Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors
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 patt...
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| Vydáno v: | The international journal of behavioral nutrition and physical activity Ročník 21; číslo 1; s. 48 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
BioMed Central
26.04.2024
BioMed Central Ltd BMC |
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| ISSN: | 1479-5868, 1479-5868 |
| On-line přístup: | Získat plný text |
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| 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
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 |
|---|---|
| AbstractList | 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).
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.
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
: 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP.
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.
ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684. 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).BACKGROUNDSedentary 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).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.METHODSThe 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.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.RESULTSAt 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.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.CONCLUSIONIn 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.ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.TRIAL REGISTRATIONClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684. 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 $$\hat{\beta }$$ 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 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 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 [formula omitted]: 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; Keywords: Functional Principal Component Analysis (FPCA), Multilevel FPCA, Sedentary Behavior (SB), Accelerometer 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). 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. 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 [formula omitted]: 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. 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. 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 |
| ArticleNumber | 48 |
| Audience | Academic |
| Author | LaCroix, Andrea Z. Natarajan, Loki Hartman, Sheri J. Zou, Jingjing Rosenberg, Dori E. Carlson, Jordan A. Hibbing, Paul R. Di, Chongzhi Dillon, Lindsay Zablocki, Rong W. Greenwood-Hickman, Mikael Anne |
| Author_xml | – sequence: 1 givenname: Rong W. surname: Zablocki fullname: Zablocki, Rong W. organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego – sequence: 2 givenname: Sheri J. surname: Hartman fullname: Hartman, Sheri J. organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego – sequence: 3 givenname: Chongzhi surname: Di fullname: Di, Chongzhi organization: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center – sequence: 4 givenname: Jingjing surname: Zou fullname: Zou, Jingjing organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego – sequence: 5 givenname: Jordan A. surname: Carlson fullname: Carlson, Jordan A. organization: Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy Kansas City – sequence: 6 givenname: Paul R. surname: Hibbing fullname: Hibbing, Paul R. organization: Department of Kinesiology and Nutrition, University of Illinois Chicago – sequence: 7 givenname: Dori E. surname: Rosenberg fullname: Rosenberg, Dori E. organization: Kaiser Permanente Washington Health Research Institute – sequence: 8 givenname: Mikael Anne surname: Greenwood-Hickman fullname: Greenwood-Hickman, Mikael Anne organization: Kaiser Permanente Washington Health Research Institute – sequence: 9 givenname: Lindsay surname: Dillon fullname: Dillon, Lindsay organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego – sequence: 10 givenname: Andrea Z. surname: LaCroix fullname: LaCroix, Andrea Z. organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego – sequence: 11 givenname: Loki orcidid: 0000-0001-5719-828X surname: Natarajan fullname: Natarajan, Loki email: lnatarajan@health.ucsd.edu organization: Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38671485$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/gerona/glw250 10.1111/j.2517-6161.1995.tb02031.x 10.1249/MSS.0000000000001315 10.1007/s10552-014-0433-z 10.1214/08-AOAS206 10.1002/clc.23303 10.1038/s41569-021-00547-y 10.1186/s12984-015-0081-x 10.1249/MSS.0000000000001257 10.1249/MSS.0b013e3181ed61a3 10.1007/s40279-017-0716-0 10.1093/biostatistics/kxs051 10.1111/j.1541-0420.2012.01808.x 10.1111/j.1467-9868.2005.00530.x 10.1016/j.apergo.2016.10.006 10.1136/bmjsem-2016-000152 10.1249/MSS.0000000000000840 10.1016/j.pmedr.2021.101405 10.1136/bjsm.2006.030262 10.1016/j.cjca.2020.02.003 10.1186/s12966-022-01274-4 10.1016/j.jsams.2015.04.006 10.1249/MSS.0b013e31820ce174 10.1123/jpah.2019-0088 10.1161/HYPERTENSIONAHA.118.11190 10.20463/jenb.2018.0013 10.1139/h2012-024 10.2196/28684 10.3390/ijerph17238864 10.1016/j.jsams.2013.07.002 10.1249/MSS.0000000000002705 10.1161/CIRCRESAHA.118.312669 10.1007/s00125-013-3102-y 10.1097/HJH.0000000000001463 10.1161/HYPERTENSIONAHA.118.12373 10.1016/j.apergo.2017.03.012 10.1371/journal.pcbi.1002375 10.1371/journal.pone.0164928 10.1016/j.sleh.2015.09.002 10.1016/j.jsams.2011.12.001 10.1007/s40279-020-01325-5 10.3389/fpubh.2018.00288 10.1016/j.jsams.2011.04.003 10.1007/s40279-015-0322-y 10.1097/HJH.0000000000001101 10.1007/s12561-019-09237-3 10.1016/j.gaitpost.2015.04.008 10.1016/j.numecd.2014.04.011 10.1186/s12966-017-0525-8 10.1186/s12966-018-0749-2 10.1093/ajcn/72.6.1451 10.1016/j.gaitpost.2021.06.004 10.1186/s12966-020-00938-3 |
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| Keywords | Functional Principal Component Analysis (FPCA) Accelerometer Sedentary Behavior (SB) Multilevel FPCA |
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| References | DM Bhammar (1585_CR49) 2017; 49 E Oliveros (1585_CR47) 2020; 43 K Wijndaele (1585_CR2) 2014; 57 1585_CR26 1585_CR25 DA Jacobs (1585_CR56) 2015; 12 F Yao (1585_CR42) 2006; 68 AJ Wiseman (1585_CR3) 2014; 25 WJ Grooten (1585_CR52) 2017; 60 CZ Di (1585_CR16) 2009; 3 N Chung (1585_CR55) 2018; 22 CJ Lavie (1585_CR4) 2019; 124 MS Tremblay (1585_CR10) 2017; 14 Y Benjamini (1585_CR34) 1995; 57 L Choi (1585_CR27) 2011; 43 PH Lee (1585_CR23) 2015; 45 P Khatri (1585_CR40) 2012; 8 JM Zeitzer (1585_CR19) 2018; 73 1585_CR35 1585_CR32 JH Migueles (1585_CR11) 2017; 47 S Kozey-Keadle (1585_CR14) 2011; 43 A Narayanan (1585_CR43) 2020; 17 J Gertheiss (1585_CR33) 2013; 14 SY Xu (1585_CR17) 2019; 11 A Holtermann (1585_CR7) 2017; 63 C Paterson (1585_CR20) 2020; 50 M Quante (1585_CR38) 2015; 1 EL Schiffrin (1585_CR46) 2020; 36 Q Xiao (1585_CR18) 2022; 19 MA Greenwood-Hickman (1585_CR44) 2021; 53 KC Heesch (1585_CR36) 2018; 15 N Aguilar-Farías (1585_CR39) 2014; 17 RN Larsen (1585_CR51) 2014; 24 SK Keadle (1585_CR1) 2017; 49 PC Dempsey (1585_CR22) 2018; 72 GA Koepp (1585_CR54) 2016; 2 KE Webster (1585_CR30) 2021; 23 MJ Wheeler (1585_CR21) 2019; 73 J Goldsmith (1585_CR41) 2013; 69 JA Levine (1585_CR53) 2000; 72 JE Sasaki (1585_CR28) 2011; 14 D Thivel (1585_CR5) 2018; 6 PM Grant (1585_CR15) 2006; 40 SJ Hartman (1585_CR24) 2021; 10 AM McGarty (1585_CR13) 2016; 11 J Bames (1585_CR9) 2012; 37 B Barone Gibbs (1585_CR48) 2017; 35 RP Kuster (1585_CR6) 2020; 17 PC Dempsey (1585_CR50) 2016; 34 SA Prince (1585_CR8) 2020; 17 K Ellis (1585_CR45) 2016; 48 1585_CR58 1585_CR12 TV Barreira (1585_CR37) 2015; 42 DW Dunstan (1585_CR29) 2021; 18 E Meinders (1585_CR57) 2021; 88 SP Hooker (1585_CR31) 2016; 19 |
| References_xml | – volume: 73 start-page: 682 issue: 5 year: 2018 ident: 1585_CR19 publication-title: J Gerontol A. doi: 10.1093/gerona/glw250 – volume: 57 start-page: 289 issue: 1 year: 1995 ident: 1585_CR34 publication-title: J R Stat Soc Ser B Methodol. doi: 10.1111/j.2517-6161.1995.tb02031.x – volume: 49 start-page: 2119 issue: 10 year: 2017 ident: 1585_CR49 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0000000000001315 – volume: 25 start-page: 1309 issue: 10 year: 2014 ident: 1585_CR3 publication-title: Cancer Causes Control. doi: 10.1007/s10552-014-0433-z – volume: 3 start-page: 458 issue: 1 year: 2009 ident: 1585_CR16 publication-title: Ann Appl Stat. doi: 10.1214/08-AOAS206 – volume: 43 start-page: 99 issue: 2 year: 2020 ident: 1585_CR47 publication-title: Clin Cardiol. doi: 10.1002/clc.23303 – volume: 18 start-page: 637 issue: 9 year: 2021 ident: 1585_CR29 publication-title: Nat Rev Cardiol. doi: 10.1038/s41569-021-00547-y – volume: 12 start-page: 1 issue: 1 year: 2015 ident: 1585_CR56 publication-title: J Neuroengineering Rehabil. doi: 10.1186/s12984-015-0081-x – volume: 49 start-page: 1572 issue: 8 year: 2017 ident: 1585_CR1 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0000000000001257 – volume: 43 start-page: 357 issue: 2 year: 2011 ident: 1585_CR27 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0b013e3181ed61a3 – volume: 47 start-page: 1821 issue: 9 year: 2017 ident: 1585_CR11 publication-title: Sports Med. doi: 10.1007/s40279-017-0716-0 – ident: 1585_CR25 – volume: 14 start-page: 447 issue: 3 year: 2013 ident: 1585_CR33 publication-title: Biostatistics. doi: 10.1093/biostatistics/kxs051 – ident: 1585_CR58 – volume: 69 start-page: 41 issue: 1 year: 2013 ident: 1585_CR41 publication-title: Biometrics. doi: 10.1111/j.1541-0420.2012.01808.x – volume: 68 start-page: 3 issue: 1 year: 2006 ident: 1585_CR42 publication-title: J R Stat Soc Ser B Stat Methodol. doi: 10.1111/j.1467-9868.2005.00530.x – volume: 60 start-page: 1 year: 2017 ident: 1585_CR52 publication-title: Appl Ergon. doi: 10.1016/j.apergo.2016.10.006 – volume: 2 start-page: e000152 issue: 1 year: 2016 ident: 1585_CR54 publication-title: BMJ Open Sport Exerc Med. doi: 10.1136/bmjsem-2016-000152 – volume: 48 start-page: 933 issue: 5 year: 2016 ident: 1585_CR45 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0000000000000840 – volume: 23 start-page: 101405 year: 2021 ident: 1585_CR30 publication-title: Prev Med Rep. doi: 10.1016/j.pmedr.2021.101405 – volume: 40 start-page: 992 issue: 12 year: 2006 ident: 1585_CR15 publication-title: Br J Sports Med. doi: 10.1136/bjsm.2006.030262 – volume: 36 start-page: 648 issue: 5 year: 2020 ident: 1585_CR46 publication-title: Can J Cardiol. doi: 10.1016/j.cjca.2020.02.003 – volume: 19 start-page: 1 issue: 1 year: 2022 ident: 1585_CR18 publication-title: Int J Behav Nutr Phys Act. doi: 10.1186/s12966-022-01274-4 – volume: 19 start-page: 336 issue: 4 year: 2016 ident: 1585_CR31 publication-title: J Sci Med Sport. doi: 10.1016/j.jsams.2015.04.006 – volume: 43 start-page: 1561 issue: 8 year: 2011 ident: 1585_CR14 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0b013e31820ce174 – volume: 17 start-page: 360 issue: 3 year: 2020 ident: 1585_CR43 publication-title: J Phys Act Health. doi: 10.1123/jpah.2019-0088 – volume: 72 start-page: 1037 issue: 5 year: 2018 ident: 1585_CR22 publication-title: Hypertension. doi: 10.1161/HYPERTENSIONAHA.118.11190 – volume: 22 start-page: 23 issue: 2 year: 2018 ident: 1585_CR55 publication-title: J Exerc Nutr Biochem. doi: 10.20463/jenb.2018.0013 – volume: 37 start-page: 540 year: 2012 ident: 1585_CR9 publication-title: Appl Physiol Nutr Metab-Physiol Appl Nutr Metab. doi: 10.1139/h2012-024 – ident: 1585_CR26 – ident: 1585_CR32 – volume: 10 start-page: e28684 issue: 5 year: 2021 ident: 1585_CR24 publication-title: JMIR Res Protoc. doi: 10.2196/28684 – volume: 17 start-page: 8864 issue: 23 year: 2020 ident: 1585_CR6 publication-title: Int J Environ Res Public Health. doi: 10.3390/ijerph17238864 – volume: 17 start-page: 293 issue: 3 year: 2014 ident: 1585_CR39 publication-title: J Sci Med Sport. doi: 10.1016/j.jsams.2013.07.002 – volume: 53 start-page: 2445 issue: 11 year: 2021 ident: 1585_CR44 publication-title: Med Sci Sports Exerc. doi: 10.1249/MSS.0000000000002705 – volume: 124 start-page: 799 issue: 5 year: 2019 ident: 1585_CR4 publication-title: Circ Res. doi: 10.1161/CIRCRESAHA.118.312669 – volume: 57 start-page: 305 issue: 2 year: 2014 ident: 1585_CR2 publication-title: Diabetologia. doi: 10.1007/s00125-013-3102-y – volume: 35 start-page: 2411 issue: 12 year: 2017 ident: 1585_CR48 publication-title: J Hypertens. doi: 10.1097/HJH.0000000000001463 – volume: 73 start-page: 859 issue: 4 year: 2019 ident: 1585_CR21 publication-title: Hypertension. doi: 10.1161/HYPERTENSIONAHA.118.12373 – volume: 63 start-page: 41 year: 2017 ident: 1585_CR7 publication-title: Appl Ergon. doi: 10.1016/j.apergo.2017.03.012 – volume: 8 start-page: e1002375 issue: 2 year: 2012 ident: 1585_CR40 publication-title: PLoS Comput Biol. doi: 10.1371/journal.pcbi.1002375 – volume: 11 start-page: e0164928 issue: 10 year: 2016 ident: 1585_CR13 publication-title: PloS one. doi: 10.1371/journal.pone.0164928 – volume: 1 start-page: 275 issue: 4 year: 2015 ident: 1585_CR38 publication-title: Sleep Health. doi: 10.1016/j.sleh.2015.09.002 – ident: 1585_CR12 doi: 10.1016/j.jsams.2011.12.001 – volume: 50 start-page: 1929 issue: 11 year: 2020 ident: 1585_CR20 publication-title: Sports Med. doi: 10.1007/s40279-020-01325-5 – volume: 6 start-page: 288 year: 2018 ident: 1585_CR5 publication-title: Front Public Health. doi: 10.3389/fpubh.2018.00288 – volume: 14 start-page: 411 issue: 5 year: 2011 ident: 1585_CR28 publication-title: J Sci Med Sport. doi: 10.1016/j.jsams.2011.04.003 – volume: 45 start-page: 867 issue: 6 year: 2015 ident: 1585_CR23 publication-title: Sports Med. doi: 10.1007/s40279-015-0322-y – volume: 34 start-page: 2376 issue: 12 year: 2016 ident: 1585_CR50 publication-title: J Hypertens. doi: 10.1097/HJH.0000000000001101 – volume: 11 start-page: 403 issue: 2 year: 2019 ident: 1585_CR17 publication-title: Stat Biosci. doi: 10.1007/s12561-019-09237-3 – volume: 42 start-page: 70 issue: 1 year: 2015 ident: 1585_CR37 publication-title: Gait Posture. doi: 10.1016/j.gaitpost.2015.04.008 – ident: 1585_CR35 – volume: 24 start-page: 976 issue: 9 year: 2014 ident: 1585_CR51 publication-title: Nutr Metab Cardiovasc Dis. doi: 10.1016/j.numecd.2014.04.011 – volume: 14 start-page: 1 issue: 1 year: 2017 ident: 1585_CR10 publication-title: Int J Behav Nutr Phys Act. doi: 10.1186/s12966-017-0525-8 – volume: 15 start-page: 1 issue: 1 year: 2018 ident: 1585_CR36 publication-title: Int J Behav Nutr Phys Act. doi: 10.1186/s12966-018-0749-2 – volume: 72 start-page: 1451 issue: 6 year: 2000 ident: 1585_CR53 publication-title: Am J Clin Nut. doi: 10.1093/ajcn/72.6.1451 – volume: 88 start-page: 225 year: 2021 ident: 1585_CR57 publication-title: Gait Posture. doi: 10.1016/j.gaitpost.2021.06.004 – volume: 17 start-page: 1 issue: 1 year: 2020 ident: 1585_CR8 publication-title: Int J Behav Nutr Phys Act. doi: 10.1186/s12966-020-00938-3 |
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Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers... Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB... Background Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers... BACKGROUND: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers... Abstract Background Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable... |
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