1 Department of Epidemiology, School of Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC.
2 Department of Biostatistics, School of Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC.
3 Division of General Medicine, Columbia College of Physicians and Surgeons, and Division of Epidemiology, School of Public Health, Columbia University, New York, NY.
4 Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
exercise; exertion; health promotion; leisure activities; life style; retirement
Abbreviations: ARIC, Atherosclerosis Risk in Communities; CI, confidence interval
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
One such life event is retirement, which may be a period when physical activity patterns could change due to a decline in occupational demands (3, 4
). Empirical evidence supporting a change in leisure-time physical activity at retirement arises from cross-sectional surveillance studies, where light and moderate intensity activities increase around retirement age but overall activity continues to decline (5
7
). To confirm this finding, prospective population-based studies are needed to characterize leisure activity patterns through retirement among the same persons. Therefore, we sought to prospectively examine the patterns of leisure activity associated with retirement over a 6-year period among African-American and White persons initially aged 4564 years.
![]() |
MATERIALS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Physical activity assessment
Physical activity was assessed at the baseline examination using the Baecke questionnaire (10). The questionnaire was administered by an interviewer and yielded three semicontinuous scores from 1 (low) to 5 (high) for sport, leisure, and work. A few modifications to the original version of the Baecke questionnaire were made and are detailed elsewhere (11
). The sport score was derived from three questions regarding the frequency of overall sport and exercise participation, the frequency of sweating, and a subjective comparison of physical activity with that of others one's own age. A fourth component on the frequency, intensity, and duration of up to four activities also contributed to the sport score. The leisure score was designed to capture leisure activity and consisted of four questions on television viewing, bicycling, walking, and time spent walking and bicycling to and from work or shopping. The work score was calculated from eight items. Participants were asked how often while at work they sit, stand, walk, lift heavy loads, sweat, and leave work physically tired. They were also asked to compare their work activity with that of others their own age. The last component of the work score consisted of a ranking (low, medium, or high) of activity based upon occupational job title (12
). Participants not reporting any occupational activity were assigned a work score of 1. Each component of the sport, leisure, or work score contributed equally to the final scores.
The reliability and validity of the Baecke questionnaire have been evaluated in several populations. The Baecke questionnaire more accurately assesses heavy intensity activity, which tends to be well-defined activities performed repetitively by participants (e.g., bicycling, jogging, swimming), as compared with low intensity activity (13). Moderate correlations have been observed between sport and leisure indices and activity diaries (13
15
). However, lower correlations were observed between the work index and occupational activity diaries (16
) or maximal oxygen consumption (15
). Short-term reliability, assessed by a 1-month test-retest Pearson correlation coefficient adjusted for age and gender, was 0.90 for sport, 0.86 for leisure, and 0.78 for work (15
). Longer term reliability (35 months) has also been demonstrated for men and women, ranging from 0.71 to 0.88 for sport, from 0.74 to 0.83 for leisure, and from 0.80 to 0.89 for work (10
, 14
, 17
).
Changes in sport and leisure scores over 6 years were computed by subtracting the baseline value from the 6-year value (range, from -4 to 4). Hypothetically, sport and leisure indices could increase by one point if a person moved from the lowest (score = 1) to the highest (score = 5) category for any one of the questions, holding all other responses to component questions constant. For example, if a participant reported never sweating during leisure at baseline and reported sweating very often at the follow-up visit, the sport score would increase by one point if all the other responses to sport component questions remained identical. Similarly, if a participant reported never walking at baseline and reported walking very often at the follow-up visit, the leisure score would increase by one point given that all other responses to leisure component questions remained identical.
In addition to the scores described above, physical activity was also characterized in several other ways. Participants were defined as engaging in vigorous activity if they reported at least one sport that was classified as vigorous (>6 metabolic equivalents). Participants were also defined as engaging in regular activity if they reported participation in at least one activity at least 1 hour per week for 10 or more months per year. Participants were asked whether they had participated in any sport or exercise (yes or no) at baseline and year 6, referred to as "sport or exercise participation." Based on self-report, participants were classified into four mutually exclusive categories: 1) maintainers, if they reported participating in at least one sport or exercise at baseline and year 6; 2) sedentary, if they reported not participating in any sport or exercise at baseline and year 6; 3) adopters, if they did not report any sport or exercise at baseline but did at year 6; and 4) stoppers, if they did report any sport or exercise at baseline but not at year 6.
Measurement of other study variables
Participants were asked to define occupational status at baseline and at year 6 of the study. Only participants who identified themselves as "employed" at baseline and as "employed," "retired and working," or "retired and not working" at the year 6 follow-up visit were included in these analyses. Education was self-reported at the home interview and defined for these analyses categorically by years of education (less than high school, high school, or greater than high school). Baseline distributions of education and occupation in the ARIC Study have been previously reported (18). Perceived health status was assessed by telephone just prior to the year 6 visit with the question, "Compared with other people your age, would you say that your health has been excellent, good, fair, or poor?"
Exclusion criteria
The original ARIC Study cohort comprised 15,792 persons. Participants not identifying themselves as African American or White (n = 49) and African Americans living in Minneapolis (n = 22) or Washington County (n = 33) were excluded. Participants not providing complete information on physical activity (n = 73) or education (n = 24) and not between 45 and 64 years of age (n = 158) were also excluded. Those not working at baseline (n = 4,987), those not returning or having incomplete physical activity or health status information at the year 6 visit (n = 1,726), those retiring for health reasons (n = 250), and those having incomplete or inconsistent information on work status at the follow-up visits (n = 688) were excluded, leaving 7,782 for these analyses.
Statistical methods
All analyses were calculated by race-gender group. Multivariable linear regression models were used to examine the adjusted sport, leisure, and sport + leisure scores, as well as individual change scores, comparing retirees with workers. These models were recalculated, excluding persons who reported "retired and working" at year 6. Unconditional multivariable logistic regression models were used to examine the maintenance of any exercise compared with stopping and adoption of any exercise compared with remaining sedentary by retirement status. All race- and gender-specific models were adjusted for age (55 years), center, education, and perceived health status. We did not adjust for the season of the year, because the questionnaire queried physical activity during the past year and clinic visits occurred throughout the year. For the study population, the distribution of perceived health status was as follows: 35.4 percent, excellent; 51.2 percent, good; 11.8 percent, fair; and 1.5 percent, poor. To control for perceived health status in statistical modeling, we created two indicator variables that compared excellent health or good health with the referent (those who reported either fair or poor health). Tracking was defined as the ability to maintain over time one's relative physical activity ranking among cohort members (19). Adjusted Spearman's rank correlation coefficients were used to assess 6-year physical activity tracking, with confidence intervals calculated using a bootstrap procedure (20
).
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
|
|
For retirees, we explored whether work activity was replaced with sport and leisure activity. To do this, we determined individual change in sport and leisure scores after grouping retirees according to three baseline work activity groups (work scores 11.9, 22.9, 35) (data not shown). For sport scores, there were no significant differences for sport scores when comparing different baseline work values for African-American and White women. However, for African-American men, sport scores declined significantly across baseline work categories: work score 11.9 (n = 19): 0.84 (95 percent CI: 0.54, 1.13); work score 22.9 (n = 88): 0.42 (95 percent CI: 0.28, 0.56); and work score 35 (n = 57): 0.16 (95 percent CI: -0.02, 0.33). For White men, the differences in sport scores were not as pronounced: work score 11.9 (n = 160): 0.26 (95 percent CI: 0.15, 0.36); work score 22.9 (n = 639): 0.17 (95 percent CI: 0.12, 0.23); and work score 35 (n = 321): 0.19 (95 percent CI: 0.11, 0.27). There were no significant differences for leisure scores at follow-up when comparing across different baseline work values for each race-gender group.
Adjusted Spearman's rank correlation coefficients for sport, leisure, and sport + leisure scores at baseline and year 6 were calculated by retirement status across race-gender groups (table 3). The sport score generally tracked more consistently than the leisure score (except for African-American men), as indicated by higher Spearman's rank correlation coefficients. Retirees were more likely to change their sport score ranking than were those who continued to work.
|
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
It is unclear from our data whether the overall net activity increased with retirement. There is a possibility that work activity was replaced with sport and exercise participation for retirees and that no real change in overall activity occurred. However, if work activity did not contribute meaningfully to overall physical activity, then there may be an overall gain. We attempted to examine this in our data by modeling individual changes in sport and leisure scores for retirees by baseline work score, which provides some indication of their activity at work. Across race-gender groups, the largest gains in sport scores occurred among those with the lowest work scores (e.g., least active at work), reaching significance for African-American and White men. No differences were found for leisure scores across work categories. This indicates that retirees' sport and exercise participation was probably adding additional activity and not replacing work activity, for those who worked in the least active occupations. However, we have no precise quantitative estimate of the amount of physical activity needed to cause a one-point increase in scores and whether this amount is consistent for sport, leisure, and work scores. Therefore, estimates of absolute gains or losses in physical activity are not possible.
Tracking data are helpful to determine the consistency of activity over time, as well as to guide how often physical activity should be assessed in prospective study designs. In this study, lower tracking, as measured by correlation coefficients, was noted for sport scores among those retiring from work during the follow-up period. These findings have implications for prospective studies that examine disease outcomes. Many studies assess physical activity at only one time period (baseline), assuming it is a valid estimate of activity over the entire follow-up period. Our findings suggest that this practice may lead to misclassification if the follow-up period spans over the retirement years, because population levels of sport and exercise participation changed with retirement. These persons may require more frequent measures of physical activity to better represent their activity patterns.
These results should be considered in light of the study's limitations. This study is based on participants from four geographic communities and may not reflect national patterns. African Americans were represented at only two sites, and Whites were represented at only three sites. Therefore, it is difficult to separate racial and geographic effects. Another potential drawback to the generalizability of this study was the response rate at the follow-up visit. Attrition is inherent in prospective closed cohort studies and, in this study, baseline sport, leisure, and work scores were higher for those returning to both examinations when compared with those not returning. Therefore, the group we reported on had higher average baseline activity scores than did the original full cohort. This may have caused selection bias if the changes in the patterns of activity differed by participation status.
In this study we do not know the exact date when retirement occurred, only that it did happen between baseline and the year 6 visit. Future studies should incorporate more frequent ascertainment of both working status and physical activity. Additionally, it is not known if the changes that we observed in physical activity would continue beyond this study period of no longer than 6 years. Finally, the definitions of working and retirement are not mutually exclusive. In our analyses, we classified those who self-reported being "retired and working" as retired. No important differences were observed when analyses were restricted to those "retired and not working" except among White men, for whom an increase in the sport score was greater for "retired and not working" than for "retired and working."
Further research is warranted to examine how other specific milestones, such as having children and changing marital status, affect health behaviors to better understand the population patterns of physical activity and to tailor interventions (22, 23
). Patterns should be examined across the life span, because physical activity participation is a lifelong process that is probably influenced by life experiences and stages of development (23
, 24
). Better characterization of the components of physical activity is also needed to quantify and to compare across domains whether overall physical activity, and therefore energy expenditure, increased with retirement.
Our results highlight the importance of a life course approach to understand the determinants of physical activity and to plan effective interventions that promote healthier lifestyles. Because of the high prevalence of physical inactivity in the United States, interventions to improve these patterns must expand beyond the individual level and also target population groups (25). For example, incorporating physical activity guidance into preretirement planning may further increase participation (26
). By better understanding how adults alter physical activity as other important life domains change, researchers can anticipate changes in activity and target interventions appropriately (27
).
![]() |
ACKNOWLEDGMENTS |
---|
The authors thank the staff and participants in the ARIC Study for their important contributions. They would also like to thank Dr. Aaron Folsom, Dr. Moyses Szklo, and Joy Wood for their helpful contributions.
The questionnaires and data collection forms used in this study are available at the following Web site: http://www.bios.unc.edu/cscc/ARIC/.
![]() |
NOTES |
---|
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|