The HAAS has been used to investigate the health outcomes associated with an active lifestyle and has shown associations between activity and reduced risk for cancer and cardiovascular disease.2,5
The validity of the traditional scales derived from the HAAS has been tested in a number of settings but not extensively in multiethnic, low income, urban populations.8
As in other studies, physiologic parameters, BMI, systolic, and diastolic blood pressure, known to be affected by physical activity were used as criteria against which to assess the construct validity of the traditional PAI scale in our study population.15,23–28
The results show that after control for confounding factors, the traditional PAI scales were generally not associated with these parameters in this population and thus probably have low construct validity in this population. However, the TWA scale does have construct validity, and a one standard deviation change in the TWA scale score is equivalent to a predicted difference of 0.99 units for BMI, −3.96 mmHg in systolic blood pressure, and −2.97 mmHg in diastolic blood pressure.
The populations used in past studies showing the validity of the HAAS have often not been well described but appear to be predominantly Caucasian and middle or upper middle class. The Study of Activity, Fitness and Exercise (SAFE) validated the HAAS in a population recruited from the University of Minnesota community, which was 94% Caucasian, highly educated, and predominantly employed in administrative or professional positions.7,14
A second large validation study that included the HAAS was conducted in a population described as being mostly comprised of employees at the US Department of Agriculture.17
Similarly, another validity study of the HAAS was conducted in a population described only as “hospital employees,” and mean PAI results were not reported.16
A large, well-described study of physiological correlates of the HAAS was conducted in the Boston Metropolitan area and recruited study subjects from areas with below average, average, and above average household incomes.15,29
However, the results were not shown by income defined areas, and income was not included in the multivariate models, so the role of socioeconomic class is unclear in this study.15
Lastly, evidence for the validity of the HAAS was found in a study of Latinos.8
This population is perhaps the closest to ours, although they would appear to have a higher socioeconomic status as 70% had a family income over $25,000/year, and 98% had completed high school.8
Compared to the PAI-2 data presented here, with a mean energy expenditure of 1,721 kcal/day, these studies have found similar or slightly higher average energy expenditures: 1,736 kcal/week in the SAFE study, 2,049 kcal/week in the Department of Agriculture Study, and 1,805 kcal/week in the Boston study. Clearly, past validity studies have used study populations substantially different from the population studied here and have not considered how socioeconomic class may influence validity.
The PAI scale reflects, to a large degree, leisure time activity; however, past studies suggest that individuals of lower socioeconomic status are less likely to engage in leisure time activity and are more likely to have higher levels of occupational and home activity.10–12
The lack of association between the PAI scale and physiological parameters is probably because of the study subjects being active in domains not measured by the scale.
The TWA scale was derived using questions on weekday and weekend hours spent asleep, engaged in sitting, light, moderate, and vigorous activity, and the TWA scale is conceptually similar to the IPAQ. These questions are framed to reflect the past year of activity, and the examples provided for each level of activity include household, occupational, and leisure time activities. Our multivariate analyses show that this scale is significantly associated with all three of the physiological measures. A comparison of the content covered by the two scales and their relations to the physiological parameters suggests that occupational and household activities are important contributors to activity in this population. This interpretation is consistent with past literature on activity patterns in lower socioeconomic groups and non-Caucasians.10–12,30
Prior national surveys have shown recreational activity levels to decline with age and men to have higher activity levels than women.31
These trends were reflected in our data although more so with the traditional PAI scale than the TWA scale, which is consistent with the PAI scale focusing on leisure time activity. We did not find activity estimates calculated by the traditional PAI scale to vary significantly by race/ethnicity, but the Caucasians scored lower on the TWA scale than Hispanics or African Americans. Prior representative surveys have almost entirely been confined to measuring leisure time physical activity and have shown Caucasians to have higher levels than African Americans.31
Thus, the types of activity measured in these national surveys are most comparable to the traditional PAI scale, which was not associated with race/ethnicity in our population. It is possible that the associations between leisure time activity and racial/ethnicity observed in the overall population do not hold in lower socioeconomic groups such as our study population. The 1990 National Health Interview Survey, a representative survey that did assess occupational activity, found that African Americans had higher levels of occupational activity than Caucasians.32
Thus, the higher TWA scores for African Americans in our study could reflect higher occupational activity in this group.
The use of physiological parameters in validity studies reflects an analysis of construct validity.28,33
The construct validity of a measurement is defined as the extent to which the measure correlates with other factors or measures known to correlate with the underlying construct that the measurement of interest is supposed to reflect.33
Physical activity is known to influence body size and BMI, and past validity studies have used measures of BMI and adiposity as validation criteria.7,15,28
Physical activity and exercise are known to reduce blood pressure and are prescribed as a treatment for hypertension, and past validity studies have used blood pressure as validity criterion23–25,27,28
. As such, analyses of the association between measured physical activity levels and BMI and blood pressure serve as a good test of the construct validity of the physical activity questionnaire.28
However, as these physiological measures are not a gold standard measure of physical activity, these analyses do not constitute an assessment of criterion validity, which is a limitation of this study.33
Although these physiological measures have been used in past validity studies, their use raises several issues that should be considered. The first is that high blood pressure is a commonly treated condition. At enrollment, the study subjects were asked about the use of blood pressure medication, and subjects that were using blood pressure medication were excluded from the analyses, however these questions were not included in the follow-up interviews. It is possible that, in the 12 months of follow-up, some additional study subjects began treatment for high blood pressure. However, this study population appears to be quite medically underserved. Of the 40 subjects whose systolic blood pressure was higher than 140 or diastolic blood pressure was greater than 90, only two subjects reported taking blood pressure medication. Thus, we do not think it likely that many of the study subjects began taking medication during the 12-month follow-up. The second consideration is that increased BMI and elevated blood pressure are chronic conditions that probably developed over a number of years. Conceptually, activity levels engaged in over a period of multiple years should be most strongly associated with the physiological parameters; yet, the HAAS only captures past year activity data. However, any misspecification of the appropriate timeframe applies all of the scales used here and is unlikely to explain why the TWA scale is strongly associated with the physiological parameters and the PAI-1 and PAI-2 scales are not.
One further limitation of this research is that it was restricted to a study population of smokers. Confounding by extent of smoking does not appear to explain our results. The results were consistent after controlling for a host of smoking-related questionnaire variables and after control for blood cotinine, a biomarker of smoking. That the subjects were all smokers may limit the generalizability of our results but only if smoking alters how activity is partitioned across domains of activity. For instance, if household or occupational activity were less important sources of physical activity in low-income nonsmokers than low-income smokers, the TWA scale would likely perform differently in nonsmokers compared to smokers. However, whereas smoking may be associated with the overall extent of activity, we do not think it is likely that smoking alters how activity is partitioned across domains of activity.
The USA faces an epidemic of obesity, and increases in obesity and sedentary lifestyles are likely to lead to increases in cardiovascular disease, diabetes, and certain cancers. However, the data presented here and the observation that most prior research to validate physical activity questionnaires has occurred in wealthier, suburban populations suggests that current research tools may not be adequate for surveillance and research on activity patterns in the urban environment. This represents a health disparity that needs to be addressed. Existing questionnaires should be studied for their validity in the urban environment, and new questionnaires and survey tools may need to be designed. The traditional PAI scales appear to have poor construct validity in our study population; however, the scale we created that includes measures of occupational and household activity does appear to have construct validity for measuring physical activity. These results highlight the need for validity studies that are more demographically varied and consider stratification by race/ethnicity and socioeconomic status.