- Email: [email protected]
Comparison of the college alumnus questionnaire physical activity index with objective monitoring
Comparison of the College Alumnus Questionnaire Physical Activity Index with Objective Monitoring SCOTT J. STRATH, PHD, DAVID R. BASSETT JR, PHD, AND ANN M. SWARTZ, PHD
PURPOSE: Two methods of measuring physical activity (PA) were compared over a consecutive 7-day period among 25 adults (12 men and 13 women). METHODS: Each day estimates of energy expended in light, moderate, vigorous, and total PA were derived from the simultaneous heart-rate motion sensor (HR+M) technique. At the end of the 7-day period participants completed the College Alumnus Questionnaire Physical Activity Index (CAQ-PAI) and results were compared with HR⫹M technique estimates. RESULTS: Correlations between the two methods in the four activity categories ranged from r ⫽ 0.20 to r ⫽ 0.47, with vigorous and total PA showing higher associations than light and moderate PA. Mean levels of PA (MET-min·wk⫺1) obtained using the two methods were similar in the moderate and vigorous categories, but individual differences were large. Energy expended in light PA was significantly underestimated on the CAQ-PAI, resulting in lower total activity scores on this questionnaire as compared with the HR⫹M. CONCLUSIONS: The CAQ-PAI accurately reflected mean moderate and vigorous activity in comparison with the HR⫹M technique. The results are consistent with other studies which have shown that physical activity questionnaires are better at assessing vigorous PA than ubiquitous light-moderate activities. Ann Epidemiol 2004;14:409–415. 쑕 2004 Elsevier Inc. All rights reserved. KEY WORDS:
Accuracy, Validity, Exercise, Heart Rate, Accelerometers.
INTRODUCTION Evidence is available to support the importance of regular physical activity (PA) to promote good health and prevent chronic disease, including coronary heart disease, hypertension, diabetes, and some cancers (1). One of the most widely used assessment techniques for estimating PA levels in epidemiological investigations is the questionnaire. The feasibility and practicality, in both time and cost, of questionnaires makes them the instrument of choice in large-scale studies examining the relationships between PA and selected health parameters (2). One commonly used questionnaire to estimate PA is the College Alumnus Questionnaire (CAQ) (3). The CAQ physical activity index (CAQ-PAI) is an overall measure of the kilocalories expended in leisure-time physical activity, and is computed from the sum of city blocks walked, flights of stairs climbed, and sports, recreation, and other physical activities. One of the main advantages of the CAQ-PAI is its From the Department of Health and Exercise Science, The University of Tennessee, Knoxville, TN. Address correspondence to: Scott J. Strath, Ph.D., University of Wisconsin-Milwaukee, Department of Human Movement Sciences, Enderis Hall, Room 435, P.O. Box 413, Milwaukee, WI 53201. Tel: (414) 229-3666; Fax: (414) 229-2619. E-mail: [email protected] Received February 5, 2003; accepted July 17, 2003. 쑕 2004 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010
ability to rank individuals according to physical activity level in large-scale population based studies. More specifically, this instrument has been used to examine associations between total PA and hypertension (4), cancer (5–7), coronary heart disease (8), stroke (9), mortality (10, 11), and longevity (10, 12). In addition, the CAQ-PAI provides information on intensity allowing the effects of moderate vs. vigorous activity to be compared (13). The limitations associated with recalled PA on questionnaires have been well documented (14–16) and include a low accuracy for recalling light to moderate activities. To date, the lack of a suitable criterion standard has been a methodological limitation to examine the accuracy of PA questionnaires during field-based PA investigations. Recently, research has demonstrated that the simultaneous heart rate-motion sensor (HR⫹M) technique is an accurate, objective method for quantifying energy expenditure (EE) during specified lifestyle tasks (17), and for quantifying minute-by-minute EE, total EE, and time spent in varying activity intensities during free-living activity (18). This technique utilizes individual calibration curves between HR ˙ O2) developed in the laboratory for and oxygen uptake (V both arm and leg exercise. Then, in the field setting, motion sensors are used to discriminate between arm and leg move˙ O2 from the corresment, and HR is used to predict the V ponding individualized regression equation. Although the accuracy of the CAQ-PAI has been reported previously 1047-2797/04/$–see front matter doi:10.1016/j.annepidem.2003.07.001
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(19), at this time, the CAQ-PAI has not been compared with an objective assessment criterion able to accurately discriminate between light, moderate, and vigorous intensity PA. Therefore, the primary purpose of this study was to compare respondent estimates of intensity-specific PA assessed by the CAQ-PAI to those measured by the HR+M technique over 7 days of regular daily activity. A secondary purpose was to compare the ability of the CAQ-PAI to estimate total PA levels in comparison with the HR⫹M technique over the same time period.
METHODS Participants The study population consisted of a convenience sample of 25 individuals (12 men and 13 women) with varying activity levels aged between 20 and 56 years (Table 1). Volunteers participating in this study were recruited through word of mouth and from posted announcements in the local community. Ninety-two percent of the participant sample were Caucasian and 8% were African American. Sixty-four percent of the study sample were white-collar workers, 8% were involved with manual labor occupations, and the remaining 28% were attending college. The participants provided both written and verbal informed consent in accordance with the guidelines of the University’s Institutional Review Board prior to beginning the study. Study Design Study participants completed laboratory testing including anthropometric and body composition measures and mea˙ O2 during both leg and arm exercise sures of HR and V testing. Leg and arm exercise tests were performed to develop ˙ O2 regression equations. individualized leg and arm HR- V Following the completion of all initial assessments participants were monitored with a HR recording device and arm and leg motion sensors (HR⫹M technique) for 7 consecutive days. During this monitoring phase participants were asked to follow their normal daily routines. The motion sensors were used to discriminate between upper and lower ˙ O2 from the body activity and HR was used to predict V TABLE 1. Participant characteristics (mean⫾SD) Variable
Mean (n ⫽ 12)
Women (n ⫽ 13)
All (n ⫽ 25)
30.6 ⫾ 9.9 1.83 ⫾ 0.1 79.9 ⫾ 11.3 23.8 ⫾ 3.2 16.1 ⫾ 6.9
29.5 ⫾ 11.4 1.63 ⫾ 0.1 65.4 ⫾ 12.1 24.7 ⫾ 5.2 29.5 ⫾ 9.3
30.0 ⫾ 10.5 1.73 ⫾ 0.1 72.4 ⫾ 11.7 24.3 ⫾ 4.3 22.5 ⫾ 10.5
Age (yr) Height (m) Body mass (kg) *BMI (kg. m⫺2) Body fat (%) *BMI: Body mass index.
corresponding individual laboratory regression equation. Following the last day of monitoring, participants completed the CAQ-PAI. Measurements Anthropometric and Body Composition. Body mass was measured to the nearest 0.01 kg using the Bod Pod쑓 Body Composition System (Life Measurement Instruments, Concord, CA) and height was measured to the nearest 0.1 cm using a stadiometer (Seca Corp., Columbia, MD). Body mass index was calculated using the formula body mass (kg) divided by height squared (m2). Body volume was measured using whole body air displacement plethysmography (Bod Pod쑓 Body Composition System). Body density was determined from the ratio of body mass to corrected body volume, and percent body fat was then calculated from body density using the Siri equation (20). Submaximal Treadmill Exercise Test. This consisted of a continuous incremental walking treadmill (Quinton Instrument Co., Q65, Bothell, WA) protocol with 3-minute stage durations. Initial speed was 67 m·min⫺1 and increased to 94 m·min⫺1 during the second stage, after which speed remained constant. Initial grade was zero for stage one and two, after which grade increased by 2% each stage. Once the participant reached 80 to 85 percent of age-predicted maximal HR the test was terminated. Throughout the treadmill test, HR was measured by a Polar Vantage HR watch (Polar NV, Polar Oy Finland). The validity of Polar HR technology has been shown to be valid in comparison with electrocardiographic measurements during both laboratory and field studies (21–23). Oxygen consumption was measured throughout the treadmill test with a computerized metabolic measurement system (TrueMax 2400, ParvoMedics, Salt Lake City, UT). The validity of the TrueMax 2400 system has previously been demonstrated in adults across wide physiological ranges (24). Minute-by-minute HR and gas exchange data were imported into a Windowsbased program for the development of individualized HR˙ O2 regression equations for leg activity. V Submaximal Arm Ergometer Exercise Test. The arm exercise test consisted of successive three-minute stages on a Monark arm ergometer (Monark 881E, Varberg, Sweden). The cadence throughout the test was set at 50 rpm, while the resistance was initially set at 0 kiloponds (kp) and increased 0.25 kp per stage. The test ended once the participant reached 80 to 85 percent of age-predicted maximal ˙ O2 were measured using the aforemenHR. Heart rate and V tioned methods. Minute-by-minute HR and gas exchange data were used for the development of individualized HR˙ O2 regression equations for arm activity. V 7-day Field Test. Instructions for use and placement of the HR monitoring device and the two motion sensors
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(Manufacturing Technology Inc. [MTI] (formerly Computer Science Applications [CSA] Inc.) model 7164 accelerometer) were provided to each participant. Both the HR recording device and the motion sensors were worn for all waking hours, except when bathing and swimming. Both HR and motion sensor monitoring commenced the day following the submaximal exercise tests and continued for 7 consecutive days. The HR watch used in both laboratory and field-testing is capable of storing 134 hours of HR information in 60second epochs, and was hence able to continuously record minute-by-minute HR information for the 7-day period. Heart rate data were immediately downloaded following the 7-day field test via an interface and imported into a digital file. MTI accelerometers were placed on the dominant wrist and the dominant leg throughout the 7-day time frame. One accelerometer was attached using a Velcro strap to the posterior aspect of the dominant hand, over the centerline of the wrist. A second accelerometer was attached via an elastic bandage to the mid-axillary line of the dominant thigh, orientated vertically along the femur. The specifications of this accelerometer have been reported previously (25). The MTI accelerometers were programmed to record data each minute. Accelerometer data were downloaded following the 7-day period and imported into a digital file. Estimation Of Energy Expenditure from HRⴙM. Arm and leg motion sensors were used to determine whether the activity was primarily upper body, lower body, or a combination of the two. Once the activity was classified, ˙ O2 regression equations were respective individualized HR-V utilized to predict EE from recorded minute-by-minute HR. Inactivity was determined by using an accelerometer threshold for both arm and leg motion sensors. A more detailed description of this technique is provided elsewhere (17, 18). Estimation Of Energy Expenditure From CAQPAI. The CAQ survey data in this study was scored at the Harvard School of Public Health. This version of the CAQ has three activity questions, asking about usual PA over the preceding 7 days. Activities identified include: the number of city blocks walked per day; the number of flights of stairs climbed per day; and the frequency and duration of sports, recreational and other activities. Walking 1 city block (1 city block ⫽ 1/12 mile) is estimated at 8 kcals (8). Climbing 1 flight of stairs (1 flight ⫽ 10 steps) is estimated at 2 kcals (8). All kcal values were transformed into MET values using standard constants: 1 L O2 ⫽ 4.85 Kcal, 1 MET ⫽ 3.5 mL·kg⫺1·min⫺1. For each sport, recreational activity, or other physical activities a MET level was assigned using the Compendium (26). All values, city blocks walked, flights of stairs climbed, and sport or recreational activity, from the CAQ-PAI were computed as MET-min·wk⫺1 to express activities independent of body
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weight. MET-min·wk⫺1 were summed to derive total weekly activity, and were also grouped into classifications of light (⬍4 METs), moderate (4–6 METs), and vigorous activities (⬎6 METs) (8). To establish intensity classifications from the CAQ-PAI, city blocks walked were categorized as a moderate activity, flights of stairs climbed as a vigorous activity, and sports, recreation and other activities denoted into categories depending on MET intensity values published in the Compendium (26). Data Analysis During the 7 days of objective assessment, the HR data being transmitted from the chest strap to the watch-receiver was sometimes subject to interference. This is typically discernible as a HR greater than 220 beats·min⫺1, or of recordings of 0 beats·min⫺1. Abnormal readings were replaced by the average of the previous and subsequent values, however, if more than five abnormal readings occurred in succession, the data were not used in the analysis. A total of 22 ⫾ 7 min·d⫺1 per participant were not used in data analyses for such reasons. On average, men and women had 14:00 ⫾ 0:59 h·d⫺1, and 13:31 ⫾ 0:40 h·d⫺1 of data for analysis, respectively. ˙ O2 values (mL·kg⫺1·min⫺1) derived Minute-by-minute V from the HR⫹M technique were divided by 3.5 and summed over 7 days to derive MET-min·wk⫺1. HR⫹M MET-min·wk⫺1 values were used to derive total activity, and time spent in light (⬍4 METs), moderate (4–6 METs), and vigorous activities (⬎6 METs). These intensity classifications allowed for a direct comparison of activity values derived from the HR⫹M and the CAQ-PAI. Means and standard deviations were calculated for the 4 categories of activity according to method. Paired t-tests with Bonferoni adjustment were performed to compare MET-min·wk⫺1 between the HR⫹M and the CAQ-PAI within each category of activity. Difference scores (criterion minus estimate) were graphically illustrated for moderate and vigorous activities using the technique of Bland and Altman (27). Spearman rank-order correlations were used to examine the associations between different activity levels reported from the CAQ-PAI and that measured by the HR⫹M. All analyses were performed using SPSS version 10.0.7 (Chicago, IL). The α level was set at 0.05.
RESULTS Examination of light intensity PA levels revealed that the CAQ-PAI accounted for 1.5% of values measured by the HR⫹M (124 vs. 8052 MET-min·wk⫺1 [Table 2]). This resulted in significantly lower total PA levels, with the CAQ-PAI only representing approximately 27% of values
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TABLE 2. Physical activity as assessed by the simultaneous heart rate motion sensor technique and the College Alumnus Questionnaire - Physical Activity Index. Mean MET-min. wk⫺1⫾SE Men (n ⫽ 12)
Women (n ⫽ 13)
All (n ⫽ 25)
Variable
Sim. HR⫹Ma
CAQ-PAIb
Sim. HR⫹Ma
CAQ-PAIb
Sim. HR⫹Ma
CAQ-PAIb
Total activity index Light activity Moderate activity Vigorous activity
10900 ⫾ 779 7990 ⫾ 280 1580 ⫾ 324 1330 ⫾ 305
2910 ⫾ 553 219 ⫾ 108‡ 1500 ⫾ 210 1190 ⫾ 442
10800 ⫾ 720 8110 ⫾ 396 1500 ⫾ 275 1200 ⫾ 270
2950 ⫾ 347 36 ⫾ 26†‡ 1700 ⫾ 334 1220 ⫾ 294
10900 ⫾ 518 8050 ⫾ 241 1540 ⫾ 207 1260 ⫾ 199
2929 ⫾ 314‡ 123.7 ⫾ 56‡ 1602 ⫾ 198 1204 ⫾ 248
‡
‡
a
Simultaneous heart rate motion sensor technique. College Alumnus Questionnaire-Physical Activity Index. Significantly different from men, p ⬍ 0.05. ‡ Significantly different from simultaneous heart rate-motion sensor technique, p ⬍ 0.05. b †
recorded by the HR⫹M (2929 vs. 10,850 MET-min·wk⫺1, respectively). Mean levels of moderate and vigorous PA did not differ between the CAQ-PAI and the HR+M. Individual difference scores between the CAQ-PAI and the HR⫹M are shown in Figure 1A for moderate activities and in Figure 1B for vigorous activities. Although mean difference scores did not significantly differ from zero for either moderate or vigorous activities, individual differences ranged from ⫺2423 to 2297 MET-min·wk⫺1 and ⫺2277 to 2389 MET-min·wk⫺1 for moderate and vigorous activities, respectively. Table 3 shows correlations between different intensity classifications of PA from the CAQ-PAI and the HR⫹M. Significant correlations were found between measures of vigorous activities (r ⫽ 0.47, p ⬍ 0.05).
DISCUSSION We compared components of regular daily activity estimated by the CAQ-PAI to values recorded by the HR+M technique over 7 consecutive days. The major findings of this study were that the CAQ-PAI greatly underestimated the total amount of PA as well as the amount of activity performed in the light (⬍4 METs) intensity category in comparison with the HR+M. Group levels of moderate and vigorous PA were similar between the two methods. Individual difference scores for moderate and vigorous activity showed wide limits of agreement, even though mean levels were not significantly different from zero (Figures 1A and 1B). Correlation coefficients between the CAQ-PAI and the HR⫹M technique for all activity levels were in the order of r ⫽ 0.20–0.47, with vigorous activities yielding the only significant association. The associations presented between the CAQ-PAI and the HR⫹M technique for total PA in this study (r ⫽ 0.35) are similar in magnitude to previous studies reporting on the accuracy of the CAQ-PAI. Similar correlations have been found between the CAQ-PAI in comparison with Caltrac activity (r ⫽ 0.29) (19), physical activity diaries
(r ⫽ 0.42) (19), energy intake (r ⫽ 0.49) (28), and doubly labeled water (r ⫽ 0.37) (29). Quantitative comparisons between the CAQ-PAI and 48-hour PA records reported by Ainsworth et al. (19) were on average 1270 ⫾ 1086 METmin·wk⫺1 vs. 3856 ⫾ 1711 MET-min·wk⫺1, respectively, for total activity. Thus, the CAQ-PAI was only one-third (70% underestimation) of the total activity recorded by the PA records. Albanes et al. (28) and Bonnefoy et al. (29) reported total PA underestimations in the region of 30% from the CAQ-PAI in comparison with energy intake and doubly labeled water, respectively. Results from the present study support large underestimations, with the amount of total daily PA accounted for by the CAQ-PAI being just 27% (73% underestimation) of the values recorded by the HR⫹M. The underestimations reported by Ainsworth et al. (19) stemmed from overall underestimations for activity in the categories of sports and recreation, city blocks walked, and stairs climbed. Similar to the studies of Albanes et al. (28) and Bonnefoy et al. (29), we were unable to evaluate specific activities in the present investigation. Instead, we categorized activity as light, moderate, vigorous, and total PA. We found that the large underestimations for total activity were mainly due to underestimations within the light intensity category. This is seen in the quantitative comparisons of light activity between the CAQ-PAI and the HR⫹M (124 ⫾ 56 vs. 8052 ⫾ 241 MET-min·wk⫺1, respectively). The CAQ-PAI was originally designed to assess activities intentionally performed for sport, exercise, or leisure in Harvard Alumni; these activities were typically moderate to vigorous in nature (3). Thus, the original design of the CAQ-PAI was not to assess daily living activities that constitute light intensity activities, such as occupational or household activities. This may explain why much of the light activity accumulated throughout the day was not accounted for by the CAQ-PAI when results were compared with the HR⫹M. This finding does have particular significance for researchers wanting to use this activity questionnaire to quantify total PA levels, as large underestimations are likely to result due to the inability to accurately assess light intensity activity. If it is total PA that is the variable of
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FIGURE 1. Difference scores (criterion minus estimate) for (A) moderate activities, and (B) vigorous activities. The horizontal solid line represents the mean difference, and the dashed lines represent the 95% confidence interval. (n ⫽ 25).
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TABLE 3. Matrix of Spearman rank-order correlation coefficients for the simultaneous heart rate motion sensor technique and the College Alumnus Questionnaire-Physical Activity Index (n ⫽ 25) Sim. HR⫹Ma (MET-min wk⫺1) Men ( n ⫽ 12) b
⫺1
CAQ-PAI . (MET-min wk )
Total
Light
Total Light Moderate Vigorous
0.34 0.36 0.02 ⫺0.15
0.29 ⫺0.18 ⫺0.49
Women (n ⫽ 13)
Moderate Vigorous
0.26 ⫺0.25
0.42*
Total
Light
0.36 ⫺0.36 0.10 0.17 ⫺0.13 0.45* 0.27
Moderate
0.29 0.13
All (n ⫽ 25) Vigorous Total
Light
0.35 0.18 0.10 0.11
0.20 0.14 ⫺0.08
0.59*
Moderate Vigorous
0.27 ⫺0.09
0.47*
a
Simultaneous heart rate motion sensor technique College Alumnus Questionnaire-Physical Activity Index. *P ⬍ 0.05 level. b
interest other questionnaires may be deemed more appropriate to capture this behavior. Results show that the CAQ-PAI is a good instrument for classifying moderate and vigorous group PA, with no significant differences apparent between mean levels of selfreported activity and mean levels measured by the HR⫹M technique for both activity intensities. However, when individual data were examined, large differences were apparent between the two measures ranging from ⫺2423 to 2297 MET-min·wk⫺1 for moderate activities and ⫺ 2277 to 2389 MET-min·wk⫺1 for vigorous activities. Correlation analysis between the CAQ-PAI and the HR⫹M technique only revealed a significant association for vigorous intensity activities (r ⫽ 0.47, p ⬍ 0.05). These results are consistent with suggestions that individuals are likely to more accurately report vigorous activities compared with ubiquitous, light, and moderate activities (16). The greater ability to recall vigorous activities is likely due to the conscious decision to take part in such behaviors. This study provides a comparison of the CAQ-PAI with an objective monitoring technique able to accurately distinguish between different intensities of PA during regular daily activity. Limitations to the study include the fact that the HR⫹M was not able to distinguish between specific activities such as the amount of energy expended within sports and recreation activity or walking activity. In addition, the current sample was not representative of the general population, they were more active than the general population, and the majority were college-educated. In summary, the direct comparison of the CAQ-PAI with the HR+M shows that both methods gave similar estimates for group moderate and vigorous activity levels. Therefore, this questionnaire represents a good measurement instrument to assess weekly group or population levels of moderate and/or vigorous activity. Significant point estimate associations were identified between the two methods for vigorous activity only. Total daily activity levels were greatly underestimated by the CAQ-PAI compared with the HR⫹M method, due to the inability of this questionnaire to account for all light activities. Future studies exploring ways to improve estimates of total activity based on self-report would prove beneficial.
The authors wish to acknowledge Sarah Freeman and Oguma Yuko for scoring the College Alumnus Questionnaires, and Dr. I-Min Lee for her helpful comments in preparing this manuscript.
REFERENCES 1. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996. 2. LaPorte R, Montoye H, Caspersen C. Assessment of physical activity in epidemiological research: Problems and prospects. Public Health Rep. 1985;100:131–147. 3. Paffenbarger RS Jr, Wing AL, Hyde RT. Physical activity as an index of heart attack risk in college alumni. Am J Epidemiol. 1978;108:161–175. 4. Paffenbarger RS Jr, Wing AL, Hyde RT, Jung DL. Physical activity and incidence of hypertension in college alumni. Am J Epidemiol. 1983; 117:245–257. 5. Sesso H, Paffenbarger RS Jr, Lee I-M. Physical activity and breast cancer risk in the college alumni health study. Cancer Causes Control. 1998; 9:433–439. 6. Lee I-M, Sesso H, Paffenbarger RS Jr. Physical activity and risk of lung cancer. Int J Epidemiol. 1999;28:620–625. 7. Lee I-M, Paffenbarger RS Jr, Hsieh C. Physical activity and risk of prostatic cancer among college alumni. Am J Epidemiol. 1992;135:169–179. 8. Lee I-M, Sesso H, Paffenbarger RS Jr. Physical activity and coronary heart disease risk in men: Does the duration of episodes predict risk?. Circulation. 2000;102:981–986. 9. Lee I-M, Paffenbarger RS Jr. Physical activity and stroke incidence: The Harvard Alumni Health Study. Stroke. 1998;29:2049–2054. 10. Paffenbarger RS Jr, Hyde R, Wing A, Hsieh C. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314:605–613. 11. Paffenbarger RS Jr, Hyde RT, Wing AL, Lee I-M, Jung DL, Kampert JB. The association of changes in physical activity and other lifestyle characteristics with mortality among men. N Engl J Med. 1993;8:538–545. 12. Paffenbarger RS Jr, Kambert J, Lee I-M, Hyde R, Leung R, Wing A. Changes in physical activity and other lifestyle patterns influencing longevity. Med Sci Sports Exerc. 1994;26:857–865. 13. Lee I-M, Hsieh CC, Paffenbarger RS Jr. Exercise intensity and longevity in men: The Harvard Alumni Health Study. JAMA. 1995;273:1179–1184. 14. Ainsworth BE, Richardson MT, Jacobs DR, Leon AS, Sternfeld B. Accuracy of recall of occupational physical activity by questionnaire. J Clin Epidemiol. 1999:219–227. 15. Philippaerts RM, Westerterp KP, Lefevre J. Doubly labeled water validation of three physical activity questionnaires. Int J Sports Med. 1999:284–289.
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16. Wareham NJ, Rennie KL. The assessment of physical activity in individuals and populations: Why try to be more precise about how physical activity is assessed? Int J Obes. 1998;22:S30–S38. 17. Strath SJ, Bassett DR Jr, Swartz AM, Thompson DL. Simultaneous heart rate motion sensor technique to estimate energy expenditure. Med Sci Sports Exerc. 2001;33:2118–2123. 18. Strath SJ, Bassett DR Jr, Swartz AM, Thompson DL. Validity of the simultaneous heart rate motion sensor technique to estimate energy expenditure. Med Sci Sports Exerc. 2002;34:888–894. 19. Ainsworth BE, Leon AS, Richardson MT, Jacobs DR, Paffenbarger RS Jr. Accuracy of the college alumnus physical activity questionnaire. J Clin Epidemiol. 1993;46:1403–1411. 20. Siri W. Body composition from fluid spaces and density: analysis of methods. In: Brozek J, Henschel A, eds. Techniques for Measuring Body Composition. Washington, DC: National Academy of Sciences–National Research Council; 1961:223–224. 21. Karvonen J, Chwalbinska-Moneta J, Saynajakangas S. Comparison of heart rates measured by ECG and microcomputer. Phys Sportsmed. 1984;12: 65–69. 22. Leger L, Thivierge M. Heart rate monitors: Validity, stability, and functionality. Phys Sportsmed. 1988;16:143–151.
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23. Treiber FA, Musante L, Hartdagan S, Davis H, Levy M, Strong WB. Validation of a heart rate monitor with children in laboratory and field settings. Med Sci Sports Exerc. 1989;21:338–342. 24. Bassett DR Jr, Howley ET, Thompson DL, King GA, Strath SJ, McLaughlin JE, et al. Validity of inspiratory and expiratory methods of measuring gas exchange with a computerized system. J Appl Physiol. 2001;91:218–224. 25. Tryon WW, Williams R. Fully proportional actigraphy: A new instrument. Behav Res Methods Instrum Comput. 1996;28:392–403. 26. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: An update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32:S498–S516. 27. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;338:1622–1623. 28. Albanes D, Conway JM, Taylor PR, Moe PW, Judd J. Validation and comparison of eight physical activity questionnaires. Epidemiology. 1990; 1:65–71. 29. Bonnefoy M, Normand S, Pachiaudi C, Lacour JR, Laville M, Kostka T. Simultaneous validation of ten physical activity questionnaires in older men: A doubly labeled water study. J Am Geriatr Soc. 2001;49:28–35.
PURPOSE: Two methods of measuring physical activity (PA) were compared over a consecutive 7-day period among 25 adults (12 men and 13 women). METHODS: Each day estimates of energy expended in light, moderate, vigorous, and total PA were derived from the simultaneous heart-rate motion sensor (HR+M) technique. At the end of the 7-day period participants completed the College Alumnus Questionnaire Physical Activity Index (CAQ-PAI) and results were compared with HR⫹M technique estimates. RESULTS: Correlations between the two methods in the four activity categories ranged from r ⫽ 0.20 to r ⫽ 0.47, with vigorous and total PA showing higher associations than light and moderate PA. Mean levels of PA (MET-min·wk⫺1) obtained using the two methods were similar in the moderate and vigorous categories, but individual differences were large. Energy expended in light PA was significantly underestimated on the CAQ-PAI, resulting in lower total activity scores on this questionnaire as compared with the HR⫹M. CONCLUSIONS: The CAQ-PAI accurately reflected mean moderate and vigorous activity in comparison with the HR⫹M technique. The results are consistent with other studies which have shown that physical activity questionnaires are better at assessing vigorous PA than ubiquitous light-moderate activities. Ann Epidemiol 2004;14:409–415. 쑕 2004 Elsevier Inc. All rights reserved. KEY WORDS:
Accuracy, Validity, Exercise, Heart Rate, Accelerometers.
INTRODUCTION Evidence is available to support the importance of regular physical activity (PA) to promote good health and prevent chronic disease, including coronary heart disease, hypertension, diabetes, and some cancers (1). One of the most widely used assessment techniques for estimating PA levels in epidemiological investigations is the questionnaire. The feasibility and practicality, in both time and cost, of questionnaires makes them the instrument of choice in large-scale studies examining the relationships between PA and selected health parameters (2). One commonly used questionnaire to estimate PA is the College Alumnus Questionnaire (CAQ) (3). The CAQ physical activity index (CAQ-PAI) is an overall measure of the kilocalories expended in leisure-time physical activity, and is computed from the sum of city blocks walked, flights of stairs climbed, and sports, recreation, and other physical activities. One of the main advantages of the CAQ-PAI is its From the Department of Health and Exercise Science, The University of Tennessee, Knoxville, TN. Address correspondence to: Scott J. Strath, Ph.D., University of Wisconsin-Milwaukee, Department of Human Movement Sciences, Enderis Hall, Room 435, P.O. Box 413, Milwaukee, WI 53201. Tel: (414) 229-3666; Fax: (414) 229-2619. E-mail: [email protected] Received February 5, 2003; accepted July 17, 2003. 쑕 2004 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010
ability to rank individuals according to physical activity level in large-scale population based studies. More specifically, this instrument has been used to examine associations between total PA and hypertension (4), cancer (5–7), coronary heart disease (8), stroke (9), mortality (10, 11), and longevity (10, 12). In addition, the CAQ-PAI provides information on intensity allowing the effects of moderate vs. vigorous activity to be compared (13). The limitations associated with recalled PA on questionnaires have been well documented (14–16) and include a low accuracy for recalling light to moderate activities. To date, the lack of a suitable criterion standard has been a methodological limitation to examine the accuracy of PA questionnaires during field-based PA investigations. Recently, research has demonstrated that the simultaneous heart rate-motion sensor (HR⫹M) technique is an accurate, objective method for quantifying energy expenditure (EE) during specified lifestyle tasks (17), and for quantifying minute-by-minute EE, total EE, and time spent in varying activity intensities during free-living activity (18). This technique utilizes individual calibration curves between HR ˙ O2) developed in the laboratory for and oxygen uptake (V both arm and leg exercise. Then, in the field setting, motion sensors are used to discriminate between arm and leg move˙ O2 from the corresment, and HR is used to predict the V ponding individualized regression equation. Although the accuracy of the CAQ-PAI has been reported previously 1047-2797/04/$–see front matter doi:10.1016/j.annepidem.2003.07.001
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(19), at this time, the CAQ-PAI has not been compared with an objective assessment criterion able to accurately discriminate between light, moderate, and vigorous intensity PA. Therefore, the primary purpose of this study was to compare respondent estimates of intensity-specific PA assessed by the CAQ-PAI to those measured by the HR+M technique over 7 days of regular daily activity. A secondary purpose was to compare the ability of the CAQ-PAI to estimate total PA levels in comparison with the HR⫹M technique over the same time period.
METHODS Participants The study population consisted of a convenience sample of 25 individuals (12 men and 13 women) with varying activity levels aged between 20 and 56 years (Table 1). Volunteers participating in this study were recruited through word of mouth and from posted announcements in the local community. Ninety-two percent of the participant sample were Caucasian and 8% were African American. Sixty-four percent of the study sample were white-collar workers, 8% were involved with manual labor occupations, and the remaining 28% were attending college. The participants provided both written and verbal informed consent in accordance with the guidelines of the University’s Institutional Review Board prior to beginning the study. Study Design Study participants completed laboratory testing including anthropometric and body composition measures and mea˙ O2 during both leg and arm exercise sures of HR and V testing. Leg and arm exercise tests were performed to develop ˙ O2 regression equations. individualized leg and arm HR- V Following the completion of all initial assessments participants were monitored with a HR recording device and arm and leg motion sensors (HR⫹M technique) for 7 consecutive days. During this monitoring phase participants were asked to follow their normal daily routines. The motion sensors were used to discriminate between upper and lower ˙ O2 from the body activity and HR was used to predict V TABLE 1. Participant characteristics (mean⫾SD) Variable
Mean (n ⫽ 12)
Women (n ⫽ 13)
All (n ⫽ 25)
30.6 ⫾ 9.9 1.83 ⫾ 0.1 79.9 ⫾ 11.3 23.8 ⫾ 3.2 16.1 ⫾ 6.9
29.5 ⫾ 11.4 1.63 ⫾ 0.1 65.4 ⫾ 12.1 24.7 ⫾ 5.2 29.5 ⫾ 9.3
30.0 ⫾ 10.5 1.73 ⫾ 0.1 72.4 ⫾ 11.7 24.3 ⫾ 4.3 22.5 ⫾ 10.5
Age (yr) Height (m) Body mass (kg) *BMI (kg. m⫺2) Body fat (%) *BMI: Body mass index.
corresponding individual laboratory regression equation. Following the last day of monitoring, participants completed the CAQ-PAI. Measurements Anthropometric and Body Composition. Body mass was measured to the nearest 0.01 kg using the Bod Pod쑓 Body Composition System (Life Measurement Instruments, Concord, CA) and height was measured to the nearest 0.1 cm using a stadiometer (Seca Corp., Columbia, MD). Body mass index was calculated using the formula body mass (kg) divided by height squared (m2). Body volume was measured using whole body air displacement plethysmography (Bod Pod쑓 Body Composition System). Body density was determined from the ratio of body mass to corrected body volume, and percent body fat was then calculated from body density using the Siri equation (20). Submaximal Treadmill Exercise Test. This consisted of a continuous incremental walking treadmill (Quinton Instrument Co., Q65, Bothell, WA) protocol with 3-minute stage durations. Initial speed was 67 m·min⫺1 and increased to 94 m·min⫺1 during the second stage, after which speed remained constant. Initial grade was zero for stage one and two, after which grade increased by 2% each stage. Once the participant reached 80 to 85 percent of age-predicted maximal HR the test was terminated. Throughout the treadmill test, HR was measured by a Polar Vantage HR watch (Polar NV, Polar Oy Finland). The validity of Polar HR technology has been shown to be valid in comparison with electrocardiographic measurements during both laboratory and field studies (21–23). Oxygen consumption was measured throughout the treadmill test with a computerized metabolic measurement system (TrueMax 2400, ParvoMedics, Salt Lake City, UT). The validity of the TrueMax 2400 system has previously been demonstrated in adults across wide physiological ranges (24). Minute-by-minute HR and gas exchange data were imported into a Windowsbased program for the development of individualized HR˙ O2 regression equations for leg activity. V Submaximal Arm Ergometer Exercise Test. The arm exercise test consisted of successive three-minute stages on a Monark arm ergometer (Monark 881E, Varberg, Sweden). The cadence throughout the test was set at 50 rpm, while the resistance was initially set at 0 kiloponds (kp) and increased 0.25 kp per stage. The test ended once the participant reached 80 to 85 percent of age-predicted maximal ˙ O2 were measured using the aforemenHR. Heart rate and V tioned methods. Minute-by-minute HR and gas exchange data were used for the development of individualized HR˙ O2 regression equations for arm activity. V 7-day Field Test. Instructions for use and placement of the HR monitoring device and the two motion sensors
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(Manufacturing Technology Inc. [MTI] (formerly Computer Science Applications [CSA] Inc.) model 7164 accelerometer) were provided to each participant. Both the HR recording device and the motion sensors were worn for all waking hours, except when bathing and swimming. Both HR and motion sensor monitoring commenced the day following the submaximal exercise tests and continued for 7 consecutive days. The HR watch used in both laboratory and field-testing is capable of storing 134 hours of HR information in 60second epochs, and was hence able to continuously record minute-by-minute HR information for the 7-day period. Heart rate data were immediately downloaded following the 7-day field test via an interface and imported into a digital file. MTI accelerometers were placed on the dominant wrist and the dominant leg throughout the 7-day time frame. One accelerometer was attached using a Velcro strap to the posterior aspect of the dominant hand, over the centerline of the wrist. A second accelerometer was attached via an elastic bandage to the mid-axillary line of the dominant thigh, orientated vertically along the femur. The specifications of this accelerometer have been reported previously (25). The MTI accelerometers were programmed to record data each minute. Accelerometer data were downloaded following the 7-day period and imported into a digital file. Estimation Of Energy Expenditure from HRⴙM. Arm and leg motion sensors were used to determine whether the activity was primarily upper body, lower body, or a combination of the two. Once the activity was classified, ˙ O2 regression equations were respective individualized HR-V utilized to predict EE from recorded minute-by-minute HR. Inactivity was determined by using an accelerometer threshold for both arm and leg motion sensors. A more detailed description of this technique is provided elsewhere (17, 18). Estimation Of Energy Expenditure From CAQPAI. The CAQ survey data in this study was scored at the Harvard School of Public Health. This version of the CAQ has three activity questions, asking about usual PA over the preceding 7 days. Activities identified include: the number of city blocks walked per day; the number of flights of stairs climbed per day; and the frequency and duration of sports, recreational and other activities. Walking 1 city block (1 city block ⫽ 1/12 mile) is estimated at 8 kcals (8). Climbing 1 flight of stairs (1 flight ⫽ 10 steps) is estimated at 2 kcals (8). All kcal values were transformed into MET values using standard constants: 1 L O2 ⫽ 4.85 Kcal, 1 MET ⫽ 3.5 mL·kg⫺1·min⫺1. For each sport, recreational activity, or other physical activities a MET level was assigned using the Compendium (26). All values, city blocks walked, flights of stairs climbed, and sport or recreational activity, from the CAQ-PAI were computed as MET-min·wk⫺1 to express activities independent of body
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weight. MET-min·wk⫺1 were summed to derive total weekly activity, and were also grouped into classifications of light (⬍4 METs), moderate (4–6 METs), and vigorous activities (⬎6 METs) (8). To establish intensity classifications from the CAQ-PAI, city blocks walked were categorized as a moderate activity, flights of stairs climbed as a vigorous activity, and sports, recreation and other activities denoted into categories depending on MET intensity values published in the Compendium (26). Data Analysis During the 7 days of objective assessment, the HR data being transmitted from the chest strap to the watch-receiver was sometimes subject to interference. This is typically discernible as a HR greater than 220 beats·min⫺1, or of recordings of 0 beats·min⫺1. Abnormal readings were replaced by the average of the previous and subsequent values, however, if more than five abnormal readings occurred in succession, the data were not used in the analysis. A total of 22 ⫾ 7 min·d⫺1 per participant were not used in data analyses for such reasons. On average, men and women had 14:00 ⫾ 0:59 h·d⫺1, and 13:31 ⫾ 0:40 h·d⫺1 of data for analysis, respectively. ˙ O2 values (mL·kg⫺1·min⫺1) derived Minute-by-minute V from the HR⫹M technique were divided by 3.5 and summed over 7 days to derive MET-min·wk⫺1. HR⫹M MET-min·wk⫺1 values were used to derive total activity, and time spent in light (⬍4 METs), moderate (4–6 METs), and vigorous activities (⬎6 METs). These intensity classifications allowed for a direct comparison of activity values derived from the HR⫹M and the CAQ-PAI. Means and standard deviations were calculated for the 4 categories of activity according to method. Paired t-tests with Bonferoni adjustment were performed to compare MET-min·wk⫺1 between the HR⫹M and the CAQ-PAI within each category of activity. Difference scores (criterion minus estimate) were graphically illustrated for moderate and vigorous activities using the technique of Bland and Altman (27). Spearman rank-order correlations were used to examine the associations between different activity levels reported from the CAQ-PAI and that measured by the HR⫹M. All analyses were performed using SPSS version 10.0.7 (Chicago, IL). The α level was set at 0.05.
RESULTS Examination of light intensity PA levels revealed that the CAQ-PAI accounted for 1.5% of values measured by the HR⫹M (124 vs. 8052 MET-min·wk⫺1 [Table 2]). This resulted in significantly lower total PA levels, with the CAQ-PAI only representing approximately 27% of values
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TABLE 2. Physical activity as assessed by the simultaneous heart rate motion sensor technique and the College Alumnus Questionnaire - Physical Activity Index. Mean MET-min. wk⫺1⫾SE Men (n ⫽ 12)
Women (n ⫽ 13)
All (n ⫽ 25)
Variable
Sim. HR⫹Ma
CAQ-PAIb
Sim. HR⫹Ma
CAQ-PAIb
Sim. HR⫹Ma
CAQ-PAIb
Total activity index Light activity Moderate activity Vigorous activity
10900 ⫾ 779 7990 ⫾ 280 1580 ⫾ 324 1330 ⫾ 305
2910 ⫾ 553 219 ⫾ 108‡ 1500 ⫾ 210 1190 ⫾ 442
10800 ⫾ 720 8110 ⫾ 396 1500 ⫾ 275 1200 ⫾ 270
2950 ⫾ 347 36 ⫾ 26†‡ 1700 ⫾ 334 1220 ⫾ 294
10900 ⫾ 518 8050 ⫾ 241 1540 ⫾ 207 1260 ⫾ 199
2929 ⫾ 314‡ 123.7 ⫾ 56‡ 1602 ⫾ 198 1204 ⫾ 248
‡
‡
a
Simultaneous heart rate motion sensor technique. College Alumnus Questionnaire-Physical Activity Index. Significantly different from men, p ⬍ 0.05. ‡ Significantly different from simultaneous heart rate-motion sensor technique, p ⬍ 0.05. b †
recorded by the HR⫹M (2929 vs. 10,850 MET-min·wk⫺1, respectively). Mean levels of moderate and vigorous PA did not differ between the CAQ-PAI and the HR+M. Individual difference scores between the CAQ-PAI and the HR⫹M are shown in Figure 1A for moderate activities and in Figure 1B for vigorous activities. Although mean difference scores did not significantly differ from zero for either moderate or vigorous activities, individual differences ranged from ⫺2423 to 2297 MET-min·wk⫺1 and ⫺2277 to 2389 MET-min·wk⫺1 for moderate and vigorous activities, respectively. Table 3 shows correlations between different intensity classifications of PA from the CAQ-PAI and the HR⫹M. Significant correlations were found between measures of vigorous activities (r ⫽ 0.47, p ⬍ 0.05).
DISCUSSION We compared components of regular daily activity estimated by the CAQ-PAI to values recorded by the HR+M technique over 7 consecutive days. The major findings of this study were that the CAQ-PAI greatly underestimated the total amount of PA as well as the amount of activity performed in the light (⬍4 METs) intensity category in comparison with the HR+M. Group levels of moderate and vigorous PA were similar between the two methods. Individual difference scores for moderate and vigorous activity showed wide limits of agreement, even though mean levels were not significantly different from zero (Figures 1A and 1B). Correlation coefficients between the CAQ-PAI and the HR⫹M technique for all activity levels were in the order of r ⫽ 0.20–0.47, with vigorous activities yielding the only significant association. The associations presented between the CAQ-PAI and the HR⫹M technique for total PA in this study (r ⫽ 0.35) are similar in magnitude to previous studies reporting on the accuracy of the CAQ-PAI. Similar correlations have been found between the CAQ-PAI in comparison with Caltrac activity (r ⫽ 0.29) (19), physical activity diaries
(r ⫽ 0.42) (19), energy intake (r ⫽ 0.49) (28), and doubly labeled water (r ⫽ 0.37) (29). Quantitative comparisons between the CAQ-PAI and 48-hour PA records reported by Ainsworth et al. (19) were on average 1270 ⫾ 1086 METmin·wk⫺1 vs. 3856 ⫾ 1711 MET-min·wk⫺1, respectively, for total activity. Thus, the CAQ-PAI was only one-third (70% underestimation) of the total activity recorded by the PA records. Albanes et al. (28) and Bonnefoy et al. (29) reported total PA underestimations in the region of 30% from the CAQ-PAI in comparison with energy intake and doubly labeled water, respectively. Results from the present study support large underestimations, with the amount of total daily PA accounted for by the CAQ-PAI being just 27% (73% underestimation) of the values recorded by the HR⫹M. The underestimations reported by Ainsworth et al. (19) stemmed from overall underestimations for activity in the categories of sports and recreation, city blocks walked, and stairs climbed. Similar to the studies of Albanes et al. (28) and Bonnefoy et al. (29), we were unable to evaluate specific activities in the present investigation. Instead, we categorized activity as light, moderate, vigorous, and total PA. We found that the large underestimations for total activity were mainly due to underestimations within the light intensity category. This is seen in the quantitative comparisons of light activity between the CAQ-PAI and the HR⫹M (124 ⫾ 56 vs. 8052 ⫾ 241 MET-min·wk⫺1, respectively). The CAQ-PAI was originally designed to assess activities intentionally performed for sport, exercise, or leisure in Harvard Alumni; these activities were typically moderate to vigorous in nature (3). Thus, the original design of the CAQ-PAI was not to assess daily living activities that constitute light intensity activities, such as occupational or household activities. This may explain why much of the light activity accumulated throughout the day was not accounted for by the CAQ-PAI when results were compared with the HR⫹M. This finding does have particular significance for researchers wanting to use this activity questionnaire to quantify total PA levels, as large underestimations are likely to result due to the inability to accurately assess light intensity activity. If it is total PA that is the variable of
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FIGURE 1. Difference scores (criterion minus estimate) for (A) moderate activities, and (B) vigorous activities. The horizontal solid line represents the mean difference, and the dashed lines represent the 95% confidence interval. (n ⫽ 25).
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TABLE 3. Matrix of Spearman rank-order correlation coefficients for the simultaneous heart rate motion sensor technique and the College Alumnus Questionnaire-Physical Activity Index (n ⫽ 25) Sim. HR⫹Ma (MET-min wk⫺1) Men ( n ⫽ 12) b
⫺1
CAQ-PAI . (MET-min wk )
Total
Light
Total Light Moderate Vigorous
0.34 0.36 0.02 ⫺0.15
0.29 ⫺0.18 ⫺0.49
Women (n ⫽ 13)
Moderate Vigorous
0.26 ⫺0.25
0.42*
Total
Light
0.36 ⫺0.36 0.10 0.17 ⫺0.13 0.45* 0.27
Moderate
0.29 0.13
All (n ⫽ 25) Vigorous Total
Light
0.35 0.18 0.10 0.11
0.20 0.14 ⫺0.08
0.59*
Moderate Vigorous
0.27 ⫺0.09
0.47*
a
Simultaneous heart rate motion sensor technique College Alumnus Questionnaire-Physical Activity Index. *P ⬍ 0.05 level. b
interest other questionnaires may be deemed more appropriate to capture this behavior. Results show that the CAQ-PAI is a good instrument for classifying moderate and vigorous group PA, with no significant differences apparent between mean levels of selfreported activity and mean levels measured by the HR⫹M technique for both activity intensities. However, when individual data were examined, large differences were apparent between the two measures ranging from ⫺2423 to 2297 MET-min·wk⫺1 for moderate activities and ⫺ 2277 to 2389 MET-min·wk⫺1 for vigorous activities. Correlation analysis between the CAQ-PAI and the HR⫹M technique only revealed a significant association for vigorous intensity activities (r ⫽ 0.47, p ⬍ 0.05). These results are consistent with suggestions that individuals are likely to more accurately report vigorous activities compared with ubiquitous, light, and moderate activities (16). The greater ability to recall vigorous activities is likely due to the conscious decision to take part in such behaviors. This study provides a comparison of the CAQ-PAI with an objective monitoring technique able to accurately distinguish between different intensities of PA during regular daily activity. Limitations to the study include the fact that the HR⫹M was not able to distinguish between specific activities such as the amount of energy expended within sports and recreation activity or walking activity. In addition, the current sample was not representative of the general population, they were more active than the general population, and the majority were college-educated. In summary, the direct comparison of the CAQ-PAI with the HR+M shows that both methods gave similar estimates for group moderate and vigorous activity levels. Therefore, this questionnaire represents a good measurement instrument to assess weekly group or population levels of moderate and/or vigorous activity. Significant point estimate associations were identified between the two methods for vigorous activity only. Total daily activity levels were greatly underestimated by the CAQ-PAI compared with the HR⫹M method, due to the inability of this questionnaire to account for all light activities. Future studies exploring ways to improve estimates of total activity based on self-report would prove beneficial.
The authors wish to acknowledge Sarah Freeman and Oguma Yuko for scoring the College Alumnus Questionnaires, and Dr. I-Min Lee for her helpful comments in preparing this manuscript.
REFERENCES 1. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996. 2. LaPorte R, Montoye H, Caspersen C. Assessment of physical activity in epidemiological research: Problems and prospects. Public Health Rep. 1985;100:131–147. 3. Paffenbarger RS Jr, Wing AL, Hyde RT. Physical activity as an index of heart attack risk in college alumni. Am J Epidemiol. 1978;108:161–175. 4. Paffenbarger RS Jr, Wing AL, Hyde RT, Jung DL. Physical activity and incidence of hypertension in college alumni. Am J Epidemiol. 1983; 117:245–257. 5. Sesso H, Paffenbarger RS Jr, Lee I-M. Physical activity and breast cancer risk in the college alumni health study. Cancer Causes Control. 1998; 9:433–439. 6. Lee I-M, Sesso H, Paffenbarger RS Jr. Physical activity and risk of lung cancer. Int J Epidemiol. 1999;28:620–625. 7. Lee I-M, Paffenbarger RS Jr, Hsieh C. Physical activity and risk of prostatic cancer among college alumni. Am J Epidemiol. 1992;135:169–179. 8. Lee I-M, Sesso H, Paffenbarger RS Jr. Physical activity and coronary heart disease risk in men: Does the duration of episodes predict risk?. Circulation. 2000;102:981–986. 9. Lee I-M, Paffenbarger RS Jr. Physical activity and stroke incidence: The Harvard Alumni Health Study. Stroke. 1998;29:2049–2054. 10. Paffenbarger RS Jr, Hyde R, Wing A, Hsieh C. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314:605–613. 11. Paffenbarger RS Jr, Hyde RT, Wing AL, Lee I-M, Jung DL, Kampert JB. The association of changes in physical activity and other lifestyle characteristics with mortality among men. N Engl J Med. 1993;8:538–545. 12. Paffenbarger RS Jr, Kambert J, Lee I-M, Hyde R, Leung R, Wing A. Changes in physical activity and other lifestyle patterns influencing longevity. Med Sci Sports Exerc. 1994;26:857–865. 13. Lee I-M, Hsieh CC, Paffenbarger RS Jr. Exercise intensity and longevity in men: The Harvard Alumni Health Study. JAMA. 1995;273:1179–1184. 14. Ainsworth BE, Richardson MT, Jacobs DR, Leon AS, Sternfeld B. Accuracy of recall of occupational physical activity by questionnaire. J Clin Epidemiol. 1999:219–227. 15. Philippaerts RM, Westerterp KP, Lefevre J. Doubly labeled water validation of three physical activity questionnaires. Int J Sports Med. 1999:284–289.
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16. Wareham NJ, Rennie KL. The assessment of physical activity in individuals and populations: Why try to be more precise about how physical activity is assessed? Int J Obes. 1998;22:S30–S38. 17. Strath SJ, Bassett DR Jr, Swartz AM, Thompson DL. Simultaneous heart rate motion sensor technique to estimate energy expenditure. Med Sci Sports Exerc. 2001;33:2118–2123. 18. Strath SJ, Bassett DR Jr, Swartz AM, Thompson DL. Validity of the simultaneous heart rate motion sensor technique to estimate energy expenditure. Med Sci Sports Exerc. 2002;34:888–894. 19. Ainsworth BE, Leon AS, Richardson MT, Jacobs DR, Paffenbarger RS Jr. Accuracy of the college alumnus physical activity questionnaire. J Clin Epidemiol. 1993;46:1403–1411. 20. Siri W. Body composition from fluid spaces and density: analysis of methods. In: Brozek J, Henschel A, eds. Techniques for Measuring Body Composition. Washington, DC: National Academy of Sciences–National Research Council; 1961:223–224. 21. Karvonen J, Chwalbinska-Moneta J, Saynajakangas S. Comparison of heart rates measured by ECG and microcomputer. Phys Sportsmed. 1984;12: 65–69. 22. Leger L, Thivierge M. Heart rate monitors: Validity, stability, and functionality. Phys Sportsmed. 1988;16:143–151.
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23. Treiber FA, Musante L, Hartdagan S, Davis H, Levy M, Strong WB. Validation of a heart rate monitor with children in laboratory and field settings. Med Sci Sports Exerc. 1989;21:338–342. 24. Bassett DR Jr, Howley ET, Thompson DL, King GA, Strath SJ, McLaughlin JE, et al. Validity of inspiratory and expiratory methods of measuring gas exchange with a computerized system. J Appl Physiol. 2001;91:218–224. 25. Tryon WW, Williams R. Fully proportional actigraphy: A new instrument. Behav Res Methods Instrum Comput. 1996;28:392–403. 26. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: An update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32:S498–S516. 27. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;338:1622–1623. 28. Albanes D, Conway JM, Taylor PR, Moe PW, Judd J. Validation and comparison of eight physical activity questionnaires. Epidemiology. 1990; 1:65–71. 29. Bonnefoy M, Normand S, Pachiaudi C, Lacour JR, Laville M, Kostka T. Simultaneous validation of ten physical activity questionnaires in older men: A doubly labeled water study. J Am Geriatr Soc. 2001;49:28–35.