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HYPOTHESIS: Physically active occupations may protect against the risk of abdominal obesity. OBJECTIVES: This study assessed the interaction between non-occupational physical activity (NOA) (leisure-time, transport and domestic activity) and occupational activity (OA) in relation to abdominal obesity. METHODS: A total of 3539 adults over the age of 20, with no work limitations, employed in one of the 17 occupations classified as low OA (LOA) or high OA (HOA) were identified in the 1999-2004 National Health and Nutrition Examination Survey. Waist circumference (WC) was used to categorize individuals into either non-obese or abdominally obese (WC>88 cm in women and >102 cm in men) categories. NOA was divided into three categories based upon physical activity guidelines: (1) no NOA; (2) insufficient NOA; and (3) sufficient NOA. Logistic regression was used to examine possible associations between NOA, OA and abdominal obesity. RESULTS: In those who are sedentary outside of work, a high-activity occupation reduces the odds risk ratio of being categorized with abdominal obesity to 0.37 in comparison with those who work in low-activity occupations. For people working in low-activity occupations, there was a clear association with activity outside of work and the odds risk ratio of being categorized with abdominal obesity. In these adults, a reduced odds ratio was found only among those who met the physical activity guidelines through NOA (odds ratio=0.55; 95% confidence interval (CI)=0.40-0.75). CONCLUSION: HOA is associated with a reduced risk of abdominal obesity. Thus, it is important to include OA in studies seeking to understand the association between physical activity and abdominal adiposity.

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We determined the validity of the Nike+ device for estimating speed, distance, and energy expenditure (EE) during walking and running. Twenty trained individuals performed a maximal oxygen uptake test and underwent anthropometric and body composition testing. Each participant was outfitted with a Nike+ sensor inserted into the shoe and an Apple iPod nano. They performed eight 6-min stages on the treadmill, including level walking at 55, 82, and 107 m x min(-1), inclined walking (82 m x min(-1)) at 5 and 10% grades, and level running at 134, 161, and 188 m x min(-1). Speed was measured using a tachometer and EE was measured by indirect calorimetry. Results showed that the Nike+ device overestimated the speed of level walking at 55 m x min(-1) by 20%, underestimated the speed of level walking at 107 m x min(-1) by 12%, but closely estimated the speed of level walking at 82 m x min(-1), and level running at all speeds (p<0.05). Similar results were found for distance. The Nike+ device overestimated the EE of level walking by 18-37%, but closely estimated the EE of level running (p<0.05). In conclusion the Nike+ in-shoe device provided reasonable estimates of speed and distance during level running at the three speeds tested in this study. However, it overestimated EE during level walking and it did not detect the increased cost of inclined locomotion.

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BACKGROUND: The goal of this study was to establish preliminary criterion-referenced cut points for adult pedometer-determined physical activity (PA) related to weight status defined by body mass index (BMI). METHODS: Researchers contributed directly measured BMI and pedometer data that had been collected (1) using a Yamax-manufactured pedometer, (2) for a minimum of 3 days, (3) on ostensibly healthy adults. The contrasting groups method was used to identify age- and gender-specific cut points for steps/d related to BMI cut points for normal weight and overweight/obesity (defined as BMI <25 and >or=25 kg/m2, respectively). RESULTS: Data included 3127 individuals age 18 to 94 years (976 men, age = 46.8 +/- 15.4 years, BMI = 27.3 +/- 4.9; 2151 women, age = 47.4 +/- 14.9 years, BMI = 27.6 +/- 6.4; all gender differences NS). Best estimated cut points for normal versus overweight/obesity ranged from 11,000 to 12,000 steps/d for men and 8000 to 12,000 steps/d for women (consistently higher for younger age groups). CONCLUSIONS: These steps/d cut points can be used to identify individuals at risk, or the proportion of adults achieving or falling short of set cut points can be reported and compared between populations. Cut points can also be used to set intervention goals, and they can be referred to when evaluating program impact, as well as environmental and policy changes.

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BACKGROUND/OBJECTIVE: The Actiheart (Mini Mitter, Sunriver, OR, USA) uses heart rate (HR) and activity data to predict activity energy expenditure (AEE). Currently, the Actiheart has only been tested during laboratory conditions. Therefore, the objective of this study was to validate the Actiheart prediction method against indirect calorimetry during a wide range of activities in a field setting. SUBJECTS/METHODS: Forty-eight participants (age: 35+/-11.4 years) were recruited for the study. Eighteen activities were split into three routines of six activities and each routine was performed by 20 participants. During each routine, the participants wore an Actiheart and simultaneously, AEE was measured with a Cosmed K4b(2) portable metabolic system. The manufacturer's HR algorithm, activity algorithm, and combined activity and HR algorithm were used to estimate AEE. RESULTS: The mean error (and 95% prediction intervals) for the combined activity and HR algorithm, HR algorithm, and activity algorithm versus the Cosmed K4b(2) were 0.02 kJ kg(-1) min(-1) (-0.17, 0.22 kJ kg(-1) min(-1)), -0.03 kJ kg(-1) min(-1) (-0.24, 0.18 kJ kg(-1) min(-1)), and 0.14 kJ kg(-1) min(-1) (-0.12, 0.40 kJ kg(-1) min(-1)), respectively. CONCLUSION: The Actiheart combined activity and HR algorithm and HR algorithm provide similar estimates of AEE on both a group and individual basis.

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OBJECTIVE: The objective of this study was to develop a new 2-regression model relating Actical activity counts to METs. METHODS: Forty-eight participants (mean (SD) age 35 (11.4) years) performed 10 min bouts of various activities ranging from sedentary behaviours to vigorous physical activities. Eighteen activities were split into three routines with each routine being performed by 20 individuals. Forty-five routines were randomly selected for the development of a new 2-regression model and 15 tests were used to cross-validate the new 2-regression model and compare it against existing equations. During each routine, the participant wore an Actical accelerometer on the hip and oxygen consumption was simultaneously measured by a portable metabolic system. The coefficient of variation (CV) of four consecutive 15 s epochs was calculated for each minute. For each activity, the average CV and the counts min(-1) were calculated for minutes 4-9. If the CV was < or =13% a walk/run regression equation was used and if the CV was >13% a lifestyle/leisure time physical activity regression was used. RESULTS: An exponential regression line (R(2) = 0.912; standard error of the estimate (SEE) = 0.149) was used for activities with a CV< or =13%, and a cubic regression line (R(2) = 0.884, SEE = 0.804) was used for activities with a CV>13%. In the cross-validation group the mean estimates, using the new 2-regression model with an inactivity threshold, were within 0.56 METs of measured METs for each of the activities performed (p> or =0.05), except cycling (p<0.05). CONCLUSION: For most activities examined the new 2-regression model predicted METs more accurately than currently available equations for the Actical accelerometer.

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Electronic pedometers are accurate for assessing steps taken while walking in normal weight adults but the accuracy of these devices has not been tested in overweight and obese men and women. The primary purpose of this study was to assess the accuracy of an electronic pedometer for measuring steps taken at various walking speeds in groups of adults with variations in body mass index (BMI). The secondary purpose was to determine if the manufacturer recommended position is the best placement position for overweight and obese adults. Participants were categorized into one of three BMI categories identified by the World Health Organization: normal (N = 25; < 25 kg x m(-2)), overweight (N = 24; 25 - 29.9 kg x m(-2)), or obese (N = 17; > or = 30 kg x m(-2)). Participants walked on a treadmill for 3 min at 54, 67, 80, 94, and 107 m x min(-1) for a total of 15 min. During the treadmill walking, three electronic pedometers tallied steps taken. The pedometers were placed at the waist level, one on the anterior mid-line of the thigh (front; manufacturer recommended placement), one on the mid-axillary line (side), and one on the posterior mid-line of the thigh (back). Concurrently, a researcher counted steps using a hand-tally counter. Category of BMI did not affect the accuracy of the pedometer at any walking speed (54 m x min(-1), p = 0.991; 67 m x min(-1), p = 0.556; 80 m x min(-1), p = 0.591; 94 m x min(-1), p = 0.426; 107 m x min(-1), p = 0.869). At 54 m x min(-1), the front, side, and back pedometers significantly underestimated hand-tally counted steps by 20 % (p < 0.001), 33 % (p < 0.001), and 26 % (p < 0.001), respectively. At 67 m x min(-1) the front, side, and back pedometers significantly underestimated hand-tally counted steps by 7 % (p = 0.027), 13 % (p < 0.001), 11 % (p = 0.002), respectively. The steps recorded by the electronic pedometers placed at the front, side and back of the waist were not significantly different than steps counted by the hand-tally counter at speeds of 80 m x min(-1) and higher for all subjects combined. An electronic pedometer accurately quantified steps walked at speeds of 80 m x min(-1) or faster in persons with a normal BMI and those classified as overweight or obese. The placement of the pedometer on the front, side or back of the waistband did not affect accuracy of the pedometer for counting steps.

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The purpose of this study was to establish the accuracy of five published accelerometer regression equations that predict time spent in different intensity classifications during free-living activities. Ten participants completed physical tasks in a field setting for a near-continuous 5 - 6 h-period while oxygen uptake and accelerometer data were collected. The amount of time spent in resting/light, moderate and hard activity was computed from 3 and 6 MET cut-points associated with five existing regression formulas relating accelerometer counts x min -1 to energy expenditure. The Freedson cut-points over-estimated resting/light activity by 34 min (13 %) and under-estimated moderate activity by 38 min (60 %). The Hendelman cut-points for all activities underestimated resting/light activity by 77 min (29 %), and overestimated moderate activity by 77 min (120 %). The Hendelman cut-points developed from walking activities over-estimated resting/light activity by 37 min (14 %) and under-estimated moderate activity by 38 min (60 %). Estimates from the Swartz cut-points for estimating time spent in resting/light, moderate and hard intensity activity were not different from the criterion measure. The Nichols cut-points over-estimated resting/light activity by 31 min (12 %) and under-estimated moderate activity by 35 min (55 %). Even though the Swartz method did not differ from measured time spent in moderate activity on a group basis, on an individual basis, large errors were seen. This was true for all regression formulas. These errors highlight some of the limitations to using hip-mounted accelerometers to reflect physical activity patterns. The finding that different accelerometer cut-points gave substantially different estimates of time spent data has important implications for researchers using accelerometers to predict time spent in different intensity categories.

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PURPOSE: Heart rate (HR) and motion sensors represent promising tools for physical activity (PA) assessment, as each provides an estimate of energy expenditure (EE). Although each has inherent limitations, the simultaneous use of HR and motion sensors may increase the accuracy of EE estimates. The primary purpose of this study was to establish the accuracy of predicting EE from the simultaneous HR-motion sensor technique. In addition, the accuracy of EE estimated by the simultaneous HR-motion sensor technique was compared to that of HR and motion sensors used independently. METHODS: Thirty participants (16 men: age, 33.1 +/- 12.2 yr; BMI, 26.1 +/- 0.7 kg.m(-2); and 14 women: age, 31.9 +/- 13.1 yr; BMI, 27.2 +/- 1.1 kg.m(-2) (mean +/- SD)) performed arm and leg work in the laboratory for the purpose of developing individualized HR-VO2 regression equations. Participants then performed physical tasks in a field setting for 15 min each. CSA accelerometers placed on the arm and leg were to discriminate between upper and lower body movement, and HR was then used to predict EE (METs) from the corresponding arm or leg laboratory regression equation. A hip-mounted CSA accelerometer and Yamax pedometer were also used to predict EE. Predicted values (METs) were compared to measured values (METs), obtained via a portable metabolic measurement system (Cosmed K4b(2)). RESULTS: The Yamax pedometer and the CSA accelerometer on the hip significantly underestimated the energy cost of selected physical activities, whereas HR alone significantly overestimated the energy cost of selected physical activities. The simultaneous HR-motion sensor technique showed the strongest relationship with VO(2) (R(2) = 0.81) and did not significantly over- or underpredict the energy cost (P = 0.341). CONCLUSION: The simultaneous HR-motion sensor technique is a good predictor of EE during selected lifestyle activities, and allows researchers to more accurately quantify free-living PA.

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PURPOSE: The American College of Sports Medicine and the Centers for Disease Control and Prevention (ACSM-CDC) recommend 30 min of daily moderate-intensity physical activity for health; however, the effectiveness of this recommendation in lowering blood pressure (BP) in hypertensives is unclear. The present study tested the hypothesis that walking activity following the ACSM-CDC physical activity recommendation would lower BP in postmenopausal women with high BP. METHODS: Resting BP was measured in 24 postmenopausal women with borderline to stage 1 hypertension at baseline, 12 wk, and 24 wk. Fifteen women in the exercise (EX) group walked 3 km.d-1 above their daily lifestyle walking, whereas 9 women in the control (CON) group did not change their activity. Walking activity was self-measured with a pedometer in both groups. RESULTS: Resting systolic BP was reduced in the EX group after 12 wk by 6 mm Hg (P < 0.005) and was further reduced by 5 mm Hg at the end of 24 wk (P < 0.005). There was no change in diastolic BP with walking. The CON group experienced no change in BP at either 12 or 24 wk. Body mass was modestly reduced by 1.3 kg in the EX group after 24 wk (P < 0.05); however, it was not correlated with the change in BP. There were no changes in selected variables known to impact BP including percent body fat, fasting plasma insulin, or dietary intake. CONCLUSION: In conclusion, a 24-wk walking program meeting the ACSM-CDC physical activity recommendation is effective in lowering systolic BP in postmenopausal women with borderline to stage 1 hypertension.

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The purpose of this investigation was to assess the accuracy of the COSMED K4 b2 portable metabolic measurement system against the criterion Douglas bag (DB) method. During cycle ergometry on consecutive days, oxygen consumption (VO2), carbon dioxide production (VCO2), minute ventilation (VE), and respiratory exchange ratio (R) were measured at rest and during power outputs of 50, 100, 150, 200, and 250W. No significant differences (P > 0.05) were observed in VO2 between the K4 b2 and DB at rest and at 250W. Though the K4 b2 values were significantly higher (P<0.05) than DB values at 50, 100, 150, and 200 W, the magnitude of these differences was small (0.088, 0.092, 0.096, and 0.088 L x min(-1), respectively). VCO2 and VE values from the K4 b2 were significantly lower than the DB at 200 and 250 W, while no significant differences were observed from rest through 150W. The slight overestimation of VO2 (50-200 W) combined with the underestimation of VCO2 (200 and 250W) by the K4 b2 resulted in significantly lower R values at every stage. These findings suggest the COSMED K4 b2 portable metabolic measurement system is acceptable for measuring oxygen uptake over a fairly wide range of exercise intensities.

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The accuracy of a computerized metabolic system, using inspiratory and expiratory methods of measuring ventilation, was assessed in eight male subjects. Gas exchange was measured at rest and during five stages on a cycle ergometer. Pneumotachometers were placed on the inspired and expired side to measure inspired (VI) and expired ventilation (VE). The devices were connected to two systems sampling expired O(2) and CO(2) from a single mixing chamber. Simultaneously, the criterion (Douglas bag, or DB) method assessed VE and fractions of O(2) and CO(2) in expired gas (FE(O(2)) and FE(CO(2))) for subsequent calculation of O(2) uptake (VO(2)), CO(2) production (VCO(2)), and respiratory exchange ratio. Both systems accurately measured metabolic variables over a wide range of intensities. Though differences were found between the DB and computerized systems for FE(O(2)) (both inspired and expired systems), FE(CO(2)) (expired system only), and VO(2) (inspired system only), the differences were extremely small (FE(O(2)) = 0.0004, FE(CO(2)) = -0.0003, VO(2) = -0.018 l/min). Thus a computerized system, using inspiratory or expiratory configurations, permits extremely precise measurements to be made in a less time-consuming manner than the DB technique.

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OBJECTIVE: To assess the interaction between leisure-time physical activity (LTPA) and occupational activity (OA) on the prevalence of obesity. DESIGN: Secondary data analysis of a population based cross-sectional US national sample (NHANES III). SUBJECTS: A total of 4889 disease-free, currently employed adults over age 20 y. MEASUREMENTS: Subjects body mass index (BMI) was categorized as (1) obese (BMI> or =30 kg/m(2)), or (2) non-obese (BMI<30 kg/m(2)). LTPA was divided into four categories: (1) no LTPA; (2) irregular LTPA; (3) regular moderate intensity LTPA; and (4) regular vigorous intensity LTPA. OA was grouped as (1) high OA and (2) low OA. Age, gender, race-ethnicity, smoking status, urbanization classification, alcohol consumption and income were statistically controlled. RESULTS: In all, 16.8% (s.e. 0.7) of the total subject population were obese (15.1% (s.e. 1.1) of men and 19.1% (s.e. 1.1) of women). Logistic regression revealed that compared to those who engage in no LTPA and have low levels of OA, the likelihood of being obese is 42% (95% CI 0.35, 0.96) lower for those who engage in no LTPA and have high OA, 48% (95% CI 0.32, 0.83) lower for those who have irregular LTPA and have high levels of OA, and about 50% lower for all those who have regular LTPA through moderate or vigorous activity levels regardless of OA level. CONCLUSION: When considering disease free adults above 20 y of age employed in high and low activity occupations, a high level of occupational activity is associated with a decreased likelihood of being obese.

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PURPOSE: This study was designed to establish prediction models that relate hip and wrist accelerometer data to energy expenditure (EE) in field and laboratory settings. We also sought to determine whether the addition of a wrist accelerometer would significantly improve the prediction of EE (METs), compared with a model that used a hip accelerometer alone. METHODS: Seventy participants completed one to six activities within the categories of yardwork, housework, family care, occupation, recreation, and conditioning, for a total of 5 to 12 participants tested per activity. EE was measured using the Cosmed K4b2 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist and one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the CSA data. RESULTS: The wrist, hip, and combined hip and wrist regression equations accounted for 3.3%, 31.7%, and 34.3% of the variation in EE, respectively. The addition of the wrist accelerometer data to the hip accelerometer data to form a bivariate regression equation, although statistically significant (P = 0.002), resulted in only a minor improvement in prediction of EE. Cut points for 3 METs (574 hip counts), 6 METs (4945 hip counts), and 9 METs (9317 hip counts) were also established. CONCLUSION: The small amount of additional accuracy gained from the wrist accelerometer is offset by the extra time required to analyze the data and the cost of the accelerometer.

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PURPOSE: Three methods for measuring time spent in daily physical activity (PA) were compared during a 21-d period among 83 adults (38 men and 45 women). METHODS: Each day, participants wore a Computer Science and Applications, Inc. (CSA) monitor and completed a 1-page, 48-item PA log that reflected time spent in household, occupational, transportation, sport, conditioning, and leisure activities. Once a week, participants also completed a telephone survey to identify the number of minutes spent each week in nonoccupational walking and in moderate intensity and hard/very hard-intensity PA. Data were analyzed using descriptive statistics and Spearman rank-order correlations. Three equations developed to compute CSA cut points for moderate and hard/very hard PA were also compared with the PA logs and PA survey. RESULTS: There was modest to good agreement for the time spent in different PA intensity categories between the three CSA cut point methods (r = 0.43-0.94, P < 0.001). Correlations between the CSA and PA logs ranged from r = 0.22 to r = 0.36, depending on the comparisons. Correlations between the survey items and PA logs were r = 0.26-0.54 (P < 0.01) for moderate and walking activities and r < 0.09 (P > 0.05) for hard/very hard activities. Correlations between the survey items and the CSA min per day varied according to the method used to compute the CSA intensity cut points. CONCLUSIONS: The results were consistent with findings from other PA validation studies that show motion sensors, PA logs, and surveys reflect PA; however, these methods do not always provide similar estimates of the time spent in resting/light, moderate, or hard/very hard PA.

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UNLABELLED: To further develop our understanding of the relationship between habitual physical activity and health, research studies require a method of assessment that is objective, accurate, and noninvasive. Heart rate (HR) monitoring represents a promising tool for measurement because it is a physiological parameter that correlates well with energy expenditure (EE). However, one of the limitations of HR monitoring is that training state and individual HR characteristics can affect the HR-VO2 relationship. PURPOSE: The primary purpose of this study was to examine the relationship between HR (beats x min(-1)) and VO2 (mL x kg(-1 x -1) min(-1)) during field- and laboratory-based moderate-intensity activities. In addition, we examined the validity of estimating EE from HR after adjusting for age and fitness. This was done by expressing the data as a percent of heart rate reserve (%HRR) and percent of VO2 reserve (%VO2R). METHODS: Sixty-one adults (18-74 yr) performed physical tasks in both a laboratory and field setting. HR and VO2 were measured continuously during the 15-min tasks. Mean values over min 5-15 were used to perform linear regression analysis on HR versus VO2. HR data were then used to predict EE (METs), using age-predicted HRmax and estimated VO2max. RESULTS: The correlation between HR and VO2 was r = 0.68, with HR accounting for 47% of the variability in VO2. After adjusting for age and fitness level, HR was an accurate predictor of EE (r = 0.87, SEE = 0.76 METs). CONCLUSION: This method of analyzing HR data could allow researchers to more accurately quantify physical activity in free-living individuals.

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PURPOSE: This study tested the validity of four motion sensors for measuring energy expenditure (EE) during moderate intensity physical activities in field and laboratory settings. We also evaluated the accuracy of the EE values for selected moderate activities listed in the 1993 Compendium of Physical Activities. METHODS: A total of 81 participants (age 19-74 yr) completed selected tasks from six general categories: yardwork, housework, occupation, family care, conditioning, and recreation. Twelve individuals performed each of the 28 activities examined. During each activity, EE was measured using a portable metabolic measurement system. Participants also wore three accelerometers (Computer Science and Applications [CSA], Inc. model 7164; Caltrac; and Kenz Select 2) and the Yamax SW-701 electronic pedometer. For the CSA device, three previously developed regression equations were used to convert accelerometer scores to EE. RESULTS: The mean error scores (indirect calorimetry minus device) across all activities were: CSA1, 0.97 MET; CSA2, 0.47 MET, CSA3, 0.05 MET; Caltrac, 0.83 MET; Kenz, 0.96 MET; and Yamax, 1.12 MET. The correlation coefficients between indirect calorimetry and motion sensors ranged from r = 0.33 to r = 0.62. The energy cost for power mowing and sweeping/mopping was higher than that listed in the 1993 Compendium (P < 0.05), and the cost for several household and recreational activities was lower (P < 0.05). CONCLUSION: Motion sensors tended to overpredict EE during walking. However, they underpredicted the energy cost of many other activities because of an inability to detect arm movements and external work. These findings illustrate some of the limitations of using motion sensors to predict EE in field settings.

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We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

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Major technical advances have occurred within the last 10 years in the field of physical activity monitoring. The biggest one is real-time data acquisition, and the development of computer microchips that allow vast amounts of information to be stored and later recalled. It is evident that no single motion sensor will provide an accurate estimate of energy expenditure across all activities. Future directions for research include the use of combined instruments such as an accelerometer plus questionnaire, multiple motion sensors, or dual HR-motion sensor technology.

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PURPOSE: This study compared measurements of daily walking distance on the College Alumnus questionnaire (CAQ) and a pedometer. METHODS: A total of 96 men and women (25-70 yr of age) with a wide range of physical activity habits were studied. Physical activity index was computed from the College Alumnus questionnaire (PAI-CAQ) as the sum of the energy expended in stair climbing, walking, and sports and recreational physical activity. Data on walking distance were compared with values obtained from the Yamax electronic pedometer (DW-500B). Participants wore the pedometer for 7 consecutive days, except when sleeping, showering, or performing sports and recreational activities. RESULTS: Subjects underestimated their daily walking distance on the CAQ, compared with the pedometer (1.43 +/- 1.01 vs 4.17 +/- 1.61 km x d(-1)). The energy expended in walking was correspondingly lower on the CAQ, compared with the pedometer-derived values (555 +/- 405 versus 1608 +/- 640 kcal x wk(-1)). CONCLUSIONS: These findings suggest that electronic pedometers are useful for examining questions about walking distance on physical activity questionnaires.

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