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Changes in the 24-h movement behaviors during the transition to retirement: compositional data analysis

INTERNATIONAL JOURNAL OF BEHAVIORAL NUTRITION AND PHYSICAL ACTIVITY 2022年第1期19卷 1-11页

作者： Suorsa, Kristin Leskinen, Tuija Pasanen, Jesse Pulakka, Anna Myllyntausta, Saana Pentti, Jaana Chastin, Sebastien Vahtera, Jussi Stenholm, Sari Univ Turku Dept Publ Hlth Turku Finland Turku Univ Hosp Turku Finland Univ Turku Ctr Populat Hlth Res Turku Finland Univ Oulu Fac Med Ctr Life Course Hlth Res Oulu Finland Finnish Inst Hlth & Welf Populat Hlth Unit Helsinki Finland Univ Turku Dept Psychol & Speech Language Pathol Turku Finland Univ Helsinki Fac Med Clin Helsinki Finland Glasgow Caledonian Univ Sch Hlth & Life Sci Glasgow Lanark Scotland Univ Ghent Dept Movement & Sports Sci Ghent Belgium

Background Transition to retirement is shown to affect sleep, sedentary time and physical activity, but no previous studies have examined how retirement changes the distribution of time spent daily in these movement behaviors. The aim of this study was to examine longitudinally how the composition of 24-h movement behaviors changes during the transition to retirement using compositional data analysis (CoDA). Methods We included 551 retiring public sector workers (mean age 63.2 years, standard deviation 1.1) from the Finnish Retirement and Aging study. The study participants wore a wrist-worn ActiGraph accelerometer for one week 24 h per day before and after retirement, with one year between the measurements. The daily proportions to time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were estimated using the GGIR package. Changes in the daily proportions of movement behaviors were examined using compositional data analysis version of linear mixed models. Results In general, the proportion of time spent in active behaviors decreased relative to time spent in passive behaviors after retirement (p < .001). This change depended on occupation (occupation*time interaction p < .001). After retirement manual workers increased the proportions of both sleep and SED in relation to active behaviors, whereas non-manual workers increased the proportion of sleep in relation to active behaviors and SED. The proportion of MVPA decreased relatively more than the proportion of LPA (p = 0.01), independently of gender and occupation. Conclusions Retirement induced a decrease in the proportion of time spent in active behaviors, especially time spent in MVPA. Future studies are needed to find ways to maintain or increase daily physical activity levels at the cost of sedentary behaviors among retirees.

关键词： Accelerometer Occupation Sleep Sedentary time Physical activity compositional data analysis

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Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis

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BIOSTATISTICS 2019年第4期20卷 698-713页

作者： Tang, Zheng-Zheng Chen, Guanhua Univ Wisconsin Dept Biostat & Med Informat Madison WI 53715 USA Wisconsin Inst Discovery Madison WI 53715 USA

There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation-maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.

关键词： compositional data analysis Differential abundance Hierarchical model Microbiome Score test Zero-inflated model

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Association of physical activity with loss of knee joint space width over two years: a compositional data analysis in the Osteoarthritis Initiative

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OSTEOARTHRITIS AND CARTILAGE 2023年第8期31卷 1091-1100页

作者： Voinier, D. Neogi, T. Arbeeva, L. Voinier, S. Master, H. Thoma, L. M. Brunette, M. Jakiela, J. T. White, D. K. Univ Delaware Dept Phys Therapy Newark DE USA Boston Univ Dept Med Sect Rheumatol Sch Med Boston MA USA Univ N Carolina Thurston Arthrit Res Ctr Chapel Hill NC USA Univ Delaware Dept Mech Engn Newark DE USA Vanderbilt Univ Vanderbilt Inst Clin & Translat Res Med Ctr Nashville TN USA Univ N Carolina Div Phys Therapy Chapel Hill NC USA Evidat Hlth Inc San Mateo CA USA 540 S Coll Ave Newark DE 19713 USA

Objective: There is continued debate as to how engaging in physical activity (PA), including moderate-to-vigorous PA (MVPA), light PA (LPA), and sedentary time (SED), affects one's risk for knee osteoarthritis (OA). Traditional regression methods do not account for the codependence of these categories of PA, whereby when one category increases, the others must decrease. Thus, we used compositional data analysis (CoDA) to examine time spent in each category of PA, or PA composition, and its association with loss of knee joint space width (JSW), a common indicator of knee OA ***: We performed a secondary analysis of data from a subset of participants in the Osteoarthritis Initiative. These participants had minute-by-minute activity data collected over 7 days at baseline;we then categorized each minute as MVPA, LPA, or SED. Our exposure, PA composition, represented min/day spent in each category. Our outcome, medial JSW loss, was the difference in medial tibiofemoral JSW from baseline to 2 years later. We employed CoDA, using an isometric log-ratio transformation, to examine the association of PA composition with medial JSW loss over 2 years, adjusting for potential ***: We included 969 participants (age: 64.5 years, 56% female, body mass index [BMI]: 28.8 kg/m2). Mean PA composition was: MVPA 9.1 min/day, LPA 278 min/day, SED 690 min/day. Per adjusted regression models, higher MVPA was not associated with greater medial JSW loss (I3 =-0.0005, P = 0.97), nor was LPA (I3 = 0.06, P = 0.27) or SED (I3 =-0.06, P = 0.21).Conclusion: Using CoDA, PA composition was not associated with medial JSW loss over 2 years.& COPY;2023 Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.

关键词： Osteoarthritis Physical activity compositional data analysis

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Volatile profiles of green Spanish-style table olives: Application of compositional data analysis for the segregation of their cultivars and production areas

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TALANTA 2017年 169卷 77-84页

作者： Garrido-Fernandez, A. Montano, A. Sanchez-Gomez, A. H. Cortes-Delgado, A. Lopez-Lopez, A. CSIC Inst Grasa Dept Biotecnol Alimentos Campus Univ Pablo de OlavideEdificio 46 Seville 41013 Spain

The work presents the application of compositional data methodology to analytical results, taking as an example the study of the volatile profiles of green Spanish-style table olives according to cultivars and production areas. For this purpose, the volatile compounds (analysed by GC-MS and expressed as percentages of the total area) were considered as a compositional data set in the Simplex space and, as a result, analysed by their specific new statistical tools. Application of compositional exploratory tools (variation array, tertiary graphs, biplots, or codadendrogram) allowed differentiating cultivars and production areas based on their volatile profiles. Also, the application of Cluster and Principal Component analysis to the ilr transformed values (coordinates), following the new methodology, led to more realistic results than the formally incorrect implementation of the standard multivariate analysis (developed for data from the Euclidean space) to percentages (data in the Simplex). Therefore, the work presents a novel consideration of the volatile profiles of table olives as compositional data and shows their proper analysis by statistical tools specifically developed for them.

关键词： Green table olives Volatile profiles Fermentation compositional data analysis Multivariate analysis

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Individual categorisation of glucose profiles using compositional data analysis

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STATISTICAL METHODS IN MEDICAL RESEARCH 2019年第12期28卷 3550-3567页

作者： Biagi, Lyvia Bertachi, Arthur Gimenez, Marga Conget, Ignacio Bondia, Jorge Antoni Martin-Fernandez, Josep Vehi, Josep Univ Girona Inst Informat & Aplicac Girona Spain Fed Univ Technol Parana UTFPR Guarapuava Brazil Hosp Clin Univ Endocrinol & Nutr Dept Diabet Unit IDIBAPS Inst Invest Biomed August Pi & Sunyer Barcelona Spain Ctr Invest Biomed Red Diabet & Enfermedades Metab Madrid Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia Spain Univ Girona Dept Informat Matemat Aplicada & Estadist Girona Spain

The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information;therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control.

关键词： Continuous glucose monitoring type 1 diabetes compositional data analysis decision support system diabetes management

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Forecasting causes of death by using compositional data analysis: the case of cancer deaths

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2019年第5期68卷 1351-1370页

作者： Kjaergaard, Soren Ergemen, Yunus Emre Kallestrup-Lamb, Malene Oeppen, Jim Lindahl-Jacobsen, Rune Univ Southern Denmark Odense Denmark Aarhus Univ Aarhus Denmark Copenhagen Business Sch Copenhagen Denmark

Cause-specific mortality forecasting is often based on predicting cause-specific death rates independently. Only a few methods have been suggested that incorporate dependence between causes. An attractive alternative is to model and forecast cause-specific death distributions, rather than mortality rates, as dependence between the causes can be incorporated directly. We follow this idea and propose two new models which extend the current research on mortality forecasting using death distributions. We find that adding age, time and cause-specific weights and decomposing both joint and individual variation between different causes of death increased the forecast accuracy of cancer deaths by using data for French and Dutch populations.

关键词： Cancer forecast Cause-specific mortality compositional data analysis Forecasting methods Population health

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Associations of neighborhood walkability with moderate to vigorous physical activity: an application of compositional data analysis comparing compositional and non-compositional approaches

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INTERNATIONAL JOURNAL OF BEHAVIORAL NUTRITION AND PHYSICAL ACTIVITY 2022年第1期19卷 1-10页

作者： Bird, Madeleine Datta, Geetanjali D. Chinerman, Deanna Kakinami, Lisa Mathieu, Marie-Eve Henderson, Melanie Barnett, Tracie A. Ctr Hosp Univ CHU St Justine Ctr Rech Montreal PQ Canada Univ Montreal Dept Med Sociale & Prevent Ecole Sante Publ Montreal PQ Canada Publ Hlth Agcy Canada Off Int Natl Affairs Hlth Portfolio Ottawa ON Canada Ctr Hosp Univ Montreal CRCHUM Ctr Rech Montreal PQ Canada Cedars Sinai Med Ctr Dept Med Los Angeles CA 90048 USA McGill Univ Dept Family Med Fac Med 5858 Cote des Neiges Rd Montreal PQ H3S 1Z1 Canada Concordia Univ Dept Math & Stat Montreal PQ Canada Univ Montreal Sch Kinesiol & Phys Act Sci Montreal PQ Canada Univ Montreal Dept Pediat Montreal PQ Canada

Background: We compared the relation between neighborhood features and moderate to vigorous physical activity (MVPA) using linear regression analysis and the more novel compositional data analysis (CoDA). compositional data analysis allows us to take the time children allocate to different movement behaviours during a 24-hour time period into account. Methodology: data from youth participants (n =409) in the QUALITY (QUebec Adipose and Lifestyle InvesTigation in Youth) cohort were included. Time spent in MVPA, light physical activity, sedentary behavior, and sleep ("24-hour movement behaviours") was measured using accelerometers. Neighborhood data were collected using a geographic information system and through direct observation. In CoDA models, we used orthogonal logratio coordinates, which allows for the association of neighbourhood walkability with MVPA to be estimated with respect to the average composition of all other behaviours within a 24-hour time frame. In baseline linear regression models, MVPA was regressed cross-sectionally on neighborhood walkability. All models were stratified by sex, and controlled for BMI z-scores, pubertal development, seasonal variation, parental education, and neighbourhood safety. Results: Based on CoDA, girls who lived in more walkable neighborhoods had 10% higher daily MVPA (95% CI: 2%, 19%), taking into account all other movement behaviours. Based on linear regression, girls who resided in more walkable neighborhoods engaged in 4.2 (95% confidence interval [CI]: 1.2, 6.6) more minutes of MVPA per day on average than girls residing in less walkable neighborhoods. Conclusions: Unlike with traditional linear models, all movement behaviours were included in a single model using CoDA, allowing for a more complete picture of the strength and direction of the association between neighbourhood Walkability and MVPA. Application of CoDA to investigate determinants of physical activity provides additional insight into potential mechani

关键词： Built environment compositional data analysis Moderate-to-vigorous physical activity QUALITY cohort Sedentary behaviour Walkability Youth 24-hour movement behaviour

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A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults

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APPLIED PHYSIOLOGY NUTRITION AND METABOLISM 2020年第10期45卷 S248-S257页

作者： Janssen, Ian Clarke, Anna E. Carson, Valerie Chaput, Jean-Philippe Giangregorio, Lora M. Kho, Michelle E. Poitras, Veronica J. Ross, Robert Saunders, Travis J. Ross-White, Amanda Chastin, Sebastien F. M. Queens Univ Sch Kinesiol & Hlth Studies Kingston ON K7L 3N6 Canada Queens Univ Dept Publ Hlth Sci Kingston ON K7L 3N6 Canada Univ Alberta Fac Kinesiol Sport & Recreat Edmonton AB T6G 2H9 Canada Childrens Hosp Hlth Act Living & Obes Res Grp Eastern Ontario Res Inst Ottawa ON K1H 8L1 Canada Univ Waterloo Dept Kinesiol Waterloo ON N2L 3G1 Canada Univ Waterloo Schlegel UW Res Inst Aging Waterloo ON N2L 3G1 Canada McMaster Univ Sch Rehabil Sci Hamilton ON L8S 1C7 Canada Univ Prince Edward Isl Dept Appl Human Sci Charlottetown PE C1A 4P3 Canada Queens Univ Stauffer Lib Kingston ON K7L 3N6 Canada Glasgow Caledonia Univ Sch Hlth & Life Sci Glasgow G4 0BA Lanark Scotland Univ Ghent Dept Movement & Sport Sci Ghent Belgium

This systematic review determined if the composition of time spent in movement behaviours (i.e., sleep, sedentary behaviour (SED), light physical activity, and moderate-to-vigorous physical activity (MVPA)) is associated with health in adults. Five electronic databases were searched in August 2019. Studies were eligible for inclusion if they were peer-reviewed, examined community-dwelling adults, and used compositional data analysis to examine the associations between the composition of time spent in movement behaviours and health outcomes. Eight studies (7 cross-sectional, 1 prospective cohort) of >12 000 unique participants were included. Findings indicated that the 24-h movement behaviour composition was associated with all-cause mortality (1 of 1 analyses), adiposity (4 of 4 analyses), and cardiometabolic biomarkers (8 of 15 analyses). Reallocating time into MVPA from other movement behaviours was associated with favourable changes to most health outcomes and taking time out of SED and reallocating it into other movement behaviours was associated with favourable changes to all-cause mortality. The quality of evidence was very low for all health outcomes. In conclusion, these findings support the notion that the composition of movement across the entire 24-h day matters, and that recommendations for sleep, SED, and physical activity should be combined into a single public health guideline. (PROSPERO registration no.: CRD42019121641.) Novelty The 24-h movement behaviour composition is associated with a variety of health outcomes. Reallocating time into MVPA is favourably associated with health. Reallocating time out of SED is associated with favourable changes to mortality risk.

关键词： sleep sedentary behaviour physical activity systematic review public health compositional data analysis time-use epidemiology

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Sedentary behaviour is associated with depression symptoms: compositional data analysis from a representative sample of 3233 US adults and older adults assessed with accelerometers

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JOURNAL OF AFFECTIVE DISORDERS 2020年第0期265卷 59-62页

作者： Cruz, Borja del Pozo Alfonso-Rosa, Rosa M. McGregor, Duncan Chastin, Sebastien F. Palarea-Albaladejo, Javier del Pozo Cruz, Jesus Australian Catholic Univ Motivat & Behav Program Inst Posit Psychol & Educ Fac Hlth Sci St Level 1033 Berry St Sydney NSW 2060 Australia Univ Seville Dept Phys Educ & Sports Fac Educ Seville Spain Glasgow Caledonian Univ Sch Hlth & Life Sci Glasgow Lanark Scotland Biomath & Stat Scotland Edinburgh Midlothian Scotland Univ Ghent Dept Movement & Sport Sci Ghent Belgium

Background: Evidence supporting the individual associations of sedentary behaviours with depression symptoms commonly ignores the inherent co-dependency between physical activity, sedentary behaviours and sleep in a given 24-hour period. data analysis based on compositional methods effectively deals with this issue. Aim: To investigate the association between sedentary behaviour and depression symptoms synergistically using compositional analysis methods. Methods: Participants were a representative sample of 3233 US adults and older adults from the 2005-2006 cycle of the NHANES with valid 24-hour lifestyle behaviours data (i.e., accelerometer-derived physical activity and sedentary behaviour and self-reported sleep) and available self-reported depression symptoms (PHQ-9). The association between sedentary behaviour and depression symptoms scoring was investigated using a compositional zero-inflated Poisson regression analysis. Subsequently, the model estimates were used to evaluate the effects on depression symptoms of replacing time spent in sitting activities with physical activity of different intensifies and sleep. Limitations: The current study is limited by its cross-sectional design. Also, sleep time was self-reported, which could bias our estimations. Results: Increased sedentary behaviour relative to other behaviours was statistically significantly associated with increased depression symptoms (p < 0.001). Reallocating 60 min time from sedentary behaviours to moderate-to-vigorous physical activity (MVPA) and sleep was associated with small reductions in depression symptoms. Conclusions: A synergistic compositional analysis of accelerometer data uncovered a detrimental association between sedentary behaviour and depression symptoms. These results add to evidence from previous studies. The observed association seems to be principally driven by corresponding reductions in MVPA and sleep duration.

关键词： Sitting time Time use Depression disorders compositional data analysis

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

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Journal of Sport and Health Science 2022年第2期11卷 234-243页

作者： Simone J.J.M.Verswijveren Karen E.Lamb Josep A.Martín-Fernández Elisabeth Winkler Rebecca M.Leech Anna Timperio Jo Salmon Robin M.Daly Ester Cerin David W.Dunstan Rohan M.Telford Richard D.Telford Lisa S.Olive Nicola D.Ridgers Institute for Physical Activity and Nutrition School of Exercise and Nutrition SciencesDeakin UniversityGeelongVIC 3216Australia Melbourne School of Population and Global Health University of MelbourneParkvilleVIC 3053Australia Department of Computer Science Applied Mathematics and StatisticsUniversity of GironaGirona 17003Spain School of Public Health University of QueenslandHerstonQLD 4006Australia Mary MacKillop Institute for Health Research Australian Catholic UniversityFitzroyVIC 3065Australia School of Public Health University of Hong KongHong KongChina Baker Heart and Diabetes Institute MelbourneVIC 3004Australia Research Institute for Sport and Exercise University of CanberraBruceACT 2617Australia School of Psychology Deakin UniversityBurwoodVIC 3125Australia IMPACT Research Institute Deakin UniversityBurwoodVIC 3125Australia ANU Medical School Australian National UniversityGarranACT 2605Australia

Purpose:The study aimed to describe youth time-use compositions,focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity(PA),and to examine associations of these time-use compositions with cardiometabolic ***:Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7-13 years old were pooled(complete cases with accelerometry and adiposity marker data,n=782).A 9-component time-use composition was formed using compositional data analysis:time in shorter and longer bouts of sedentary behavior;time in shorter and longer bouts of light-,moderate-,or vigorous-intensity PA;and"other time"(i.e.,non-wear/sleep).Shorter and longer bouts of sedentary time were defined as<5 min and>5 min,*** bouts of light-,moderate-,and vigorous-intensity PA were defined as<1 min;longer bouts were defined as≥1 *** models examined associations between overall time-use composition and cardiometabolic ***,associations were derived between ratios of longer activity patterns relative to shorter activity patterns,and of each intensity level relative to the other intensity levels and"other time",and cardiometabolic ***:Confounder-adjusted models showed that the overall time-use composition was associated with adiposity,blood pressure,lipids,and the summary ***,more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score(zBMI)(β=1.79;SE=0.68)and waist circumference(β=18.35,SE=4.78).When each activity intensity was considered relative to all higher intensities and"other time",more time in light-and vigorous-intensity PA,and less time in sedentary behavior and moderate-intensity PA,were associated with lower waist ***:Accumulating PA,particularly light-intensity PA,in frequent short bursts may be more beneficial for limiting adi

关键词： Accumulation patterns Cardiometabolic health Children compositional data analysis Time-use

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