Weighted Pivot Coordinates for Compositional Data and Their Application to Geochemical Mapping

Hron, Karel; Filzmoser, Peter; Caritat, Patrice de; Fišerová, Eva; Gardlo, Alžběta

doi:10.1007/s11004-017-9684-z

Cited by 56 publications

(35 citation statements)

References 14 publications

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“…In summary, this study has systematically considered and presented CODA regression methods which are, by construction, robust to the presence of outlying observations. They were discussed in relation to standard ‘non-robust’ regression models, as those applied in previous studies using movement behavior data [ 12 , 30 , 40 ]. Robust MM regression, which aims to retain robustness and resistance against outliers whilst gaining in statistical efficiency, is a proper tool for dealing with real-world data with natural presence of extreme observations; similar goals are also achieved by robust orthogonal regression.…”

Section: Discussionmentioning

confidence: 99%

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Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

Štefelová

Dygrýn

Hron

et al. 2018

IJERPH

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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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Section: Discussionmentioning

confidence: 99%

“…There are many ways to construct ilr coordinates but their interpretation might be quite difficult. Therefore, it is appealing to choose an orthonormal coordinate system that highlights the role of a single compositional part against the others, which have been recently called pivot coordinates [ 29 , 30 ]. They are constructed as follows.…”

Section: Methodsmentioning

confidence: 99%

Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

Štefelová

Dygrýn

Hron

et al. 2018

IJERPH

Self Cite

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“…Compositional analyses were conducted using R open-source software (v. 3.6.2, www.r-project.org) with the packages, 'compositions' (v. 1.40-5) (60) and 'robCompositions' (v. 2.2.1) (61). Time-use compositions were expressed as four speci c sets of three isometric log ratio (ILR) coordinates called pivot coordinates (62,63). The rst coordinate of each set contained one activity behaviour (either sleep, ST, LPA, MVPA) relative to all remaining activity behaviours.…”

Section: Discussionmentioning

confidence: 99%

Cross-Sectional Associations Between 24-hour Activity Behaviours and Mental Health Indicators in Children and Adolescents: A Compositional Data Analysis

Fairclough

Tyler

Dainty

et al. 2020

Preprint

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Background. The prevalence of mental ill health increases with age through childhood and adolescence and likely impairs mental and physical health in adulthood. Little is known about the combined influence of sleep, sedentary time (ST), and physical activity on youth mental health. This study examined associations between youth 24-hour activity behaviour compositions and mental health indicators, and investigated predicted differences in mental health when time was reallocated between activity behaviours.Methods. Demographic information and anthropometric data were collected from 359 participants (aged 9-13 years; 50.7% girls). 24-hour activity behaviours (sleep, ST, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA)) were assessed using a wrist accelerometer, worn for 7 days. Validated questionnaires measured self-esteem, depressive symptomology, overall emotional and behavioural mental health, internalising problems, externalising problems, and prosocial behaviour. A computerised cognitive test battery assessed executive functions of switching, spatial working memory, and inhibition. Linear mixed models examined associations between activity behaviour compositions and mental health indicators for all participants and separately by primary and secondary school. Post-hoc analyses modelled the influence of reallocating fixed durations of time between activity behaviours on mental health.Results. For all participants ST was associated with worse internalising problems, relative to the other activity behaviours. Among primary school participants, relative to the other activity behaviours, ST was associated with poorer prosocial behaviour, and LPA was associated with worse switching and inhibition test scores. For all participants, reallocating time to ST from sleep and MVPA was associated with higher internalising problems. Among primary school participants, reallocating time to ST from any other behaviour was associated with poorer prosocial behaviour, and reallocating time to LPA from any other behaviour was associated with lower cognitive function.Conclusions. Relative to other activity behaviours, ST and LPA were significantly and unfavourably associated with some mental health indicators. These associations were strongest among primary school participants with no significant relationships observed for the secondary school participants. Replacing MVPA with ST or LPA reflected the greatest unfavourable predicted changes in mental health indicators. Our results provide further evidence for the influence of daily activity behaviours on youth mental health.

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“…Daily work and leisure time-use compositions were expressed using pivot isometric log-ratio (ilr) coordinates [31]. The rst pivot-coordinate was calculated as the normalised log ratio of the rst compositional part, relative to the geometric mean of the remaining parts within each of the work and leisure time compositions.…”

Section: Discussionmentioning

confidence: 99%

Day-to-Day Pattern of Work and Leisure Time Physical Behaviours: Are Low Socioeconomic Status Adults Couch Potatoes or Work Warriors?

Rasmussen

Dumuid

Hron

et al. 2020

Preprint

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BackgroundMost studies on day-to-day patterns of physical behaviours (i.e. physical activities and sedentary behaviour) are based on adult populations of high socioeconomic status (SES) and without differentiating between work and leisure time. Thus, we aimed to characterise the day-to-day leisure time physical behaviours patterns among low SES adults and investigate if a day of the week, workday, work duration, and work physical behaviours influence these leisure-time patterns.MethodsThis cross-sectional study included 1012 adults from typical low SES occupations (e.g. manufacturing, cleaning and transportation). The participants wore accelerometers for 1-7 days to measure daily physical behaviours during work and leisure time, expressed as 3-part time-use compositions consisting of time spent 1) sedentary, 2) standing or 3) being active (i.e. walking, running, stair climbing, or cycling). Multivariate multilevel log-ratio models were used to regress leisure time-use composition on day-of-week and the interaction between day-of-week and (1) type of day, (i.e., work/non-work), (3) work duration, and (4) work time-use composition. Models were adjusted for sex, age, BMI, and smoking-status. ResultsOver the course of a week, the workers were primarily sedentary during leisure time, while spending most work time standing or being active. Leisure time physical behaviours were associated with type of day (p < 0.005, more sedentary on workdays vs. non-workdays), day-of-week (p < 0.005, more sedentary on Friday, Saturday and Sunday), standing work (p < 0.005, more sedentary and less active leisure time on Sunday), active work (p < 0.005, less sedentary and more active leisure time on Sunday), and work duration (p < 0.005, less active leisure time on Friday). ConclusionsOur findings suggest that while low SES adults’ leisure time is mostly sedentary, their work time is predominantly standing or active. Leisure time was spent more sedentary on workdays compared to non-workdays, indicating that work factors influenced day-to-day leisure time behaviours. Accordingly, work physical behaviours and work duration were differently associated with leisure time physical behaviours. Our findings support the value of considering the influence of work factors when planning strategies aiming to increase health-enhancing leisure time activities among low SES adults.

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Weighted Pivot Coordinates for Compositional Data and Their Application to Geochemical Mapping

Cited by 56 publications

References 14 publications

Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

Robust Compositional Analysis of Physical Activity and Sedentary Behaviour Data

Cross-Sectional Associations Between 24-hour Activity Behaviours and Mental Health Indicators in Children and Adolescents: A Compositional Data Analysis

Day-to-Day Pattern of Work and Leisure Time Physical Behaviours: Are Low Socioeconomic Status Adults Couch Potatoes or Work Warriors?

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