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Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate Patty S. Freedson & Kelly Miller Published online: 13 Feb 2015.
To cite this article: Patty S. Freedson & Kelly Miller (2000) Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate, Research Quarterly for Exercise and Sport, 71:sup2, 21-29, DOI: 10.1080/02701367.2000.11082782 To link to this article: http://dx.doi.org/10.1080/02701367.2000.11082782
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Freedson andMiller
Research Quarterly for Exercise and Sport ©2000 bythe American Alliance for Health, Physical Education, Recreation and Dance Vol. 71,No.2, pp.21-29
Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate
Downloaded by [FU Berlin] at 05:54 02 July 2015
Patty S. Freedson and Kelly Miller
Keywords: physical activity, motion sensors, heart rate monitoring, accelerometers hysical activity is a behavior that is characterized by any bodily movement that results in an increase in energy expenditure above resting levels (Caspersen, 1989). This behavior has been linked to a reduced risk of premature mortality (Haskell, 1994) and several diseases such as heart disease, certain types of cancer, noninsulin dependent diabetes mellitus, hypertension, and osteoporosis (Haskell, 1994). However, it is not clear how much physical activity is required to reduce risk for these diseases. There are a number of risk factors that have been linked to increased health risk, particularly for coronary heart disease. Hyperlipidemia, hypertension, smoking, and obesity have all received widespread attention in the research literature as well as in the popular press as increasing one's risk for coronary heart disease. All of these 'risk factors' are relatively easy to quantify either at home or through routine medical examination procedures. In addition, these measures exhibit a fairly high degree of stability day-to-day. However, assessment of physical activity behavior is much more difficult to characterize as it is based on individual habits and practices that vary considerably day-to-day. Often self-report measures are used to assess physical activity and are commonly subject to recall errors and inaccurate perception of one's activity behavior. With the current public health emphasis on the exposure (i.e., accumulating 30 minutes of moderate intensity physical activity), it becomes imperative that we use more objective methods for quantifying this be-
P
PattyS. Freedson is a Professor andGraduate Program Directorin the Exercise Science Department at the University of Massachusetts/Amherst. KellyMiller was a graduate studentin the department.
ROES: June 2000
havior in the same way we measure and monitor blood lipids, blood pressure, and body mass. In order to determine the dose of physical activity, an objective assessment instrument should be used to quantify physical activity. The ideal objective instrument would be low in cost, easy to administer to large groups, unobtrusive to the subject, and accurate. Two of these methods include motion sensors and heart rate. This paper reviews the characteristics and methods of data assessment for both of these types of instruments. The authors refer the readers to a text that provides a detailed analysis of objective methods to assess physical activity (Montoye et al., 1996).
Motion Sensors Motion sensors are mechanical and electronic devices that pick up motion or acceleration of a limb or trunk, depending on where the monitor is attached to the body. There are several different types of motion sensors that range in complexity and cost from the pedometer to the triaxial accelerometer.
Pedometers The first pedometer was designed approximately 500 years ago by Leonardo DaVinci (Montoye et al., 1996). Currently, pedometers are predominately used for assessing amount of locomotion by counting steps. The pedometer counts steps by responding to vertical acceleration, triggering a lever arm to move vertically and a ratchet to rotate. The main advantages of pedometers are that they are generally small and low in cost. For example,
21
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Freedson andMiller
one of the newer pedometers, the Yamax Digiwalker, is 52 x 39 x 19 mm in size, and priced at approximately $20.00 per unit. However, the pedometer has limited application for measuring habitual activity for several reasons. First of all, it does not provide any temporal information about activity patterns, as it does not store data over a specified time interval. Additionally, it is not sensitive to activity that does not involve locomotion, isometric exercise, or activity that involves the upper body (Melanson & Freedson, 1996). Differences in spring tension in the pedometers can lead to high variability between models and among units, making comparisons between studies difficult (Melanson & Freedson, 1996). In addition, they are not as accurate at very slow or very fast walking speeds (Bassett et aI., 1996; Washburn et aI., 1980). Although the limitations of the pedometer make it less suitable for assessing habitual physical activity patterns, it is potentially very useful in walking intervention studies where participants can be given specific pedometer step goals that can be self-monitored very easily.
Data Output Pedometer output in its simplest form is in steps accumulated. However if stride length is provided, distance walked may be determined. Some pedometers will also estimate the total number of calories expended if body weight is provided. Energy expenditure from a pedometer is most accurate when walking comprises most of activity. Bassett et al. (1996) reported that the Yamax Digiwalker was accurate for counting steps on concrete sidewalks with 100.6 % and 100.7% of actual steps recorded. In addition, there were no significant differences in accuracy between sidewalk and rubberized track surfaces. The Yamax was also very accurate at counting the number of steps over speeds ranging from 50 to 110 rn.rnirr l range. The Yamax has also been studied as a tool for assessing moderate intensity overground walking by Hendelman et al. (in press). Steps were highly correlated with walking speed (r=0.86) and V0 2 (r=0.75) for speeds ranging from 63.2 to 111.2 m.min- 1. The data suggested that pedometry may be useful for the assessmen t of total activity if walking is the predominant form of activity.
Other Populations The pedometer has not been widely used in a variety of populations for the assessment of physical activity. However, Eston et al. (1998) investigated the utility of the Yamax Digiwalker for monitoring physical activity in children. A correlation of r = 0.78 was reported between pedometer steps and oxygen uptake for treadmill walking. For unregulated play activities, correlations ofr=0.92
22
and r=0.88 were observed between steps and oxygen uptake and heart rate, respectively. The various limitations of the pedometer do not make it the ideal instrument for assessing physical activity. However, if overall walking activity is the outcome being assessed, the pedometer is a useful and inexpensive instrument, particularly in walking intervention studies where participants can self-monitor their behavior to determine if they are attaining specified goals.
Uniaxial Accelerometers The uniaxial accelerometer is a more complex instrument than the pedometer. It measures acceleration in a single plane (usually vertical) and can be attached to the trunk and/or limbs to measure acceleration of the trunk and/ or limbs. The theoretical basis underlying the use of an accelerometer to assess physical activity is that acceleration is directly proportional to the muscular forces and therefore is related to energy expenditure (Melanson & Freedson, 1996; Montoye et aI., 1996). Recent advances in accelerometers make them a unique and useful technology for measuring physical activity. These units are small, unobtrusive instruments with large memory capacity that allow for monitoring and storage of temporal patterns of activity in relatively small time intervals over a period of days or weeks. Additionally, the accelerometer measures both the amount and intensity of movement. However, not all activity is reflected in acceleration or deceleration such as load carriage or on a grade (Montoye et aI., 1996; Haymes & Byrnes, 1993). The Caltrac accelerometer (Muscle Dynamics Fitness Network, Torrence, CAl (7 x 7 x 2 cm) was one of the first accelerometers to be commercially marketed for research applications and for use by practitioners to provide clients with the ability to estimate daily caloric expenditure. It is typically worn at the hip and provides a measure of trunk acceleration. Inputting individual age, stature, mass, and gender estimates and accumulates resting metabolic rate over the monitoring period. When trunk acceleration occurs, an estimate of energy expenditure associated with this movement is added to the resting metabolic rate. The estimate of Caltrac total caloric expenditure can be inactivated by inputting constants for age, stature, mass and gender variables so that the data output is in accelerometer counts and only assesses the quality and quantity of movement. The Caltrac functions through a piezoelectric bender element consisting of two layers of piezo-ceramic material with a brass center. When the trunk accelerates, the transducer bends and produces a charge that is proportional to the force exerted by the subject (Montoye et aI., 1996). This creates an acceleration-deceleration
ROES: June 2000
Downloaded by [FU Berlin] at 05:54 02 July 2015
Freedson andMiller
wave and the area under this wave is summed, yielding the final count value. The major limitation of the Caltrac is that the output is in total counts accumulated for the entire sampling period so that patterns of activity cannot be assessed. The Computer Science and Applications, Inc. actigraph (Shalimar, FL) is a newer accelerometer that is 5.1 x 3.8 x 1.5 centimeters in size (42.6 gm) and records accelerations from 0.05 - 2 G's, Movement results in an acceleration that acts on a cantilevered beam creating a bending moment that results in a strain on the piezoceramic cantilever beam (Tyron & Williams, 1996). The accelerometer sensor then produces a charge that is proportional to the strain (Tyron & Williams, 1996). The signal is filtered by an analog bandpass filter and digitized by an 8 bit AID converter at a rate of 10 samples per second (Tyron & Williams, 1996). Each signal is summed over a user specified time interval and at the end of this time, the activity counts are stored and the counter is reset to zero (Tyron & Williams, 1996). The CSA actigraph is initialized and downloaded using a reader interface that is connected to the serial port of a PC compatible computer. The initialization procedure allows the tester to set start time and sampling interval (epoch). Downloading consists of transferring the information from the monitor to the computer where it can be imported to a software program to be analyzed. The Actillume actigraph (Ambulatory Monitoring, Inc., Ardsely, NY) is 7 x 3.8 x 2.2 ern in size (100 gm). It contains a uniaxial piezoresistive accelerometer that samples accelerations at a rate of 20 times per second. The signal is digitized by an 8 bit AID converter and amplified through a low-pass filter. The signal is then stored as a byte of data over a specified time interval. Another commercially available uniaxial accelerometer is the Kenz accelerometer (Select 2 Model, Nagoya, Japan) (5 x 3 x 1 cm). It estimates gross and net energy expenditure and can store 7 days of data 1 day sampling intervals. The Biotrainer (1M Systems, Baltimore, MD) uniaxial accelerometer is similar in design to the CSA actigraph but uses high speed sampling rather than accumulating counts over a user specified sampling interval. This device permits long term monitoring and evaluation of temporal patterns of activity.
Data Output Sallis et al. (1990) compared Caltrac raw counts to net activity heart rate. Significant moderate correlations (r = 0.42-0.54) were reported between the two methods across two days of monitoring field activity. Additionally, Sallis et al. (1990) revealed a high correlation (r = 0.82) between oxygen consumption and Caltrac counts. Similarly, Maliszewski et al. (1991) reported a high correla-
RUES: June 2000
tion (r = 0.88) between oxygen consumption and Caltrac counts for walking in men. Several studies have investigated the utility of the Caltrac in determining energy expenditure. Pambianco et al. (1990) revealed that the Caltrac significantly overestimated energy expenditure compared to indirect calorimetry during treadmill walking. The overestimation was greater in normal weight subjects at higher walking speeds. Maliszewski et al. (1991) reported a significant correlation (r = 0.90) between Caltrac energy expenditure and indirect calorimetry during treadmill walking. Haymes and Byrnes (1993) reported that the Caltrac overestimated energy expenditure at all walking and running speeds. The CSA and Actillume actigraph monitor data are in counts per unit time which is sampling interval dependent (i.e., counts. 20sec 1, counts. min-I). Although there is no direct physiological translation of counts to energy expenditure or oxygen consumption, laboratory-based studies suggest that there is a linear relationship between counts per minute and physiological measures such as energy expenditure and oxygen consumption. Melanson and Freedson (1996) compared CSA activity counts and energy expenditure from indirect calorimetry during treadmill walking in adults. The results revealed significant correlations between energy expenditure and CSA counts (r = 0.66-0.82) and between relative oxygen consumption and CSA counts (r = 0.77-0.89). Hendelman et al. (in press) compared measured oxygen consumption and CSA counts for overground walking and reported a correlation of r = 0.77 between CSA counts and METs. In a field trial, Matthews et al. (in press) observed a high correlation (r = 0.92) between total minutes of activity per day determined by a 24 hour recall and number of activity minutes per day using the Actillume accelerometer. These studies reveal that the CSA and Actillume accelerometer output in the form of counts per minute is useful for determining activity and activity patterns in adults. A more practical method of interpreting CSA accelerometer output was developed by Freedson et al. (1998). This study examined the relationship between CSA accelerometer counts and oxygen consumption during treadmill walking and running. Count ranges corresponding to MET levels of intensity were derived from regression analyses. Figures 1 and 2 illustrate counts. min- 1 data from two individual 3 days of monitoring using the CSA actigraph. Figure 1 is a less vigorously active subject. Table 1 presents the data using the counts. min-I cut-points for moderate and vigorous activity described by Freedson et al. (1998). Figure 2 and Table 2 present data for a vigorously active individual (bouts of purposeful exercise on all days) over 3 days. The data reveal some very distinct activity pattern differences and the numbers of minutes spent in the vigorous activity
23
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cat egory is qui te di ffe re n t with the active subject spe nding over 2 hou rs per day in vigoro us activity where the inactive subject ha d ~n tiall)' no minutes of vigorous activity. The mean counts. mi n-I ranged fro m 236 - ~36 COl" the less active 5U~ (fable l) and 1122·1169 counts, min-I for the \'igorously active 5U~ H endel ma n «:1 al. (in press) developed individua l regression equations for METs based on CSA acce terometer co unts from overgro und walking activityan d applied th em to selec ted recr eat io nal and household activitie s in cluding gardening. lawn m owing. dusting, washing windows. vacu uming and playing go lf (walking a nd p ullingclubs). O xygen consum ption predicted from the CSA co unts was significantly lowe r than meas ured VO , for all activities. Based on these data. regression equations for deri ving point estimates of r nr rgy expe nditure based on accelero me ter coun ts should be deve loped for the acti vity in which rhey are to be used.
OtherPopulations Both the Caltrac and GSAacce lerome te rs have been widely used in physical acti\ity research Iovo lving ad ults,
,
children. and ove rweight child re n . The Cahrac as a tool for assessin g children 's activity has been investiga ted in both field an d laboratory setti ngs . Sallis e t at ( 1990) revealed moderate correlations between Caltrac coun ts and mean activity heart rate for two days (I' = 0.54 day I, r ~ 0.42 day 2) of free-living activity, The a uthors suggested th at the correlat ions were lower tha n these observed in adults because o f the wide varie ty of activity performed by child ren . In the la bo ra tory. Maliszewski e t al. (199 1) reported no significant diffe rences betw een measured o xygen consum ption an d estimate d ene rKY expenditure by Ca ltrac in c hildre n d urin g tread mill wal king a t speeds ranging from 3.35 to 6.7 km .hr 1. The GSA accel eromete r has been evalua ted for use with c hild re n in con tro lled and free-living labora tory cond itio ns, as well as f ield settings. Tro st et OIl. (1lJYH) eval ua ted th e GSA in a con trolled labo ratory situa tion , d u ring tr ead mill walk ing a nd running. Cou nts we re highlycorrelated with e nergy expe ndi ture (I' = 0 .86.0.87) . oxygen consumption (r ~ 0 .86. 0 .87) , hea rt ra te (r = 0.77) . and treadmill speed (r ~ 0.9. 0.89) . Addi tio nally. th er e was high iturr..jnstru menl reliability {int raclass R .0.87) . Energy ex pendi ture estimated from a regression
Sample Accelerometer Data
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figure 2.Three d.ys of minute-by-minut. CSA I ctigraph.ceeler-
T.bl.1. Accelerometer dati for three daysof monitoring: l . ss Active
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Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20
Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate Patty S. Freedson & Kelly Miller Published online: 13 Feb 2015.
To cite this article: Patty S. Freedson & Kelly Miller (2000) Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate, Research Quarterly for Exercise and Sport, 71:sup2, 21-29, DOI: 10.1080/02701367.2000.11082782 To link to this article: http://dx.doi.org/10.1080/02701367.2000.11082782
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Freedson andMiller
Research Quarterly for Exercise and Sport ©2000 bythe American Alliance for Health, Physical Education, Recreation and Dance Vol. 71,No.2, pp.21-29
Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate
Downloaded by [FU Berlin] at 05:54 02 July 2015
Patty S. Freedson and Kelly Miller
Keywords: physical activity, motion sensors, heart rate monitoring, accelerometers hysical activity is a behavior that is characterized by any bodily movement that results in an increase in energy expenditure above resting levels (Caspersen, 1989). This behavior has been linked to a reduced risk of premature mortality (Haskell, 1994) and several diseases such as heart disease, certain types of cancer, noninsulin dependent diabetes mellitus, hypertension, and osteoporosis (Haskell, 1994). However, it is not clear how much physical activity is required to reduce risk for these diseases. There are a number of risk factors that have been linked to increased health risk, particularly for coronary heart disease. Hyperlipidemia, hypertension, smoking, and obesity have all received widespread attention in the research literature as well as in the popular press as increasing one's risk for coronary heart disease. All of these 'risk factors' are relatively easy to quantify either at home or through routine medical examination procedures. In addition, these measures exhibit a fairly high degree of stability day-to-day. However, assessment of physical activity behavior is much more difficult to characterize as it is based on individual habits and practices that vary considerably day-to-day. Often self-report measures are used to assess physical activity and are commonly subject to recall errors and inaccurate perception of one's activity behavior. With the current public health emphasis on the exposure (i.e., accumulating 30 minutes of moderate intensity physical activity), it becomes imperative that we use more objective methods for quantifying this be-
P
PattyS. Freedson is a Professor andGraduate Program Directorin the Exercise Science Department at the University of Massachusetts/Amherst. KellyMiller was a graduate studentin the department.
ROES: June 2000
havior in the same way we measure and monitor blood lipids, blood pressure, and body mass. In order to determine the dose of physical activity, an objective assessment instrument should be used to quantify physical activity. The ideal objective instrument would be low in cost, easy to administer to large groups, unobtrusive to the subject, and accurate. Two of these methods include motion sensors and heart rate. This paper reviews the characteristics and methods of data assessment for both of these types of instruments. The authors refer the readers to a text that provides a detailed analysis of objective methods to assess physical activity (Montoye et al., 1996).
Motion Sensors Motion sensors are mechanical and electronic devices that pick up motion or acceleration of a limb or trunk, depending on where the monitor is attached to the body. There are several different types of motion sensors that range in complexity and cost from the pedometer to the triaxial accelerometer.
Pedometers The first pedometer was designed approximately 500 years ago by Leonardo DaVinci (Montoye et al., 1996). Currently, pedometers are predominately used for assessing amount of locomotion by counting steps. The pedometer counts steps by responding to vertical acceleration, triggering a lever arm to move vertically and a ratchet to rotate. The main advantages of pedometers are that they are generally small and low in cost. For example,
21
Downloaded by [FU Berlin] at 05:54 02 July 2015
Freedson andMiller
one of the newer pedometers, the Yamax Digiwalker, is 52 x 39 x 19 mm in size, and priced at approximately $20.00 per unit. However, the pedometer has limited application for measuring habitual activity for several reasons. First of all, it does not provide any temporal information about activity patterns, as it does not store data over a specified time interval. Additionally, it is not sensitive to activity that does not involve locomotion, isometric exercise, or activity that involves the upper body (Melanson & Freedson, 1996). Differences in spring tension in the pedometers can lead to high variability between models and among units, making comparisons between studies difficult (Melanson & Freedson, 1996). In addition, they are not as accurate at very slow or very fast walking speeds (Bassett et aI., 1996; Washburn et aI., 1980). Although the limitations of the pedometer make it less suitable for assessing habitual physical activity patterns, it is potentially very useful in walking intervention studies where participants can be given specific pedometer step goals that can be self-monitored very easily.
Data Output Pedometer output in its simplest form is in steps accumulated. However if stride length is provided, distance walked may be determined. Some pedometers will also estimate the total number of calories expended if body weight is provided. Energy expenditure from a pedometer is most accurate when walking comprises most of activity. Bassett et al. (1996) reported that the Yamax Digiwalker was accurate for counting steps on concrete sidewalks with 100.6 % and 100.7% of actual steps recorded. In addition, there were no significant differences in accuracy between sidewalk and rubberized track surfaces. The Yamax was also very accurate at counting the number of steps over speeds ranging from 50 to 110 rn.rnirr l range. The Yamax has also been studied as a tool for assessing moderate intensity overground walking by Hendelman et al. (in press). Steps were highly correlated with walking speed (r=0.86) and V0 2 (r=0.75) for speeds ranging from 63.2 to 111.2 m.min- 1. The data suggested that pedometry may be useful for the assessmen t of total activity if walking is the predominant form of activity.
Other Populations The pedometer has not been widely used in a variety of populations for the assessment of physical activity. However, Eston et al. (1998) investigated the utility of the Yamax Digiwalker for monitoring physical activity in children. A correlation of r = 0.78 was reported between pedometer steps and oxygen uptake for treadmill walking. For unregulated play activities, correlations ofr=0.92
22
and r=0.88 were observed between steps and oxygen uptake and heart rate, respectively. The various limitations of the pedometer do not make it the ideal instrument for assessing physical activity. However, if overall walking activity is the outcome being assessed, the pedometer is a useful and inexpensive instrument, particularly in walking intervention studies where participants can self-monitor their behavior to determine if they are attaining specified goals.
Uniaxial Accelerometers The uniaxial accelerometer is a more complex instrument than the pedometer. It measures acceleration in a single plane (usually vertical) and can be attached to the trunk and/or limbs to measure acceleration of the trunk and/ or limbs. The theoretical basis underlying the use of an accelerometer to assess physical activity is that acceleration is directly proportional to the muscular forces and therefore is related to energy expenditure (Melanson & Freedson, 1996; Montoye et aI., 1996). Recent advances in accelerometers make them a unique and useful technology for measuring physical activity. These units are small, unobtrusive instruments with large memory capacity that allow for monitoring and storage of temporal patterns of activity in relatively small time intervals over a period of days or weeks. Additionally, the accelerometer measures both the amount and intensity of movement. However, not all activity is reflected in acceleration or deceleration such as load carriage or on a grade (Montoye et aI., 1996; Haymes & Byrnes, 1993). The Caltrac accelerometer (Muscle Dynamics Fitness Network, Torrence, CAl (7 x 7 x 2 cm) was one of the first accelerometers to be commercially marketed for research applications and for use by practitioners to provide clients with the ability to estimate daily caloric expenditure. It is typically worn at the hip and provides a measure of trunk acceleration. Inputting individual age, stature, mass, and gender estimates and accumulates resting metabolic rate over the monitoring period. When trunk acceleration occurs, an estimate of energy expenditure associated with this movement is added to the resting metabolic rate. The estimate of Caltrac total caloric expenditure can be inactivated by inputting constants for age, stature, mass and gender variables so that the data output is in accelerometer counts and only assesses the quality and quantity of movement. The Caltrac functions through a piezoelectric bender element consisting of two layers of piezo-ceramic material with a brass center. When the trunk accelerates, the transducer bends and produces a charge that is proportional to the force exerted by the subject (Montoye et aI., 1996). This creates an acceleration-deceleration
ROES: June 2000
Downloaded by [FU Berlin] at 05:54 02 July 2015
Freedson andMiller
wave and the area under this wave is summed, yielding the final count value. The major limitation of the Caltrac is that the output is in total counts accumulated for the entire sampling period so that patterns of activity cannot be assessed. The Computer Science and Applications, Inc. actigraph (Shalimar, FL) is a newer accelerometer that is 5.1 x 3.8 x 1.5 centimeters in size (42.6 gm) and records accelerations from 0.05 - 2 G's, Movement results in an acceleration that acts on a cantilevered beam creating a bending moment that results in a strain on the piezoceramic cantilever beam (Tyron & Williams, 1996). The accelerometer sensor then produces a charge that is proportional to the strain (Tyron & Williams, 1996). The signal is filtered by an analog bandpass filter and digitized by an 8 bit AID converter at a rate of 10 samples per second (Tyron & Williams, 1996). Each signal is summed over a user specified time interval and at the end of this time, the activity counts are stored and the counter is reset to zero (Tyron & Williams, 1996). The CSA actigraph is initialized and downloaded using a reader interface that is connected to the serial port of a PC compatible computer. The initialization procedure allows the tester to set start time and sampling interval (epoch). Downloading consists of transferring the information from the monitor to the computer where it can be imported to a software program to be analyzed. The Actillume actigraph (Ambulatory Monitoring, Inc., Ardsely, NY) is 7 x 3.8 x 2.2 ern in size (100 gm). It contains a uniaxial piezoresistive accelerometer that samples accelerations at a rate of 20 times per second. The signal is digitized by an 8 bit AID converter and amplified through a low-pass filter. The signal is then stored as a byte of data over a specified time interval. Another commercially available uniaxial accelerometer is the Kenz accelerometer (Select 2 Model, Nagoya, Japan) (5 x 3 x 1 cm). It estimates gross and net energy expenditure and can store 7 days of data 1 day sampling intervals. The Biotrainer (1M Systems, Baltimore, MD) uniaxial accelerometer is similar in design to the CSA actigraph but uses high speed sampling rather than accumulating counts over a user specified sampling interval. This device permits long term monitoring and evaluation of temporal patterns of activity.
Data Output Sallis et al. (1990) compared Caltrac raw counts to net activity heart rate. Significant moderate correlations (r = 0.42-0.54) were reported between the two methods across two days of monitoring field activity. Additionally, Sallis et al. (1990) revealed a high correlation (r = 0.82) between oxygen consumption and Caltrac counts. Similarly, Maliszewski et al. (1991) reported a high correla-
RUES: June 2000
tion (r = 0.88) between oxygen consumption and Caltrac counts for walking in men. Several studies have investigated the utility of the Caltrac in determining energy expenditure. Pambianco et al. (1990) revealed that the Caltrac significantly overestimated energy expenditure compared to indirect calorimetry during treadmill walking. The overestimation was greater in normal weight subjects at higher walking speeds. Maliszewski et al. (1991) reported a significant correlation (r = 0.90) between Caltrac energy expenditure and indirect calorimetry during treadmill walking. Haymes and Byrnes (1993) reported that the Caltrac overestimated energy expenditure at all walking and running speeds. The CSA and Actillume actigraph monitor data are in counts per unit time which is sampling interval dependent (i.e., counts. 20sec 1, counts. min-I). Although there is no direct physiological translation of counts to energy expenditure or oxygen consumption, laboratory-based studies suggest that there is a linear relationship between counts per minute and physiological measures such as energy expenditure and oxygen consumption. Melanson and Freedson (1996) compared CSA activity counts and energy expenditure from indirect calorimetry during treadmill walking in adults. The results revealed significant correlations between energy expenditure and CSA counts (r = 0.66-0.82) and between relative oxygen consumption and CSA counts (r = 0.77-0.89). Hendelman et al. (in press) compared measured oxygen consumption and CSA counts for overground walking and reported a correlation of r = 0.77 between CSA counts and METs. In a field trial, Matthews et al. (in press) observed a high correlation (r = 0.92) between total minutes of activity per day determined by a 24 hour recall and number of activity minutes per day using the Actillume accelerometer. These studies reveal that the CSA and Actillume accelerometer output in the form of counts per minute is useful for determining activity and activity patterns in adults. A more practical method of interpreting CSA accelerometer output was developed by Freedson et al. (1998). This study examined the relationship between CSA accelerometer counts and oxygen consumption during treadmill walking and running. Count ranges corresponding to MET levels of intensity were derived from regression analyses. Figures 1 and 2 illustrate counts. min- 1 data from two individual 3 days of monitoring using the CSA actigraph. Figure 1 is a less vigorously active subject. Table 1 presents the data using the counts. min-I cut-points for moderate and vigorous activity described by Freedson et al. (1998). Figure 2 and Table 2 present data for a vigorously active individual (bouts of purposeful exercise on all days) over 3 days. The data reveal some very distinct activity pattern differences and the numbers of minutes spent in the vigorous activity
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cat egory is qui te di ffe re n t with the active subject spe nding over 2 hou rs per day in vigoro us activity where the inactive subject ha d ~n tiall)' no minutes of vigorous activity. The mean counts. mi n-I ranged fro m 236 - ~36 COl" the less active 5U~ (fable l) and 1122·1169 counts, min-I for the \'igorously active 5U~ H endel ma n «:1 al. (in press) developed individua l regression equations for METs based on CSA acce terometer co unts from overgro und walking activityan d applied th em to selec ted recr eat io nal and household activitie s in cluding gardening. lawn m owing. dusting, washing windows. vacu uming and playing go lf (walking a nd p ullingclubs). O xygen consum ption predicted from the CSA co unts was significantly lowe r than meas ured VO , for all activities. Based on these data. regression equations for deri ving point estimates of r nr rgy expe nditure based on accelero me ter coun ts should be deve loped for the acti vity in which rhey are to be used.
OtherPopulations Both the Caltrac and GSAacce lerome te rs have been widely used in physical acti\ity research Iovo lving ad ults,
,
children. and ove rweight child re n . The Cahrac as a tool for assessin g children 's activity has been investiga ted in both field an d laboratory setti ngs . Sallis e t at ( 1990) revealed moderate correlations between Caltrac coun ts and mean activity heart rate for two days (I' = 0.54 day I, r ~ 0.42 day 2) of free-living activity, The a uthors suggested th at the correlat ions were lower tha n these observed in adults because o f the wide varie ty of activity performed by child ren . In the la bo ra tory. Maliszewski e t al. (199 1) reported no significant diffe rences betw een measured o xygen consum ption an d estimate d ene rKY expenditure by Ca ltrac in c hildre n d urin g tread mill wal king a t speeds ranging from 3.35 to 6.7 km .hr 1. The GSA accel eromete r has been evalua ted for use with c hild re n in con tro lled and free-living labora tory cond itio ns, as well as f ield settings. Tro st et OIl. (1lJYH) eval ua ted th e GSA in a con trolled labo ratory situa tion , d u ring tr ead mill walk ing a nd running. Cou nts we re highlycorrelated with e nergy expe ndi ture (I' = 0 .86.0.87) . oxygen consumption (r ~ 0 .86. 0 .87) , hea rt ra te (r = 0.77) . and treadmill speed (r ~ 0.9. 0.89) . Addi tio nally. th er e was high iturr..jnstru menl reliability {int raclass R .0.87) . Energy ex pendi ture estimated from a regression
Sample Accelerometer Data
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fig..... 1. Three d. ys of minut.·byminute CSAI ctigr. ph. ceel.r· omet.r data: Individual not vigorously. ctive.
figure 2.Three d.ys of minute-by-minut. CSA I ctigraph.ceeler-
T.bl.1. Accelerometer dati for three daysof monitoring: l . ss Active
T,bl.2. Accelerometer det. for three deys ot monitoring: More Active
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Min Vigorous Activity 1 2
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emeter data: Individull vigorouslyIclive.
Total Counts
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