Workmen had higher indices concerning physical activity during work than clerks and managers. Congruent validity, studied by means of principal-components.
American Journal of Eptdemtotogy Copyright C 1998 by The Johns Hopkins University School of Hygiene and Public Health AH rights reserved
Vol. 147, No. 10
Printed In USA.
Reliability and Validity of Three Physical Activity Questionnaires in Flemish Males
Renaat M. Philippaerts and Johan Lefevre
activities of daily living; epidemiologic methods; life style; questionnaires
thicknesses, body composition, heart rate monitoring, food intake diaries, and accelerometers (8, 10-14). Maximal oxygen uptake is commonly used for validation purposes, although functional capacity is influenced by age, gender, and other factors (15). The variety of questionnaires and validation techniques makes it difficult to compare data. Nevertheless, Baranowski (16) and Kriska and Caspersen (17) gave a good overview of validity and reliability of different physical activity questionnaires. However, only a few studies simultaneously investigated (concurrent and congruent) validity and reliability of different physical activity surveys in order to find one simple questionnaire that is valid, reliable, and easy to complete (7, 10, 14, 18-20). Moreover, there is too little information on congruent validity as to whether questions or questionnaires measure the same dimension of physical activity (21, 22). Three physical activity questionnaires are being administered in the Leuven Longitudinal Study on Lifestyle, Fitness, and Health, the Five-City Project Questionnaire (FCQ), the Baecke Questionnaire (BAQ), and an adapted version of the Tecumseh Community Health Study Questionnaire (TCQ). Although there are several studies on reliability and validity of these questionnaires (18, 21, 23—26), no data about reliability and validity in adult Flemish males were available. The purposes of this study were 1) to investigate 1-month test-retest reliability of three commonly used
Physical activity is an important characteristic of human lifestyle and is related to morbidity and mortality (1-4). However, physical activity in epidemiologic (longitudinal) studies is hard to quantify. Growing interest in the evaluation of physical activity resulted in the development of specific measurement techniques to investigate health benefits of physical activity in a wide range of study designs. Laporte et al. (5) in their overview concluded that more than 30 different techniques have been used for assessing physical activity in population studies. However, in large-scale studies the questionnaire technique is the most popular way to assess daily physical activity, even in the absence of an acceptable validation standard to which a questionnaire can be compared (6, 7). The questionnaire technique is a relatively easy and inexpensive method although it has limitations (8). Ainsworth et al. (9) described 39 different physical activity survey questionnaires and interview techniques. The validity of different questionnaires is investigated in relation to different validation techniques, i.e., maximal oxygen uptake, skinfold Received for publication March 6, 1997, and accepted for publication September 20, 1997. Abbreviations: BAQ, Baecke Questionnaire; FCQ, Five-City Project Questionnaire; MET, metabolic equivalent: ratio of activity metabolism to basal metabolism; TCQ: Tecumseh Community Health Study Questionnaire. From the Department of Kinesiology, Center for Physical Development Research, Katholleke Universitett Leuven, Leuven, Belgium.
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The reliability and validity of three physical activity questionnaires were studied using 90 Flemish males (30 aged 30 years, 30 aged 35 years, and 30 aged 40 years). Intraclass correlations (R) and kappa values were caJculated to verify within judge and between judges reliability (objectivity) and test-retest reliability (stability) of the Tecumseh Community Health Study Questionnaire, the Five City Project Questionnaire, and the Baecke Questionnaire. Results showed high for within judge and between judges reliability. R coefficients for stability varied between 0.47 and 0.95. Kappa values varied between 0.20 and 0.73. Concurrent validity was investigated by comparing three levels of professional status. Workmen had higher indices concerning physical activity during work than clerks and managers. Congruent validity, studied by means of principal-components analysis, confirmed subdivision of habitual physical activity into three entities, physical activity during work, sports activities, and general leisure time. These results indicate that reliable and valid data can be obtained in Flemish males by three interviewer-assisted physical activity questionnaires. Am J Epidemiol 1998; 147: 982-90.
Validity of Physical Activity Questionnaires
physical activity questionnaires in Flemish males, 2) to study concurrent validity by comparing physical activity between three levels of professional status, and 3) to assess congruent validity by means of a principal-components analysis study. MATERIALS AND METHODS Subjects
The sample consisted of 90 Flemish male volunteers equally divided into three groups, 30 males each aged 30 years, 30 males each aged 35 years, and 30 males each aged 40 years. Within each age group, three levels of professional status were included in the study design, 10 workmen (welder, construction worker, etc.), 10 clerks (administration, secretary work, etc.), and 10 managers (office work, organizing business meetings, etc.). At the time of first interview, subjects were not informed about the second visit of the interviewer after 1 month. All subjects completed the study.
The first questionnaire is an adapted version of the questionnaire of Reiff et al. (23) used in the Tecumseh Community Health Study (TCQ). The adaptation of the questionnaire was made in the leisure-time supplement. The adapted leisure-time questionnaire recorded participation in sports activities during the preceding year, but in a simple and brief manner (27). It covered three aspects of sports participation, types of sports activity, diversity of sports activities, and amount of time spent on each activity (calculation of hours per week over a 1-year period). During a personal interview a detailed inventory of an average week of the last year was made about 1) occupation and transportation to and from work, 2) sports participation, leisure-time activities (active and quiet), and home repair and maintenance activities, and 3) sleeping and eating time. The hours-per-week estimates for the different working and active leisure time activities were multiplied by their respective metabolic rates (expressed as metabolic equivalent (MET) values) in order to obtain energy expenditure during both work and active leisure time (expressed as kcal/kg/week). Work index and active leisure time index (both in MET values) were calculated by dividing the respective energy expenditures by time working and by time spent on active leisure activities. A table of metabolic rates (expressed as a ratio of work metabolism to basal metabolism) for the various major activities, as described by Reiff et al. (23), was used in order to calculate the activity scores and a total activity index. Finally, each subject was asked to rate his physical Am J Epidemiol
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activity on a 7-point scale (1 = not active, 7 = very active). The second questionnaire (FCQ) (25) was used in the Five-City Project. Four physical activity indices were derived from inquiries concerning the subject's leisure-time participation. Subjects were asked to quantify their weekly participation for the last 3 months in a variety of activities (strenuous racquet sports and other strenuous sports involving running, bicycling, and swimming). This information was used to calculate the 3-month index by multiplying the reported hours by their corresponding metabolic equivalents, as described by Kohl et al. (25). A runwalk-jog index was calculated from the reported frequency, distance, and average speed for those subjects who reported running, walking, or jogging in their physical activity program. Frequency (times per week) of sweating during vigorous physical activity (sweat index) was also asked. Also, two questions concerning the time spent on vigorous and moderate activities during the last 7 days were asked (7-day index). Data from the 7-day recall were used to calculate energy expenditure by multiplying the hours of moderate activities by 4 (4 X resting metabolic rate) and the hours of vigorous activities by 8. The 7-day index could be calculated by including sleeping time and light activities (28). The third questionnaire (BAQ) used in this study is described by Baecke et al. (21). Four indices were calculated, physical activity at work, sports activities during leisure time, physical activity during leisure time excluding sports, and the total activity index as the sum of the three previous indices. All responses were precoded on five-point scales with exception of the questions concerning the title of the respondents' main occupation and the types of sports in which they participated. Procedure
Prior to administration, all questionnaires were translated into Dutch. Before starting the experiment, the interviewers were instructed during two training sessions—encoding difficulties and problems with interpretation of some of the questions were discussed. After the second training session was completed, subjects were selected, according to age and profession, from population registers from two different regions in the Flemish part of Belgium. All subjects of the same age group were visited at home by one particular interviewer. One month after the first visit, the subjects were visited a second time by the same interviewer to again administer the questionnaires; this was done in order to study stability. In total, five encoding administrations were done for each subject, as shown
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Questionnaires
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in table 1. Order of administration of the test questionnaires was fixed as follows: TCQ, FCQ, BAQ. The retest questionnaires were administered randomly. Double-entry procedures and range and consistency checks were used to guarantee the quality of the data input. Statistical analysis
All data were analyzed using SAS procedures (29). In order to calculate objectivity and stability, intraclass correlation coefficients (R) were calculated first using a one-way ANOVA (analysis of variance) model analysis, because between-judges variance was considered an error variance since all judges used the same encoding standards. R was calculated using the following formula: R = (MSs - MSw)/(MSs + (N/N' -
l)(MSw))
TABLE 1. Schematic overview of the experimental protocol: administering and encoding of the physical activity questionnaire* by the three interviewers (A, B, and C) in a sample of 90 Flemish males aged 30, 35, and 40 years in 1995 Test questionnaires Interviewed Encoded by: by:
Retest questionnaires Interviewed Encoded by: by:
Age 30 years (n = 30) Age 35 years
A
A, A, B
A
A,B
(n = 30)
B
B, B, C
B
B, C
Age 40 years (n = 30)
C
C, C, A
C
C, A
RESULTS Reliability
Table 2 presents R and kappa values for objectivity and stability for the total group. Data from the different age groups were pooled since separate analysis carried out for each age group resulted in comparable R coefficients. Objectivity coefficients were high to very high (R above 0.95 and kappa values above 0.90, with a few exceptions). Even the TCQ, a more complicated questionnaire, had a high degree of consistency with the lowest R for the work index (R = 0.79). Energy expenditure during work and active leisure time index had the lowest kappa values (both 0.70). Between judges reliability was lower than within judges reliability, but the coefficients were still high enough to be considered as objective. As expected, the FCQ and the BAQ attained higher levels of between judges reliability than the TCQ due to simplicity of administration. Test-retest R coefficients were moderate to high and generally exceeded 0.80. Lower coefficients were observed only for energy expenditure during active leisure time (TCQ), the 3-month index, both 7-day indices, and the sweat index than for most of the other physical activity variables (respectively, R = 0.64, 0.59, 0.59, 0.47, and 0.60); also, except for the 3-month index, kappa values were the lowest for these same variables (respectively, K = 0.40, 0.44, 0.20, and 0.54). However, p values indicate that, in most of the variables, more than 70 percent of the subjects are classified in the same quartile of the distribution. Concurrent validity
Means and standard deviations of the physical activity characteristics by professional status are given in table 3. For the TCQ, significant differences (all/? < 0.05) were found for work variables. Managers worked significantly more (52.1 hours/week) than clerks (42.2 hours/week) and workmen (44.9 hours/ week). However, workmen had a higher energy expenditure and work index. This difference in professional status was also found for the total activity index and total daily energy expenditure. Workmen had a total activity index of 2.2 METs with a calculated energy expenditure of 3,939.8 kcal/day. Managers (1.7 METs, 3,323.9 kcal/day) and clerks (1.8 METs, 3,313.0 kcal/day) obtained lower values. The same pattern was observed for both 7-day indices and for the Baecke work index. No significant differences in sports activities and leisure time activities were found Am J Epidemiol
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where MSs is mean squares between subjects and MSw is mean squares within subjects. N is the number of repeated measures and N' is the number of repeated measures for which R is estimated. Because reliability of a single score was of more interest than the mean score for each subject in all calculations, N and N' were, respectively, 2 and 1 (30, 31). Besides R, kappa values were also calculated, based on categorization of the distributions of the physical activity variables into quartiles. Kappa values are appropriate indices to assess agreement between different observers or even between different test occasions for categorical data. Proportion of agreement (P) was also calculated, but only in the test-retest design. P can be defined as a describer of overall decision consistency across test occasions (31). To study differences between the professional levels (concurrent validity), analysis of variance was carried out for all physical activity variables (p < 0.05). Principal-components analysis with varimax rotation was used to investigate the degree of association between physical activity variables of the questionnaires. Only components with Eigenvalues
greater than 1.0 were retained for the final rotated solution.
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TABLE 2. IntracJaso correlation coefficient* (fl) and kappa values (K) for within judges, between judges, and test-retest reliability for all physical activity variables in a sample of 90 Flemish males aged 30, 35, and 40 years in 1995 Stability
Objectivity Test OuAftlktmalm
TCQf
FCQt
BAQt
VnrtnHn
Between Judges
Between Judges
R
K
R
K
R
K
R
K
F»
1.00
1.00
059
057
059
054
0.78 0.93
0.70 0.73
0.71 0.80
0.96 0.94
0.91 053
0.88 0.79
0.70 0.79
0.91 0.85
0.87 0.81
0.90 0.88
0.67 0.60
0.76 0.70
0.71
0.45
059
0.88 0.74
0.64 0.80
0.40 0.60
055 0.70
0.84 0.85 0.95 0.96 057 0.95 1.00 0.85 056 058 0.90
0.79 0.86 0.89 0.83 0.89 0.59 0.59 0.47 0.87 0.60 0.95 0.93 0.87 0.88
0.54 0.66 0.69 0.53 0.69 0.65 0.44 0.20 0.61 054 0.69 0.61 059 0.61
0.66 0.76 0.78 0.64 0.77 0.74 0.78 0.40 0.73 0.66 0.77 0.71 0.70 0.71
059 0.99
0.98 0.99 0.99 055 0.91 0.99 059 059 0.99 059 059
056 050
056 056 0.98 0.97 056 1.00 0.99 054 059 058 057
059 053
051 0.94 0.93 057 056 0.99 1.00 057 059 1.00 058
051 0.70
0.81 0.94 0.98 059 057 0.97 1.00 0.87 056 1.00 0.90
054 0.83
0.90 053 056 059 059 059 1.00 058 059 058 058
• Proportions ot agreement (P) are only given (or test-retest reBabUty. t TCQ, Tecumseh Communtty Questionnaire; MET, metabolic equivalent: ratio of activity metaboBsm to basal metabolism; FCQ, Five City Questionnaire; BAQ, Baecke Questionnaire.
between the three professions: biking, walking, jogging, and gardening were the most reported activities. Congruent validity
Table 4 contains the component-loading matrix of 19 physical activity variables after varimax rotation. Data about time spent on eating and sleeping activities (these activities are mostly interpreted as inactive) were not included in the analysis. Variables 1-7 showed high loadings on the first component. Because variables concerning work in the TCQ, respectively, energy expenditure during work (0.91) and work index (0.86), and the work index of BAQ (0.70) scored high loadings, this component can be interpreted as a dimension of physical activity at work. Also, the total activity index (0.84) and total daily energy expenditure (0.81) of the TCQ, and both 7-day indices (0.86 and 0.82) of the FCQ, showed high loadings on this component. Comprehensively, those variables contained more information about physical activity at work than physical activity during sports or leisure time. Component 1 accounted for 31.4 percent of total variance among the set of all variables. The second component may be interpreted as a dimension of physical activity during sports activities (variables 8-12). Surprisingly, the Baecke total activity index (0.87) and leisure time index (0.60) loaded Am J Epidemiol
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on this component. Component 2 accounted for 16.9 percent of total variance. Time spent on active leisure time and energy expenditure during active leisure time (variables 13 and 14) are grouped in the third component, which explained 10.7 percent of the total variance. Despite the fact that these two variables also include aspects of sports activities, principal-components analysis separated them from the variables with high loadings on the second component. Time spent on work activities (variable 15) correlated negatively with time spent during quiet leisure time (variable 16) (r = -0.61, p < 0.0001). Both variables loaded highly on component 4 (respectively 0.93 and -0.80), which is clearly bipolar. The sweat index and the subjective activity score showed high loading on component 5, respectively 0.70 and 0.62. Although both variables are supposed to show relation with sports activities, they seemed to cover a specific dimension of physical activity. Finally, the active leisure time index (variable 19) is highly correlated with the last component (0.87). Despite the fact that this aspect of physical activity included sports activities and active leisure time activities, like gardening or house maintenance, no association was found with physical activity variables loading on component 3. About 80 percent of the total
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Sub)ecUve acuVfiy score Time at work (hours/week) Energy expenditure during work (kcaV kg/week) Work Index (METf) Time acuve during leisure time (hours/ week) Energy expenditure during active leisure dme (teal/kg/week) Active leisure time hdex (MET) Time quiet during leisure time (hours/ week) Time sleeping (hours/week) Time eating (hours/week) Total acdvtty Index (MET) Total daly energy expenditure (kcal/day) 3-month hdex (kcal/kg/woek) 7-day Index (kcal/day) 7-day Index (kcaVkg/day) Run-walk-Jog Index Sweat Index Work Index Sports Index Leisure time Index Total activity Index
With In Judges
Retest
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TABLE 3. Means and standard deviations (SD)forallphysical activity variables by professional status: 30 managers ('1) 30 clerks (2), and 30 workmen (3) of the total sample of Flemish males aged 30,35, and 40 years in 11995 Professfon Questionnaire
variable
Managers (1) Mean
TCQf
TCQ*
Workmen (3)
Clerks (2) Mean
4.3 52.1
1.3 13.8
4.4
101.3 1.9
34.8 0.5
13.1 57.4 4.5 44.2 50.8 7.7 1.7 3,323.9 21.9 2,877.2 36.8 824.9 1.9 2.5 2.8 ^8 7.9
SO
Mean
Contrastt
SD
1.1 9.7
44.9
96.4 ^3
29.1 0.7
156.9 3.4
67.2 0.9
1,2x3* 1,2x3*
8.0
14.1
9.4
15.1
11.6
ns
37.9 1.0 15.8 5.6 3.1 0.3 628.6 29 538.8 4.3 774.8 2.1 0.3 1.0 0.5 1.4
55.2 4.3 52.4 50.8
35.8 1.2 13.4 7.3 2.9 0.2 820.4 42.6 542.6
67.4 4.4 49.9 50.5 7.7 3,939.8 23.0 3,399.2 44.6 761.9
59.1 1.5 12.9 5.7 ^6 0.5 1,055.6 48.2 1,163.3 11.8 937.4
3.1 3.3 ^8
2.9 0.3 1.3
2.B 8.8
0.7 1.8
8.3 1.8
3,313.0 37.5 2,766.3 35.5 784.7 2.4 2.3 3.0 2.8 8.0
3.7
954.8 1.9 0.6 1.1 0.5
1.4
1.0 11.2
4.7
2.2
ns
1x2,3*
ns ns ns ns ns 1,2x3* 1,2x3* ns
1,2x3* 1,2x3* ns ns
1,2x3* ns ns ns
• p < 0.05. t Tukey a posteriori test 1 x 2 , 3 •> significant difference between managers and clerks, and between managers and workmen, no significant difference between clerks and workmen; 1,2x3 - significant difference between managers and workmen, and between clerks and workmen, no significant difference between managers and clerks; ns - no significant difference between three levels of professional status. $ TCX), Tecumseh Community Questionnaire; MET, metabolic equivalent ratio of activity metabolism to basal metabolism; FCQ, Five City Questionnaire; BAQ, Baecke Questionnaire.
were above 0.80. These coefficients are comparable to data (some coefficients are even higher) reported by the original authors and others, with respect to the different test-retest time intervals (7, 10, 18, 19, 21, 28, 32, 33), where most of the test-retest coefficients DISCUSSION varied between 0.75 and 0.95. In our data, only the In this study, we investigated whether the same 3-month index and both 7-day indices had lower R information about physical activity is obtained from coefficients. These lower correlations for the 7-day three questionnaires often used in epidemiologic studindices are comparable to the correlations found by ies. Ninety Flemish males between 30 and 40 years of Sallis et al. (34), r = 0.34 (kcal/kg/day) and r = 0.66 age participated in this study. The three questionnaires (kcal/day). Analogous conclusions can be made when we used represent a general evolution in the developconsidering the kappa values. All kappa values indiment of physical activity questionnaires (32). Montoye cate a moderate to substantial reliability, according to et al. (8) developed a lengthy method which requires the criteria of Landis and Koch (35). about 30 minutes to administer. The BAQ and the Validity of the diree questionnaires has recently FCQ are easy to administer and demand no subjective been well documented (17, 18). Most of the validation interpretation from the interviewer. However, our data studies showed satisfactory relations with activity show that the TCQ did not differ in reliability when records, maximal oxygen uptake, percentage body fat, compared with the BAQ and FCQ. and Caltrac readings (18, 36-40). In the present samObjectivity coefficients were very high for both of ple, three levels of professional status were contrasted the statistical methods. Test-retest R coefficients for in order to study concurrent validity, i.e., managers, most of the different scores of the three questionnaires variation in the set of the physical activity variables can be explained by the six extracted components (table 4).
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BAQ*
Subjective activity score Time at work (hours/week) Energy expenditure during work (teal/ kg/week) Work index (MET*) Time active during leisure time (hours/ week) Energy expenditure during active leisure time (kcal/kg/week) Active leisure time index (MET) Time quiet during leisure time (hours/week) Time sleeping (hours/week) Time eating (hours/week) Total activity index (MET) Daily energy expenditure (kcal/day) 3-month index (kcal/kg/week) 7-day index (kcal/day) 7-day index (kcal/kg/day) Run-walk-jog index Sweat index Work index Sports index Leisure time index Total activity index
SD
Validity of Physical Activity Questionnaires
987
TABLE 4. Component-loading matrix of the physical activity variables after varimax rotation in a sample of 90 Flemish males aged 30, 35, and 40 years in 1995 variable (questionnaire)
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
Energy expenditure during work (TCQ*) 7-day index (kcal/kg/day) (FCQ*) Work index (TCQ) Total activity index (TCQ) 7-day index (kcal/day) (FCQ) Daily energy expenditure (TCQ) Work index (BAQ*) Total activity index (BAQ) Sport index (BAQ) 3-month index (FCQ) Leisure time index (BAQ) Run-walk-jog index (FCQ) Time active during leisure time (TCQ) Energy expenditure during active leisure time (TCQ) Time at work (TCQ) Time quiet during leisure time (TCQ) Sweat index (FCQ) Subjective activity score (TCQ) Active leisure time index (TCQ)
2
3
4
5
6
0.91 0.86 0.86 0.84 0.82 0.81 0.70 0.23 -0.05 -0.02 -0.09 0.05 0.10 0.21 0.17 -0.05 0.27 0.19 0.14
-0.03 0.08 0.04 0.06 -0.05 -0.09 0.09 0.87 0.86 0.79 0.60 0.58 0.16 0.14 -0.10 -0.08 0.22 0.26 0.08
-0.03 -0.03 0.15 0.47 -0.04 0.36 0.03 0.15 0.14 0.03 0.10 -0.13 0.93 0.92 -0.19 -0.51 -0.10 0.26 0.01
0.33 0.07 -0.25 0.05 0.09 0.07 -0.03 -0.02 -0.11 -0.05 0.20 -0.15 -0.01 0.05 0.93 -0.80 -0.10 0.02 0.02
0.09 0.07 0.13 0.11 0.06 0.07 0.24 0.23 0.08 -0.02 0.20 -0.58 0.08 0.06 -0.03 -0.02 0.70 0.62 0.11
-0.08 0.20 -0.25 0.08 0.30 0.21 -0.41 -0.06 0.18 0.02 -0.04 -0.10 -0.18 0.19 0.11 0.10 0.04 0.04 0.87
5.96 31.4 31.4
3.21 16.9 48.2
2.04 10.7 59.0
1.60 8.4 67.4
1.27 6.7 74.1
1.11 5.8
79.9
* TCQ, Tecumseh Community Questionnaire; FCQ, Five City Questionnaire; BAQ, Baecke Questionnaire. t Components with Eigenvalues greater than 1.0 were retained for the final rotated solution. i Eigenvalues, proportional and cumulative explained variation, are given for each component
clerks, and workmen. Workmen worked on the average 44.9 hours per week. Clerks and managers worked, respectively, 42.2 and 52.1 hours per week. However, our data also show that the workmen obtained the highest values for energy expended during work and the work index of the TCQ, and that the TCQ total activity index and total daily energy expenditure, the BAQ work index, and the 7-day indices (FCQ) were all higher in workmen. It is also important to note that the values for the FCQ 7-day index (expressed in kcal/day and kcal/kg/day) for managers and clerks are lower than the values reported by Sallis et al. (34) (about 3,200-3,300 kcal/day) and by Blair et al. (28) (41-42 kcal/kg/day) for the same age and sex; however, 7-day index values for workmen (3,399.2 kcal/day and 44.6 kcal/kg/day) were of the same magnitude. In contrast with the time spent on working activities, workmen spent, respectively, 1 and 2 hours per week more (but not significant) on active leisure time activities than clerks and managers. Also, time spent on quiet leisure time activity was higher (but not significant) in workmen compared with managers. The difference in working time in favor of the managers is countered by the differences in time spent on active and quiet leisure time in favor of the workmen. No differences were found in eating and sleeping time between the three professional levels. Am J Epidemiol
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Principal-components analysis was carried out to verify the degree of association between the three questionnaires. This multivariate correlation technique allows for the examination of theoretic constructs of a number of variables with no delineation of dependent or independent measures. Principal-components analysis also allows for clustering of variables, that are logically related to one another, into common factors. Explained variance obtained from principalcomponents analysis indicates the relative importance of the corresponding factor in the total variance of the measured behavior. This is a very elegant way to conduct congruent validity in the absence of a standard measurement technique (31). However, only a few studies using this statistical technique for validation purposes are found in the literature. Baecke et al. (21) used principal-components analysis to identify meaningful underlying dimensions of habitual physical activity. Pols et al. (22) used this technique in order to investigate whether various methods, or different questionnaires, can measure the same facet of activity. The first component consisted of aspects of occupational activity. Energy expenditure during work (0.91) and the work index (0.86) of the TCQ, and the work index (0.70) of the BAQ, loaded very highly on this component. A key finding is the strong association between the work index of the TCQ and the work
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Eigenvalue! Proportion explained variation^ (%) Cumulative explained variation}: (%)
Component 1
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Component 2 can be interpreted as a more specific aspect of leisure time activity, namely sports activity. The sports indices of the FCQ and the sports index, the leisure time index, and the total activity index of the BAQ had positive correlations on component 2, varying from 0.58 (run-walk-jog index) to 0.87 (BAQ total activity index). The 3-month index (FCQ) correlated with the BAQ sports index (r = 0.65, p < 0.0001). Also, a significant correlation (r — 0.30, p < 0.01) between the run-walk-jog index (FCQ) and the sports index (BAQ) supposed specific measurement of sports activities. No data were found concerning the relation between questionnaires or variables used in this study in the literature. However, correlations found in this study are somewhat lower than those reported in 73 males and females by Jacobs et al. (18). Corresponding components of the Minnesota Leisure Time Physical Activity Questionnaire and the Four Week History are highly correlated, with coefficients above 0.70. The CARDIA heavy score (r = 0.83) and the BAQ sports index (r = 0.71) also correlated highly with the heavy intensity score of the Four Week History. Albanes et al. (43) reported comparable correlation coefficients with our data between eight different physical activity questionnaires. Baecke et al. (21)
found, in their analysis, that sports index and leisure time index measured two different aspects of physical activity, which is not the case in our study. It could be expected that the Baecke leisure time index showed a stronger relation with time spent on and energy expenditure during active leisure time of the TCQ. The third component is difficult to interpret. Time spent during active leisure time and energy expenditure during active leisure time (TCQ) had very high loadings on component 3, respectively, 0.93 and 0.92. The correlation coefficient between these two variables was 0.86 (p < 0.0001). Since questions about active leisure time in the Tecumseh questionnaire include, besides sports participation, aspects of house maintenance and gardening, the third component probably represents general leisure time activity. Surprisingly, the active leisure time index correlated very high on component 6 (0.87). The active leisure time index is probably indicating a different aspect of physical activity than component 3. Because this index doesn't give information about the time aspect of the activities and is indicating work metabolism over basal metabolism (MET value) for active leisure time activities, it contains information about the average intensity of those active leisure time activities. Interesting to note is the inverse relation between the time spent on professional activities (time at work) and the time spent on quiet leisure time activities, with a correlation coefficient of -0.61 (p < 0.0001). This indicates that subjects spending more time in occupational activities first cut their quiet leisure time activities, because no significant differences were found in active leisure time activities between the three occupational levels. Therefore, component 4 can be interpreted as an indicator of time management. Godin and Shephard (10), Kohl et al. (25), and Jacobs et al. (18) already reported the validity of frequency of sweat-inducing exercise. In our study, the sweat index correlated significantly with the subjective activity score (r = 0.34, p < 0.01), but also correlated significantly (0.27 < r < 0.32, p < 0.01) with most of the variables loading high on component 1. Moreover, table 3 indicates that workmen reported the highest frequency of sweating; managers reported the lowest frequency. Both the sweat index and the subjective activity score had high loadings on component 5, which can indicate that subjects with a higher frequency of producing sweat generally see themselves as more active persons. In conclusion, this study confirms that the three questionnaires have an acceptable level of reliability and validity in Flemish males. Moreover, very simple questionnaires can be as informative as longer and detailed questionnaires. All questionnaires used in this Am J Epidemiol
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index of the BAQ, with a correlation coefficient of 0.73 {p < 0.0001) (data not shown). Energy expenditure during work (TCQ) also correlated with the work index (BAQ) (r = 0.64, p < 0.0001). Moreover, a very strong relation was found between total energy expenditure (kcal/day) of the TCQ and both 7-day indices of the FCQ (r = 0.80 and 0.70, respectively; p < 0.0001). The association between the different occupational scores in our study is stronger than that found in the literature. Jacobs et al. (18) found no relation between four occupational scores and the validation techniques used. However, Buskirk et al. (24) found a significant correlation coefficient of 0.52 (p < 0.05) in 197 males between the Tecumseh occupational score and the Health Insurance Plan occupational score. In a sample of 43 females, Wilbur et al. (41) reported a correlation coefficient of 0.45 {p < 0.05) between the Tecumseh occupational score and the Saltin and Grimby occupational score. These data and the loadings on component 1 indicate that questions about occupation on two different questionnaires can measure the same entity, namely physical activity during work. Physical activity during work accounts for the greater part of variance in total activity; total activity index and total daily energy expenditure (TCQ) and the 7-day indices (FCQ) are also situated in component 1. The Tecumseh study showed also that occupational activity contributed more to total activity than did leisure time activity (42).
Validity of Physical Activity Questionnaires
study were able to distinguish blue-collar workers with a higher energy expenditure during work from whitecollar workers. Two separate dimensions of physical activity are very clearly identified, physical activity during work and sports activities during leisure time. The third dimension, active leisure time activity (component 3), must be interpreted with caution since sports activities, household activities, and maintenance activities were all asked in the same section of the TCQ. Although some evidence for validity of the three physical activity questionnaires is presented, more investigation in this field is needed. One of the major problems in physical activity research is the lack of a single appropriate method against which questionnaires can be compared. For the last decade, double labeled water has often been cited as the gold standard in validation studies, although information about activity patterns is not available from this technique (44, 45). A combination of this technique with the registration of body movements by electronic devices can be a promising solution.
The authors are grateful to Lieve Lutters, Joos Louwagie, and Jurgen Huybrechts for their assistance in the data collection for this study. We also wish to thank Dr. K. Westerterp, Dr. G. Beunen and Dr. A. Claessens for their constructive comments in preparing the manuscript.
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ACKNOWLEDGMENTS
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