Pedometer Step Counts and Metabolic Syndrome Risk Factors in Middle School Students in Rural Michigan

Jan Perkins¹, Michael Jack Wierenga^2, , William A. Saltarelli³ and Miranda Moncada-Sullivan⁴

¹Doctoral Program in Physical Therapy, Herbert H. and Grace A. Dow College of Health Professions, Central Michigan University, Mt Pleasant, MI 48859

²Exercise Physiology, Herbert H. and Grace A. Dow College of Health Professions, Central Michigan University, Mt Pleasant, MI 48859

³School of Health Sciences, Central Michigan University, Mount Pleasant, MI 48859

⁴Hayes Green Beach Memorial Hospital, 321 East Harris Street, Charlotte, MI 48813

Cite this paper:
Jan Perkins, Michael Jack Wierenga, William A. Saltarelli and Miranda Moncada-Sullivan. Pedometer Step Counts and Metabolic Syndrome Risk Factors in Middle School Students in Rural Michigan. Journal of Physical Activity Research. 2017; 2(1):32-38. doi: 10.12691/jpar-2-1-6

Abstract

BACKGROUND: Childhood metabolic syndrome increases health risks in later life. Physical activity may moderate risk factors and incidence. Pedometers are a valid means of tracking physical activity in children. This study was developed to examine the impact of physical activity, as measured by pedometer counts, on risk factors in middle school children in a rural Midwestern community. METHODS: The Cardiovascular Health Intervention Program (CHIP) measures cardiovascular disease risk factors in rural Midwestern middle school students, and includes blood pressure, waist circumference, fasting blood glucose, high density lipoprotein, and triglyceride levels, allowing determination of metabolic syndrome incidence. In one community activity of fifth and sixth grade students pedometer monitoring was added for two separate weeks (December and April). Analysis was done with one way ANOVA and T-Tests. RESULTS: Fifty-four students participated in CHIP, 36 in the pedometer project. Winter and Spring step counts were different (p=0.00). Boys had a trend for higher step counts than girls, but sample size prevented this reaching significance (p=0.16). Two (3.7%) students met criteria for metabolic syndrome. Lower step counts were associated with HDL levels meeting risk factor criteria (p=0.028). CONCLUSIONS: Rates of metabolic syndrome and component risk were similar to those seen nationally. Seasonal weather variation may explain differences between December and April step counts. Additional studies of rural students are suggested, allowing pooling of populations to attain greater sample sizes.

Keywords:
adolescents physical activity assessment metabolic syndrome risk factors pedometers

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Zimmet P, Alberti KG, Kaufman F, et al. The metabolic syndrome in children and adolescents—an IDF consensus report. Pediatr Diabetes. 2007; 8(5): 299-306.
[2]	Balagopal P. Physical activity and cardiovascular health in children. Pediatr Ann. 2006; 35(11): 814-8, 820-821.
[3]	Carnethon MR, Gidding SS, Nehgme R, Sidney S, Jacobs DR Jr, Liu K. Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors. JAMA. 2003; 290(23): 3092-30100.
[4]	Chang C, Liu W, Zhao X, Li S, Yu C. Effects of supervised exercise intervention on metabolic risk factors and physical fitness in Chinese obese children in early puberty. Obes Rev. 2008; 9 Suppl 1: 135-141.
[5]	Gutin B, Barbeau P, Owens S, et al. Effects of exercise intensity on cardiovascular fitness, total body composition, and visceral adiposity of obese adolescents. Am J Clin Nutr. 2002; 75(5): 818-826.
[6]	Pan Y, Pratt CA. Metabolic syndrome and its association with diet and physical activity in US adolescents. J Am Diet Assoc. 2008; 108(2): 276-286.
[7]	Metzger JS, Catellier DJ, Evenson KR, Treuth MS, Rosamond WD, Siega-Riz AM. Associations between patterns of objectively measured physical activity and risk factors for the metabolic syndrome. Am J Health Promot. 2010; 24(3): 161-169.
[8]	Kelishadi R. Childhood overweight, obesity, and the metabolic syndrome in developing countries. Epidemiol Rev. 2007; 29(1): 62-76.
[9]	Okosun IS, Boltri JM, Lyn R, Davis-Smith M. Continuous metabolic syndrome risk score, body mass index percentile, and leisure time physical activity in American children. J Clin Hypertens (Greenwich). 2010; 12(8): 636-644.
[10]	Morrison JA, Friedman LA, Wang P, Glueck CJ. Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. J Pediatr. 2008; 152(2): 201-206.
[11]	Morrison JA, Glueck CJ, Umar M, Daniels S, Dolan LM, Wang P. Hyperinsulinemia and metabolic syndrome at mean age of 10 years in black and white schoolgirls and development of impaired fasting glucose and type 2 diabetes mellitus by mean age of 24 years. Metabolism. 2011;60(1):24-31.
[12]	Morrison JA, Friedman LA, Gray-McGuire C. Metabolic syndrome in childhood predicts adult cardiovascular disease 25 years later: the Princeton Lipid Research Clinics Follow-up Study. Pediatrics. 2007; 120(2): 340-345.
[13]	Magnussen CG, Koskinen J, Chen W, et al. Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone: the Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study. Circulation. 2010; 122(16): 1604-1611.
[14]	Weiss R, Dziura J, Burgert TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004; 350(23): 2362-2374.
[15]	Ford ES, Ajani UA, Mokdad AH. The metabolic syndrome and concentrations of C-reactive protein among U.S. youth. Diabetes Care. 2005; 28(4): 878-881.
[16]	Lambert M, Delvin EE, Paradis G, O’Loughlin J, Hanley JA, Levy E. C-reactive protein and features of the metabolic syndrome in a population-based sample of children and adolescents. Clin Chem. 2004; 50(10): 1762-1768.
[17]	Friend A, Craig L, Turner S. The prevalence of metabolic syndrome in children: a systematic review of the literature. Metab Syndr Relat Disord. 2013; 11(2): 71-80
[18]	Brownson RC, Chriqui JF, Burgeson CR, Fisher MC, Ness RB. Translating epidemiology into policy to prevent childhood obesity: the case for promoting physical activity in school settings. Ann Epidemiol. 2010; 20(6): 436-444.
[19]	McMurray RG, Harrell JS, Bangdiwala SI, Deng S. Cardiovascular disease risk factors and obesity of rural and urban elementary school children. J Rural Health. 1999; 15(4): 365-374.
[20]	Hartley D. Rural health disparities, population health, and rural culture. Am J Public Health. 2004; 94(10): 1675-1678.
[21]	Moore JB, Davis CL, Baxter SD, Lewis RD, Yin Z. Physical activity, metabolic syndrome, and overweight in rural youth. J Rural Health. 2008; 24(2): 136-142.
[22]	Tucker P, Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007; 121(12): 909-922.
[23]	Beighle A, Alderman B, Morgan CF, Le Masurier G. Seasonality in children’s pedometer-measured physical activity levels. Res Q Exerc Sport. 2008; 79(2): 256-260.
[24]	McKee DP, Murtagh EM, Boreham CA, Nevill AM, Murphy MH. Seasonal and annual variation in young children’s physical activity. Med Sci Sports Exerc. 2012; 44(7): 1318-1324.
[25]	Fisher A, Reilly JJ, Montgomery C, et al. Seasonality in physical activity and sedentary behavior in young children. Pediatr Exerc Sci. 2005; 17(1): 31-41.
[26]	Carson V, Spence JC, Cutumisu N, Boule N, Edwards J. Seasonal variation in physical activity among preschool children in a northern Canadian city. Res Q Exerc Sport. 2010; 81(4): 392-399.
[27]	Beighle A, Erwin H, Morgan CF, Alderman B. Children’s in-school and out-of-school physical activity during two seasons. Res Q Exerc Sport. 2012; 83(1): 103-107.
[28]	Riddoch CJ, Mattocks C, Deere K, et al. Objective measurement of levels and patterns of physical activity. Arch Dis Child. 2007; 92(11): 963-969.
[29]	Hardman CA, Horne PJ, Rowlands AV. Children’s pedometer-determined physical activity during school-time and leisure-time. J Exerc Sci Fit. 2009; 7(2): 129-134.
[30]	Treuth MS, Catellier DJ, Schmitz KH, et al. Weekend and weekday patterns of physical activity in overweight and normal-weight adolescent girls. Obesity (Silver Spring). 2007; 15(7): 1782-1788.
[31]	Godard C, Román M, Rodríguez Mdel P, Leyton B, Salazar G. Variability of physical activity in 4 to 10-year-old children: a study by accelerometry. Arch Argent Pediatr. 2012; 110(5): 388-393.
[32]	Vander Ploeg KA, Wu B, McGavock J, Veugelers PJ. Physical activity among Canadian children on school days and nonschool days. J Phys Act Health. 2012; 9(8): 1138-1145.
[33]	Trost SG, Pate RR, Dowda M, Saunders R, Ward DS, Felton G. Gender differences in physical activity and determinants of physical activity in rural fifth grade children. J Sch Health. 1996; 66(4): 145-150.
[34]	Sarkin JA, McKenzie TL, Sallis JF. Gender differences in physical activity during fifth grade physical education and recess periods. J Teach Phys Educ. 1997; 17(1): 99-106.
[35]	Tudor-Locke CE, Myers AM. Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Res Q Exerc Sport. 2001; 72(1): 1-12.
[36]	Tudor-Locke C, Lee SM, Morgan CF, Beighle A, Pangrazi RP. Children’s pedometer-determined physical activity during the segmented school day. Med Sci Sports Exerc. 2006; 38(10): 1732-1738.
[37]	Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001; 31(6): 439-454.
[38]	Tudor-Locke C, Williams JE, Reis JP, Pluto D. Utility of pedometers for assessing physical activity: convergent validity. Sports Med. 2002; 32(12): 795-808.
[39]	Rowlands AV, Eston RG, Ingledew DK. Measurement of physical activity in children with particular reference to the use of heart rate and pedometry. Sports Med. 1997; 24(4): 258-272.
[40]	Foley JT, Beets MW, Cardinal BJ. Monitoring children’s physical activity with pedometers: reactivity revisited. J Exerc Sci Fit. 2011; 9(2): 82-86.
[41]	Gao Z, Lee AM, Solmon MA, et al. Validating pedometer-based physical activity time against accelerometer in middle school physical education. ICHPER—SD J Research. 2010; 5(1): 20-25.
[42]	Tudor-Locke C, Pangrazi RP, Corbin CB, et al. BMI-referenced standards for recommended pedometer determined steps/day in children. Prev Med. 2004; 38(6): 857-864.
[43]	Peterson MD, Liu D, IglayReger HB, Saltarelli WA, Visich PS, Gordon PM. Principal component analysis reveals gender-specific predictors of cardiometabolic risk in 6th graders. Cardiovasc Diabetol. 2012; 11: 146.
[44]	Devaney JM, Thompson PD, Visich PS, et al. The 1p13.3 LDL (C)-associated locus shows large effect sizes in young populations. Pediatr Res. 2011; 69(6): 538-543.
[45]	Fletcher GF, Balady GJ, Amsterdam EA, et al. Exercise standards for testing and training: a statement for healthcare professionals from the American Heart Association. Circulation. 2001; 104(14): 1694-1740.
[46]	Brusseau TA, Kulinna PH, Tudor-Locke C, Ferry M, van der Mars H, Darst PW. Pedometer-determined segmented physical activity patterns of fourth- and fifth-grade children. J Phys Act Health. 2011; 8(2): 279-286.
[47]	Brusseau TA, Hannon JC. Pedometer-determined physical activity of youth while attending school: a review. Sport Sci Rev. 2013; 22(5-6): 329-342.
[48]	Bélanger M, Gray-Donald K, O’Loughlin J, Paradis G, Hanley J. Influence of weather conditions and season on physical activity in adolescents. Ann Epidemiol. 2009; 19(3): 180-186.
[49]	Rowlands AV, Eston RG. Comparison of accelerometer and pedometer measures of physical activity in boys and girls, ages 8-10 years. Res Q Exerc Sport. 2005; 76(3): 251-257.
[50]	Rush E, Coppinger T, Obolonkin V, et al. Use of pedometers to identify less active children and time spent in moderate to vigorous physical activity in the school setting. J Sci Med Sport. 2012; 15(3): 226-230.
[51]	Smith JD, Schroeder CA. Assessing pedometer accuracy while walking, skipping, galloping, sliding, and hopping. J Strength Cond Res. 2008; 22(1): 276-282.