Validation of an Electronic Pedometer for Measurement of Physical Activity in Children

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1 Pediatric Exercise Science, 1999, 11, O 1999 Human Kinetics Publishers, Inc. Validation of an Electronic Pedometer for Measurement of Physical Activity in Children Colleen K. Kilanowski, Angela R. Consalvi, and Leonard H. Epstein Activity measurement using a uniaxial electronic pedometer was compared to a triaxial accelerometer and behavioral observation measurements for ten 7-12-year-old children studied during high intensity recreational and low intensity classroom periods. Correlations between all measures were significant for recreational and classroom periods combined, and recreational periods alone (r's >.90, p <,001). Correlations between the pedometer and accelerometer were significantly lower during classroom versus recreational activities (0.98 vs. 0.50, p <.05). This may be due in part to the uniaxial pedometer being sensitive only to vertical and not back and forward or side to side movement. The development of a cost efficient, unobtrusive, and valid method of measuring physical activity in children is a major research priority (15). Current physiological methods of measuring physical activity, such as doubly labeled water (14, 17) and heart rate monitoring (3, are accurate but limited by cost and subject intrusiveness. Behavioral observation of activity also provides an accurate method for differentiating activity levels, but it is labor intensive (11, 13). Motion sensors provide an alternative objective method for measuring physical activity. Triaxial accelerometers correlate highly with oxygen consumption, doubly labeled water, and heart rate methods for measuring physical activity (2, 3, 5), and they provide continuous measurement of activity and information on patterns of physical activity. However, they are relatively obtrusive and may not be worn during some physical activities (3). The electronic pedometer is a uniaxial motion sensor that assesses integrated physical activity in a unobtrusive and cost-effective manner (5,20). Eston and colleagues tested the vddity of triaxial accelerometers, uniaxial pedometers, and heart rate for predicting energy cost in children (5) while walking and running on a treadmill, as well as during two brief recreational activities and one seden tary =ti-vity,tkrfisultsshowed each-of-the-measures to be significantly correlated with energy expenditure, with the accelerometer to be the best predictorotexpenditure, and pedometers and heart rate similarly related to expenditure. However, there may be potentially important differences in sensitivity of the pedometer to activity The authors are with the Department of Psychology at the University of Buffalo, Buffalo, NY

2 64 - Kilanowski, Consalvi, and Epstein counts during moderate to intense physically active play versus low intensity sedentary activities, with the pedometer less sensitive to low intensity sedentary activities (5, 19). Similarly, Bouten and colleagues (2,3) have shown in adults that accelerometers have stronger relationships with higher intensity activities than with sedentary activities. Additional research on pedometer validity is needed using longer observational periods in the child's natural environment, rather than during brief, structured laboratory tasks (5). The purpose of the present study was to compare activity measurement during recreational physical activity and classroom activities in the natural environment of children using an electronic pedometer to two previously validated measures: triaxial accelerometer (5) and behavioral observation (1 3). Subjects Method Participants were seven male and three female children enrolled in a summer dayschool program for children at the University at Buffalo. This study was approved by the Human Subjects Review Board, and informed consent was obtained from each parent and child prior to their participation. Participants received $10 for their participation. Procedure Each child was studied during a normal classroom and an active recreational period. Classroom activities included desk work, computer work, and art work and recreational activities included soccer, basketball, baseball, dodge ball, and dancing. Six children were first observed during the active recreational period prior to the classroom period, and four children were observed in the opposite order. The average amount of time observed during recreational physical activity and sedentary periods were min (range, 73 to 132) and 48.6.t 7.9 min (85 to 148), respectively. Each of two observers monitored the activity level of one child during each observational period. At the beginning of each observation period, observers placed an elastic belt with velcro fasteners around a child's waist so that: the pedometer was to the left and the triaxial accelerometer was to the right of the navel at the hip, and the observer then reset the pedometer. The pedometer counts (step counts) were accumulated during each type of activity, and an observer recorded the counts at the end of each observational period. Measurement Body Mass Index (BMI) (kg/m2) was calculated based on participants' height and weight and compared to population data (8,9) to establish BMI percentiles. Pedometers collect uniaxial motion data by measuring vertical oscillations and step counts, which are accumulated until the pedometer is reset. The pedometer used was the Yamax Digiwalker SW-200 (Tokyo) electronic pedometer, which has been validated against energy cost in children (5). A similar Yarnax pedometer was demonstrated to be the most accurate for assessing distance walked in adults (1). The triaxial accelerometer is a motion sensor that collects movement data from three planes. The accelerometer used was the TriTrac-R3D Model T303 Research Ergometer (Professional Products, Reining International, Madison, WI).

3 Child Activity Levels - 65 This device collects minute by minute data by measuring motion side to side, forward and backward, and vertical, as well as a composite vector magnitude score. The TriTrac is strongly related to energy expenditure in children (5). Behavioral observation was conducted using the Children's Activity Rating Scale (CARS) (12). This scale rates the activity level of the children on five levelsstationary with no movement; stationary with movement; translocation, slow, easy movement; translocation, medium, moderate movement; and translocation, fast very intense movement-at 30-s intervals. The CARS has been validated as a measure of child activity (10,12). Reliability of behavioral observations was calculated by comparing ratings of two independent observers for 1,545 of the 1,793 observations (86% of the intervals). The percent agreement between observers was 86%. Results The average participant was years old, f 12.2 cm in height, and kg in weight. Mean BMI and BMI percentile were 19.7 f 3.3 and , respectively. Four children were above the 85 BMI percentile and were considered obese. The data for recreational and classroom activities are presented in Table 1. One-way analyses of variance (ANOVA) testing differences in methods as a function of the level of activity showed significant differences between the active and sedentary ranges of physical activity for the pedometer step counts (F[l, 91 = 33.00, p <.001), accelerometer vector magnitude (F[1, , p <.001), and behavioral observation ratings (F[1,9] = 16.13, p <.003). Correlations between each of the measures for combined recreational and classroom activity shown in Table 2 were all significant (p <.001). Similarly, correlations between each set of measures for recreational activities alone were significant (p <.001). Correlations between the measures were reduced for classroom activities, with significant differences (p <.05) observed for the correlation between pedometer and accelerometer (.98 vs. SO) Discussion This study provides additional support for the use of an electronic pedometer as a measure of physical activity when observing children engaged in a variety of mod- Table 1 Mean, Standard Deviation, and Range of the Different Measurements During the Active and Sedentary Observation Intervals Recreation activities Classroom activities -'Fype+frnea~u~e- ----XLSD--R-. - M5SD -.- R Pedometer stepcountslrnin Accelerometer vector f magnitudelmin Behavioral observation 2.08 f ratingslmin

4 66 - Kilanowski, Consalvi, and Epstein Table 2 Correlations of Measures Across All Activities, and Separately for Recreational Physical Activities and Sedentary Classroom Activities Combined Recreational Classroom Pedometer versus accelerometer 0.99 < < <.41* Pedometer versus behavioral 0.96 < < <.02 observation Accelerometer versus behavioral 0.95 < < <.07 observation *p <.05 comparing correlations between measurement during recreational and classroom periods. erate to high intensity recreational activity as well as less intense classroom activities (5,20). Pedometer, accelerometer, and behavioral observation measures were highly correlated for combined activities as well as recreational activities, equaling or exceeding r = The correlation between motion sensors during recreational activity periods indicate that the pedometer is able to measure high activity levels similar to the triaxial accelerometer and behavioral observation (5, 12). Additional research is needed with larger samples to ensure generalizability of the results to other samples, increase power of the statistical analyses, and improve stability of the correlations. The strong correlation between pedometer and accelerometer measures during physical activity suggest the pedometer may be very useful in clinical studies to provide an objective measure of activity levels and feedback on activity. Most interventions designed to increase physical activity rely on self-report diaries to assess physical activity, but self-report of physical activity is often overestimated in adults (7) and children (4). Feedback to participants is then provided based on inaccurate self-reports. Providing feedback for inaccurate levels of physical activity may inadvertently be reinforcing people for less activity than reported and possibly teaching them to be inaccurate observers of their physical activity. The availability of inexpensive and valid pedometers could improve recording of activity levels, and thus allow feedback to be contingent on true changes in physical activity. Consistent with other investigations (2, 3, 5, 19), the relationship between accelerometer and pedometer was higher during recreational activities than during the more sedentary classroom activities. This is relevant to the measurement of activity, since the activity pattern of most children is brief bursts of moderate or high intensity physical activity in combination with periods of low intensity activity (6, 16). Pedometers are designed to register activity in the vertical direction, and many classroom and sedentary behaviors are engaged in while sitting and may involve little vertical movement. Thus, it is not surprising that pedometers may be less sensitive to low intensity activities that do not involve vertical movement (5). The relative insensitivity of the pedometer to behaviors that do not involve vertical movement may produce differences in the distribution of values relating pe-

5 Child Activity Levels - 67 dometer to other objective measures of physical activity values during low intensity sedentary versus more intense physically active periods. Examination of the plots relating pedometer to energy expenditure by Eston and colleagues (5) showed greater similarity of scores and less variability for the sedentary versus physically active behaviors. In the present sample, there was over 65 times as much variance for the pedometer during recreational versus sedentary activities (372.9 vs. 5.7) and for accelerometer readings during recreational versus classroom activities (219,563.3 vs. 3,281.8). Examination of a plot of the residuals from a regression model predicting accelerometer from pedometer data in the present study showed an even spread of residuals at low values of the estimated variable. As the level of the estimated variable increased, the range of the residuals spread out, evidence of heteroscedasticity, which weakens the strength of the correlational analysis (18). A factor that may influence the decision to use a pedometer is the distribution of moderate to vigorous physical activity versus sedentary activity patterns during observation intervals. Research that varies the percentage of sedentary and physically active response intervals is needed to test whether validity will be higher when the percentage of physically active intervals to total observation time is high, and to establish minimum amounts of physical activity per observation interval that are needed for valid measurement. There are several limitations to the use of pedometers versus triaxial accelerometers. Pedometers provide only estimates of cumulative activity and do not record or store activity data by time. It is not possible to determine parameters of activity such as the duration or intensity of an exercise bout or the number of discrete exercise bouts per day. The one-dimensional nature of pedometers may be a particular problem for children, who may engage in play behaviors that involve greater diversity of movement than many repetitive aerobic activities engaged in by adults. In summary, pedometers are highly correlated with physical activity in children. They provide an inexpensive and valid method for assessing levels of physical activity for large samples and for use as a measure and source of feedback in intervention studies. Pedometers are better suited to assess higher rather than lower intensity activity and would be the choice if the goal was to assess differences in activity among moderate to high intensity behaviors. The unobtrusive size and economical cost makes the pedometer a useful objective measure of physical activity in children. References 1. Bassett, D.R., B.E. Ainsworth, S.R. Leggett, C.A. Mathien, J.A. Main, D.C. Hunter, and G.E. Duncan. Accuracy of five electronic pedometers for measuring distance walked. Med. Sci. Sports Exerc. 28: , Bouten, C.V., W.P.H.G. Verboeket-Van de Venne, K.R. Westerterp, M. Verduin, and J;D;-Janssert.-BailypkysicaLactivi~son between movement reg- -- istration and doubly labeled water. J. Appl. Physiol. 8 1: , Bouten, C.V., K.A. Westerterp, M. Verduin, and J.D. Jaussen. Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med. Sci. Sports Exerc. 26: , Coleman, K.J., B.E. Saelens, M.D. Wiedrich-Smith, J.D. Finn, and L.H. Epstein. Relationships between Tritrac-R3D vectors, heart rate and self-report in obese children. Med. Sci. Sports Exerc. 29: , 1997.

6 68 - Kilanowski, Consalvi, and Epstein 5. Eston, R.G., A.V. Rowlands, and D.K. Ingledew. Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children's activities. J. Appl. Physiol. 84: , Gilliam, T.B., P.S. Freedson, D.L. Geenen, and B. Shahraray. Physical activity patterns determined by heart rate monitoring in 6-7-year-old children. Med. Sci. Sports Exerc. 13:65-67, Lichtman, S.W., K. Pisarska, E.R. Berman, M. Pestone, H. Dowling, E. Offenbacher, H. Weisel, S. Heshka, D.W. Matthews, and S.B. Heymsfield. Discrepancy between self-reported and actual caloric intake in obese subjects. N. Engl. J. Med. 327: , Must, A,, G.E. Dallal, and W.H. Dietz. Reference data for obesity: 85th and 95th percentiles of body mass index (wtlht7-a correction. Am. J. Clin. Nutr. 54:773, Must, A., G.E. Dallal, and W.H. Dietz. Reference data for obesity: 85th and 95th percentiles of body mass index (wt/ht2) and triceps skinfold thickness. Am. J. Clin. Nutr. 53: , Noland, M., F. Danner, K. DeWalt, M. McFadden, and J.M. Kotchen. The measurement of physical activity in young children. Res. Q. Exerc. Sport. 61: , Pate, R.R. Physical activity assessment in children and adolescents. Crit. Rev. Food Sci. Nutr. 33: , Puhl, J., K. Greaves, T. Baranowski, D. Gruben. and D. Seale. Descriptions and calibration of a Children's Activity Rating Scale (CARS). Res. Q. Exerc. Sport. 61: , Puhl, J., K. Greaves, M. Hoyt, and T. Baranowski. Children's activity rating scale (CARS): Description and calibration. Res. Q. Exerc. Sport. 61:26-36, Ravussin, E., I.T. Harper, R. Rising, and C. Bogardus. Energy expenditure by doubly labeled water: Validation in lean and obese subjects. Am. J. Physiol. 261:E , Rowlands, A.V., R.G. Eston, and D.K. Ingledew. Measurement of physical activity in children with particular reference to the use of heart rate and pedometry. Sports Medicine. 24: , Sallo, M., and R. Silla. Physical activity with moderate to vigorous intensity in preschool and first-grade schoolchildren. Ped. Exerc. Sci. 4:44-54, Schoeller, D.A., and P.B. Taylor. Precision of the doubly labeled water method using the two-point calculation. Hum. Nut,: Clin. Nutr. 41C: , Tabachnick, B.G., and L.S. Fidell. Using Multivariate Statistics (2nd ed.). New York: HarperCollins, Welk, G.J., and C.B. Corbin. The validity of the TriTrac-R3D Activity Monitor for the assessment of physical activity in children. Res. Q. Exerc. Sport. 66:l-8, Weston, A.T., R. Petosa, and R.R. Pate. Validation of an instrument for measurement of physical activity in youth. Med. Sci. Sport Exerc. 29: , Acknowledgments This research is supported by grants HD and HD awarded to the third author. Appreciation is expressed to Drs. Cheri Sharipo and William Pelham of the Summer Treatment Program for access to the children, Seana Murphy for behavioral observation data collection, and Rocco Paluch for help with data analysis.

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