Daily step count and cardiometabolic risk in young people: a systematic review

Gordia, Alex Pinheiro; Quadros, Teresa Maria Bianchini de; Silva, Luciana Rodrigues

doi:10.5007/1980-0037.2018v20n5p468

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Revista Brasileira de Cineantropometria & Desempenho Humano

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Original Article • Rev. bras. cineantropom. desempenho hum 20 (5) • Sep-Dec 2018 • https://doi.org/10.5007/1980-0037.2018v20n5p468 copy

Daily step count and cardiometabolic risk in young people: a systematic review

Número de passos por dia e risco cardiometabólico em jovens: revisão sistemática

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The aim of this systematic review was to analyze the association between the number of daily steps and cardiometabolic risk factors in children and adolescents. Studies published from 1 January 2000 to 31 May 2016 in the PubMed, SciELO and LILACS databases were analyzed. Manual searches of the reference lists of the selected articles were also performed. Of the 4,454 articles retrieved, eight studies met the eligibility criteria and were included in the analysis. Seven of these studies reported some significant association between step count and cardiometabolic risk factors. However, the data suggested the lack of an association with hyperglycemia, were inconclusive regarding lipid profile alterations, divergent for high blood pressure, and indicated an association with insulin resistance and metabolic syndrome only in males. The findings of this systematic review do not permit to determine the association between step count and cardiometabolic risk factors in young people. The small number of articles that met the eligibility criteria and the different methodological problems of the studies reviewed were the main factors limiting inferences. Observational studies with a longitudinal design (cohort and casecontrol) and accurate methodological control are necessary.

Key words
Adolescent; Cardiovascular diseases; Child; Motor activity; Review; Risk factors

Study	Year of publication	Location	Study design	Population	Evaluation of the number of steps	Cardiometabolic risk factor (outcome)	Statistical analysis
Schofield et al.¹⁸18 Schofield G, Schofield L, Hinckson EA, Mummery WK. Daily step counts and selected coronary heart disease risk factors in adolescent girls. J Sci Med Sport 2009;12(1):148-55.	2009	Queensland, Australia	Cross sectional	415 adolescent girls, mean age of 16 years	Monitoring for 4 days with a Yamax Digi-walker SW-700 pedometer	High blood pressure	Logistic regression
Telford et al.²⁰20 Telford RD, Cunningham RB, Shaw JE, Dunstan DW, Lafferty AR, Reynolds GJ, et al. Contrasting longitudinal and cross-sectional relationships between insulin resistance and percentage of body fat, fitness, and physical activity in children-the LOOK study. Pediatr Diabetes 2009;10(8):500-7.	2009	Canberra, Australia	Cohort	498 children (257 girls), follow-up from 8 to 10 years	Monitoring for 7 days with a Walk4Life pedometer	Insulin resistance^a a Insulin resistance evaluated by homeostatic model assessment of insulin resistance (HOMA-IR).	Generalized linear mixed model
Moreira et al.²⁵25 Moreira C, Santos R, de Farias Junior JC, Vale S, Santos PC, Soares-Miranda L, et al. Metabolic risk factors, physical activity and physical fitness in Azorean adolescents: a cross-sectional study. BMC Public Health 2011;11:214.	2011	Açores, Portugal	Cross sectional	417 adolescents (243 girls), age from 15 to 18 years	Monitoring for 7 days with a Kenz Lifecorder Plus pedometer	Presence of one or more of the following cardiometabolic risk factors: abdominal obesity, hyperglycemia, low HDL-cholesterol, hypertriglyceridemia, and high blood pressure	Logistic regression
Telford et al.21	2012	Canberra, Australia	Cohort	534 children (278 girls), follow-up from 8 to 12 years	Monitoring for 7 days with a Walk4Life pedometer	Insulin resistance^a a Insulin resistance evaluated by homeostatic model assessment of insulin resistance (HOMA-IR).	Generalized linear mixed model
Andaki et al.²⁴24 Andaki ACR TA, Andaki Júnior R, Santos A, Brito CJ, Mendes EL. Physical activity level as a predictor of cardiovascular risk factors in children. Motriz: Rev Educ Fis 2013;19(3):S8-15.	2014	Viçosa, Minas Gerais, Brazil	Cross sectional	187 children (106 girls), mean age of 9.90 years	Monitoring for 7 days with a DX-8897 pedometer	Hypertriglyceridemia, low HDL-cholesterol, high blood pressure, and metabolic syndrome^b b Metabolic syndrome was assessed using the criteria of Ferranti et al.24	ROC curve
Andaki et al.²³23 Andaki AC, Tinoco AL, Mendes EL, Andaki Junior R, Hills AP, Amorim PR. Anthropometry and physical activity level in the prediction of metabolic syndrome in children. Public Health Nutr 2014;17(10):2287-94.	2014	Viçosa, Minas Gerais, Brasil	Cross sectional	187 children (106 girls), mean age of 9.90 years	Monitoring for 7 days with a DX-8897 pedometer	Metabolic syndrome^b b Metabolic syndrome was assessed using the criteria of Ferranti et al.24	ROC curve
Telford et al.22	2015	Canberra, Australia	Cohort	469 children (240 girls), follow-up from 8 to 12 years	Monitoring for 7 days with a Walk4Life pedometer	Altered lipid profile	Generalized linear mixed model
Quadros et al.¹⁹19 Quadros TM, Gordia AP, Silva LR, Silva DA, Mota J. Epidemiological survey in schoolchildren: determinants and prevalence of cardiovascular risk factors. Cad Saude Publica 2016;32(2):e00181514.	2016	Amargosa, Bahia, Brazil	Cross sectional	1,139 children and adolescents (633 girls), age from 6 to 18 years	Monitoring for 7 days with a Yamax Digi-walker SW-200 pedometer	Dyslipidemia, hyperglycemia, and high blood pressure	Poisson regression

Study		Main findings
Schofield et al.¹⁸18 Schofield G, Schofield L, Hinckson EA, Mummery WK. Daily step counts and selected coronary heart disease risk factors in adolescent girls. J Sci Med Sport 2009;12(1):148-55.		There was no association between step count and high blood pressure.
Telford et al.²⁰20 Telford RD, Cunningham RB, Shaw JE, Dunstan DW, Lafferty AR, Reynolds GJ, et al. Contrasting longitudinal and cross-sectional relationships between insulin resistance and percentage of body fat, fitness, and physical activity in children-the LOOK study. Pediatr Diabetes 2009;10(8):500-7.		In cross-sectional analysis, no significant association was found between step count and insulin resistance in boys or girls, while longitudinal analysis revealed an association only in boys.
Moreira et al.²⁵25 Moreira C, Santos R, de Farias Junior JC, Vale S, Santos PC, Soares-Miranda L, et al. Metabolic risk factors, physical activity and physical fitness in Azorean adolescents: a cross-sectional study. BMC Public Health 2011;11:214.		Adolescents in the 4^th quartile of step count were less likely to have one or more metabolic risk factors compared to those in the 1^st quartile (OR = 0.56).
Telford et al.²¹21 Telford RD, Cunningham RB, Telford RM, Kerrigan J, Hickman PE, Potter JM, et al. Effects of changes in adiposity and physical activity on preadolescent insulin resistance: the Australian LOOK longitudinal study. PLoS One 2012;7(10):e47438.		Variations in step count explained changes in insulin resistance only in boys (boys: β = -0.007, p = 0.039; girls: β = 0.0005, p = 0.85).
Andaki et al.²⁴24 Andaki ACR TA, Andaki Júnior R, Santos A, Brito CJ, Mendes EL. Physical activity level as a predictor of cardiovascular risk factors in children. Motriz: Rev Educ Fis 2013;19(3):S8-15.		Step count was a predictor of low HDL-cholesterol and metabolic syndrome only in boys (AUC = 0.891 and AUC = 0.817, respectively).
Andaki et al.²³23 Andaki AC, Tinoco AL, Mendes EL, Andaki Junior R, Hills AP, Amorim PR. Anthropometry and physical activity level in the prediction of metabolic syndrome in children. Public Health Nutr 2014;17(10):2287-94.		Step count was a predictor of metabolic syndrome only in boys (AUC = 0.891).
Telford et al.²²22 Telford RD, Cunningham RB, Waring P, Telford RM, Potter JM, Hickman PE, et al. Sensitivity of blood lipids to changes in adiposity, exercise, and diet in children. Med Sci Sports Exerc 2015;47(5):974-82.		There was no evidence of a longitudinal relationship between step count and lipid profile in either sex. In cross-sectional analysis, boys (but not girls) with a larger number of steps had higher HDL-cholesterol and lower triglycerides (p = 0.04 and p = 0.03, respectively).
Quadros et al.¹⁹19 Quadros TM, Gordia AP, Silva LR, Silva DA, Mota J. Epidemiological survey in schoolchildren: determinants and prevalence of cardiovascular risk factors. Cad Saude Publica 2016;32(2):e00181514.		There was an association of step count with high blood pressure (PR = 1.32; p = 0.034), but not with dyslipidemia (p = 0.281) or hyperglycemia (p = 0.379).

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[1] CORRESPONDING AUTHOR. Alex Pinheiro Gordia. Federal University of Reconcavo of Bahia. Av. Nestor de Melo Pita, 535-Centro, CEP: 45300-000, Amargosa, Bahia, Brazil. E-mail: alexgordia@gmail.com