Foundational Movement Skills and Play Behaviors during Recess among Preschool Children: A Compositional Analysis

This study aimed to examine the associations between play behaviors during preschool recess and foundational movement skills (FMS) in typically developing preschool children. One hundred and thirty-three children (55% male; mean age 4.7 ± 0.5 years) from twelve preschools were video-assessed for six locomotor and six object-control FMS using the Champs Motor Skill Protocol. A modified System for Observing Children’s Activity and Relationships during Play assessed play behaviors during preschool recess. Associations between the composition of recess play behaviors with FMS were analyzed using compositional data analysis and linear regression. Results: Relative to time spent in other types of play behaviors, time spent in play without equipment was positively associated with total and locomotor skills, while time spent in locomotion activities was negatively associated with total and locomotor skills. No associations were found between activity level and group size play behavior compositions and FMS. The findings suggest that activity type play behaviors during recess are associated with FMS. While active games without equipment appear beneficial, preschool children may need a richer playground environment, including varied fixed and portable equipment, to augment the play-based development of FMS.

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The 24-h Movement Compositions in Weekday, Weekend Day or Four-Day Periods Differentially Associate with Fundamental Movement Skills

Children ◽

10.3390/children8100828 ◽

2021 ◽

Vol 8 (10) ◽

pp. 828

Author(s):

Clare M. P. Roscoe ◽

Michael J. Duncan ◽

Cain C. T. Clark

Keyword(s):

Preschool Children ◽

Compositional Data ◽

Economic Status ◽

Compositional Data Analysis ◽

Motor Competence ◽

Object Control ◽

Socio Economic Status ◽

Movement Skills ◽

Fundamental Movement Skills ◽

The Relationship

The aim of this study was to investigate the relationship between weekday, weekend day and four-day physical activity (PA) behaviours and fundamental movement skills (FMS) in British preschool children from a low socio-economic status background using compositional data analysis (CoDA). One hundred and eighty-five preschool children aged 3–4 years provided objectively assessed PA and sedentary behaviour (SB) data (GENEActiv accelerometer) and FMS (TGMD-2). The association of 24-h movement behaviours with FMS was explored using CoDA and isotemporal substitution (R Core Team, 3.6.1). When data were considered compositionally (SB, light PA (LPA), moderate and vigorous PA (MVPA)) and adjusted for age, BMI and sex, the weekday-derived composition predicted total motor competence (r2 = 0.07), locomotor (r2 = 0.08) and object control skills (r2 = 0.09); the weekend day-derived composition predicted total motor competence (r2 = 0.03) and object control skills (r2 = 0.03), the 4-day-derived composition predicted total motor competence (r2 = 0.07), locomotor (r2 = 0.07) and object control skills (r2 = 0.06) (all p < 0.05). Reallocation of 5 min of LPA at the expense of any behaviour was associated with significant improvements in total motor competence, locomotor and object control skills; for weekend-derived behaviours, MVPA was preferential. Considering movement behaviours over different time periods is required to better understand the effect of the 24-h movement composition on FMS in preschool children.

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Associations of Physical Behaviours and Behavioural Reallocations with Markers of Metabolic Health: A Compositional Data Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15102280 ◽

2018 ◽

Vol 15 (10) ◽

pp. 2280 ◽

Cited By ~ 18

Author(s):

Gregory Biddle ◽

Charlotte Edwardson ◽

Joseph Henson ◽

Melanie Davies ◽

Kamlesh Khunti ◽

...

Keyword(s):

Insulin Sensitivity ◽

Compositional Data ◽

Compositional Analysis ◽

Statistical Modelling ◽

Metabolic Health ◽

Sitting Time ◽

Compositional Data Analysis ◽

Sensitivity Index ◽

Model Assessment ◽

Physical Behaviour

Standard statistical modelling has shown that the reallocation of sitting time to either standing or stepping may be beneficial for metabolic health. However, this overlooks the inherent dependency of time spent in all behaviours. The aim is to examine the associations between physical behaviours and markers of metabolic health (fasting glucose, fasting insulin, 2-h glucose, 2-h insulin, Homeostasis Model Assessment of Insulin Sensitivity (HOMA-IS), Matsuda Insulin Sensitivity Index (Matsuda-ISI) while quantifying the associations of reallocating time from one physical behaviour to another using compositional analysis. Objectively measured physical behaviour data were analysed (n = 435) using compositional analysis and compositional isotemporal substitutions to estimate the association of reallocating time from one behaviour to another in a population at high risk of type 2 diabetes mellitus (T2DM). Stepping time was associated with all markers of metabolic health relative to all other behaviours. Reallocating 30 min from sleep, sitting, or standing to stepping was associated with 5–6 fold lower 2-h glucose, 15–17 fold lower 2-h insulin, and higher insulin sensitivity (10–11 fold via HOMA-IS, 12–15 fold via Matsuda-ISI). Associations of reallocating time from any behaviour to stepping were maintained for 2-h glucose, 2-h insulin, and Matsuda-ISI after further adjusting for body mass index (BMI). Relocating time from stepping into sleep, sitting, or standing was associated with lower insulin sensitivity. Stepping time may be the most important behavioural composition when promoting improved metabolic health in adults at risk of T2DM.

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The Mathematics of Compositional Analysis

Austrian Journal of Statistics ◽

10.17713/ajs.v45i4.142 ◽

2016 ◽

Vol 45 (4) ◽

pp. 57-71 ◽

Cited By ~ 33

Author(s):

Carles Barcelo-Vidal ◽

Josep-Antoni Martín-Fernández

Keyword(s):

Scale Invariance ◽

Compositional Data ◽

Quotient Space ◽

Compositional Analysis ◽

Space Structure ◽

Compositional Data Analysis ◽

New Developments ◽

Quotient Spaces ◽

Subcompositional Coherence ◽

Logratio Transformations

The term compositional data analysis is historically associated to the approach based on the logratio transformations introduced in the eighties. Two main principles of this methodology are scale invariance and subcompositional coherence. New developments and concepts emerged in the last decade revealed the need to clarify the concepts of compositions, compositional sample space and subcomposition. In this work the mathematics of compositional analysis based on equivalence relation is presented. The two principles are essential attributes of the corresponding quotient space. A logarithmic isomorphism between quotient spaces induces a metric space structure for compositions. Using this structure, the statistical analysis of compositions consists of analysing logratio coordinates.

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A field guide for the compositional analysis of any-omics data

GigaScience ◽

10.1093/gigascience/giz107 ◽

2019 ◽

Vol 8 (9) ◽

Cited By ~ 22

Author(s):

Thomas P Quinn ◽

Ionas Erb ◽

Greg Gloor ◽

Cedric Notredame ◽

Mark F Richardson ◽

...

Keyword(s):

Data Analysis ◽

General Solution ◽

Compositional Data ◽

Compositional Analysis ◽

Compositional Data Analysis ◽

Nucleotide Synthesis ◽

Library Size ◽

Next Generation Sequencing Ngs ◽

Concise Guide ◽

Log Ratio

Abstract Background Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: their magnitude is determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when heterogeneous samples are compared. Results Methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. Herein, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. Conclusions In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, “Relative to some important activity of the cell, what is changing?”

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A field guide for the compositional analysis of any-omics data

10.1101/484766 ◽

2018 ◽

Cited By ~ 3

Author(s):

Thomas P. Quinn ◽

Ionas Erb ◽

Greg Gloor ◽

Cedric Notredame ◽

Mark F. Richardson ◽

...

Keyword(s):

Data Analysis ◽

General Solution ◽

Compositional Data ◽

Compositional Analysis ◽

Compositional Data Analysis ◽

Sequencing Data ◽

Nucleotide Synthesis ◽

Library Size ◽

Concise Guide ◽

Log Ratio

AbstractNext-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. Today, NGS is routinely used to understand many important topics in biology from human disease to microorganism diversity. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: the magnitude of the counts are determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged, and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when comparing heterogeneous samples (e.g., samples collected across distinct cancers or tissues). Instead, methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. In this manuscript, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. In doing so, we review zero replacement, differential abundance analysis, and within-group and between-group coordination analysis. We then discuss how this pipeline can accommodate complex study design, facilitate the analysis of vertically and horizontally integrated data, including multiomics data, and further extend to single-cell sequencing data. In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, “Relative to some important activity of the cell, what is changing?”. Taken together, this manuscript establishes the first fully comprehensive analysis protocol that is suitable for any and all -omics data.

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Compositional analysis of dietary patterns

Statistical Methods in Medical Research ◽

10.1177/0962280218790110 ◽

2018 ◽

Vol 28 (9) ◽

pp. 2834-2847 ◽

Cited By ~ 2

Author(s):

M Solans ◽

G Coenders ◽

R Marcos-Gragera ◽

A Castelló ◽

E Gràcia-Lavedan ◽

...

Keyword(s):

Data Analysis ◽

Dietary Patterns ◽

Pattern Analysis ◽

Compositional Data ◽

Compositional Analysis ◽

Principal Component ◽

Compositional Data Analysis ◽

Trade Offs ◽

Dietary Pattern Analysis ◽

The Relationship

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others.

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Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15102248 ◽

2018 ◽

Vol 15 (10) ◽

pp. 2248 ◽

Cited By ~ 7

Author(s):

Nikola Štefelová ◽

Jan Dygrýn ◽

Karel Hron ◽

Aleš Gába ◽

Lukáš Rubín ◽

...

Keyword(s):

Physical Activity ◽

Light Intensity ◽

Robust Statistics ◽

Compositional Data ◽

Compositional Analysis ◽

Public Health Research ◽

Movement Behavior ◽

Moderate Intensity ◽

Compositional Data Analysis ◽

Aberrant Observations

Although there is an increasing awareness of the suitability of using compositional data methodology in public health research, classical methods of statistical analysis have been primarily used so far. The present study aims to illustrate the potential of robust statistics to model movement behaviour using Czech adolescent data. We investigated: (1) the inter-relationship between various physical activity (PA) intensities, extended to model relationships by age; and (2) the associations between adolescents’ PA and sedentary behavior (SB) structure and obesity. These research questions were addressed using three different types of compositional regression analysis—compositional covariates, compositional response, and regression between compositional parts. Robust counterparts of classical regression methods were used to lessen the influence of possible outliers. We outlined the differences in both classical and robust methods of compositional data analysis. There was a pattern in Czech adolescents’ movement/non-movement behavior—extensive SB was related to higher amounts of light-intensity PA, and vigorous PA ratios formed the main source of potential aberrant observations; aging is associated with more SB and vigorous PA at the expense of light-intensity PA and moderate-intensity PA. The robust counterparts indicated that they might provide more stable estimates in the presence of outlying observations. The findings suggested that replacing time spent in SB with vigorous PA may be a powerful tool against adolescents’ obesity.

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Tree Ecosystem Services, for Everyone? A Compositional Analysis Approach to Assess the Distribution of Urban Trees as an Indicator of Environmental Justice

Sustainability ◽

10.3390/su12031215 ◽

2020 ◽

Vol 12 (3) ◽

pp. 1215 ◽

Cited By ~ 1

Author(s):

Marco Cruz-Sandoval ◽

María Isabel Ortego ◽

Elisabet Roca

Keyword(s):

Socioeconomic Status ◽

Ecosystem Services ◽

Environmental Justice ◽

Green Infrastructure ◽

Compositional Data ◽

Compositional Analysis ◽

Well Being ◽

Urban Trees ◽

Compositional Data Analysis ◽

Equal Distribution

Trees provide a broad amount of ecosystem services in urban areas. Although it is well documented that trees are essential for the well-being and livability of cities, trees are often not evenly distributed. Studies have found that urban residents with a deprived socioeconomic status are associated with a lower coverage and access to urban trees in their communities, yet a fair distribution of trees contributes to the sustainability and resilience of cities. In this context, the environmental justice movement seeks to ensure equal distribution of green infrastructure and its benefits throughout a territory. The objective of this study is threefold: (i) to determine whether urban trees in Guadalajara, Mexico, are distributed equally; (ii) to assess the association between urban trees and socioeconomic status; and (iii) to introduce compositional data analysis to the existing literature. Due to the compositional nature of the data, compositional analysis techniques are applied. We believe this novel approach will help define the proper management of data used in the literature. The outcomes provide insights for urban planners working towards the Sustainable Development Goals to help eradicate the uneven distribution of urban trees in cities.

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