Bjornson, Kristie F. PhC, PT, PCS
Lack of physical activity is a striking concern for today’s youth. More than 75% of children and adolescents, in grades four to 12, do not engage in daily vigorous physical activity in the United States.1 A 1997 health survey documented that less than 25% of youths participate in even 30 minutes of any type of physical activity daily. The obesity epidemic is a major clinical issue. In the past 20 years, obesity has doubled in youths between the ages of six and 11 years and has tripled in those aged 12 to 19 years.2,3 The obesity epidemic and the availability of new treatments for children with mobility limitations like cerebral palsy are two major clinical issues. In response to these striking health data, the U.S. Surgeon General has issued a call for action and recommends at least 60 minutes of moderate intensity activity per day for children.4 More research is needed to understand how inactivity affects the lives of youths with disability and define how much and what types of activity will produce positive health outcomes. Researchers and clinicians working with pediatric populations will need valid measures of ambulatory physical activity to understand the relationship between activity level and health outcomes.
Heart rate (HR) monitoring, direct observation, and self-report questionnaires have traditionally been used to assess patterns of physical activity in children.5 Advantages of HR monitoring include established reliability of measurement over a long period and direct measurement of cardiovascular status. The influence of other factors such as emotional stress, or body position on HR suggests that HR may not accurately reflect activity levels and/or patterns especially in youths with mobility impairments.6 HR monitors are clearly not a direct measure of physical activity, are costly to use, and require intensive time to download. HR response also tends to lag behind changes in physical movement. The rapid transitions seen in the movement patterns of children and adolescents may not be captured by HR monitoring. HR monitors may in fact mask potentially important information during monitoring of children, such as duration of bursts of activity and/or patterns of movement over time.5,6
Patterns of children’s activity have been documented by direct observations to be variable and of limited duration. In 1995, Bailey and colleagues7 used an observation system to study duration and intensity of activity in 15 children ages six to 10 years as they went about their typical daily activities at school, home, and play. Collecting data every three seconds, they reported median duration of low- and medium-intensity activities at a mere six seconds, high-intensity activity of three seconds’ duration, and none of those levels longer than 22.5 seconds total.7 Even 10 to 20 minutes of continuous activity in children has not been documented in the activity literature.8 Children appear to transition into and out of levels of activity quickly, and prolonged activity is not a part of their natural activity pattern. Saris9 proposed that this pattern of activity may be due to the shorter attention span naturally found in children, which may have negatively biased previously reported activity levels in children. Activity monitoring in youths may be warranted due to the potential transitory nature of their physical activity and their lack of sustained exercise.
The most common method of physical activity measurement, self-report questionnaires allow for low participant burden and cost, qualitative and quantitative information, and quick administration.10 Self-report techniques include a variety of measurement approaches (ie, activity diaries, proxy report, interviewer and self-administered questionnaires). Limitations to self-report are noted to be potential misinterpretation of the questions, erroneous recall of time or intensity of the physical activity, and/or deliberate misinformation. Dimensions of physical activity such as frequency, type, intensity, and duration may not be accurately detected across the full life-span with self-report measures.10,11
A challenge in pediatric activity measurement is the lower age limit for which children and youths can accurately recall what they did, let alone the intensity and/or duration. In a 2000 review, Sallis and Saelens11 reported that self-report measures do exist that have adequate reliability, content validity, and relative criterion validity for adolescent youths. Recently validated, the Activities Scale for Kids (ASK) is a child self-report measure of physical disability designed for children and youths ages five to 15 years who are experiencing physical activity limitations due to musculoskeletal disorders.12–16 The performance scale of this measure (ASKp) was developed to query what a youth “usually does,” taking into account the environment in which the child functions. Reliability is excellent (ICC = 0.97) and has been validated against the parent-reported Child Health Questionnaire (CHQ), clinician global ratings of change and observations, with significant correlations ranging from 0.81 to 0.92 for children with mobility limitations.
In response to poor reliability of self-report measures in children, the intrusiveness of direct observation and the complexity of HR monitoring, activity and step counters have been developed as a possible method to accurately record activity.17 An extensive discussion of activity measurement by Tyron18 states that “the step is the preferred unit of measure, since it is the natural unit of ambulation.” Tyron emphasized human walking activity because it can be obtained longitudinally from a person’s daily life and it has obvious clinical relevance. Newer electronic monitors were validated first in adults by indirect calorimetry and/or calibrated in the measurement units of resting metabolic equivalents (MET). The established adult MET values may be inaccurate for children since the resting metabolic rate of children declines by approximately half between the ages of five and 18 years of age.19
The overall purpose of this paper is to highlight issues relevant to ambulatory activity monitoring in children and youths. For this discussion, ambulatory physical activity monitoring is defined as direct measurement of the amount of walking and/or steps taken over time.20 A review of the currently available pedometers and accelerometers for ambulatory physical activity monitoring is presented. Elements of device selection are discussed, and critical criteria are proposed for the ambulatory activity monitoring of children and adolescents. Finally, examples of pediatric physical activity monitoring are presented in the context of clinical practice and research.
DEVICES TO MONITOR PEDIATRIC AMBULATORY ACTIVITY: PEDOMETERS AND ACCELEROMETERS
A relatively simple electronic device, a pedometer is designed to estimate mileage walked and/or steps taken over a period of time. Leonardo da Vinci is credited with the invention of the pedometer; his 15th-century drawings described the basic principles behind the mechanical pedometer of today.21 Thomas Jefferson is thought to have brought the first pedometer to the United States from France.22 These older mechanical-style pedometers are less accurate than the electronic pedometers developed in the past 10 years. Today, pedometers are battery operated and contain a spring-suspended horizontal level arm that moves up and down.23 An electrical circuit is opened and closed by the movement of this lever arm when the pedometer is attached at the waist or hip. The movement of the lever arm is in response to the vertical movement of pelvis during walking. Electronic circuitry allows summation of steps or counts taken and provides a digital read out. Device, manufacturer, attachment site, strengths, limitations, and general comments in Table 1 summarize pedometers currently available in the United States. Information presented in the table is a summary of the published research, product information, and author/colleagues clinical and research experience.
Low cost and ease of use are the primary advantages of pedometers over accelerometers.24 The 16 different pedometer models presented in Table 1 range in cost from $15.00 to $50.00, each with some vendors who offer price discounts for large quantities and/or research applications. All the pedometers in Table 1 are relatively small and unobtrusive with attachments at the waist. Several devices do not have a cover over the LCD display, so that blinding of data collection would require modifications. Covers for LCD displays and buttons also guard against the accidental resetting of the device. One pedometer reviewed does not use replaceable batteries, thus requiring replacement of the actual monitor. All these pedometers require someone to visually read the LCD display and record the information. Only one brand has published pediatric applications (Yamax, Inc.).25–28
Pedometers are capable of producing outcome data in steps, distance and calories. Thus, the device can be used to estimate energy expenditure during walking. Distance is calculated by multiplying the number of steps by the stride length (distance = steps × stride length). Walking speed, height, age, gender, and/or physical impairments can all influence stride length through out development.23 Only one model listed in Table 1 (Accusplit) reported auto stride length adjustment. Tudor-Locke and Myers20 have suggested that research outcomes should only be reported as steps since this is the most direct output of pedometers. Four devices give cumulative step or activity counts. By virtue of their simplicity, none of the pedometers are capable of generating time series data (ie, steps per time minute over seven days).
The first published pediatric application of pedometers was by Nishikido and colleagues28 in 1982. Nishikido compared the Yamasa AM-5 pedometer to direct observation of activity in 50 typically developing children aged five to six years old. The correlations with mothers’ and teachers’ direct observations of activity were relatively low at 0.14 and 0.25, respectively. This specific monitor is no longer available and has been replaced by the Digiwalker series. In contrast, a strong association (r = 0.97) was documented by Kilanowski and colleagues26 between the Digiwalker DW-200 pedometer and a direct observation system, the Children’s Activity Rating Scare (CARS). When compared with HR and oxygen consumption (Vo2), Eston et al25 reported correlations with the Digiwalker DW-200 pedometer of 0.82 and 0.78, respectively in youths eight to 11 years old. Louie and colleagues27 replicated this study in 1999, documenting Pearson product-moment correlations of 0.77, 0.68, and −0.45, respectively, for hip, ankle, and wrist attachments during treadmill walking activity compared with oxygen uptake scaled for body mass (SVo2) in Hong Kong Chinese boys aged eight to 10 years old who were typically developing. The negative correlation suggests that the wrist attachment may not be a valid placement for measuring ambulatory activity of children with this pedometer.
In contrast to pedometers, accelerometers are more complex electronic devices that specifically measure accelerations produced as a body segment or limb part moves through space.29 Acceleration is the change in velocity over time of the body part as it moves, eg, of the ankle during stepping. During walking, the portions of the lower extremities accelerate and decelerate with each step. Accelerometers record a constant acceleration signal or count during repetitive steady activity such as walking. The accelerations experienced by a person as they ride in a car usually are of a lower magnitude and duration and are not recorded.29 Electric transducers and microprocessors convert the accelerations recorded into a digital signal, which are the counts or steps.
Accelerometry-based devices have been employed in human movement study for the past 30 years. One of the first accelerometers was the Large-Scale Integrated (LSI) Motor Activity Monitor, developed in the 1970s to record the movements of psychiatric patients.30 The LSI was first used in physical activity research by LaPorte and colleagues31,32 in the late 1970s and early 1980s. This early activity research documented the potential of this type of measurement, spurring further development and research. Devices have become smaller and more sophisticated in the past 10 years, thus allowing direct application to activity monitoring.29 Currently available accelerometers are summarized in Table 2.
Attachment sites for the accelerometers listed include the waist, wrist, and ankle. Prices for these devices including download hardware and software range from approximately $100 to $3,000. Of the accelerometers listed, five are unidimensional or single-plane devices. The Step Watch employs a two-plane sensor specifically designed for the accelerations of the ankle during walking. Two devices (RT3 & Actical) are multidimensional, capable of recording movements in all planes of motion. Size varies from that of a wristwatch face to a digital pager with attachments via wristbands, belt clips, pouches, and/or knit cuffs. Programmability of sampling epochs is available with seven of the devices. The majority of the devices have replaceable batteries except the RT3, which uses a charger, and the Step Watch, which has a battery life of five to six years. All devices except the Caltrac employ some type of hardwire cable or telemetry for electronic computer downloading of data with various levels of analysis with either device specific software and/or data export capabilities.
Six of the accelerometers have been employed in studies with children. The Caltrac was compared with direct observations of activity in a sample of 30 two- to four-year-old children.33 Spearman rank-order correlations of 0.05 and 0.37 were noted for walking and running, respectively, with total activity step counts per 10 hours of monitoring ranging from 85 to 142. The low counts and correlations suggest that the Caltrac underestimates ambulatory activity. Early validation of accelerometers appears to have focused on comparisons with HR and Vo2. The CSA model 7164 (now marketed as the MTI Actigraph) was studied during treadmill walking by Trost and colleagues.34 Monitoring 30 youths ages 10 to 14 years, the authors reported Pearson product-moment correlations of 0.86, 0.77, and 0.86 to HR, Vo2, and energy expenditure, respectively. Strong correlations to HR (r = 0.85) and Vo2 (r = 0.88) were documented with the TriTrac R3D, model T303, in a group of 30 youths ages eight to 11 years by Eston and colleagues.25
Louie and colleagues27 compared the CSA model 7164 and the TriTrac R3D with Vo2 during four-minute treadmill walking and running activities in a sample of 21 Hong Kong Chinese boys ages eight to 10 years old. Correlations with Vo2 were reported as slightly stronger for the TriTrac (r = 0.93) versus the CSA (r = 0.81). Step counts were similar for the four-minute walking with the CSA, measuring a larger relative count range (5,991–6,737) than the TriTrac (4,653–5,192). Troutman et al35 validated the Mini-Logger 2000 against Vo2 and HR in 31 youths, ages 10 to 16 years old. Good reliability was documented for hip and ankle placement during treadmill walking and running. The hip placement was consistently less reliable (r = 0.61–0.66) than the ankle placement (r = 0.84) for both treadmill activities.
StepWatch measurement accuracy as compared with manual steps counts taken by direct observation (r = 0.99) in 21 boys and 21 girls, aged seven to 21 years old was documented by McDonald and colleagues.36 The authors reported average total steps per day of 5,428, with low, medium, and high activity level minutes per day (246, 82, and 53, respectively). The same research team established initial discriminative validity of the StepWatch in a study of 20 obese and 27 nonobese youths aged eight to 10 years.37 The percentage and total minutes of high activity per day for the youths with obesity was approximately one half of the results documented for the youths without obesity. Total steps per day for the nonobese youths were reported to be 6,037 (SD ±359), whereas youths with obesity averaged 3,898 steps (SD ±516). Youths with obesity averaged 1,480 steps per day in a high activity level compared with 2,590 for the nonobese youths.
Bjornson et al38 collected a total of six weeks of daily StepWatch data on 20 typically developing youths in the age groups five to seven years and nine to 11 years using a two-week sampling epoch. StepWatch measurement accuracy was documented for walking and running with correlations of 0.97 and 0.96, respectively, when compared with directly observed steps. A longer time sample may be indicated to capture the variability of daily step activity in children since total steps per day ranged from 4,864 to 14,591, The average percentage of time spent in low, medium, and high activity levels was 67.7%, 21.7%, and 23.7%, respectively, for a two-week time sample.
The CSA model 7164 was compared with the Mini Mitter Actiwatch during 10-minute samples of treadmill walking and jogging and track walking and jogging in children aged six to 16 years old by Puyau et al17 in 2002. A comparison of the two devices at the hip and head of the fibula documented a correlation of 0.88 for the hip and 0.89 for the leg attachments for steps taken. The CSA monitor documented higher average counts per day for walking compared with the Actiwatch regardless of attachment site. A large discrepancy in step counts between the CSA (7,354) and the Actiwatch (2,959) was noted for the leg placement. CSA hip versus leg attachments were also found to have a large difference in step counts (8,804 vs 26,562), suggesting that the leg placement is picking up more than four times more movements than the hip attachment. Actiwatch leg attachment was also found to have a larger average daily step count than the hip (5,644 vs 3,318).
ELEMENTS AND CRITICAL CRITERIA FOR ACTIVITY DEVICE SELECTION
Monitoring devices need to have established reliability before output can be considered useful.39 Physical activity researchers have recently proposed a total daily step count as the most accurate descriptor of ambulatory activity when measured by pedometers and accelerometers.20,40 Activity monitoring specifically for ambulation has the gold standard criterion of the number of steps per unit of time. Hence, device performance can be related directly to the field criterion of observed steps taken or raw step counts.
Criterion-related validity for a device can be confirmed by direct observation and manual count of steps taken per standardized time period or distance walked if stride length is known. A comparison with the criterion (actual steps taken) before and after each use and/or periodically over time will document intra- and interdevice reliability. Thus, step criterion validity and the reliability of a specific device can be tested within the specific activity (ie, walking to school, running, playing soccer, biking) or time frame (ie, morning/afternoon, school/nonschool hours). Social validation of the devices can be addressed by qualitative methods (ie, personal activity diaries completed by the youths or caregivers). Individualized outcome measures with level of performance and relative importance of activity to the youth, caregiver, and/or family can be quantified to socially validate the output of ambulatory activity devices.
The reliability of data capture can be addressed with electronic downloading methods for most accelerometers. With pedometers, the method of visually reading the LCD display and recording the data accurately in pediatric populations will need to be verified. Youths may use a hard copy diary, palm device, or Email or may call the investigator to report the pedometer output at a specific time each day (eg, bedtime, on wakening). Data capture methods may have to involve adult caregivers due to age, cognition, reading level, fine motor, writing, and/or visual acuity issues experienced by youths with disabling conditions. The reliable recording by participants, masking of investigators and/or participants to decrease bias, the methods of returning the monitor (ie, via U.S. Postal Service), and adherence to wearing the device are all issues that may affect daily step counts.
If masking of the participants to their step activity performance is desired clinically or for research purposes, covers can be placed over the LCD display of the pedometers. Most accelerometers do not give visual feedback to the participants. Noncompliance is more difficult to quantify with pedometers than with the accelerometers due to the manual data recording and LCD display feedback to the participant. Noncompliance is obvious with review of the data download for most accelerometers.
Besides the specific monitoring device, clinicians and researchers will need to determine the most appropriate duration of sampling to provide a confident estimate of the typical ambulatory activity for the pediatric populations of interest. The measurement time frame appears to be dependent on device, population, or research question.20 Data collection and/or recording procedures will also affect the monitoring duration. In a study directed at documenting age-related trends, Trost et al34 found that seven days of consecutive monitoring would provide sufficient estimates of daily activity. The two-week samples of step data documented by Bjornson and colleagues38 support selection of a similar sampling epoch.
Validation of site attachment against direct observation of actual steps taken should be confirmed between hip or ankle attachments. Significant error between the hip and leg attachments of the same device has been documented for walking.17 Attachment consistency will affect the output from pedometers as well as accelerometers. Attachment can be influenced by the physical size or shape of the individual. For example, a child who is obese may not be able to consistently keep a pedometer or accelerometer vertical during data collection at the waist due to abdominal tissue. Youths with hip limitations due to arthritis may not be able to reach their ankle easily or consistently to apply a device. Fine motor limitations seen in youths with cerebral palsy, juvenile forms of arthritis, and spina bifida may limit which device can be put on independently. The fit and/or type of clothing worn may also affect the consistency of the device attachment.22 Youth fashion trends in the year 2004 find the waist bands often well below the top of the pelvis, thus potentially biasing the input to the monitoring device. The use of devices that are consistently attached (ie, wristband, waist belt, ankle cuff, or strap) would address this measurement challenge.
The actual ease of using the buttons, attaching to the device to the body, opening the cover, and reading the LCD output of the pedometer needs to be verified for children and youths with developmental disabilities. Change in stride length over time is expected due to growth in children. The ability to adjust a device for stride length changes or the ability for the device to detect this change would be important for longitudinal measurement. Device selection will need to be sensitive to the movement disorders seen in pediatric chronic conditions. The device chosen for a child with cerebral palsy who presents with spasticity may need to be different from the device and/or attachment site chosen for a child with ataxia, dystonia, and/or athetosis. A device to monitor steps may need to be programmed and/or specifically selected to accommodate the extraneous movements seen in varying and/or mixed movement disorders seen in cerebral palsy. Sirard and Pate41 suggest that the frequently employed single-plane (vertical) accelerometer may be potentially limited in its ability to detect the variable movement patterns of children and adolescents. Thus, the type of accelerometer (unidimensional vs multidimensional) may need to be validated for a specific movement disorder depending on the clinical or research question.
Musculoskeletal biomechanics and functional movement patterns are known to change as part of the natural history of some pediatric mobility impairments. A device will need to be sensitive and/or capable of calibration to effectively monitor the increasing crouched gait pattern seen in ambulatory youths with cerebral palsy or the progressing Trendelenberg gait pattern of boys with muscular dystrophy.22 Changes in the assistive devices used for walking often seen in the natural history of some pediatric mobility impairments (ie, walker to quad canes with cerebral palsy) may also affect which device is chosen to measure walking activity. For mobility impairments that result from conditions such as juvenile arthritis, in which walking fluctuates between independent walking to the use of a walker and/or wheelchair, an ankle device may be preferred over a wrist device.
A consideration with the Step Watch monitor is that it was designed to be worn specifically on the outside of the right ankle. If worn on the left side or upside down, step counts will be extremely low.42 The ability to calibrate the StepWatch to unique walking patterns on the right leg (ie, toe-walking, crouch gait, stiff knee gait) makes it ideal for abnormal as well as asymmetrical gait patterns. This also allows sensitivity to gait changes after interventions (ie, orthopedics surgery, orthotics, injection therapy, prosthesis). Clearly marking the device, repeated instruction in its application, and confirmation of appropriate device attachment can decrease missing data. Youths are physically active in a variety of environments; thus, monitoring devices need to demonstrate durability across time and activities. Sealed waterproof devices would be appropriate in pediatric monitoring to limit possible tampering with the device and allow recording across a variety of life situations.
In summary, critical criteria for pediatric ambulatory activity monitoring device selection should include (1) clinical or research questions and/or design, (2) validity and reliability of measurement established against actual steps taken, (3) documented sensitivity to the within- and between-group differences, (4) attachment site appropriate for age, height, weight, size, and physical and mobility skill level as well as study question and design, (5) consistent and reliable attachment of the device, (6) reliable and unbiased data capture and/or download from the device, (7) a sampling time frame adequate to confidently estimate typical ambulation activity of interest, (8) durability for use over time, (9) appropriate cost for population-based studies versus clinical patient application, (10) ease of data management as needed for clinical outcomes or research, and (11) acceptability to the youth.
APPLICATION OF ACTIVITY MONITORING IN PEDIATRIC RESEARCH AND PRACTICE
Valid and reliable ambulatory activity monitoring is needed in some areas of research and clinical practice such as establishing normative levels by age, surveillance, screening, program evaluation, and intervention efficacy.20 The purpose of the study, the clinical question, and the unique characteristics of the population measured will affect device selection.43 For example, a population-based tracking of ambulatory activity in children who are typically developing over time would require a device that is of lower cost and durable and provides cumulative total step counts and reliable data capture. Most of the available pedometers would be useful in this situation due to cost and ease of use compared with accelerometers. In contrast, in an intervention study for a treatment of hyperactivity where overall movement is the focus, a triaxial accelerometer with wrist attachment may have greater sensitivity to detect treatment effects than pedometers. In this example, masking the child to his/her performance level would also be feasible with most accelerometers.
Research questions for which the outcomes are related to patterns of activity or endurance for functional tasks (eg, restricted pulmonary disease, cystic fibrosis) would require devices with the capacity to reliably monitor over two- to three-week time frames. In this scenario, pedometers and/or accelerometers may be employed depending on the reliability of data capture, sample size, and the magnitude of the treatment effect predicted. Monitoring the ambulatory activity of boys with Duchenne muscular dystrophy every three to six months from age at diagnosis to termination of independent walking would be an example of such a project. Another example of clinical application could be the monitoring of ambulatory activity before and after a limb salvage procedure for bone cancer. In this scenario, accelerometers may be more appropriate due to their potential sensitivity to the changes in walking patterns.
When the interventions and/or impairments of a specific population are clearly related to upright mobility or walking (eg, cerebral palsy, spina bifida), accelerometers validated against actual steps taken will be most appropriate. Accelerometers would also be indicated in clinical or research applications in which patterns of ambulatory activity are the desired outcome. Preference for a particular monitor may then be driven by the software capabilities to allow a variety of sampling epochs and/or analysis flexibility depending on the clinical or research question. If a comparison of ambulatory activity level during school hours versus nonschool hours was desired, an accelerometer with masked recording, times series capability, and flexible data analysis would be needed.
Activity monitoring may also be employed to triangulate outcomes with self-report measures of activity.29 Sallis and colleagues44 reported differing relationships when activity was measured by self-report versus objectively recorded with an activity monitor. These relationships have been interpreted to suggest that either the two types of activity measurement assess different types of activity patterns or psychosocial mediators have differential effects on the two indicators. Welk29 has suggested the use of activity level as the dependent variable in studies designed to predict physical activity. Such research application would require an adequate time frame for sampling to ensure that typical activity levels were sampled along with screening for compliance of wearing protocols.
SUGGESTIONS FOR FUTURE CLINICAL RESEARCH
The focus of future research needs to first address the criterion-related validity of the specific pedometers and accelerometers. The specific and/or variety of movements counted by the monitors need to be established with respect to the clinical or research questions. Monitors with various attachment sites will need documentation of the sensitivity of the device to the movement of interest in various pediatric populations (ie, wrist versus ankle attachment to measure actual steps taken in youths with cerebral palsy versus spina bifida). Methodologies for reliable data capture with the devices need to be confirmed in young toddlers to teenagers. The sampling time frame required to adequately address clinical/research questions needs to be established (ie, one week versus three weeks for children with pulmonary limitations). Finally, further work combining activity monitoring with pedometers and accelerometers with methods of physical activity monitoring (ie, self-report, direct observation) needs to be explored to understand children’s physical activity from a broader perspective.
A variety of measurement methods for measuring physical activity in children and youths exist today. Clearly, pedometers and accelerometers can provide valuable information on daily ambulatory performance. Pedometers allow a relatively low-cost measure of physical activity with outcomes reported in steps, distance, and/or calories. The more costly and complicated accelerometers appear to have improved sensitivity to movements in a variety of planes. Monitor selection should be based on the clinical or research question and/or design as well as the validity and reliability with respect to the physical behaviors of interest (ie, steps to actual steps taken versus general body movements). Population-based clinical problems looking at general physical activity may be addressed by using pedometers and/or low-cost accelerometers. Clinical activity questions related to ambulation (ie, pre-/post-surgery, orthotics, botulinum toxin injections) may require accelerometers validated to actual steps taken.
Considerable evidence exists that activity level can be changed.45 A 1996 meta-analysis of a broad spectrum of interventions to increase physical activity across the life-span conducted by Dishman and Buckworth46 documented an overall 0.75 standard deviation effect size. A noted limitation of this meta-analysis was the variability in measurement of physical activity. It is not known whether the act of wearing an activity monitoring device actually changes normal daily activity patterns.24 Pedometers and accelerometers are easy for youths to wear and do not appear to interfere with daily activity or normal movement patterns. Current recommendations for physical activity have been summarized by various professional organizations in the statement “some is better than none and more is better than some.”45,47,48 To know whether youths are doing “some” or “more,” healthcare providers and researchers will need to directly measure physical activity within the context of their daily lives.
1. International Life Sciences Institute. Improving Children's Health Through Physical Activity: A New Opportunity. Atlanta: International Life Institute, 1997.
2. US Department of Health and Human Services. Physical Activity Fundamental to Preventing Disease
. Accessed 10–10–2004.
3. Pastor PN, Makuc DM, Reuben C, et al. Chartbook on Trends in the Health of Americans. Health in the United States. Hyattsville, MD: National Center for Health Statistics; 2002.
4. Office of Surgeon General. Surgeon General's Call to Action to Prevent and Decrease Overweight and Obesity. Rockville, MD: Public Health Service, U.S. Department of Health and Human Services; 2001.
5. Kohl HW, Fulton JE, Castellan NJ. Assessment of physical activity among children and adolescent: a review and synthesis. Prev Med. 1999;31:S54–S76.
6. Trost SG. Objective measurement of physical activity in youth: current issues, future directions. Exerc Sport Sci Rev. 2001;29:32–36.
7. Bailey RC, Olson J, Porszasa J, et al. The level and tempo of children's physical activities: an observational study. Med Sci Sport Exerc. 1995;27:1033–1041.
8. Rowlands AV, Eston RG, Ingledew DK. Measurement of physical activity in youth with particular reference to the use of heart rate and pedometry. Sports Med. 1997;24:258–272.
9. Saris WHM. Habitual activity in children: methodology and findings in health and disease. Med Sci Sports Exerc. 1986;18:253–263.
10. Dale D, Welk GJ, Mathews CE. Methods for assessing physical activity and challenges for research. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:107–123.
11. Sallis JF, Saelens BE. Assessment of physical activity by self-report: limitations and future directions. Res Q Exerc Sports. 2000;71:S1–S14.
12. Pencharz J, Kin BSc, Young N, et al. Comparison of three outcomes instruments in children. J Pediatr Orthop. 2001;21:425–432.
13. Plint AC, Gaboury I, Owen J, et al. Activities Scale for Kids: an analysis of normals. J Pediatr Orthop. 2003;23:788–190.
14. Wai EK, Owen J, Fehlings DL, et al. Assessing physical disability in children with spina bifida and scoliosis. J Pediatr Orthop. 2000;20:765–770.
15. Young N, Williams JI, Yoshida KK, et al. Measurement properties of the Activities Scale for Kids. J Clin Epidemiol. 2000;53:125–137.
16. Young NL, Yoshida KK, Williams JI, et al. The role of children in reporting their physical disability. Arch Phys Med Rehabil. 1995;76:913–918.
17. Puyau MR, Adolph AL, Vohra FA, et al. Validation and calibration of physical activity monitors in children. Obes Res. 2002;10:150–157.
18. Tyron WW. Activity Measurement in Psychology and Medicine. New York: Plenum Press; 1991.
19. Schofield WN. Predicting basal Metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(suppl 1):5–41.
20. Tudor-Locke CE, Myers AM. Methodological Considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Res Q Exerc Sports. 2001;72:1–12.
21. Gibbs-Smith C. The Inventions of Leonardo da Vinci. London: Phaidon Press; 1978.
22. Belza B, Bodies in Motion: Using Motion Sensors to Measure Physical Activity. Orlando, FL: American College of Rheumatology; 2003.
23. Bassett DR, Strath SJ. Use of pedometers to assess physical activity. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:163–177.
24. Welk GJ. Introduction to physical activity research. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:3–18.
25. Eston RG, Rowlands AV, Ingledew DK. Validity of heart rate, pedometry, and accelerometry for predicting the energy costs of children's activities. J Appl Physiol. 1998;84:362–371.
26. Kilanowski CK, Conslavi AR, Epstein RS. Validation of an electronic pedometer for measurement of physical activity in children. Pediatr Exerc Sci. 1999;11:63–68.
27. Louie L, Eston RG, Rowlands AV, et al. Validity of heart rate, pedometry and accelerometry for estimating the energy cost of activity in Hong Kong Chinese boys. Pediatr Exerc Sci. 1999;11:229–239.
28. Nishikido N, Kashiwazaki H, Suzuki T. Preschool children's daily activities: direct observation, pedometry or questionnaire. J Hum Ergol. 1982;11:214–218.
29. Welk GJ. Use of accelerometry-based activity monitors to assess physical activity. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:125–141.
30. Montoye HJ, Kemper HC, Saris WHM, et al. Measuring Physical Activity and Energy Expenditure. Champaign, IL: Human Kinetics; 1996.
31. LaPorte RR, Kupfer DJMR, Matthews GCC. An objective measure of physical activity for epidemiologic research. J Epidemiol. 1979;109:158–168.
32. LaPorte RR, Cauley JA, Kinsey CM, et al. The epidemiology of physical activity in children, college students, middle-aged men, menopausal females and monkeys. J Chronic Dis. 1982;35:787–795.
33. Klesges LM, Klesges RC. The assessment of children's physical activity: a comparison of methods. Med Sci Sports Exerc. 1986;19:511–517.
34. Trost SG, Ward DS, Moorehead SM, et al. Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc. 1998;30:629–633.
35. Troutman SR, Allor KM, Hartmann DC, et al. Mini-Logger reliability and validity for estimating energy expenditure and heart rate in adolescents. Res Q Exerc Sports. 1999;70:10–74.
36. McDonald CJ, Walsh D, Widman L, et al. Use of the step activity monitor for continuous objective 3-day physical activity monitoring of children. Dev Med Child Neurol. 1999;41:36.
37. McDonald CJ, Walsh D, Widman L, Walsh S. Use of the step activity monitor for continuous objective physical activity assessment in children with obesity. Dev Med Child Neurol. 2000;42:22–23.
38. Bjornson KF, Song K, Coleman K, et al. Application of the step activity monitor to define normal activity levels in children. Dev Med Child Neurol. 2000;42:23.
39. Morrow JF. Measurement issues for the assessment of physical activity. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:37–49.
40. Rowlands AV, Eston RG, Ingledew DK. Relationship between activity levels, aerobic fitness, and body fat in 8 to 10 year-old children. J Appl Physiol. 1999;86:1428–1435.
41. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001;31:439–454.
42. Coleman K, Smith GV, Bonne D, et al. Step activity monitor: long-term, continuous recording of ambulatory function. J Rehabil Res Dev. 1999;36:8–18.
43. Mahar MR, Rowe DA. Construct validity in physical activity research. In: Welk GJ, ed. Physical Activity Assessments for Health Related Research. Champaign, IL: Human Kinetics; 2002:51–72.
44. Sallis JF, McKenzie RL, Elder JP, et al. Sex and ethnic differences in children's physical activity: discrepancy between self-report and objective measures. Pediatr Exerc Sci. 1998;10:277–284.
45. Nigg CR. Population activity assessment issues in population-based interventions: a staged approach. In: Welk GJ, ed. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002:227–239.
46. Dishman RK, Buckworth J. Increasing physical activity: a quantitative synthesis. Med Sci Sports Exerc. 1996;28:706–719.
47. Pate RR, Pratt M, Blair SN, et al. Physical activity and public health. A recommendation from the Centers for Disease control and Prevention and the American College of Sports Medicine. JAMA. 1995;273:402–407.
48. U.S. Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 1996.
© 2005 Lippincott Williams & Wilkins, Inc.