physical activity, energy intake and obesity prevalance among urban and rural schoolchildren aged...

Physical activity, energy intake, and obesityprevalence among urban and rural schoolchildrenaged 1112 years in Japan

Aya Itoi, Yosuke Yamada, Yoshiyuki Watanabe, and Misaka Kimura

Abstract: The prevalence of childhood overweight and obesity has been shown to differ among regions, including ruralurban regional differences within nations. This study obtained simultaneous accelerometry-derived physical activity, 24 hactivity, and food records to clarify the potential contributing factors to ruralurban differences in childhood overweightand obesity in Japan. Sixth-grade children (n = 227, 1112 years old) from two urban elementary schools in Kyotoand four rural elementary schools in Tohoku participated in the study. The children were instructed to wear a pedome-ter that included a uniaxial accelerometer and, assisted by their parents, keep minute-by-minute 24 h activity and foodrecords. For 12 children, the total energy expenditure was measured by the doubly labeled water method that was usedto correct the Lifecorder-predicted activity energy expenditure and physical activity level. The overweight and obesityprevalence was significantly higher in rural than in urban children. The number of steps per day, activity energy expen-diture, physical activity level, and duration of walking to school were significantly lower in rural than in urban chil-dren. In contrast, the reported energy intake did not differ significantly between the regions. The physical activity andduration of the walk to school were significantly correlated with body mass index. Rural children had a higher preva-lence of overweight and obesity, and this may be at least partly caused by lower physical activity, especially less timespent walking to school, than urban children.

Key words: walking to school, active commuting, physical activity level, dietary intake, obesity, urban and rural regions,schoolchildren.

Rsum : Daprs des tudes, la prvalence de surpoids et de lobsit diffre dune rgion lautre et on note des diffren-ces ruraleurbaine dans une mme nation. Cette tude prsente des observations en matire dactivit physique issues duport dun acclromtre et des carnets dactivit physique et dalimentation sur une priode de 24 h, et ce, pour clarifierlimportance des facteurs contributifs dans les diffrences ruraleurbaine chez des enfants prsentant un surpoids et de lob-sit au Japon. Des enfants de 6e anne (n = 227, 1112 ans) provenant de deux coles lmentaires en milieu urbain(Kyoto) et de quatre coles lmentaires en milieu rural (Tohoku) participent cette tude. On demande aux enfants de por-ter un podomtre comprenant un acclromtre uniaxial et, avec laide des parents, dinscrire toutes les minutes les activitseffectues et lapport alimentaire sur une priode de 24 h. Chez 12 enfants, on value la dpense totale dnergie par la m-thode de leau deux isotopes et on se sert des rsultats pour estimer avec plus de prcision la dpense dnergie pour lac-tivit physique et le niveau dactivit physique . La prvalence de surpoids/obsit est significativement plus grande chez lesenfants en milieu rural quen milieu urbain. Le nombre de pas effectus dans une journe, lnergie pour lactivit physique,le niveau dactivit physique et la dure de la marche vers lcole sont significativement plus faibles chez les enfants en mi-lieu rural quen milieu urbain. Par contre, on nobserve pas de diffrences significatives dapport alimentaire consign dunmilieu lautre. Lactivit physique et la dure de la marche vers lcole sont significativement corrles avec lIMC. Laprvalence de surpoids et de lobsit des enfants en milieu rural est plus grande quen milieu urbain et cest probablement cause dun niveau plus faible dactivit physique et notamment de la plus faible dure de marche vers lcole.

Motscls : marche vers lcole, dplacements actifs, niveau dactivit physique, apport alimentaire, obsit, rgions urbaineset rurales, coliers.

[Traduit par la Rdaction]

Received 9 February 2012. Accepted 1 June 2012. Published at www.nrcresearchpress.com/apnm on 31 October 2012.

A. Itoi. Department of Health, Sports and Nutrition, Faculty of Health and Welfare, Kobe Womens University, 4-7-2Minatojimanakamachi, Chuo-ku, Kobe, Japan; Department of Epidemiology for Community Health and Medicine, Graduate School ofMedical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan.Y. Yamada. Laboratory of Applied Health Science, Graduate School of Nursing for Health Care Science, Kyoto Prefectural University ofMedicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan; Research Fellow, Japan Society for the Promotion of Science, Tokyo,Japan.Y. Watanabe. Department of Epidemiology for Community Health and Medicine, Graduate School of Medical Science, Kyoto PrefecturalUniversity of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan.M. Kimura. Laboratory of Applied Health Science, Graduate School of Nursing for Health Care Science, Kyoto Prefectural University ofMedicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602-8566, Japan.

Corresponding author: Yosuke Yamada (e-mail: [email protected]).

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Appl. Physiol. Nutr. Metab. 37: 11891199 (2012) doi:10.1139/H2012-100 Published by NRC Research Press

Introduction

The prevalence of childhood obesity and its related chronicdiseases has increased in the last few decades (van Cleave etal. 2010), and it has become a major public health problemin both developed and developing countries (Benson et al.2009; Esquivel and Gonzalez 2010). Childhood obesity oftencontinues into adolescence and adulthood (Guo and Chumlea1999; Wang et al. 2000) and is related to adult all-cause andcardiovascular mortality (Gunnell et al. 1998). The EndocrineSociety's Clinical Practice Guidelines recommend controllingcaloric intake and engaging in 60 min of daily moderate tovigorous physical activity (PA) for the treatment of pediatricobesity (August et al. 2008). The prevalence of obese chil-dren in Japan is approximately 10% compared with 6% threedecades ago (Ministry of Education Culture Sports Scienceand Technology Japan 2010).The prevalence of childhood obesity differs among re-

gions, and previous studies have indicated that there are ru-ralurban differences in the prevalence of overweight andobese children even within a nation (Bertoncello et al. 2008;Lewis et al. 2006; McMurray et al. 1999; Plotnikoff et al.2004; Tognarelli et al. 2004). McMurray et al. (1999) indi-cated that living in a rural area was an independent risk fac-tor for obesity in third- to fourth-grade children in NorthCarolina, USA. Joens-Matre et al. (2008) examined bodymass index (BMI) and questionnaire-derived PA of fourth- tosixth-grade children in Iowa, USA. The prevalence of beingoverweight was higher and PA was lower, particularly aroundlunchtime, among rural children compared with urban chil-dren. In contrast, Bathrellou et al. (2007) reported that theprevalence of being overweight or obese and questionnaire-derived vigorous or moderate to vigorous physical activitiesdid not differ between urban and rural areas in Cyprus. Bas-sett et al. (2007) found that Old Order Amish youth had highdaily physical activity levels, as measured by a step counterand that obesity prevalence was rare.These previous studies suggest that there is variation in

ruralurban differences in the prevalence of childhood obe-sity that might be explained by PA differences. However,despite the role of low PA levels and (or) excess energy in-take (EI) in the etiology of obesity (de Gouw et al. 2010;van der Sluis et al. 2010), no previous studies have exam-ined the ruralurban differences in PA energy expenditure(EE) and EI simultaneously. In Japan, Tohoku is a regionwith many rural and agricultural areas, and it has a high prev-alence of childhood overweight and obesity (Ministry of Edu-cation Culture Sports Science and Technology Japan 2010).The reasons for the difference in overweight and obesity be-tween rural and urban regions, specifically the contributionsof PA and dietary intake, remain unknown. The low birth-rate in Japan has played a role in the decrease in populationin rural areas. In addition, the amalgamation of elementaryschools in rural areas has increased the size of each schoolzone. The result is that many children cannot walk toschool or to a friends house after school in these areasand are forced to use a car or bus to commute to school.In contrast, children in urban areas are able to walk toschool or to a friends house because walkways to schoolare generally well maintained and neighborhood adults vol-unteer to assist with road crossing.

The 24 h daily physical activity energy expenditure (PAEE)can be calculated most accurately using a combination of to-tal energy expenditure (TEE), measured by the doubly labeledwater (DLW) method, and resting metabolic rate (Schoeller etal. 1986; Westerterp et al. 1986). However, access to theDLW method is limited because of the costs of the isotopesand the methodological effort involved. Accelerometers havebeen used to monitor PA in a range of populations from chil-dren to the elderly. Accelerometers were initially developed inlaboratory settings and validated using indirect calorimetry.They were also validated under free-living conditions usingthe DLW method (Chen and Sun 1997; Kumahara et al.2004; Plasqui and Westerterp 2007; Tanaka et al. 2007;Westerterp 1999; Yamada et al. 2009b). The Kenz Lifecor-der is a uniaxial accelerometer that can accurately assessstep counts and various intensities of activity. The PAEEestimated by the Lifecorder is highly correlated with thePAEE measured by the DLW method in children and adults(Adachi et al. 2007; Rafamantanantsoa et al. 2002). How-ever, it significantly underestimates PAEE compared withthe DLW method (Rafamantanantsoa et al. 2002; Yamadaet al. 2009b). A previous study found that the Lifecordercould not accurately measure EE during sedentary activities,light intensity activities, or several vigorous intensity activities(running, cycling, swimming, and hill climbing) (Yamada etal. 2009b). However, if the output is corrected, the dailyPAEE can be estimated reasonably accurately in children.A systematic review concluded that active commuting (i.e.,

walking or cycling to school) is associated with daily PA(Davison et al. 2008; Mendoza et al. 2011b). However, therelationship between active commuting and weight status isinconsistent among studies, with some reporting a positive as-sociation (Heelan et al. 2005), no association (Ford et al.2007; Metcalf et al. 2004), or an inverse association (Gordon-Larsen et al. 2005; Rosenberg et al. 2006). Mendoza et al.(2011b) suggested that these inconsistent findings, whichmay confound the relationship with energy balance, may berelated to subjective measurements of PA, sampling from lo-cal or regional populations, or not controlling for dietary EI.The purpose of the present study was to examine the daily

PA levels and EI of Japanese rural and urban children. Wehypothesized that (i) rural children would have a higher prev-alence of overweight and obesity, a lower daily PA becauseof lower active commuting, and a higher EI compared withurban children; and (ii) the lower daily PA and lower activecommuting would be associated with weight status.

Materials and methods

ParticipantsA total of 227 sixth-grade children (1112 years old) from

six elementary schools in Japan participated in this study.The children comprised 77 boys and 79 girls from two ele-mentary schools in an urban area of Kyoto, and 45 boys and26 girls at four elementary schools in a rural area of Tohoku.These schools were selected by convenience sampling. Allmeasurements were conducted in the fall (OctoberNovember)of 2000. Informed consent was obtained from the childrenand their parents and teachers according to the Declarationof Helsinki, and the study was approved by the Kyoto Pre-fectural University of Medicine Ethics Committee.

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Anthropometric characteristicsPhysical characteristics of the children were assessed in ad-

dition to age, grade, and sex. Body mass (kg) and height(cm) were measured to the nearest 0.1 kg and 0.1 cm, respec-tively, in one layer of light clothing, without shoes, using aprofessional physicians scale and stadiometer. We used a sta-diometer that passed the Measurement Act established by theMinistry of Economy, Trade and Industry, Japan. The scaleand stadiometer are calibrated every 2 years using standar-dized methods outlined in the Measurement Act. BMI(kgm2) for each child was calculated using their body mass(kg) divided by height squared (m2). Classifications of over-weight and obesity were determined using the internationaldefinitions for childhood obesity developed in a workshop or-ganized by the International Obesity Task Force (IOTF)(Cole et al. 2000). The IOTF used six nationally representa-tive growth studies and constructed BMI growth curves suchthat the curves at 18 years of age passed through the BMIcutoff points of 25 and 30 for adults. The resulting curveswere then averaged to arrive at age- and sex-specific cutoffpoints for overweight and obesity (Bassett et al. 2007). How-ever, because a Japanese population was not included in thesurvey, we conducted an additional group classification usingdomestic definitions. Two different national definitions werereleased by the Japanese government, one from the Ministryof Education, Culture, Sports, Science and Technology(MEXT) and the other from the Ministry of Health, Labourand Welfare (MHLW) (2010). These equations and cutoffswere also used to define overweight and obesity.

AccelerometerA uniaxial accelerometer (Kenz Lifecorder/Calorie counter;

Suzuken Co. Ltd., Nagoya, Japan; 72.5 41.5 27.5 mm,weighing 60 g including the battery) was continuously andrigidly attached to the waistband of the children during allwaking hours for one week, excluding time spent bathing orin water. The participants were requested to record the timeand date that they did not wear the Lifecorder. The recordsand Lifecorder data were checked and the children were inter-viewed if a lack of compliance was suspected. The devicewas previously validated against an indirect calorimeter, aswell as the DLW method in adults (Kumahara et al. 2004;Rafamantanantsoa et al. 2002; Yamada et al. 2009b). Thetechnical and estimation equation details of the uniaxial ac-celerometer have been described elsewhere (Kumahara et al.2004, 2010; Yamada et al. 2009b). Briefly, the device meas-ures acceleration in the vertical direction ranging from 0.06to 1.94 times the acceleration of gravity at a sampling fre-quency of 32 Hz. The accelerometer is designed to estimatethe daily EE in kilocalories from the subjects characteristicsand the accelerometry signals caused by body movements.The number of steps taken per day was also determinedfrom the accelerometric signals. The reported margin of errorregarding the number of steps was less than 3%. In addition,the accelerometer has a superior step counting accuracyunder controlled and free-living conditions in comparisonwith other instruments.Previous studies indicated that the TEE and AEE estimated

by the uniaxial accelerometer were highly correlated with theTEE and AEE measured by the DLW method. However, theaccelerometer significantly underestimated the TEE and AEE

in young, middle-aged, and aged adults (Kumahara et al.2004, 2010; Yamada et al. 2009b). Kumahara et al. (2004b,2010) reported that the Lifecorder underestimated EE by8%9% for adults. Rafamantanantsoa et al. (2002) reportedthat the Lifecorder underestimated EE by 20% for adults,while Yamada et al. (2009b) reported that the Lifecorderunderestimated EE by 11% for elderly people relative to 14-day DLW method measurements. In children, Adachi et al.(2007) reported correlations (r = 0.7090.828) between theoutput of the Lifecorder and AEE (kcalkg1) measured bythe DLW method but did not mention any underestimation.Therefore, we conducted an additional experiment in thepresent study in which we measured the TEE using theDLW method and the accelerometer simultaneously in 12children (1113 years old) to obtain a correction factor. Thedetailed DLW method was described in previous reports (Ya-mada et al. 2009a, 2009b).

Activity and dietary records for 24 hThe 5-day minute-by-minute activity record was used to

assess the level of PA (Noda et al. 2006). Participants used aspecially designed form to record each activity minute by mi-nute to facilitate diary maintenance. They were instructed torecord only when there was a change in activity by drawing aline at the end of one activity under the corresponding indi-cator of time. A detailed demonstration and an example of acompleted sample were given to the teachers, parents, andchildren before the recording . We asked the teachers and pa-rents to assist children in completing the activity records. Theactivity records were checked and any missing informationwas obtained. The literacy rate is 100% in Japan, and allschools use similar textbooks approved by the Japan OfficialCommission on Textbooks; therefore, there was no differencein language capability between urban and rural areas. All par-ticipants were requested to continue with their regular dailyactivities and to keep a record of all activities, assisted bytheir parents or teachers if required.Values for nutrient intakes were obtained from the food re-

cords that were maintained during the usual school hours(MondayFriday). All participants received a detailed verbalexplanation and written instructions about keeping the foodrecord. The participants were requested to maintain theirusual dietary habits and to be as accurate as possible in re-cording the amount and type of food, fluid, and drinks con-sumed, excluding unsugared tea or water. Examples ofcommon household measures, such as cups, tablespoons, andspecific information about the quantity of each measurement(grams, etc.) were given. After completion of the activity anddietary logging period, the recording sheets were collectedand checked to reduce under recording. The administrator ofan urban school and a boy in a rural school refused to com-plete the food records because of the complexity. Therefore,the numbers of participants for the dietary examination werereduced to 60 urban and 70 rural children.

Statistical analysisAnalyses were conducted using PASW statistics (Windows

Version 18; SPSS Inc., Chicago, Illinois). The results aregiven as mean SD. Differences in the physical characteris-tics, PA, and dietary intake values were analyzed using two-way ANOVA with region (rural or urban) and sex (boy or

Itoi et al. 1191


girl) as between-subject factors. Differences in the distribu-tion of weight status, rate of walking to school, and rate ofplaying outdoors were examined using the c2 test. Signifi-cance was set at P < 0.05.

ResultsThe mean SD values for the childrens anthropometric

characteristics are shown in Table 1. The weight and BMIwere significantly higher in the rural children than in the ur-ban children for both sexes. The prevalences of overweightand obese children in the rural schools were 26.8%, 21.1%,and 25.4%, using the IOTF, MEXT, and MHLW definitions,respectively. These values were significantly higher than theprevalences in the urban schools, which were 8.4%, 6.4%,and 6.4%, using the IOTF, MEXT, and MHLW definitions,respectively (P < 0.02).The mean SD values for the childrens step count, esti-

mated EE, and duration or engaged rate of activities areshown in Table 2. There were no significant interactions forregion sex for any of the variables. Boys had significantlyhigher step counts, TEE, AEE, and PAL than girls. The ruralchildren had significantly lower step counts, AEE, and PALthan the urban children for both sexes (P < 0.001). TEE didnot differ significantly between the two regions because ofthe higher weight and lower PA in the rural children com-pared with the urban children (P = 0.143).The mean duration of walking to school was significantly

shorter for the rural children than for the urban children. Thepercentage of children who walked to school was almost100% in the urban region but was only 25% in the rural re-gion. The majority of the rural children were taken to schoolby car. The durations of playing indoors and studying weresignificantly shorter in the rural region (P 0.001); in con-trast, sleeping duration was significantly longer (P = 0.019).The mean SD values for reported food intake are shown

in Table 3. The reported EI was significantly higher in theboys than in the girls, but it did not differ significantly be-tween the two regions (P = 0.631). The reported EI dividedby weight had a significant interaction for region sex. Spe-cifically, the rural boys had a lower EI per weight than theurban boys. The energy balance described as EITEE1 didnot differ between the two regions (P = 0.924). There weresignificantnutritional differences in the two regions; specifi-cally fat, iron, vitamin B2, vitamin C, eggs, sugar, and cook-ing oil were all consumed at lower levels in the rural regioncompared with the urban region.The children were divided into three categories (tertiles)

using step counts to examine the relationship between walk-ing and BMI. For both sexes, the children who had lowerstep counts per day had a higher BMI (Fig. 1). The childrenof both sexes who demonstrated a shorter duration of walk-ing to school also had a higher BMI.

DiscussionThe purpose of the present study was to examine the dif-

ferences in obesity prevalence, daily PA, active commuting,and EI between rural and urban Japanese children. The ruralchildren had higher BMIs and obesity prevalence and lowerstep counts, AEE, and PAL compared with the urban chil-dren. Fewer rural children walked to school, and they also

had a lower mean duration of walking to school. In contrast,the reported EI and EITEE did not differ significantly be-tween the two regions, with a lower fat intake in the ruralchildren than in the urban children. Sleeping duration waslonger in the rural children than in the urban children. Thestep count and duration of walking to school were signifi-cantly related to the weight status.Several previous studies have examined the differences in

obesity prevalence and PA between urban and rural areas.However, in those studies the obesity prevalence was calcu-lated using BMI, and PA status was obtained by a question-naire that is less accurate than accelerometer data or a 24 hactivity record. In contrast, the present study used an acceler-ometer and a 24 h activity record. Previous studies reportedthat the TEE and AEE obtained by the Lifecorder are highlycorrelated with TEE and AEE measured using the DLWmethod but significantly underestimated TEE and AEE inadults aged 1887 years (Kumahara et al. 2010; Rafamanta-nantsoa et al. 2002; Yamada et al. 2009b). In children, Ada-chi et al. (2007) found a high correlation between theLifecorder output and AEE measured using the the DLWmethod but did not mention any underestimation. Therefore,we examined the relationship between the DLW method andthe Lifecorder in 12 children. The TEE and AEE estimatedby the Lifecorder were highly correlated with the TEE andAEE estimated by the DLW method, but the Lifecorder sig-nificantly underestimated EE, as expected. Therefore, usingthe correction equation developed from this analysis, we re-calculated the TEE, AEE, and PAL for the entire population.In the present study, the rural children had a higher BMIs

and obesity prevalence and lower step counts and PA. Al-most all the urban children, but only 25% of rural children,walked to school. The other rural children commuted by caror bus. Joens-Matre et al. (2008) reported that the prevalenceof overweight was higher among rural children than in chil-dren from urban areas and small cities. Urban children werethe least active overall, particularly around lunchtime while atschool. Children from small cities reported the highest levelsof physical activity. Sjolie and Thuen (2002) reported that94% of children commuted by car or bus in a rural munici-pality in Rendalen, Norway. Similarly, Kobayashi and Ozawa(2007) and Ozawa et al. (2006) reported that rural schoolchildren commuted by car or bus more often than urban chil-dren in Japan. The primary underlying reason for these find-ings is that larger school districts now exist in the ruralregions. Previous studies reported that children who walkedto school had higher moderate to vigorous PA and stepcounts compared with children who did not walk to school(Cooper et al. 2003, 2005, 2010; Loucaides and Jago 2008;Sirard et al. 2005). The present results are consistent withthese previous findings.Tudor-Locke et al. (2004) recommended 15 000 and

12 000 steps per day for boys and girls, respectively. In thepresent study, the children who reached the recommendedstep counts were 83.1% and 13.3% for the urban and ruralboys (P < 0.001), respectively, and 79.7% and 17.7% for theurban and rural girls (P < 0.001), respectively. The measuredstep counts in the present study were 15003000 steps higherin the urban children, but 40005000 steps lower in the ruralchildren, compared with previously reported values by Dun-can et al. (2008) (16 100 and 14 200 steps for boys and girls,



respectively) and Toda et al. (2007) (16 657 and 13 690 stepsfor boys and girls, respectively).Pabayo et al. (2011) reported that urban settings were sig-

nificant predictors of active transportation to school com-pared with rural settings in the Canadian NationalLongitudinal Survey of Children and Youth (odds ratio 3.66(95% confidence interval: 3.234.15)). van Sluijs et al.(2009) reported that the proportion of active travelers de-creased from 83.8% to 0.0% across the increasing distancetravelled to school from

Table 3. Food intake of the children.

Boys Girls P value of two-way ANOVA

Item Urban (n = 30) Rural (n = 44) Urban (n = 30) Rural (n = 26) Region SexRegion Sex

Energy intake (kcalday1) 2171333 2006433 1864435 1960368 0.631 0.015 0.071Protein (g) 80.419.0 76.521.5 75.719.4 72.516.0 0.355 0.253 0.923Fat (g) 73.319.3 59.823.7 64.333.4 54.717.1 0.010 0.114 0.658Carbohydrate (g) 288.756.5 278.056.3 237.943.7 285.154.7 0.062 0.026 0.003Calcium (mg) 727189 707230 696210 635140 0.269 0.161 0.570Iron (mg) 9.62.9 7.12.5 10.03.9 7.02.1 0.000 0.809 0.668Vitamin A (g RE) 1087654 9451021 1053508 1025913 0.565 0.874 0.699Vitamin B1 (mg) 1.060.35 1.220.47 0.940.30 1.200.44 0.004 0.345 0.503Vitamin B2 (mg) 1.660.37 1.310.54 1.460.37 1.340.75 0.015 0.378 0.229Vitamin C (mg) 11158 8036 10145 8952 0.014 0.953 0.264Salt (g) 8.12.6 8.52.8 8.12.5 8.22.2 0.581 0.837 0.792Energy rate of protein (%) 15.11.9 15.52.1 16.13.2 15.11.7 0.488 0.561 0.138Energy rate of fat (%) 27.04.3 26.26.2 24.86.7 24.74.7 0.752 0.136 0.767Energy rate of carbohydrate (%) 56.14.0 56.46.2 57.08.4 58.34.6 0.547 0.277 0.680Animal protein ratio (%) 57.36.0 55.510.3 57.012.9 53.89.0 0.247 0.660 0.737Animal fat ratio (%) 51.39.2 46.716.8 50.614.7 40.814.0 0.030 0.311 0.424Green and yellow vegetables ratio (%) 36.020.3 24.612.8 35.515.3 28.511.3 0.005 0.596 0.491Energy rate of grain (%) 42.67.5 44.39.6 46.510.9 45.46.3 0.884 0.195 0.445Energy rate of confectionery and drink (%) 9.69.7 6.17.1 5.35.3 8.88.2 0.985 0.654 0.041Energy rate of breakfast (%) 21.26.1 22.29.2 18.47.6 19.94.8 0.461 0.122 0.858Energy rate of lunch (%) 34.07.4 37.19.4 39.65.8 38.47.7 0.595 0.057 0.230Energy rate of dinner (%) 35.18.8 33.010.7 36.56.9 31.57.2 0.074 0.992 0.458Energy rate of between-meal eating (%) 6.57.0 3.96.3 4.44.4 7.06.0 0.981 0.698 0.056Energy rate of late-evening snacking (%) 3.25.0 3.97.0 1.12.5 3.24.6 0.262 0.251 0.534Fish and seafood (%) 71.066.4 60.453.9 74.871.7 58.141.8 0.235 0.948 0.788Meat (%) 174.9116.6 158.7147.2 176.5205.2 128.294.5 0.235 0.594 0.554Eggs (%) 112.181.7 47.754.5 114.679.6 42.546.0 0.000 0.912 0.753Beans (%) 73.350.8 82.550.4 102.158.2 80.041.3 0.485 0.159 0.093Milk (%) 155.079.4 135.661.5 140.764.7 128.955.6 0.197 0.384 0.750Vegetables (%) 50.120.7 40.920.9 53.029.6 52.829.2 0.304 0.108 0.329Fruits (%) 40.052.7 31.426.6 32.030.1 41.729.4 0.931 0.856 0.167Grains (%) 85.321.4 80.820.3 69.419.2 83.115.2 0.198 0.056 0.011Sugar (%) 83.271.2 40.639.1 94.573.4 31.926.6 0.000 0.898 0.333Confectionery (%) 107.3103.5 62.064.9 91.1113.0 77.869.3 0.076 0.991 0.330Cooking oil (%) 106.098.4 62.252.4 78.387.7 58.347.6 0.021 0.249 0.384No. who did not eat breakfast (%) 0.0 8.9 0.0 0.0 Boys 0.565, girls N/A

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Simen-Kapeu et al. (2010) reported a high prevalence ofobesity among rural children. These obese children hadhigher PA levels but also had a poor diet characterized byhigh fat intake. The authors noted that these results mayhave been related to geographical factors and a lack ofknowledge regarding healthy diets. In contrast, the rural chil-dren in the present study consumed less fat than the urbanchildren. This was caused by significantly lower intake ofcooking oil with integrated effects of a slightly lower intakeof fish, seafood, meat, eggs, and milk. The animal fat to totalfat ratio was also significantly lower in the rural children.The reasons for this are that (i) the Tohoku district is a majorrice production area and the frequency of eating rice as thestaple food is higher in this district than in metropolitanareas, and (ii) the percentage of multigenerational householdsis higher in the Tohoku district and the dietary habits are lesswesternized than in metropolitan areas. Therefore, the higherprevalence of obese children in this Japanese rural area can-not be accounted for by eating behavior, at least according tothe results of the reported dietary intake. Dietary intake is ex-amined annually by the National Health and Nutrition Surveyin Japan (Ministry of Health Labour and Welfare Japan 2010)and is reported as the averaged general population data foreach district. Total EI was almost the same for the urbanand rural districts, but fat intake was lower in rural districts(Tohoku, Hokuriku, Shikoku, and South Kyushu) compared

with the urban districts (Greater Tokyo Area (Shuto-ken)and Kansai Metropolitan Area (Kei-han-shin)). Althoughdistrict data were not reported for the children because ofthe small sample size, the food intake results for the presentstudy are consistent with the National Health and NutritionSurvey in Japan.We recognize that the measurement of daily dietary intake

has limitations. Daily dietary intake cannot be examined ac-curately using unsupervised methods, whereas supervisedmethods cause a modification in eating behavior. In particu-lar, obese adults (Braam et al. 1998; Prentice et al. 1986) orchildren (Bratteby et al. 1998; Livingstone et al. 1992;OConnor et al. 2001) underreport their food intake. The factthat the present study and the National Health and NutritionSurvey showed no differences in reported EI between ruraland urban districts may not mean that there is no differencein the actual EI between rural and urban districts. In addition,the year-long accumulation of a small positive energy balance(approx. 30100 kcalday1) can result in an increase in obe-sity (Hill et al. 2009). There are no field methods for measur-ing daily EI with this precision. Therefore, the results of thereported dietary intake should be interpreted with caution.Food frequency questionnaires, 24 h food records and 24 h

food recall methods were developed to examine food intake.Black et al. (2000) reported that the EI calculated using a di-etary record was significantly correlated with the EE meas-

Fig. 1. (A) Body mass index (BMI) values of the children categorized by the daily step counts in boys (solid bars) and girls (open bars).There was a significant main effect for each group, and the group with the lowest step counts had a BMI that was greater than the highesttertile. (B) BMI values of the children categorized by the duration of walking to school in boys (solid bars) and girls (open bars). There was asignificant main effect for each group, and the group with the lowest duration of walking to school had a BMI that was greater than thehighest tertile. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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ured by the DLW method (r = 0.48), but EI calculated usingthe 24 h recall was not significantly correlated with the EE(r = 0.11). Therefore, we used food records rather than afood frequency questionnaire or food recall method. Recently,Burrows et al. (2010) reviewed the validity of dietary assess-ment methods in children compared with the DLW method.They concluded that there is a lack of data regarding themost suitable methods for estimating EI in children.Previous studies reported a negative association between

obesity and participation in sporting activities (Salbe et al.2002) and a positive association between obesity and the du-ration of watching TV in children (Jackson et al. 2009). Inthe present study, the duration of playing outdoors and theduration of watching TV did not differ significantly betweenrural and urban children. Previous studies found that sleepduration was negatively associated with obesity and snacking.The rural children slept longer than the urban children in thepresent study. Japanese urban children go to private prepara-tory school more frequently and study for longer periods out-side of school than rural children. Therefore, the higherprevalence of childhood obesity in the Japanese rural areascannot be accounted for by sleep disturbance.Previous studies reported that childhood obesity was re-

lated to higher food intake, a westernized diet pattern charac-terized by high intake of meats, fat, and oils (Aeberli et al.2007; Gazzaniga and Burns 1993), lower PA (Abbott andDavies 2004), and a shorter sleep duration (Padez et al.2009; Shi et al. 2010). In contrast, in the present study, therural children did not demonstrate a westernized dietary pat-tern, had a longer sleep duration, and did not have a higherfood intake compared with the urban children. These habitsmay not be related to obesity prevalence in rural children inJapan. However, the rural children demonstrated a lower PAlevel with fewer children walking to school compared withthe urban children. The promotion of walking to school orbeing more active, rather than altering dietary or sleeping be-haviors, may be useful for preventing childhood obesity inJapanese rural areas. Previous studies have demonstrated theeffectiveness of a walking school bus program (Mendoza etal. 2011a). Children joined the walking school bus at variouspoints along a set route. Students who lived far away weredropped off along the route to join the walking school bus.The program was effective in that it increased physical activ-ity levels. One method of increasing walking to school in ru-ral children would be to establish a walking school busprogram in those areas. Further studies are required to exam-ine the effect of a walking school bus program on obesityprevention in Japanese rural areas.A limitation of the present study was that the schools were

selected using convenience sampling. However, the overweightand obesity prevalences in the Tohoku and Kyoto regions aresimilar to those provided in a government report (Ministry ofEducation Culture Sports Science and Technology Japan2010). Further large-scale research is required to examinethe issue in more depth using random sampling.

ConclusionThe overweight and obesity prevalences in Japan was sig-

nificantly higher in rural regions than in urban regions. Thenumber of steps per day, AEE, PAL, and duration of walking

to school were significantly lower in rural children than inurban children. The PA and duration of the walk to schoolwere significantly correlated with BMI. In contrast, thehigher prevalence of overweight or obese children in the ruralarea cannot be accounted for by eating or sleeping behaviors.Rural children have a higher prevalence of overweight andobesity, and this may be at least partly explained by lowerphysical activity levels, especially less time spent walking toschool, in rural chidren than in urban children.

AcknowledgmentsThe authors thank Naoyuki Ebine and Satoshi Nakae (Dos-

hisha University, Kyoto, Japan), Mami Fujibayashi (KyotoUniversity, Kyoto, Japan), Soichi Ando (Kyoto PrefecturalUniversity of Medicine, Kyoto, Japan), and Yoshiko Aoki(Heian Jogakuin St. Agnes University, Osaka, Japan) for theirhelp in conducting the DLW experiments. This study wassupported by a research grant awarded to M.K. from the Jap-anese Ministry of Education, Culture, Sports, Science, andTechnology (23650408) and to A.I. (21500675).

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