Poor academic performance has been linked with particular lifestyle behaviors, such as unhealthy diet, short sleep duration, high screen time, and low physical activity. However, little is known about how lifestyle behavior patterns (or combinations of behaviors) contribute to children’s academic performance. We aimed to compare academic performance across clusters of children with common lifestyle behavior patterns. We clustered participants (Australian children aged 9-11 years, n = 284) into four mutually exclusive groups of distinct lifestyle behavior patterns, using the following lifestyle behaviors as cluster inputs: light, moderate, and vigorous physical activity; sedentary behavior and sleep, derived from 24-hour accelerometry; self-reported screen time and diet. Differences in academic performance (measured by a nationally administered standardized test) were detected across the clusters, with scores being lowest in the Junk Food Screenies cluster (unhealthy diet/high screen time) and highest in the Sitters cluster (high nonscreen sedentary behavior/low physical activity). These findings suggest that reduction in screen time and an improved diet may contribute positively to academic performance. While children with high nonscreen sedentary time performed better academically in this study, they also accumulated low levels of physical activity. This warrants further investigation, given the known physical and mental benefits of physical activity.
AllensworthD. D. (2015). Health services and health education. In BosworthK. (Ed.), Prevention science in school settings (pp. 105-121). New York, NY: Springer.
4.
AnsariD.DhitalB. (2006). Age-related changes in the activation of the intraparietal sulcus during nonsymbolic magnitude processing: An event-related functional magnetic resonance imaging study. Journal of Cognitive Neuroscience, 18, 1820-1828.
5.
Australian Curriculum Assessment and Reporting Authority. (2013). My school: Guide to understanding 2012 Index of Community Socio-Educational Advantage (ICSEA) values. Retrieved from http://www.acara.edu.au/verve/_resources/About_icsea_2014.pdf
6.
BarreiraT. V.SchunaJ. M.Jr.MireE. F.KatzmarzykP. T.ChaputJ.-P.LeducG.Tudor-LockeC. (2015). Identifying children’s nocturnal sleep using 24-h waist accelerometry. Medicine & Science in Sports & Exercise, 47, 937-943.
7.
BarreiraT. V.StaianoA. E.KatzmarzykP. T. (2013). Validity assessment of a portable bioimpedance scale to estimate body fat percentage in White and African–American children and adolescents. Pediatric Obesity, 8(2), e29-e32.
8.
BergS.Willis-StewartS.KendallS. (2016). Creating healthy schools and student engagement: The Got Health? Initiative. Revue phénEPS/PHEnex Journal, 8(1). Retrieved from http://ojs.acadiau.ca/index.php/phenex/article/view/1618
9.
BurrowsT.GoldmanS.PurseyK.LimR. (2016). Is there an association between dietary intake and academic achievement: a systematic review. Journal of Human Nutrition and Dietetics. Advance online publication. doi:10.1111/jhn.12407
10.
BuschV.LoyenA.LodderM.SchrijversA. J.van YperenT. A.de LeeuwJ. R. (2014). The effects of adolescent health-related behavior on academic performance: A systematic review of the longitudinal evidence. Review of Educational Research, 84, 245-274.
11.
CarsonV.HunterS.KuzikN.GrayC. E.PoitrasV. J.ChaputJ.-P.. . . Connor GorberS. (2016). Systematic review of sedentary behaviour and health indicators in school-aged children and youth: An update. Applied Physiology, Nutrition, and Metabolism, 41, S240-S265.
12.
ChaputJ.-P.GrayC. E.PoitrasV. J.CarsonV.GruberR.OldsT.. . . SampsonM. (2016). Systematic review of the relationships between sleep duration and health indicators in school-aged children and youth. Applied Physiology, Nutrition, and Metabolism, 41, S266-S282.
13.
ChastinS. F.Palarea-AlbaladejoJ.DontjeM. L.SkeltonD. A. (2015). Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: A novel compositional data analysis approach. PLoS One, 10(10), e0139984.
14.
CheemaJ. R. (2014). Some general guidelines for choosing missing data handling methods in educational research. Journal of Modern Applied Statistical Methods, 13(2), 3.
15.
ChinapawM.ProperK.BrugJ.Van MechelenW.SinghA. (2011). Relationship between young peoples’ sedentary behaviour and biomedical health indicators: A systematic review of prospective studies. Obesity Reviews, 12, e621-e632.
16.
CliffD.HeskethK.VellaS.HinkleyT.TsirosM.RidgersN.. . . HardyL. (2016). Objectively measured sedentary behaviour and health and development in children and adolescents: Systematic review and meta-analysis. Obesity Reviews, 17, 330-344.
17.
ColcombeS. J.EricksonK. I.ScalfP. E.KimJ. S.PrakashR.McAuleyE.. . . KramerA. F. (2006). Aerobic exercise training increases brain volume in aging humans. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61, 1166-1170.
18.
ColcombeS. J.KramerA. F.EricksonK. I.ScalfP.McAuleyE.CohenN. J.. . . ElavskyS. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proceedings of the National Academy of Sciences of the United States of America, 101, 3316-3321.
19.
de OnisM.OnyangoA.BorghiE.SiyamA.NishidaC.SiekmannJ. (2007). Development of a WHO growth reference for school-aged children and adolescents. Bulletin of the World Health Organization, 85, 660-667.
20.
DewaldJ. F.MeijerA. M.OortF. J.KerkhofG. A.BögelsS. M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review. Sleep Medicine Reviews, 14, 179-189.
21.
DumuidD.OldsT.LewisL.Martín-FernándezJ.KatzmarzykP. T.BarreiraT. V.. . . MaherC. (2016). The adiposity of children is associated with their lifestyle behaviours: A cluster analysis of school-aged children from 12 nations. Pediatric Obesity. Advance online publication. doi:10.1111/ijpo.12196
22.
DumuidD.OldsT.LewisL. K.Martin-FernandezJ.KatzmarzykP. T.BarreiraT. V.. . . MaherC. (2017). Health-related quality of life and lifestyle behavior clusters in school-aged children from 12 countries. The Journal of Pediatrics, 183, 78–183.e2
23.
EgozcueJ. J.Pawlowsky-GlahnV. (2005). Groups of parts and their balances in compositional data analysis. Mathematical Geology, 37, 795-828.
24.
EvensonK. R.CatellierD. J.GillK.OndrakK. S.McMurrayR. G. (2008). Calibration of two objective measures of physical activity for children. Journal of Sports Sciences, 26, 1557-1565.
25.
Fernández-AlviraJ. M.De BourdeaudhuijI.SinghA. S.VikF. N.ManiosY.KovacsE.. . . MorenoL. A. (2013). Clustering of energy balance-related behaviors and parental education in European children: The ENERGY-project. International Journal of Behavioral Nutrition and Physical Activity, 10, 5. doi:10.1186/1479-5868-10-5
26.
FrisardiV.PanzaF.SeripaD.ImbimboB. P.VendemialeG.PilottoA.SolfrizziV. (2010). Nutraceutical properties of Mediterranean diet and cognitive decline: Possible underlying mechanisms. Journal of Alzheimer’s Disease, 22, 715-740.
27.
GöbelS. M.Johansen-BergH.BehrensT.RushworthM. F. (2004). Response-selection-related parietal activation during number comparison. Journal of Cognitive Neuroscience, 16, 1536-1551.
28.
Gómez-PinillaF. (2008). Brain foods: The effects of nutrients on brain function. Nature Reviews Neuroscience, 9, 568-578.
GrolnickW. S.Raftery-HelmerJ. N.FlammE. S. (2013). Parent involvement in learning. In HattieJ.AndermanE. (Eds.), International guide to student achievement (pp. 101-103). New York, NY: Routledge.
31.
GubbelsJ. S.van AssemaP.KremersS. P. (2013). Physical activity, sedentary behavior, and dietary patterns among children. Current Nutrition Reports, 2, 105-112.
32.
HaapalaE. A.PoikkeusA.-M.Kukkonen-HarjulaK.TompuriT.LintuN.VäistöJ.. . . LakkaT. A. (2014). Associations of physical activity and sedentary behavior with academic skills: A follow-up study among primary school children. PLoS One, 9(9), e107031.
33.
HairJ. F.BlackW. C.BabinB. J.AndersonR. E.TathamR. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.
34.
HjorthM. F.ChaputJ. P.DamsgaardC. T.DalskovS. M.MichaelsenK. F.TetensI.SjödinA. (2012). Measure of sleep and physical activity by a single accelerometer: Can a waist-worn Actigraph adequately measure sleep in children?Sleep and Biological Rhythms, 10, 328-335.
35.
HuntE.McKayE.FitzgeraldA.PerryI. (2014). Time use and daily activities of late adolescents in contemporary Ireland. Journal of Occupational Science, 21, 42-64.
36.
KatzmarzykP. T.BarreiraT. V.BroylesS. T.ChampagneC. M.ChaputJ.-P.FogelholmM.. . . KurpadA. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): Design and methods. BMC Public Health, 13(1), 900.
LeechR. M.McNaughtonS. A.TimperioA. (2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: A review. International Journal of Behavioral Nutrition and Physical Activity, 11, 4. doi:10.1186/1479-5868-11-4
39.
MageeC. A.CaputiP.IversonD. C. (2013). Patterns of health behaviours predict obesity in Australian children. Journal of Paediatrics and Child Health, 49, 291-296.
40.
MaguireE. A.FrithC. D.MorrisR. (1999). The functional neuroanatomy of comprehension and memory: The importance of prior knowledge. Brain, 122, 1839-1850.
41.
MaherC.LewisL.KatzmarzykP. T.DumuidD.CassidyL.OldsT. (2016). The associations between physical activity, sedentary behaviour and academic performance. Journal of Science and Medicine in Sport, 19, 1004-1009.
42.
MarksB. L.MaddenD. J.BucurB.ProvenzaleJ. M.WhiteL. E.CabezaR.HuettelS. A. (2007). Role of aerobic fitness and aging on cerebral white matter integrity. Annals of the New York Academy of Sciences, 1097, 171-174.
43.
Martín-FernándezJ.-A.Daunis-i-EstadellaJ.Mateu-FiguerasG. (2015). On the interpretation of differences between groups for compositional data. SORT—Statistics and Operations Research Transactions, 39, 231-252.
44.
Martínez-GómezD.VeigaO.Gómez-MartínezS.ZapateraB.Martínez-HernándezD.CalleM. E.MarcosA. (2012). Gender-specific influence of health behaviors on academic performance in Spanish adolescents: The AFINOS study. Nutricion Hospitalaria, 27, 724-730.
45.
McIsaacJ.-L. D.KirkS. F.KuhleS. (2015). The association between health behaviours and academic performance in Canadian elementary school students: A cross-sectional study. International Journal of Environmental Research and Public Health, 12, 14857-14871.
46.
Osei-BrysonK.-M.SamoilenkoS. (2014). Overview on cluster analysis. In Osei-BrysonK.-M.SamoilenkoS. (Eds.), Advances in research methods for information systems research (pp. 127-138). New York, NY: Springer.
47.
Palarea-AlbaladejoJ.Martín-FernándezJ. A.SotoJ. A. (2012). Dealing with distances and transformations for fuzzy C-means clustering of compositional data. Journal of Classification, 29, 144-169.
48.
ParsonsT. (2014). The school class as a social system. InBallantineJ.SpadeJ. (Eds.), Schools and society: A sociological approach to education. (pp. 35-40). Thousand Oaks, CA: SAGE Publications.
49.
PedišićŽ. (2014). Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research—The focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology, 46, 135-146.
50.
PoitrasV. J.GrayC. E.BorgheseM. M.CarsonV.ChaputJ.-P.JanssenI.. . . KhoM. E. (2016). Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Applied Physiology, Nutrition, and Metabolism, 41, S197-S239.
51.
RiveraS. M.ReissA.EckertM. A.MenonV. (2005). Developmental changes in mental arithmetic: Evidence for increased functional specialization in the left inferior parietal cortex. Cerebral Cortex, 15, 1779-1790.
52.
SaloheimoT.GonzálezS.ErkkolaM.MilauskasD.MeiselJ.ChampagneC.. . . FogelholmM. (2015). The reliability and validity of a short food frequency questionnaire among 9–11-year olds: A multinational study on three middle-income and high-income countries. International Journal of Obesity Supplements, 5, S22-S28.
53.
ShinN. (2004). Exploring pathways from television viewing to academic achievement in school age children. Journal of Genetic Psychology, 165, 367-382.
54.
SibleyB. A.EtnierJ. L. (2003). The relationship between physical activity and cognition in children: A meta-analysis. Pediatric Exercise Science, 15, 243-256.
55.
SigfúsdóttirI. D.KristjánssonA. L.AllegranteJ. P. (2007). Health behaviour and academic achievement in Icelandic school children. Health Education Research, 22, 70-80.
56.
SigmanA. (2012). Time for a view on screen time. Archives of Disease in Childhood, 97, 935-942.
57.
SyväojaH.KantomaaM. T.AhonenT.HakonenH.KankaanpääA.TammelinT. H. (2013). Physical activity, sedentary behavior, and academic performance in Finnish children. Medicine & Science in Sports & Exercise, 45, 2098-2104.
58.
TremblayM. S.LeBlancA. G.KhoM. E.SaundersT. J.LaroucheR.ColleyR. C.. . . GorberS. C. (2011). Systematic review of sedentary behaviour and health indicators in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 98.
59.
TrinhL.WongB.FaulknerG. E. (2015). The independent and interactive associations of screen time and physical activity on mental health, school connectedness and academic achievement among a population-based sample of youth. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 24, 17-24.
60.
Tudor-LockeC.BarreiraT. V.SchunaJ. M.MireE. F.ChaputJ.-P.FogelholmM.. . . LambertE. V. (2015). Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). International Journal of Behavioral Nutrition and Physical Activity, 12(1), 11.
61.
Tudor-LockeC.BarreiraT. V.SchunaJ. M.Jr.MireE. F.KatzmarzykP. T. (2013). Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Applied Physiology, Nutrition, and Metabolism, 39, 53-57.
62.
van den BoogaartK. G.Tolosana-DelgadoR. (2008). “Compositions”: A unified R package to analyze compositional data. Computers & Geosciences, 34, 320-338.
63.
Van PraagH.ChristieB. R.SejnowskiT. J.GageF. H. (1999). Running enhances neurogenesis, learning, and long-term potentiation in mice. Proceedings of the National Academy of Sciences, 96, 13427-13431.
64.
VoyerD.VoyerS. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140, 1174-1204.
65.
WuA.YingZ.Gomez-PinillaF. (2004). The interplay between oxidative stress and brain-derived neurotrophic factor modulates the outcome of a saturated fat diet on synaptic plasticity and cognition. European Journal of Neuroscience, 19, 1699-1707.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
