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

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0206013 ◽  
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

scholarly journals Compositional data analysis for physical activity, sedentary time and sleep research

2017 ◽  
Vol 27 (12) ◽  
pp. 3726-3738 ◽  
Author(s):  
Dorothea Dumuid ◽  
Tyman E Stanford ◽  
Josep-Antoni Martin-Fernández ◽  
Željko Pedišić ◽  
Carol A Maher ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Health Effects ◽  
Daily Activity ◽  
Sedentary Time ◽  
Compositional Data ◽  
Time Budget ◽  
International Study ◽  
Compositional Data Analysis ◽  
Log Ratio

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children’s daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

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

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.

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

2020 ◽  
Vol 45 (1) ◽  
pp. 266-275 ◽  
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.

scholarly journals Cross-sectional and prospective associations of sleep, sedentary and active behaviors with mental health in older people: a compositional data analysis from the Seniors-ENRICA-2 study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Verónica Cabanas-Sánchez ◽  
Irene Esteban-Cornejo ◽  
Esther García-Esquinas ◽  
Rosario Ortolá ◽  
Ignacio Ara ◽  
...  
Keyword(s):  
Mental Health ◽  
Data Analysis ◽  
Older People ◽  
Time Use ◽  
Compositional Data ◽  
Global Mental Health ◽  
Health Indicators ◽  
Depression Symptoms ◽  
Compositional Data Analysis ◽  
Cross Sectional

Abstract Background Most studies on the effects of sleep, sedentary behavior (SB), and physical activity (PA) on mental health did not account for the intrinsically compositional nature of the time spent in several behaviors. Thus, we examined the cross-sectional and prospective associations of device-measured compositional time in sleep, SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) with depression symptoms, loneliness, happiness, and global mental health in older people (≥ 65 years). Methods Data were taken from the Seniors-ENRICA-2 study, with assessments in 2015–2017 (wave 0) and 2018–2019 (wave 1). Time spent in sleep, SB, LPA and MVPA was assessed by wrist-worn accelerometers. Depression symptoms, loneliness, happiness, and global mental health were self-reported using validated questionnaires. Analyses were performed using a compositional data analysis (CoDA) paradigm and adjusted for potential confounders. Results In cross-sectional analyses at wave 0 (n = 2489), time-use composition as a whole was associated with depression and happiness (all p < 0.01). The time spent in MVPA relative to other behaviors was beneficially associated with depression (γ = -0.397, p < 0.001), loneliness (γ = -0.124, p = 0.017) and happiness (γ = 0.243, p < 0.001). Hypothetically, replacing 30-min of Sleep, SB or LPA with MVPA was beneficially cross-sectionally related with depression (effect size [ES] ranged -0.326 to -0.246), loneliness (ES ranged -0.118 to -0.073), and happiness (ES ranged 0.152 to 0.172). In prospective analyses (n = 1679), MVPA relative to other behaviors at baseline, was associated with favorable changes in global mental health (γ = 0.892, p = 0.049). We observed a beneficial prospective effect on global mental health when 30-min of sleep (ES = 0.521), SB (ES = 0.479) or LPA (ES = 0.755) were theoretically replaced for MVPA. Conclusions MVPA was cross-sectionally related with reduced depression symptoms and loneliness and elevated level of happiness, and prospectively related with enhanced global mental health. Compositional isotemporal analyses showed that hypothetically replacing sleep, SB or LPA with MVPA could result in modest but significantly improvements on mental health indicators. Our findings add evidence to the emerging body of research on 24-h time-use and health using CoDA and suggest an integrated role of daily behaviors on mental health in older people.

scholarly journals The Impact of Movement Behaviors on Bone Health in Elderly with Adequate Nutritional Status: Compositional Data Analysis Depending on the Frailty Status

Nutrients ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Irene Rodríguez-Gómez ◽  
Asier Mañas ◽  
José Losa-Reyna ◽  
Leocadio Rodríguez-Mañas ◽  
Sebastien Chastin ◽  
...  
Keyword(s):  
Data Analysis ◽  
Nutritional Status ◽  
Bone Health ◽  
Compositional Data ◽  
Compositional Data Analysis ◽  
X Ray ◽  
Frailty Status ◽  
Vigorous Intensity ◽  
The Impact ◽  
The Relationship

The aim of this study was to determine the relationship between bone mass (BM) and physical activity (PA) and sedentary behavior (SB) according to frailty status and sex using compositional data analysis. We analyzed 871 older people with an adequate nutritional status. Fried criteria were used to classify by frailty status. Time spent in SB, light intensity PA (LPA) and moderate-to-vigorous intensity PA (MVPA) was assessed from accelerometry for 7 days. BM was determined by dual-energy X-ray absorptiometry (DXA). The combined effect of PA and SB was significantly associated with BM in robust men and women (p ≤ 0.05). In relation to the other behaviors, SB was negatively associated with BM in robust men while BM was positively associated with SB and negatively with LPA and MVPA in robust women. Moreover, LPA also was positively associated with arm BM (p ≤ 0.01). Finally, in pre-frail women, BM was positively associated with MVPA. In our sample, to decrease SB could be a good strategy to improve BM in robust men. In contrast, in pre-frail women, MVPA may be an important factor to consider regarding bone health.

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

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.

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

2020 ◽  
Vol 17 (7) ◽  
pp. 2220 ◽  
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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