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Skinner AM, Vlachopoulos D, Barker AR, Moore SA, Rowlands AV, Soininen S, Haapala EA, Väistö J, Westgate K, Brage S, Lakka TA. Physical activity volume and intensity distribution in relation to bone, lean and fat mass in children. Scand J Med Sci Sports 2023; 33:267-282. [PMID: 36326758 PMCID: PMC10947490 DOI: 10.1111/sms.14255] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/05/2022] [Accepted: 11/01/2022] [Indexed: 11/05/2022]
Abstract
Considering physical activity (PA) volume and intensity may provide novel insights into the relationships of PA with bone, lean, and fat mass. This study aimed to assess the associations of PA volume, PA intensity distribution, including moderate-to-vigorous PA (MVPA) with total-body-less-head bone mineral content (BMC), lean, and fat mass in children. A population sample of 290 Finnish children (158 females) aged 9-11 years from the Physical Activity and Nutrition in Children (PANIC) Study was studied. PA, including MVPA, was assessed with a combined heart rate and movement sensor, and the uniaxial acceleration was used to calculate average-acceleration (a proxy metric for PA volume) and intensity-gradient (reflective of PA intensity distribution). Linear regression analyzed the associations of PA volume, PA intensity and MVPA with BMC, lean mass, and fat mass assessed by dual-energy X-ray absorptiometry. PA volume was positively associated with BMC in females (unstandardised regression coefficient [ß] = 0.26) and males (ß = 0.47), and positively associated with lean (ß = 7.33) and negatively associated with fat mass in males (ß = -20.62). PA intensity was negatively associated with BMC in males (ß = -0.13). MVPA was positively associated with lean mass in females and males (ß = 0.007 to 0.012), and negatively associated with fat mass in females and males (ß = -0.030 to -0.029). PA volume may be important for improving BMC in females and males, and increasing lean and reducing fat mass in males, whereas MVPA may be important for favorable lean and fat outcomes in both sexes.
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Affiliation(s)
- Annie M. Skinner
- Children's Health and Exercise Research CentreUniversity of ExeterExeterUK
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
| | | | - Alan R. Barker
- Children's Health and Exercise Research CentreUniversity of ExeterExeterUK
| | - Sarah A. Moore
- School of Health and Human PerformanceDalhousie UniversityHalifaxCanada
| | - Alex V. Rowlands
- Assessment of Movement Behaviours Group (AMBer), Leicester Lifestyle and Health Research Group, Diabetes Research CentreUniversity of LeicesterLeicesterUK
- NIHR Leicester Biomedical Research CentreLeicesterUK
- Division of Health Sciences, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health ResearchUniversity of South AustraliaAdelaideSouth AustraliaAustralia
| | - Sonja Soininen
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
- Social and Health CenterVarkausFinland
| | - Eero A. Haapala
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
- Faculty of Sport and Health SciencesUniversity of JyväskyläJyväskyläFinland
| | - Juuso Väistö
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
| | - Kate Westgate
- MRC Epidemiology UnitUniversity of CambridgeCambridgeUK
| | - Soren Brage
- MRC Epidemiology UnitUniversity of CambridgeCambridgeUK
| | - Timo A. Lakka
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
- Department of Clinical Physiology and Nuclear MedicineKuopio University HospitalKuopioFinland
- Foundation for Research in Health Exercise and NutritionKuopio Research Institute of Exercise MedicineKuopioFinland
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Williams RA, Dring KJ, Morris JG, Sun FH, Cooper SB. Agreement and equivalence of estimated physical activity behaviours, using ENMO- and counts-based processing methods, for wrist-worn accelerometers in adolescents. J Sports Sci 2023; 40:2499-2508. [PMID: 36638058 DOI: 10.1080/02640414.2023.2167254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The present study examined the agreement and equivalence between two physical activity processing methods. Data were obtained from 161 Hong-Kong adolescents (74 girls, age: 12.6 ± 1.7y). Participants wore an Actigraph GT3XBT on their non-dominant wrist for 7d. Time spent sedentary, and in light-(LPA), moderate-(MPA), vigorous-(VPA), and moderate-to-vigorous physical activity (MVPA) were calculated using different processing methods (proprietary counts and Euclidean Norm Minus One (ENMO)). Intraclass correlation coefficients (ICC) were used to examine absolute agreement (ICC2) and consistency (ICC3), and equivalence was assessed using pairwise equivalence tests. Using ENMO, sedentary time and VPA were higher, whereas all other behaviours were lower (compared to counts processing). Agreement ranged from poor (ICC2:0.42(Sedentary)) to moderate (ICC2:0.86(LPA)) and consistency ranged from moderate (ICC3:0.71(sedentary)) to good (ICC3:0.91(LPA)). Methods were not considered equivalent (all p > 0.05). Due to differences in the wear-time validation of processing methods, a sensitivity analyses (sub-sample with the same valid wear time for both methods (n = 56)), resulted in minimal change. Lack of agreement and equivalence between ENMO and counts processing methods suggests that the processing method significantly affects youth physical activity estimates.
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Affiliation(s)
- Ryan A Williams
- Exercise and Health Research Group; Sport, Health and Performance Enhancement (SHAPE) Research Centre; Department of Sport Science, Nottingham Trent University, Nottingham, UK
| | - Karah J Dring
- Exercise and Health Research Group; Sport, Health and Performance Enhancement (SHAPE) Research Centre; Department of Sport Science, Nottingham Trent University, Nottingham, UK
| | - John G Morris
- Exercise and Health Research Group; Sport, Health and Performance Enhancement (SHAPE) Research Centre; Department of Sport Science, Nottingham Trent University, Nottingham, UK
| | - Feng-Hua Sun
- Department of Health and Physical Education, the Education University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Simon B Cooper
- Exercise and Health Research Group; Sport, Health and Performance Enhancement (SHAPE) Research Centre; Department of Sport Science, Nottingham Trent University, Nottingham, UK
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The Physical Behaviour Intensity Spectrum and Body Mass Index in School-Aged Youth: A Compositional Analysis of Pooled Individual Participant Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148778. [PMID: 35886629 PMCID: PMC9320124 DOI: 10.3390/ijerph19148778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/04/2022] [Accepted: 07/15/2022] [Indexed: 12/10/2022]
Abstract
We examined the compositional associations between the intensity spectrum derived from incremental acceleration intensity bands and the body mass index (BMI) z-score in youth, and investigated the estimated differences in BMI z-score following time reallocations between intensity bands. School-aged youth from 63 schools wore wrist accelerometers, and data of 1453 participants (57.5% girls) were analysed. Nine acceleration intensity bands (range: 0−50 mg to ≥700 mg) were used to generate time-use compositions. Multivariate regression assessed the associations between intensity band compositions and BMI z-scores. Compositional isotemporal substitution estimated the differences in BMI z-score following time reallocations between intensity bands. The ≥700 mg intensity bandwas strongly and inversely associated with BMI z-score (p < 0.001). The estimated differences in BMI z-score when 5 min were reallocated to and from the ≥700 mg band and reallocated equally among the remaining bands were −0.28 and 0.44, respectively (boys), and −0.39 and 1.06, respectively (girls). The time in the ≥700 mg intensity band was significantly associated with BMI z-score, irrespective of sex. When even modest durations of time in this band were reallocated, the asymmetrical estimated differences in BMI z-score were clinically meaningful. The findings highlight the utility of the full physical activity intensity spectrum over a priori-determined absolute intensity cut-point approaches.
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Longitudinal Associations Between Device-Measured Physical Activity and Early Childhood Neurodevelopment. J Phys Act Health 2022; 19:80-88. [PMID: 34983024 DOI: 10.1123/jpah.2021-0587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/17/2021] [Accepted: 11/19/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND The aim of this study was to investigate longitudinal associations between physical activity and early childhood neurodevelopment. METHODS Data from 1673 children from the 2015 Pelotas (Brazil) birth cohort study were analyzed. Physical activity was measured using accelerometers on the wrist at ages 1, 2, and 4 years. Neurodevelopment was measured using the Battelle Development Inventory at age 4 years. Linear regression models were used to test trajectories and cumulative associations of physical activity with child neurodevelopment. RESULTS Of the 3 physical activity trajectories observed, children in the medium (β = 1.17; 95% confidence interval, 0.25 to 2.10) and high (β = 2.22; 95% confidence interval, 0.61 to 3.82) trajectories showed higher neurodevelopment scores than children in the lower activity trajectory. Cumulative analyses showed that children in the highest tertile of physical activity in all follow-ups presented a mean neurodevelopment score 4.57 (95% confidence interval, 2.63 to 6.51) higher than children in the lowest tertile in all follow-ups. All analyses showed a dose-response characteristic of association, with higher physical activity indicating higher neurodevelopment scores. CONCLUSIONS Physical activity may be an important predictor of neurodevelopment through early childhood.
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Dygrýn J, Medrano M, Molina-Garcia P, Rubín L, Jakubec L, Janda D, Gába A. Associations of novel 24-h accelerometer-derived metrics with adiposity in children and adolescents. Environ Health Prev Med 2021; 26:66. [PMID: 34118885 PMCID: PMC8199825 DOI: 10.1186/s12199-021-00987-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/01/2021] [Indexed: 12/14/2022] Open
Abstract
Background Further research is required to explore the associations between 24-h movement behaviours and health outcomes in the paediatric population. Therefore, this study aimed to examine the associations between novel data-driven 24-h activity metrics and adiposity among children and adolescents. Methods The sample included 382 children (8–13 years) and 338 adolescents (14–18 years). The average acceleration (AvAcc) of activity, intensity gradient (IG), and metrics representing the initial acceleration for the most active time periods of the 24-h cycle were calculated from raw acceleration data. Adiposity measures included body mass index z-score, fat mass percentage (FM%), and visceral adipose tissue (VAT). Data analysis was performed using multiple linear regression adjusted for wear time, sex, maternal education level, and maternal overweight and obesity. Results Children demonstrated higher values in all 24-h activity metrics than did adolescents (p < 0.001 for all). For children, the initial acceleration for the most active 2, 5, 15, and 30 min of the 24-h cycle were negatively associated with FM% (p ≤ 0.043 for all) and VAT (p <0.001 for all), respectively. For adolescents, the IG was negatively associated with FM% (p = 0.002) and VAT (p = 0.007). Moreover, initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min were associated with FM% (p ≤ 0.007 for all) and with VAT (p ≤ 0.023 for all). Conclusions The intensity distribution of activity and initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min within the 24-h cycle are beneficial for the prevention of excess adiposity in the paediatric population.
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Affiliation(s)
- Jan Dygrýn
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic
| | - María Medrano
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic
| | - Pablo Molina-Garcia
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic
| | - Lukáš Rubín
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic.,Faculty of Science, Humanities and Education, Technical University of Liberec, Liberec, Czech Republic
| | - Lukáš Jakubec
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic
| | - David Janda
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic
| | - Aleš Gába
- Faculty of Physical Culture, Palacký University Olomouc, třída Míru 117, 771 11, Olomouc, Czech Republic.
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Migueles JH, Aadland E, Andersen LB, Brønd JC, Chastin SF, Hansen BH, Konstabel K, Kvalheim OM, McGregor DE, Rowlands AV, Sabia S, van Hees VT, Walmsley R, Ortega FB. GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies. Br J Sports Med 2021; 56:376-384. [PMID: 33846158 PMCID: PMC8938657 DOI: 10.1136/bjsports-2020-103604] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2021] [Indexed: 02/06/2023]
Abstract
The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.
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Affiliation(s)
- Jairo H Migueles
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain .,Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Eivind Aadland
- Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway
| | - Lars Bo Andersen
- Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway
| | - Jan Christian Brønd
- Department of Sport Science and Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Sebastien F Chastin
- School of Health and Life Science, Glasgow Caledonian University, Glasgow, UK.,Department of Movement and Sport Science, Ghent University, Ghent, Belgium
| | - Bjørge H Hansen
- Department of Sports Medicine, Norwegian School of Sport Sciences, Osloål, Norway.,Departement of Sport Science and Physical Education, University of Agder, Kristiansand, Norway
| | - Kenn Konstabel
- Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estonia.,School of Natural Sciences and Health, Tallinn University, Tallinn, Estonia.,Institute of Psychology, University of Tartu, Tartu, Estonia
| | | | - Duncan E McGregor
- School of Health and Life Science, Glasgow Caledonian University, Glasgow, UK.,Biomathematics and Statistics Scotland, Edinburgh, UK
| | - Alex V Rowlands
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Leicester General Hospital, Leicester, UK.,Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Séverine Sabia
- Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative diseases, Paris, France.,Department of Epidemiology and Public Health, University College London, London, UK
| | - Vincent T van Hees
- Accelting, Almere, The Netherlands.,Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Public and Occupational Health, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Rosemary Walmsley
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Francisco B Ortega
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain .,Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
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