Associations Among School Day Sedentary Behavior, Physical Activity, and Motor Skills: A Compositional Data Analysis

Background: To examine the relationships among school day sedentary times (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA) with gross motor skills in children using Compositional Data Analysis. Methods: Participants were 409 children (mean age = 8.4 [1.8] y) recruited across 5 low-income schools. Gross motor skills were assessed using the test for gross motor development—third edition (TGMD-3), and physical activity was assessed using accelerometers. Isometric log-ratio coordinates were calculated by quantifying the relative proportion of percentage of the school day spent in SED, LPA, and MVPA. The associations of the isometric log-ratio coordinates with the TGMD-3 scores were estimated using general linear mixed-effects models adjusted for age, body mass index, estimated aerobic capacity, and school affiliation. Results: A higher proportion of the school day spent in %MVPA relative to %SED and %LPA was significantly associated with higher TGMD-3 total scores (γMVPA = 14.44, P = .01). This relationship was also observed for the ball skills subtest scores (γMVPA = 16.12, P = .003). Conclusions: Replacing %SED and %LPA with %MVPA during school hours may be an effective strategy for improving gross motor skills, specifically ball skills, in low-income elementary school-aged children.

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Reallocating Time From Sedentary Behavior to Physical Activity in Patients With Peripheral Artery Disease: Analyzing the Effects on Walking Capacity Using Compositional Data Analysis

Journal of Physical Activity and Health ◽

10.1123/jpah.2020-0487 ◽

2021 ◽

Vol 18 (4) ◽

pp. 426-432

Author(s):

Antonio Henrique Germano-Soares ◽

Rafael M. Tassitano ◽

Breno Quintela Farah ◽

Aluísio Andrade-Lima ◽

Marília de Almeida Correia ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Light Intensity ◽

Confidence Interval ◽

Sedentary Behavior ◽

Compositional Data ◽

Compositional Data Analysis ◽

Peripheral Artery ◽

Walking Capacity ◽

Artery Disease

Background: To examine the associations between physical activity (PA) and sedentary behavior (SB) with walking capacity and the effects of reallocating time from SB to PA in patients with symptomatic peripheral artery disease (PAD) using compositional data analysis. Methods: This cross-sectional study included 178 patients (34% females, mean age = 66 [9] y, body mass index = 27.8 [5.0] kg/m2, and ankle-brachial index = 0.60 [0.18]). Walking capacity was assessed as the total walking distance (TWD) achieved in a 6-minute walk test, while SB, light-intensity PA, and moderate to vigorous-intensity PA (MVPA) were measured by a triaxial accelerometer and conceptualized as a time-use composition. Associations between time reallocation among wake-time behaviors and TWD were determined using compositional isotemporal substitution models. Results: A positive association of MVPA with TWD (relative to remaining behaviors) was found in men (βilr = 66.9, SE = 21.4, P = .003) and women (βilr = 56.5, SE = 19.8; P = .005). Reallocating 30 minutes per week from SB to MVPA was associated with higher TWD in men (6.7 m; 95% confidence interval, 2.6–10.9 m) and women (4.5 m; 95% confidence interval, 1.5–7.5 m). Conclusions: The findings highlight, using a compositional approach, the beneficial and independent association of MVPA with walking capacity in patients with symptomatic PAD, whereas SB and light-intensity PA were not associated.

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Compositional Data Analysis in Time-Use Epidemiology: What, Why, How

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072220 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2220 ◽

Cited By ~ 9

Author(s):

Dorothea Dumuid ◽

Željko Pedišić ◽

Javier Palarea-Albaladejo ◽

Josep Antoni Martín-Fernández ◽

Karel Hron ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behavior ◽

Time Use ◽

Compositional Data ◽

Compositional Data Analysis ◽

Health It ◽

Future Directions ◽

The Relationship ◽

Daily Time

In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.

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A comparison of standard and compositional data analysis in studies addressing group differences in sedentary behavior and physical activity

International Journal of Behavioral Nutrition and Physical Activity ◽

10.1186/s12966-018-0685-1 ◽

2018 ◽

Vol 15 (1) ◽

Cited By ~ 33

Author(s):

Nidhi Gupta ◽

Svend Erik Mathiassen ◽

Glòria Mateu-Figueras ◽

Marina Heiden ◽

David M. Hallman ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behavior ◽

Compositional Data ◽

Group Differences ◽

Compositional Data Analysis

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Associations of physical activity and sedentary behavior with cardiometabolic biomarkers in prediabetes and type 2 diabetes: a compositional data analysis

The Physician and Sportsmedicine ◽

10.1080/00913847.2019.1684811 ◽

2019 ◽

Vol 48 (2) ◽

pp. 222-228

Author(s):

Jenny Rossen ◽

Philip Von Rosen ◽

Unn-Britt Johansson ◽

Kerstin Brismar ◽

Maria Hagströmer

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Data Analysis ◽

Sedentary Behavior ◽

Compositional Data ◽

Compositional Data Analysis

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Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health

Journal of Sport and Health Science ◽

10.1016/j.jshs.2021.03.004 ◽

2021 ◽

Author(s):

Simone J.J.M. Verswijveren ◽

Karen E. Lamb ◽

Josep A. Martín-Fernández ◽

Elisabeth Winkler ◽

Rebecca M. Leech ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behavior ◽

Compositional Data ◽

Compositional Data Analysis ◽

Youth Health

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Effects of Two Randomized and Controlled Multi-Component Interventions Focusing On 24-Hour Movement Behavior among Office Workers: A Compositional Data Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084191 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4191

Author(s):

Lisa-Marie Larisch ◽

Emil Bojsen-Møller ◽

Carla F. J. Nooijen ◽

Victoria Blom ◽

Maria Ekblom ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Cardiorespiratory Fitness ◽

Leisure Time ◽

Compositional Data ◽

Office Workers ◽

Movement Behavior ◽

Compositional Data Analysis ◽

Intervention Effects ◽

Time In Bed

Intervention studies aiming at changing movement behavior have usually not accounted for the compositional nature of time-use data. Compositional data analysis (CoDA) has been suggested as a useful strategy for analyzing such data. The aim of this study was to examine the effects of two multi-component interventions on 24-h movement behavior (using CoDA) and on cardiorespiratory fitness among office workers; one focusing on reducing sedentariness and the other on increasing physical activity. Office workers (n = 263) were cluster randomized into one of two 6-month intervention groups, or a control group. Time spent in sedentary behavior, light-intensity, moderate and vigorous physical activity, and time in bed were assessed using accelerometers and diaries, both for 24 h in total, and for work and leisure time separately. Cardiorespiratory fitness was estimated using a sub-maximal cycle ergometer test. Intervention effects were analyzed using linear mixed models. No intervention effects were found, either for 24-h behaviors in total, or for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Despite a thorough analysis of 24-h behaviors using CoDA, no intervention effects were found, neither for behaviors in total, nor for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Although the design of the multi-component interventions was based on theoretical frameworks, and included cognitive behavioral therapy counselling, which has been proven effective in other populations, issues related to implementation of and compliance with some intervention components may have led to the observed lack of intervention effect.

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Associations between sedentary time, physical activity and bone health among older people using compositional data analysis

PLoS ONE ◽

10.1371/journal.pone.0206013 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0206013 ◽

Cited By ~ 18

Author(s):

Irene Rodríguez-Gómez ◽

Asier Mañas ◽

José Losa-Reyna ◽

Leocadio Rodríguez-Mañas ◽

Sebastien F. M. Chastin ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Older People ◽

Bone Health ◽

Sedentary Time ◽

Compositional Data ◽

Compositional Data Analysis

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Sedentary behavior patterns and adiposity in children: a study based on compositional data analysis

BMC Pediatrics ◽

10.1186/s12887-020-02036-6 ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 3

Author(s):

Aleš Gába ◽

Željko Pedišić ◽

Nikola Štefelová ◽

Jan Dygrýn ◽

Karel Hron ◽

...

Keyword(s):

Data Analysis ◽

Sedentary Behavior ◽

Compositional Data ◽

Behavior Patterns ◽

Compositional Data Analysis

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Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis

International Journal of Obesity ◽

10.1038/s41366-020-0638-z ◽

2020 ◽

Vol 45 (1) ◽

pp. 266-275 ◽

Cited By ~ 1

Author(s):

Youngwon Kim ◽

Ryan D. Burns ◽

Duck-chul Lee ◽

Gregory J. Welk

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Data Analysis ◽

Body Mass ◽

Compositional Data ◽

Activity Measurement ◽

Compositional Data Analysis ◽

Lifestyle Behaviors ◽

Linear Regression Models ◽

Index Comparison

Abstract Background/objectives Evidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA). Subjects/methods Replicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data. Results Using 24PAR, time spent in sleep (γ = −3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = −0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = −5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = −3.12, p < 0.001), and MVPA (γ = −1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R2 = 0.28) compared with the 24PAR models (R2 = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA. Conclusions Favorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.

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