Research Quarterly for Exercise and Sport

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Validity and Reliability issues in Objective Monitoring of Physical Activity David R. Bassett Jr. To cite this article: David R. Bassett Jr. (2000) Validity and Reliability issues in Objective Monitoring of Physical Activity, Research Quarterly for Exercise and Sport, 71:sup2, 30-36, DOI: 10.1080/02701367.2000.11082783 To link to this article:

Published online: 13 Feb 2015.

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Date: 05 November 2015, At: 18:59

ReMerdI a-..ty lor Ex.m. ... s,.t e 2OllO by the Ameriu n"" lilnc. tot He.1ltl. Physic.1Educ. tion,• • 1ibn .od O.IIce Va!. 11, No. 2, pp. »-36

Validity and Reliability Issues in Objective Monitoring of Physical Activity

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Oavid R. Bassett, Jr.

lVylJJfJfds: ph ysical activity, pedometers, he art rate monitari ng , acce lerome te rs

I. Introduction ccura te measurement of physical activity is needed for several reasons: 1) to quantify dose-response relatio ns hi ps be tween physical activity a nd health o utcomes, 2) to an swer questions about the re lative meri ts ofvigorous \/S . mod erate ph ysical activity,and S) tc document th e physical activi ty perfo r med in lo ngitud inal training studies . Our cur re n t understanding in these areas is based mainly on studies using physical activity questionnaires. However, questionnaires have inhe rent limitations because they are subjective in nature. While subjects can recall vigorous, structured exercise with a hig h degree of accuracy, th ey are not as good at recalling ubiquitous, moderate intensity acti vities like walking ( Basset t, Cureto n, &: Ainsworth, in press; Ratty, 1998). Hence, there is a need for accurate, objective instruments that a re low-cost and unobtrusive. An article by Freedson and Miller (2000) a ppearing in thi s issue of Rneardl Qp.amrlyf or£x,m;i.y and Sfxm deals with objective monitoring of phys ical activity. The article describe s the variou s methods used by researc hers, including heart ra te mon itoring, pedometers, and acceterometers. T hey n icely su mmarize the assumptions underlying each of th ese approaches, and the various methods for analyzing data. Freedsc n '5 research h as been important to understandi ng the relationship between acceleromete r coun ts and energy expenditure.


D,vid R. B, ss.n, Jr.';s with th. D.p,rtm.ntof &''';5' Sci, nc. , nd Sport M,n,g.m.nt.t til. Unw,rsity of TllrtnllSSIlIl.


As noted by Freedson a nd Miller, physical activity is b odily movem ent resulting in e ne rgy e xpe n d it u re (Caspersen, 1989 ). It is often see n as a mul ti-dime nsio nal construc t that includes variables such as frequency, inte nsity. duration. and circu ms tance ( Mom oye, Kemper, Saris . &: Washburn . 1995). The first three dimensions can be combined to give infonnation about the "qua ntity of e nergy expended in physical activity'" (EEPA). Total daily e nergy expenditure (TD EE) has three co mpone n ts: the resting metabolic rate (RMR) . the the rm ic effec t of feeding (TEF) . and EEPA (Figure 1). When measuring EEPA. it is preferable to adjust fo r differe nces in body size sinc e heavier individuals tend to expend more kilocalories pe r day th an lighte r ones. This increase in calori c ex penditure is d ue to great er body mass, not a n in crease in physical activity. It is also preferable to exp ress activity as ne t (rather tha n gross) e nergy expenditure. O therwise, one might concl ude that a person accu mu lates ph ysical activity even while at rest. Although EEPA is a n im portant variable. we should consider o ther di mensio ns of physical activiry as well. For instance . it is possible that vigorous exercise exerts more

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of a protective effect on cardiovascular mortality than moderate physical activity, even if the total amount .of energy expended is equal (Lee, Hsieh, & Paffenbarger, 1995). Th us, it is desirable to have information on all dimensions of physical activity, rather than simply a global measure of energy expenditure (such as can be obtained using doubly labeled H 20).

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II. Heart rate monitoring Heart rate (HR) is a physiological parameter that is closely related to energy expenditure. There is a linear relationship between HR and oxygen uptake (V0 2) over a wide range of exercise intensities. HR monitoring has undergone major advances over the last two decades (Laukkanen & Virtanen, 1998). With current technology, HR data are sent from a transmitter worn around the chest to a wristwatch or small device. The Polar Vantage NY heart watch (Polar Electro, Tampere Finland) can store 5.5 days of HR data in 1minute epochs in a wristwatch-sized device. Mini-mitter (Sunriver, Oregon) makes a small data logger that receives data from the Polar HR transmitter and will store HR and activity data for 44 days. Devices using the Polar HR detection board (PCBA Receiver 380193) have excellent validity. We validated a metabolic system (Cosmed K4b 2) using this board against a 60 second electrocardiogram. Ten subjects cycled at 0,50, 100, 150, and 200 W. The correlation between the two methods was r = 1.00 (SEE = 0.6 beats.minl). However, not all HR monitors are as accurate as the Polar models (Leger & Thivierge, 1988; Terbizan, Dolezal, & Albano, 1999) and it is necessary to evaluate these devices before using them in a field setting.

%V02reserve (Swain & Leutholtz, 1997; Swain, Leutholtz, King, Haas, & Branch, 1998). Strath et al. (in press) showed the utility of this approach in a field setting. Sixtyone adults (19-74 yrs of age) performed various activities including housework, yard work, occupation, conditioning, family care, and recreation. HR and V0 2 were measured simultaneously, over a IS-minute period. Maximal heart rate (HRrnax) was estimated from 220 minus age, and cardiorespiratory fitness was estimated from the non-exercise V0 2max prediction formulas (Jackson et aI., 1990). Over a wide range of activities, %Hl\.eserve was linearly related to %V02reserve (r=0.88, SEE = 9% V0 2maJ , indicating that this method of analyzing HR data agrees closely with measured energy expenditure in the field. Another method of analyzing HR data makes use of individualized HR-V0 2 regression equations. This technique, termed the "FLEX-HR" method, takes into account variations in the HR-V0 2 relationship resulting from differences in the age, gender, and fitness level of research participants. Flex HR denotes a change in the slope of the HR-V0 2 relationship, representing the point where a person transitions from rest to physical activity. It is usually determined by taking the average of the HR values recorded during sedentary activities (sitting, standing, etc.) and light activity. A number of studies have compared this method for estimating TDEE against criterion measures such as indirect room calorimetry and doublylabeled H 20. In general, while the group estimates are usually within 10% of these criterion measures, the individual errors are sometimes larger (Bitar et aI., 1996; Ceesay et aI., 1989; Emons, Groenenboom, Westerterp, & Saris, 1992). As Freedson and Miller (2000) note, there are potentiallimitations to using HR to estimate energy expenditure. These include the following: (a) HR is subject to emotional influences, (b) variation in age and fitness

Methods of analyzing HR data % Heart Hate Reserve vs. % V02 Reserve

Freedson and Miller (2000) discuss several different methods of analyzing HR data, including the use of %Hl\.eserve' net HR, and the FLEX-HR method. Using the first method,Janz et al. (Janz, 1992) computed the time spent in the "aerobic training zone" by determining the number of minutes spent above 60% HR reserve in children, and compared this to cardia-respiratory fitness levels (V02peak)' There were low negative correlations between these variables (r=-0.02-0.10), However, it is difficult to assess the accuracy and utility of this method since V02peak is usually not highly correlated to physical activity (Freedson & Miller, 2000). The %HRreserve method can be directly validated against %V02reserve' The latter term expresses the V0 2 as a percent of the difference between RMR and maximum oxygen uptake (V0 2max) As shown in Figure 2, there is a strong, 1:1 relationship between %HRreserve and

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Figure 2. Relationship of %HRreserve and %V02reserve onthe cycle ergometer. The regression equation was derived from data on 63 individuals. From: Swain, D. P. et al. (1998). Relationship between % heart rate reserve and % V02reserve in treadmill exercise. Med.

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level can affect the HR-V0 2 relationship, and (c) .arm exercise elicits higher HR than leg exercise at the same V02. Recently, a method has been proposed to overcome these difficulties (Haskell, Vee, Evans, & Irby, 1993). This technique makes use of simultaneous recordings of heart rate and body motion (recorded by motion sensors placed on an arm and leg). Nineteen men performed various activities (walking, running, arm cranking, cycling, Air-Dyne, and bench stepping). A multiple regression analysis was used to predict EE from HR and motion sensor data, with good results. The average individual R2 was 0.89 with a SEE = 2.3 mI.~g-l.min-l. The authors recommend that individual HR-V0 2 regressions first be determined for arm and leg exercise in the laboratory. In the field setting, motion sensors would discriminate between arm and leg movement, and HR would be used to predict the V0 2 from the corresponding regression equation (Haskell et aI., 1993).

III. Pedometers An early study found that older, mechanical style pedometers had problems with reliability and validity (Gayle, Montoye, & Philpot, 1977). In general, these devices were found to be unacceptable for research, unless individual instrument calibration is done with each one. Recently, newer electronic pedometers have become available. These belt-mounted devices are triggered by vertical accelerations of the waist that occur during walking. With each step, a horizontal, spring-suspended pendulum arm moves up and down, opening and closing an electrical circuit, and one event is recorded.

Some pedometers are very accurate for recording steps during self-paced overground walking (Bassett et aI., 1996). One model (Yamax DW-500) recorded steps and distance within 2% of actual values and had good inter-instrument agreement. Over a range of walking speeds (2-4 mph), the Yamax pedometer had significantly better accuracy for "steps" and "distance" than the other models tested (Figure 3). Though this model has been discontinued, a suitable replacement (the Yamax SW-200 pedometer) was tested in our laboratory and found to be similar in accuracy to the original digi-walker (DW500) model. The validity of the Yamax DW-500 pedometer during level walking has been examined, by comparing it against indirect calorimetry (Nelson, Leenders, & Sherman, 1998). The pedometer underestimated the energy expenditure at 2.0 mph (by 0.8 kcal . min!) and at 3.5 mph and above (by 0.32 kcal.rnin'). This corresponds to errors of around 27% and 7%, respectively. Taken together, these results suggest that the Yamax pedometer is accurate for measuring steps and kcal for walking speeds from 3.0 to 4.0 mph, but under-estimates these variables at 2.0 mph and below. Electronic pedometers have limitations as research tools (Freedson & Miller, 2000). They are fairly accurate at counting steps, but they do not distinguish vertical accelerations above a certain "threshold". Thus, pedometers cannot distinguish between walking and running, and they must assume that a person expends a constant amount of energy per step. For the Yamax this assumed value is about 0.55 cal/kg/step regardless of the speed at which the person is ambulating, which is an oversimplification (Hatano, 1993) (Figure 4). Another limitation of pedometers is that they lack internal clocks and data storage ability. Hence, they do not provide information on the duration, frequency, and intensity ofPA. Despite this, pedometers are useful for distinguishing between groups that vary in their level of walking.

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