of Electronic Publication: 2024 Apr 26.
DOI: 10.1186/s12966-024-01585-8
Abstrakt: 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 β ^ : 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.
Trial Registration: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.
(© 2024. The Author(s).)
Databáze: MEDLINE
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