National health objectives call for all Americans to increase their participation in physical activity. One of the objectives is to reduce the prevalence of no leisure-time physical activity among adults to no more than 15% by the year 2000 (28). The National Health Interview Survey (NHIS) found in 1985 that approximately 24% of U.S. adults were physically inactive during leisure time. Subsequent national surveys have confirmed that as of 1991, roughly one-quarter of the population remains inactive (3,29). The prevalence of physical inactivity and the burden of a sedentary lifestyle is more predominant among women than among men, and among non-Hispanic blacks or Mexican Americans than among non-Hispanic whites (1,5,29).
Reports on the barriers to participation in physical activity show that society’s influence on physical activity are multifactorial (20,29). Some of these factors are differences in social class (30). The measurement of social class in epidemiology is complex, and if the wrong indicator of social class is used, misleading results may be obtained (17). In providing a theoretical frame for the relationship of social class to disease, Liberatos et al. (17) recommends the use of multiple measures of social class. Most measures developed by American sociologist are based on Edwards’ and Weber’s view of three separate but linked dimensions of social class; these are occupation, education, and income (9,32). Additional indicators of social class are employment and poverty status. To our knowledge the study of physical inactivity relative to multiple indicators of social class in a national representative sample of the population has not been reported (10,24).
The extent to which men and women of different social class differ in their pattern of physical inactivity is also not fully understood. This is due, in part, to the different roles that men and women assume in the family and at work. What remains unknown is whether or not disparity in participation in physical activity among men and women can be explained solely by differences in social class. Thus, to update the estimates of physical inactivity during leisure time and to examine its relation to social class in a national sample of men and women, we analyzed data from the Third National Health and Nutrition Examination Survey (NHANES III), conducted between 1988 and 1994.
The NHANES III was conducted by the National Center for Health Statistics, Centers for Disease Control and Prevention. The Plan and Operation of the NHANES III fully describes the procedures and implementation mechanisms used (18,27). Briefly, NHANES III is a national representative sample of the civilian noninstitutionalized population of the United States aged 2 months and older. In contrast to other Health and Nutrition Examination Surveys, NHANES III did not have an upper age limit. NHANES III was conducted between 1988 and 1994 and consisted of a home interview and a detailed clinical examination performed in a mobile examination center. Subjects signed a consent form and approval was obtained from a Human Subjects Committee in the U.S. Department of Health and Human Service. Self-reported information provided the basis for the identification of race ethnicity and allowed for the over sampling of Mexican Americans and non-Hispanic blacks.
NHANES III used several questionnaires during the home interview, depending on the participant’s age. A household adult questionnaire collected information on health practices, knowledge, conditions, occupational information, and physical activity. A Family Questionnaire obtained information about educational levels, ethnicity, occupational information, health insurance coverage, family income, and physical characteristics of the house. Between 1988 and 1994 a total of 18,885 adults 20 yr of age or older responded to the household adult and family questionnaires.
Physical activity assessment.
The household adult questionnaire was used by trained interviewers to obtain information on physical activity during the past month. The physical activity instrument was adapted from the 1985 NHIS, the survey used to establish baseline estimates for several of the Healthy People 2000 physical activity objectives. Participants were asked to specify their frequency of participation in physical activities during their leisure time during the past month for the following activities: jogging or running, riding a bicycle or an exercise bicycle, swimming, aerobic dancing, other dancing, calisthenics or floor exercises, gardening or yard work, and weight lifting. Four open-ended questions assessed information on physical active hobbies, sports, or other activities not previously listed such as walking or sports specific pursuits. Participants who responded no to all leisure-time physical activity questions, including the four open-ended questions, were classified as persons who were physically inactive.
Social class assessment.
Multiple sources of social class were used following Liberatos et al. (17) recommendation on the study of social class in epidemiology. Our construct of social class indicators was based on the categorization of selected variables such as education, income, poverty index ratio, occupation, and employment information. During the administration of the family questionnaire, the interviewer asked a responsible adult household member about total combined family income. A hand-card listed specific annual family income from which we computed five annual family income categories: <$10,000, $10,000–$19,999, $20,000–$34,999, $35,000–$49,999, and $50,000 or more. The variable poverty index ratio is available in the NHANES III data sets and only applies to the U.S. population. It is prepared by the U.S. Census Bureau and is calculated using annual family income, family size, and other economic information such as annual cost of living (18,27,31). A poverty index ratio of less than 1.000 is the cutoff point to live below the poverty line. At or above the poverty line means that the participant’s poverty index ratio is 1.000 or above.
Classification of education status was based on information about the year of schooling completed, and from that the following categories were created: less than high school (<12 yr), high school (12 yr), some college (>12 yr but <16 yr of school completed), and college plus (completed ≥ 16 yr of school). Years of school attended and completed does not necessarily mean that a degree was obtained, it is just a reflection of the self-reported years of education obtained by the participant.
A standard protocol assessed employment, occupation, type of work, position, duties, activities, and time involved in a specific occupation, and was used to compute occupational scores using the 1980 U.S. census occupational categories as reference standards (25,27). Occupations were grouped into three classes: white-collar professional, white-collar others, and blue-collar. White-collar professional refers to managerial and professional specialty occupations, and white-collar other includes technical, sales, and administrative support occupations. Blue-collar refers to mostly manual labor jobs in service, farming, forestry and fishing, precision production, craft and report occupations, and laborers (25).
Employment status was determined based on whether the participant reported working at a job or business during the past 2 wk by using a standard systematic assessment of occupational involvement. This was followed by information on whether the participant was laid off, was looking for a job, was retired, worked keeping the house or homemaker, or attended school during the past 12 months. With this information, three categories of employment were established: employed, not employed, and not in labor force.
The occupational category combined information on occupation and employment status to yield six separate classifications: white-collar professional, white-collar other, blue-collar, retired, homemakers, and other.
Statistical analyses were carried out using SAS, WesVarPC, and SUDAAN (21,22). All analyses incorporated the sampling weights and the complex sample design. Age-adjusted prevalence estimates were calculated using the direct method based on the total population for the 1980 as the standard. Additionally, the prevalence of physical inactivity in the above social class indicators was adjusted for age and race/ethnicity using the general linear model procedure in SAS and the least square means option. For variance estimation, we used the balance repeated replication method in the software package WesVarPC.
About one-quarter of the U.S. adult population remains inactive during leisure time (Table 1). The prevalence of physical inactivity is higher among women than among men. Physical inactivity ranged between 20 and 26% for persons between the ages of 30–69 yr of age, and women had a higher prevalence of physical inactivity than men regardless of age (Table 1). More than half of the persons older than 80 yr of age reported being inactive.
Figure 1 shows that persons with higher educational attainment had a lower prevalence of physical inactivity. Women reported higher levels of physical inactivity during leisure time than men in every category of education. Physical inactivity was higher (47%) in households with a lower income than in households with a higher income (32%). The inverse association between income and physical inactivity reaches it lowest level among households with an annual income between $35,000 and $49,999 a year. Prevalence of physical inactivity plateaus for men, but was higher among women in households with annual incomes of $50,000 or more.
White collar professionals had lower age and race/ethnicity-adjusted prevalence of physical inactivity than white-collar workers in technical or clerical jobs and blue-collar workers (Fig. 1). The highest prevalence of physical inactivity according to occupation was observed for retired men and for women who were homemakers. Currently employed individuals were more physically active than both unemployed persons or those not in the labor force. Among women, however, the prevalence of physical inactivity, adjusted for age and race/ethnicity, was similar at about 43 and 41% for both the currently employed and the unemployed.
Consistently, people living below the poverty line were more inactive during leisure time (50%) than those living at or above the poverty line (37%). We adjusted the prevalence of physical inactivity for body mass index, and the results did not add or change our estimates. Adjustments for race/ethnicity produced estimates that were consistently higher than the age-adjusted prevalence; we, therefore, report age and race/ethnicity adjusted estimates.
Using the entire NHANES III data set (1988–1994), we have shown that 23% of the U.S. adult population continue to be inactive during leisure time. This is consistent with results from the 1991 National Health Interview Survey (NHIS) (24%) but lower than the Behavioral Risk Factor Surveillance Survey (BRFSS) (29%). The difference between NHANES III and NHIS or the BRFSS is the sampling design and instruments used to assess physical activity. NHANES III oversampled specific groups (e.g., blacks, persons of Mexican ancestry, and the very young and the very old); it includes a clinical examination by a physician, and uses an eight-item physical activity questionnaire plus four open-ended questions. The NHIS is an interview-only national survey with a 17-item physical activity assessment questionnaire. The BRFSS is a state-based survey where sampled persons can identify up to four different physical activities. This limitation to recall additional activities may play a role in explaining why estimates of physical inactivity from the BRFSS are higher than the NHIS and the NHANES III. As with other national surveys, our findings confirm that women report being less active during leisure time than men and that older segment of the population are more inactive than younger ones (3,28,29).
Social class incorporates economic, political, and cultural differences that may have an impact on health. The study of social class from an epidemiological perspective suggests that education influences health through lifestyle behaviors such as exercise and diet, problem-solving capacity, and values. This is consistent with our findings where higher levels of physical inactivity were observed among those with less education in both men and women. Income reflects access to medical care resources and good housing, abundance of food, good working conditions, and more social amenities. Unlike education, our results on income did not demonstrate a clear trend toward lower prevalence of physical inactivity with higher income, especially among women.
Occupation according to Duncan’s socioeconomic index is the intervening activity linking income to education (6,17,25). Earlier analyses by Edwards (9) used education and income to measure the socioeconomic status of an occupation. Other researchers have expanded and elaborated on the validity of occupation as measurement of social class and how to classify persons within this construct (2,6,9,11,17,19,25). As society evolves, and the physical and psychological demands of the workplace change, these occupational classifications require updating. Liberatos et al. (17) suggests that the linkage between occupation and health is a reflection of the individual personal control in the work environment, access to medical care, exposure to physically noxious and psychological stressful environment, and the ability to obtain good housing. Not surprisingly, these three social class indicators (education, income, and occupation) are in one way or another interrelated in their relations to health (17). Our results are in agreement with this interrelation, where the lowest prevalence of physical inactivity by occupational categories was observed among those in white-collar professional jobs, who also have the highest level of education and income.
In our attempt to explain lack of leisure-time physical activity as a function of social class in men and women, we created multiple measures of social class where certain segments of society have been customarily understudied (e.g., retired persons, blue-collar workers, and homemakers). In the United States, race/ethnicity is associated with social class. We have reported elsewhere that race/ethnicity is independently associated with physical inactivity, even after controlling for confounders such as age, sex, education, income, and occupation (4). In their study, Crespo et al. (4) found that non-Hispanic blacks and Mexican Americans were more inactive during leisure time than non-Hispanic whites regardless of educational attainment. Further research on the independent relationships between physical inactivity and social class in racial and ethnic minority subgroups in the United States is needed.
Overall, our results demonstrate that the prevalence of physical inactivity is associated with a variety of social class measures. Although our cross-sectional findings do not prove a cause-effect relationship, it does suggest a strong association between physical inactivity and multiple indicators of social class of the individual (e.g., education, occupation, and employment) and the family (e.g., income and poverty index ratio). The most widely used indicator of social class in physical activity epidemiology is education. In our study, we found an inverse association between the prevalence of physical inactivity and educational attainment for both men and women. Education is a fairly stable indicator for use with adults, in addition to being a precursor to income and occupational status. High education, however, does not necessarily lead to high income or high occupational standing. The reason why education has been mostly correlated with disease is not entirely understood, but its correlation with health practices and adoption of lifestyle may explain the importance of education as a predictor of physical inactivity (16,17,20,23). High educational attainment may be a reflection of the persons ability to understand and value the benefit of exercise for overall physical and psychological well-being. Hellerstedt and Jeffery (13) found that education played a significant role in explaining the association between weekly exercise sessions and the physical and psychological demands of the job, especially among women.
We observed that persons living in households with higher income reported lower levels of physical inactivity. However, in women where the household annual income was above $50,000, the prevalence of physical inactivity was higher than in women with household incomes between $35,000 and $49,999. The percentages of women who were working or were homemakers in these two income groups were very similar (working women: 20–24%; homemakers: 43–44%, data not shown). Close to 32% of the women in the high income group ($50,000+) had completed 16 yr of education compared with 23% for the women in the $35,000–$49,999 group. It is unclear as to whether this entirely explains the higher prevalence of physical inactivity observed in the high income group, especially because higher education is usually associated with lower prevalence of physical inactivity. Conversely, men in these two income brackets ($35,000–$49,999 and $50,000+) had a similar prevalence of physical inactivity, both lower than men with income groups below $35,000. The percentage of men who were homemakers was smaller in every income group (2–7%) than it was for women (39–44%). Stronks et al. (26) report that women homemakers perceive their health to be significantly worse than those who work outside the home. This negative perception of health status may affect self-efficacy when starting or maintaining a regular physical activity program. Our data also show that men who were homemakers are more active than women who are homemakers, yet the number of men (N = 160) who were homemakers in our study was very small. A possible limitation with the income variable is that it is age dependent. Earnings tend to increase during a person’s occupational career, then drop off after retirement. At any given time, employed persons under age 65 yr are likely to earn more than retired persons older than 65 yr (17). Thus, it is important that studies looking into social class by income use multiple measures of social class that includes retired persons. Another challenge is to expand research that would help us to better understand the barriers to participation in leisure-time physical activities among women who live in households with high income as well as lower income households.
We created six categories of occupation using theoretical models discussed by Duncan (6) and Stevens and Cho to group white and blue collar workers (25). Moreover, we added retired persons and homemakers, given that earlier studies of socioeconomic indexes for all occupations were mostly based on white men of working age (2,6,17,25). We further divided white collar occupations into two subcategories: those who are mostly in managerial and professional specialty occupations (white-collar professional), and those in technical, administrative and sales occupations (white-collar other). Further analysis of the data showed that the proportion of racial and ethnic minorities is greater in the “blue-collar” and “white-collar other” positions than in the “white-collar professional” jobs. In men, the most inactive groups during leisure time were retired persons. Among women in blue-collar occupations, homemakers, and retired women had the highest levels of physical inactivity. Both men and women who worked in white-collar professionals jobs had the lowest prevalence of physical inactivity. This finding is consistent with a greater participation in physical activity previously observed among persons of higher socioeconomic status (10,13,29). Hellerstedt and Jeffery (13) report that women with less job latitude engage in less physical activity than women with higher job latitude (white-collar professional). This emphasizes the importance of considering multiple domains of social class when attempting to explain gender differences relative to participation in leisure-time physical activity. Societal and cultural responsibilities in taking care of the home and the family may explain, in part, this difference (14–16,24). The traditional responsibilities of taking care of the home, plus the added responsibility of being currently employed may have implications in the amount of free time available to engage in physical activity.
Our results showed that people who live below the poverty line engage in less physical activity than people who live at or above the poverty line. The variable poverty index ratio produced by the U.S. federal government allows for comparability across household of different sizes and across years with different cost of living. Unfortunately, this variable is not available in other countries and may not have the same level of applicability as in the United States. Given that the NHANES III was conducted from 1988 to 1994, this variable allowed us to compare prevalence estimates across groups with varying family sizes and in different years. This is especially important because certain ethnic groups, such as Mexican Americans traditionally have larger sample sizes than non-Hispanic white families. Again, women regardless of their poverty status had a greater prevalence of physical inactivity than men. Of the women living below the poverty line, about half of them were homemakers, whereas only 7% of the men were homemakers. However, both men and women living below the poverty line had similar levels of education (48% and 55% respectively, had less than 12 yr of education) (data not shown).
Social and cultural expectations, time, economic resources, social support, and low self-efficacy in exercise may be critical factors in explaining these differences (8,26,29). It is also possible that persons living below the poverty line may work in occupations that require more energy expenditure and are not interested in pursuing physical activity during their leisure-time, such as those employed in blue-collar jobs. Misclassification of occupational status may be a limitation of our study. For example, not all farmers may be blue-collar workers, although a small number of participants reported being a farmer or farm manager. Albeit this limitation, we feel our data provide reasonable estimates of physical inactivity by occupational status.
The study of social class and participation in leisure-time physical activity is complex. Our analysis failed to incorporate other factors that may be part of the social class construct such as safety issues in local neighborhoods. Also, employers that provide worksite health promotion programs, although increasing in number, are few, and if available, are mostly available to white-collar workers. Availability of child care for working mothers or homemakers may also influence the prevalence of physical inactivity.
Evaluating only income levels may not be appropriate because income can vary widely within occupations. Some categories of blue-collar workers earn more than white-collar workers (12). Additionally, the assessment of leisure-time physical activity via a questionnaire may not accurately reflect physical activity from occupation, transportation, and house work. People in occupations with high-energy expenditure may be disinterested in physical activities outside work. Similarly, women who are homemakers expend many calories doing house chores and, as a result, are less inclined to participate in physical activity (1). Conversely, working outside the home may increase the likelihood of women adopting an exercise program (7).
Our findings reveal that social class indicators were closely related to physical inactivity in this national cross-sectional sample of the U.S. adult population. Additional research is needed to further explore if disparities in participation in physical activity can be solely explained by differences in social class, or if other issues such as physical education experience, family structure, geographic residence, and recreational facilities are equally important. Information is needed detailing whether the differences observed in the general population are observed in both men and women of different ethnic and racial groups.
In summary, approximately one-quarter of the U.S. adult population are inactive during leisure time. A higher prevalence of physical inactivity during leisure time occurs among the less educated, those living below the poverty line, those living in household income below $20,000, and retired persons. Consistently, in every category of social class, women experienced a higher prevalence of physical inactivity than men. Our findings highlight the need for more research to recognize how different social class indicators affect participation in physical activity in men and women.
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