594136 research-article2015
JAHXXX10.1177/0898264315594136Journal of Aging and HealthMcLaughlin et al.
Article
Gender Differences in Trajectories of Physical Activity Among Older Americans With Diabetes
Journal of Aging and Health 1–21 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: + jah.sagepub.com
Sara J. McLaughlin, PhD1, Cathleen M Connell, PhD2, and Mary R. Janevic, PhD2
Abstract Objective: The primary objective of this study was to examine gender differences in engagement in physical activity over time among older U.S. adults with diabetes. Method: Using data from the Health and Retirement Study, we investigated physical activity between 2004 and 2010 among 1,857 adults aged 65 years and above with diabetes. Results: Less than half of adults were physically active at baseline. The probability of physical activity declined over the 6-year period, with no significant gender variation in the effect of time. Because the odds of physical activity were lower for women at baseline and the effect of time did not vary by gender, the trajectory of physical activity was less favorable for women than men. Discussion: The women in this cohort of older Americans started and remained less active than their male counterparts. Investigations covering a larger portion of the life course and those examining the impact of life events and transitions on physical activity among adults with diabetes are needed.
1Miami
University, Oxford, OH, USA of Michigan, Ann Arbor, USA
2University
Corresponding Author: Sara J. McLaughlin, Department of Sociology and Gerontology, Miami University, 358 Upham Hall, Oxford, OH 45056, USA. Email: [email protected]
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
2
Journal of Aging and Health
Keywords diabetes, gender, health behavior, longitudinal, physical activity, trajectories
Introduction In the United States, approximately one in four adults aged 65 years and above has diabetes (Centers for Disease Control and Prevention [CDC], 2014). Diabetes has been linked with a variety of adverse health outcomes, including heart disease, stroke, peripheral artery disease, neuropathy, kidney disease (Engelgau et al., 2004; Fowler, 2008), and suboptimal cognitive health (Exalto, Whitmer, Kappele, & Biessels, 2012). It is, in fact, one of the leading causes of death among older adults (Federal Interagency Forum on Aging-Related Statistics, 2012). Regular physical activity is a critically important self-management behavior for those with diabetes, as it promotes glycemic control and enhances insulin action (Colberg et al., 2010). People with type 2 diabetes are encouraged to engage in a minimum of 150 min of moderate to vigorous aerobic exercise over the course of 3 or more days each week, with at most 2 days between exercise sessions (Colberg et al., 2010). Recent national estimates indicate, however, that the majority of older U.S. adults with diabetes do not engage in optimal levels of physical activity and are less likely to engage in recommended levels of physical activity than their peers without the condition (Zhao, Ford, Li, & Balluz, 2011). Although the number of nationally representative studies of physical activity among older Americans with diabetes is limited, existing research suggests that older women with diabetes are less likely to engage in physical activity than older men. Using data from the 2007 Behavioral Risk Factor Surveillance System, Zhao et al. (2011) examined correlates of meeting the American Diabetes Association’s (ADA) recommendation for physical activity. After controlling for differences in such factors as age, education, race and ethnicity, disability, and heart disease, the odds of women meeting guidelines were nearly one-quarter lower than for men. Importantly, women’s lower level of engagement in physical activity is not without consequence. Recent research suggests, for example, that midlife and older women with diabetes are at greater risk of functional health problems than their male counterparts (Chiu & Wray, 2011b). This appears to be due, in part, to women’s lower level of engagement in physical activity (Chiu & Wray, 2011a). Although the reasons for the observed gender difference in physical activity among older adults with diabetes are unclear, Bird and Rieker’s (2008) model of constrained choice offers insight. This model suggests that while individuals can and do make choices about various health-related behaviors
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
McLaughlin et al.
3
(e.g., to exercise regularly or not), those choices are influenced by a number of “constraints” (e.g., the social norms, values, and beliefs to which one is exposed and the responsibilities that accompany various social roles; Bird & Rieker, 2008). Of particular relevance to this investigation, Bird and Rieker (2008) argue that the constraints that men and women face may not be the same and/or the impact of a particular constraint (e.g., neighborhood disorder) on health-related choices may vary by gender. While there are undoubtedly many “constraints” that contribute to women’s lower level of engagement in physical activity, research suggests that women’s caregiving and familial responsibilities may play a major role (Vrazel, Saunders, & Wilcox, 2008; Wilcox, Oberrecht, Bopp, Kammermann, & McElmurray, 2005). Moreover, over the course of their lives, many older women were exposed to negative messages about women and girls’ participation in sports (Lutter, 1994) and likely had limited exposure to physically active role models who could help counter these negative messages (Vrazel et al., 2008; Wilcox et al., 2005). Thus, being physically active is unlikely to have been a normative behavior for many of today’s older women. Other factors that may contribute to lower levels of physical activity among women are concerns about personal safety (Bird & Rieker, 2008; Conn, 1998) and gender differences in health (Shaw, Liang, Krause, Gallant, & McGeever, 2010), including women’s heightened risk of functional health problems and depression (Chiu & Wray, 2011b; Crimmins, Kim, & Solé-Auró, 2011; Roy & Lloyd, 2012). In summary, we know that older adults with diabetes are less active than those without diabetes, and that older women with the condition are less likely to meet physical activity guidelines than men. We know little, however, about how physical activity among older adults with diabetes changes with time. Compared with their counterparts without the condition, older adults with diabetes are at elevated risk of a number of health problems (e.g., eye disease, heart disease, and neuropathy) that lower the odds of engaging in physical activity (Janevic, McLaughlin, & Connell, 2013). Given the potential for these added challenges to physical activity as well as evidence that suggests that physical activity mitigates the health risks associated with diabetes (Laditka & Laditka, 2015; Palmer, Espino, Dergance, Becho, & Markides, 2012; Stessman & Jacobs, 2014), efforts to understand physical activity in this large and growing segment of the older population are warranted. Gender patterns in physical activity also warrant additional attention. Although existing research indicates that women with diabetes are less physically active than men, it is possible that the influence of gender on physical activity varies over time. As individuals move through the life course, they
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
4
Journal of Aging and Health
transition into and out of social roles that influence their ability to engage in physical activity (Brown, Heesch, & Miller, 2009; Hirvensalo & Lintunen, 2011). Depending on the nature of these transitions and the extent to which they differentially “constrain” or facilitate engagement in physical activity by gender, the gap in men and women’s physical activity behavior may widen or narrow. Research by Nothwehr and Stump (2000) suggests that the gender gap may widen with time. Using data from the 1992 and 1996 waves of the Health and Retirement Study (HRS), they investigated a range of health practices among adults aged 50 to 62 years with diabetes. Although they observed no statistically significant gender difference in physical activity at baseline, women who were exercising at baseline were more likely to have stopped exercising at follow-up than their male counterparts. This finding needs to be interpreted with caution, however, because the measure of physical activity changed over the study period. Using data from a panel study of older Americans, we sought to build on the existing body of work by addressing three questions: 1. Does engagement in physical activity change over time among older adults with diabetes? 2. Among older adults with diabetes, does engagement in physical activity over time vary by gender? 3. If a gender difference in engagement in physical activity is evident, does the difference widen with time?
Method Data Source and Sample The data utilized in this investigation are from the HRS, a national panel study of adults aged 51 years and over in the United States (Juster & Suzman, 1995). HRS participants are selected using a complex sampling design that involves clustering, stratification, and disproportionate sampling of residents of Florida, those of Black race, and those of Hispanic ethnicity (Heeringa & Connor, 1995). HRS participants are interviewed every 2 years. Survey interviews cover a range of topics, including engagement in physical activity. Although the study began in 1992, the assessment of physical activity changed in 2004. As a result, we limited our investigation of physical activity to data from the 2004, 2006, 2008, and 2010 waves of data collection. At the time of this analysis, 2010 data were the latest final release data available for analysis.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
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The analytic sample was restricted to those respondents who (a) indicated that a doctor had ever told them that they had diabetes or high blood sugar as of the 2004 wave of data collection, (b) were aged 65 years and over in 2004, and (c) had non-zero sampling weights (n = 2,147). We excluded those with proxy respondents (n = 221) because they were not administered all relevant measures and those of “other” race and ethnicity due to their small subgroup size (n = 26). The resulting sample included 1,900 respondents. Of those, 43 (2.3%) were missing data for one or more of the variables included in the analysis. Thus, the final analytic sample included 1,857 individuals.
Measures Time. Between 2004 and 2010, there were a total of four waves of data collection (i.e., 2004, 2006, 2008, and 2010). Using the dates that each individual respondent completed his or her interviews, we created a time variable representing the number of years since 2004. Thus, time equal to 0 corresponds to the 2004 interview, with all subsequent interviews occurring an average of 2.0 to 6.4 years later. Physical activity. Beginning in 2004, respondents were asked about their level of engagement in light, moderate, and vigorous physical activity. To come as close as possible to the ADA recommendation (i.e., a minimum of 150 min/ week of at least moderate-intensity aerobic exercise over the course of 3 or more days per week; Colberg et al., 2010), we used only the moderate and vigorous items. To assess engagement in vigorous physical activity, participants were asked, “How often do you take part in sports or activities that are vigorous, such as running or jogging, swimming, cycling, aerobics or gym workout, tennis, or digging with a spade or shovel?” They were then asked, “And how often do you take part in sports or activities that are moderately energetic such as, gardening, cleaning the car, walking at a moderate pace, dancing, floor or stretching exercises.” Response options for both items included more than once a week, once a week, one to three times a month, and hardly ever or never. We categorized those who reported engaging in either moderate or vigorous physical activity more than once a week as physically active. All others were categorized as being physically inactive. Independent variables. The primary independent variable of interest was gender. To help understand any observed gender differences, we also examined a range of demographic and health covariates that have been found to be associated with physical activity in existing research.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
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Journal of Aging and Health
Demographic covariates. We examined several baseline demographic characteristics, including age in years (centered on the grand mean), educational level (less than a high school diploma, high school diploma or GED, some college or higher education [referent]), race and ethnicity (Hispanic, nonHispanic Black, non-Hispanic White [referent]), and quartiles of household wealth (referent = highest quartile of wealth). Health covariates. Health characteristics assessed at baseline included number of comorbid conditions, level of cognitive functioning, depressive symptoms, and mobility limitations. To capture number of comorbid conditions, a categorical variable was created based on participants’ self-reported history of arthritis, cancer, chronic lung disease, heart disease, hypertension, and stroke (0 = no comorbid conditions [referent group], 1 = one comorbid condition, 2 = two comorbid conditions, 3 = three or more comorbid conditions). Level of cognitive functioning was assessed using a modified version of the Telephone Interview for Cognitive Status (for details, see Herzog & Wallace, 1997; Ofstedal, Fisher, & Herzog, 2005). Scores range from 0 to 35, with higher scores indicating higher cognitive functioning. Depressive symptoms were assessed using an 8-item Center for Epidemiologic Studies Depression Scale (CES-D); individuals obtaining a score of 4 or more (out of a possible eight) were classified as having a clinically significant level of depressive symptoms (Steffick, 2000). The presence of mobility limitations was ascertained using participants’ responses to five items capturing difficulty walking across a room, walking one block, walking several blocks, climbing one flight of stairs without resting, and climbing several flights of stairs without resting. Response options included yes, no, can’t do, and don’t do. For purposes of this investigation, respondents who indicated that they “can’t do” or “don’t do” the task were classified as having difficulty with the task. A total mobility score was obtained by summing the number of tasks with which respondents’ reported difficulty.
Follow-Up Status Among the 1,857 individuals with complete baseline data on all independent variables utilized in the analysis, 61.2% (n = 1,137) had outcome data for all four waves of data collection, 18.2% (n = 338) had outcome data for three of the four waves, 10.7% (n = 199) had outcome data for two waves, and 9.9% (n = 183) had outcome data for just one wave. All participants with data for at least one wave were included in the analysis. To permit investigation of the impact of incomplete follow-up on our findings, we created a dichotomous variable that indicated whether or not the respondent had incomplete follow-up across the four waves.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
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Statistical Analysis To account for the fact that we have repeated measures of physical activity nested within individuals who are nested within sampling strata and sampling clusters, we used hierarchical linear modeling (HLM) to examine trajectories of physical activity over time. Specifically, we used a three-level logistic regression model to examine the effect of gender on the log odds of engaging in physical activity between 2004 and 2010. Level 1 predictors included time (in years) and a time2 term. Level 2 predictors included gender, baseline demographic and health characteristics, and follow-up status. Features of the sample design (i.e., clustering and stratification) were accounted for in Level 3. Specifically, as described by Heeringa, West, and Berglund (2010), we created a variable that captured each respondent’s sampling stratum and sampling cluster and utilized this new variable as a Level 3 identifier. To ensure that estimates reflect the population of older adults in the United States in 2004, the 2004 sampling weights provided by HRS were incorporated into the HLM algorithm. To determine how best to model the relationship between physical activity and time, we first plotted the percentage of older adults engaging in physical activity by time. This plot suggested that the relationship between time and engagement in physical activity was a curvilinear one, with the percentage of adults reporting engagement in physical activity becoming increasingly smaller with time. Thus, our initial mixed model included time and time2 as Level 1 predictors of the log odds of engaging in physical activity. To determine if there was evidence of significant between-person and between-stratum-cluster variation in the intercept and the effects of time and time2, we also included random coefficients for the intercept and slopes for time and time2 in our initial model. Results revealed significant Level 2 (p = .016) and Level 3 (p < .001) random effects for the intercept term, but no significant Level 2 or Level 3 random effects for the time or time2 slopes (p > .500 in all cases). For this reason, the Level 2 and Level 3 random effects for time and time2 were dropped from the model. After establishing how best to model time, we then examined the unadjusted relationship between gender and the log odds of engagement in physical activity. Specifically, we entered gender as a Level 2 predictor of the intercept and slopes for time and time2. In a series of subsequent models, we examined the relationship between gender and physical activity after adjusting for demographic characteristics, health characteristics, and follow-up status.
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Results Sample Characteristics Table 1 contains weighted characteristics for the sample in 2004. Mean age of the sample was 74.1 years (SE = .2); 53.3% of the sample was female, 8.8% was Hispanic, 12.4% was non-Hispanic Black, and 78.8% was nonHispanic White. More than two thirds (68%) of the sample reported a high school or higher education. With respect to health status, nearly one fifth of the sample reported a clinically significant level of depressive symptoms, approximately 86% reported one or more chronic conditions in addition to their diabetes, and more than 70% reported one or more mobility limitations. Mean cognitive score was 20.9 (SE = .2), where a score of 10 or below is suggestive of cognitive impairment (Langa et al., 2008). At baseline, less than half of the sample (45.5%) reported engaging in moderate or vigorous physical activity more than once a week. Whereas no significant gender differences were observed for age (p = .292) or cognitive score (p = .271), significant gender differences were evident for education, race and ethnicity, level of wealth, number of chronic conditions, depressive symptoms, and mobility limitations (p < .01 in all cases). Notable differences were also evident for baseline levels of physical activity, with approximately one half of men engaging in moderate or vigorous physical activity compared with about 40% of women (p < .0001).
Trajectories of Physical Activity Table 2 contains the results of our HLM analysis. As shown in Model 1, in which we examined the effect of time and time2 on the log odds of engaging in physical activity, the linear term for time was not significant (p = .840). Time2, however, was statistically significant (p < .001) and negative, suggesting a decreasing probability of engaging in physical activity over time among older adults with diabetes. Based on the coefficients for Model 1, the predicted probability of engaging in physical activity declined by 34% between baseline and Year 6. In our next model, gender was entered as a Level 2 predictor of the intercept and slopes for time and time2. Results revealed a significant main effect of gender (p < .001), but no significant gender by time (p = .256) or gender by time2 (p = .270) interaction, suggesting that the effect of time on the log odds of engaging in physical activity did not vary by gender (see Model 2). Because there was no evidence of a significant gender by time interaction, gender was removed as a Level 2 predictor of the slopes for time and time2 and the model was re-run. Exponentiating the coefficient for gender in the
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McLaughlin et al. Table 1. Sample Characteristics (n = 1,857).
Characteristic Age (years) Gender Men Women Education Less than high school High school Some college or higher Race-ethnicity Hispanic Non-Hispanic Black Non-Hispanic White Wealth quartiles Lowest Second lowest Second highest Highest Cognitive score Chronic conditions None One Two Three or more ≥4 Depressive symptoms Mobility limitations None One Two Three Four Five Physically active
Overall
Men
Women
M (SE) or % (n)
M (SE) or % (n)
M (SE) or % (n)
74.1 (.2)
73.9 (.3)
74.2 (.3)
—
—
46.7 (868) 53.3 (989)
p for gender differences .292 —
32.1 (658) 51.2 (908) 16.8 (291)
27.6 (264) 50.0 (422) 22.4 (182)
35.9 (394) 52.2 (486) 11.9 (109)
JAHXXX10.1177/0898264315594136Journal of Aging and HealthMcLaughlin et al.
Article
Gender Differences in Trajectories of Physical Activity Among Older Americans With Diabetes
Journal of Aging and Health 1–21 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: + jah.sagepub.com
Sara J. McLaughlin, PhD1, Cathleen M Connell, PhD2, and Mary R. Janevic, PhD2
Abstract Objective: The primary objective of this study was to examine gender differences in engagement in physical activity over time among older U.S. adults with diabetes. Method: Using data from the Health and Retirement Study, we investigated physical activity between 2004 and 2010 among 1,857 adults aged 65 years and above with diabetes. Results: Less than half of adults were physically active at baseline. The probability of physical activity declined over the 6-year period, with no significant gender variation in the effect of time. Because the odds of physical activity were lower for women at baseline and the effect of time did not vary by gender, the trajectory of physical activity was less favorable for women than men. Discussion: The women in this cohort of older Americans started and remained less active than their male counterparts. Investigations covering a larger portion of the life course and those examining the impact of life events and transitions on physical activity among adults with diabetes are needed.
1Miami
University, Oxford, OH, USA of Michigan, Ann Arbor, USA
2University
Corresponding Author: Sara J. McLaughlin, Department of Sociology and Gerontology, Miami University, 358 Upham Hall, Oxford, OH 45056, USA. Email: [email protected]
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
2
Journal of Aging and Health
Keywords diabetes, gender, health behavior, longitudinal, physical activity, trajectories
Introduction In the United States, approximately one in four adults aged 65 years and above has diabetes (Centers for Disease Control and Prevention [CDC], 2014). Diabetes has been linked with a variety of adverse health outcomes, including heart disease, stroke, peripheral artery disease, neuropathy, kidney disease (Engelgau et al., 2004; Fowler, 2008), and suboptimal cognitive health (Exalto, Whitmer, Kappele, & Biessels, 2012). It is, in fact, one of the leading causes of death among older adults (Federal Interagency Forum on Aging-Related Statistics, 2012). Regular physical activity is a critically important self-management behavior for those with diabetes, as it promotes glycemic control and enhances insulin action (Colberg et al., 2010). People with type 2 diabetes are encouraged to engage in a minimum of 150 min of moderate to vigorous aerobic exercise over the course of 3 or more days each week, with at most 2 days between exercise sessions (Colberg et al., 2010). Recent national estimates indicate, however, that the majority of older U.S. adults with diabetes do not engage in optimal levels of physical activity and are less likely to engage in recommended levels of physical activity than their peers without the condition (Zhao, Ford, Li, & Balluz, 2011). Although the number of nationally representative studies of physical activity among older Americans with diabetes is limited, existing research suggests that older women with diabetes are less likely to engage in physical activity than older men. Using data from the 2007 Behavioral Risk Factor Surveillance System, Zhao et al. (2011) examined correlates of meeting the American Diabetes Association’s (ADA) recommendation for physical activity. After controlling for differences in such factors as age, education, race and ethnicity, disability, and heart disease, the odds of women meeting guidelines were nearly one-quarter lower than for men. Importantly, women’s lower level of engagement in physical activity is not without consequence. Recent research suggests, for example, that midlife and older women with diabetes are at greater risk of functional health problems than their male counterparts (Chiu & Wray, 2011b). This appears to be due, in part, to women’s lower level of engagement in physical activity (Chiu & Wray, 2011a). Although the reasons for the observed gender difference in physical activity among older adults with diabetes are unclear, Bird and Rieker’s (2008) model of constrained choice offers insight. This model suggests that while individuals can and do make choices about various health-related behaviors
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
McLaughlin et al.
3
(e.g., to exercise regularly or not), those choices are influenced by a number of “constraints” (e.g., the social norms, values, and beliefs to which one is exposed and the responsibilities that accompany various social roles; Bird & Rieker, 2008). Of particular relevance to this investigation, Bird and Rieker (2008) argue that the constraints that men and women face may not be the same and/or the impact of a particular constraint (e.g., neighborhood disorder) on health-related choices may vary by gender. While there are undoubtedly many “constraints” that contribute to women’s lower level of engagement in physical activity, research suggests that women’s caregiving and familial responsibilities may play a major role (Vrazel, Saunders, & Wilcox, 2008; Wilcox, Oberrecht, Bopp, Kammermann, & McElmurray, 2005). Moreover, over the course of their lives, many older women were exposed to negative messages about women and girls’ participation in sports (Lutter, 1994) and likely had limited exposure to physically active role models who could help counter these negative messages (Vrazel et al., 2008; Wilcox et al., 2005). Thus, being physically active is unlikely to have been a normative behavior for many of today’s older women. Other factors that may contribute to lower levels of physical activity among women are concerns about personal safety (Bird & Rieker, 2008; Conn, 1998) and gender differences in health (Shaw, Liang, Krause, Gallant, & McGeever, 2010), including women’s heightened risk of functional health problems and depression (Chiu & Wray, 2011b; Crimmins, Kim, & Solé-Auró, 2011; Roy & Lloyd, 2012). In summary, we know that older adults with diabetes are less active than those without diabetes, and that older women with the condition are less likely to meet physical activity guidelines than men. We know little, however, about how physical activity among older adults with diabetes changes with time. Compared with their counterparts without the condition, older adults with diabetes are at elevated risk of a number of health problems (e.g., eye disease, heart disease, and neuropathy) that lower the odds of engaging in physical activity (Janevic, McLaughlin, & Connell, 2013). Given the potential for these added challenges to physical activity as well as evidence that suggests that physical activity mitigates the health risks associated with diabetes (Laditka & Laditka, 2015; Palmer, Espino, Dergance, Becho, & Markides, 2012; Stessman & Jacobs, 2014), efforts to understand physical activity in this large and growing segment of the older population are warranted. Gender patterns in physical activity also warrant additional attention. Although existing research indicates that women with diabetes are less physically active than men, it is possible that the influence of gender on physical activity varies over time. As individuals move through the life course, they
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
4
Journal of Aging and Health
transition into and out of social roles that influence their ability to engage in physical activity (Brown, Heesch, & Miller, 2009; Hirvensalo & Lintunen, 2011). Depending on the nature of these transitions and the extent to which they differentially “constrain” or facilitate engagement in physical activity by gender, the gap in men and women’s physical activity behavior may widen or narrow. Research by Nothwehr and Stump (2000) suggests that the gender gap may widen with time. Using data from the 1992 and 1996 waves of the Health and Retirement Study (HRS), they investigated a range of health practices among adults aged 50 to 62 years with diabetes. Although they observed no statistically significant gender difference in physical activity at baseline, women who were exercising at baseline were more likely to have stopped exercising at follow-up than their male counterparts. This finding needs to be interpreted with caution, however, because the measure of physical activity changed over the study period. Using data from a panel study of older Americans, we sought to build on the existing body of work by addressing three questions: 1. Does engagement in physical activity change over time among older adults with diabetes? 2. Among older adults with diabetes, does engagement in physical activity over time vary by gender? 3. If a gender difference in engagement in physical activity is evident, does the difference widen with time?
Method Data Source and Sample The data utilized in this investigation are from the HRS, a national panel study of adults aged 51 years and over in the United States (Juster & Suzman, 1995). HRS participants are selected using a complex sampling design that involves clustering, stratification, and disproportionate sampling of residents of Florida, those of Black race, and those of Hispanic ethnicity (Heeringa & Connor, 1995). HRS participants are interviewed every 2 years. Survey interviews cover a range of topics, including engagement in physical activity. Although the study began in 1992, the assessment of physical activity changed in 2004. As a result, we limited our investigation of physical activity to data from the 2004, 2006, 2008, and 2010 waves of data collection. At the time of this analysis, 2010 data were the latest final release data available for analysis.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
McLaughlin et al.
5
The analytic sample was restricted to those respondents who (a) indicated that a doctor had ever told them that they had diabetes or high blood sugar as of the 2004 wave of data collection, (b) were aged 65 years and over in 2004, and (c) had non-zero sampling weights (n = 2,147). We excluded those with proxy respondents (n = 221) because they were not administered all relevant measures and those of “other” race and ethnicity due to their small subgroup size (n = 26). The resulting sample included 1,900 respondents. Of those, 43 (2.3%) were missing data for one or more of the variables included in the analysis. Thus, the final analytic sample included 1,857 individuals.
Measures Time. Between 2004 and 2010, there were a total of four waves of data collection (i.e., 2004, 2006, 2008, and 2010). Using the dates that each individual respondent completed his or her interviews, we created a time variable representing the number of years since 2004. Thus, time equal to 0 corresponds to the 2004 interview, with all subsequent interviews occurring an average of 2.0 to 6.4 years later. Physical activity. Beginning in 2004, respondents were asked about their level of engagement in light, moderate, and vigorous physical activity. To come as close as possible to the ADA recommendation (i.e., a minimum of 150 min/ week of at least moderate-intensity aerobic exercise over the course of 3 or more days per week; Colberg et al., 2010), we used only the moderate and vigorous items. To assess engagement in vigorous physical activity, participants were asked, “How often do you take part in sports or activities that are vigorous, such as running or jogging, swimming, cycling, aerobics or gym workout, tennis, or digging with a spade or shovel?” They were then asked, “And how often do you take part in sports or activities that are moderately energetic such as, gardening, cleaning the car, walking at a moderate pace, dancing, floor or stretching exercises.” Response options for both items included more than once a week, once a week, one to three times a month, and hardly ever or never. We categorized those who reported engaging in either moderate or vigorous physical activity more than once a week as physically active. All others were categorized as being physically inactive. Independent variables. The primary independent variable of interest was gender. To help understand any observed gender differences, we also examined a range of demographic and health covariates that have been found to be associated with physical activity in existing research.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
6
Journal of Aging and Health
Demographic covariates. We examined several baseline demographic characteristics, including age in years (centered on the grand mean), educational level (less than a high school diploma, high school diploma or GED, some college or higher education [referent]), race and ethnicity (Hispanic, nonHispanic Black, non-Hispanic White [referent]), and quartiles of household wealth (referent = highest quartile of wealth). Health covariates. Health characteristics assessed at baseline included number of comorbid conditions, level of cognitive functioning, depressive symptoms, and mobility limitations. To capture number of comorbid conditions, a categorical variable was created based on participants’ self-reported history of arthritis, cancer, chronic lung disease, heart disease, hypertension, and stroke (0 = no comorbid conditions [referent group], 1 = one comorbid condition, 2 = two comorbid conditions, 3 = three or more comorbid conditions). Level of cognitive functioning was assessed using a modified version of the Telephone Interview for Cognitive Status (for details, see Herzog & Wallace, 1997; Ofstedal, Fisher, & Herzog, 2005). Scores range from 0 to 35, with higher scores indicating higher cognitive functioning. Depressive symptoms were assessed using an 8-item Center for Epidemiologic Studies Depression Scale (CES-D); individuals obtaining a score of 4 or more (out of a possible eight) were classified as having a clinically significant level of depressive symptoms (Steffick, 2000). The presence of mobility limitations was ascertained using participants’ responses to five items capturing difficulty walking across a room, walking one block, walking several blocks, climbing one flight of stairs without resting, and climbing several flights of stairs without resting. Response options included yes, no, can’t do, and don’t do. For purposes of this investigation, respondents who indicated that they “can’t do” or “don’t do” the task were classified as having difficulty with the task. A total mobility score was obtained by summing the number of tasks with which respondents’ reported difficulty.
Follow-Up Status Among the 1,857 individuals with complete baseline data on all independent variables utilized in the analysis, 61.2% (n = 1,137) had outcome data for all four waves of data collection, 18.2% (n = 338) had outcome data for three of the four waves, 10.7% (n = 199) had outcome data for two waves, and 9.9% (n = 183) had outcome data for just one wave. All participants with data for at least one wave were included in the analysis. To permit investigation of the impact of incomplete follow-up on our findings, we created a dichotomous variable that indicated whether or not the respondent had incomplete follow-up across the four waves.
Downloaded from jah.sagepub.com at University of British Columbia Library on November 14, 2015
McLaughlin et al.
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Statistical Analysis To account for the fact that we have repeated measures of physical activity nested within individuals who are nested within sampling strata and sampling clusters, we used hierarchical linear modeling (HLM) to examine trajectories of physical activity over time. Specifically, we used a three-level logistic regression model to examine the effect of gender on the log odds of engaging in physical activity between 2004 and 2010. Level 1 predictors included time (in years) and a time2 term. Level 2 predictors included gender, baseline demographic and health characteristics, and follow-up status. Features of the sample design (i.e., clustering and stratification) were accounted for in Level 3. Specifically, as described by Heeringa, West, and Berglund (2010), we created a variable that captured each respondent’s sampling stratum and sampling cluster and utilized this new variable as a Level 3 identifier. To ensure that estimates reflect the population of older adults in the United States in 2004, the 2004 sampling weights provided by HRS were incorporated into the HLM algorithm. To determine how best to model the relationship between physical activity and time, we first plotted the percentage of older adults engaging in physical activity by time. This plot suggested that the relationship between time and engagement in physical activity was a curvilinear one, with the percentage of adults reporting engagement in physical activity becoming increasingly smaller with time. Thus, our initial mixed model included time and time2 as Level 1 predictors of the log odds of engaging in physical activity. To determine if there was evidence of significant between-person and between-stratum-cluster variation in the intercept and the effects of time and time2, we also included random coefficients for the intercept and slopes for time and time2 in our initial model. Results revealed significant Level 2 (p = .016) and Level 3 (p < .001) random effects for the intercept term, but no significant Level 2 or Level 3 random effects for the time or time2 slopes (p > .500 in all cases). For this reason, the Level 2 and Level 3 random effects for time and time2 were dropped from the model. After establishing how best to model time, we then examined the unadjusted relationship between gender and the log odds of engagement in physical activity. Specifically, we entered gender as a Level 2 predictor of the intercept and slopes for time and time2. In a series of subsequent models, we examined the relationship between gender and physical activity after adjusting for demographic characteristics, health characteristics, and follow-up status.
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Journal of Aging and Health
Results Sample Characteristics Table 1 contains weighted characteristics for the sample in 2004. Mean age of the sample was 74.1 years (SE = .2); 53.3% of the sample was female, 8.8% was Hispanic, 12.4% was non-Hispanic Black, and 78.8% was nonHispanic White. More than two thirds (68%) of the sample reported a high school or higher education. With respect to health status, nearly one fifth of the sample reported a clinically significant level of depressive symptoms, approximately 86% reported one or more chronic conditions in addition to their diabetes, and more than 70% reported one or more mobility limitations. Mean cognitive score was 20.9 (SE = .2), where a score of 10 or below is suggestive of cognitive impairment (Langa et al., 2008). At baseline, less than half of the sample (45.5%) reported engaging in moderate or vigorous physical activity more than once a week. Whereas no significant gender differences were observed for age (p = .292) or cognitive score (p = .271), significant gender differences were evident for education, race and ethnicity, level of wealth, number of chronic conditions, depressive symptoms, and mobility limitations (p < .01 in all cases). Notable differences were also evident for baseline levels of physical activity, with approximately one half of men engaging in moderate or vigorous physical activity compared with about 40% of women (p < .0001).
Trajectories of Physical Activity Table 2 contains the results of our HLM analysis. As shown in Model 1, in which we examined the effect of time and time2 on the log odds of engaging in physical activity, the linear term for time was not significant (p = .840). Time2, however, was statistically significant (p < .001) and negative, suggesting a decreasing probability of engaging in physical activity over time among older adults with diabetes. Based on the coefficients for Model 1, the predicted probability of engaging in physical activity declined by 34% between baseline and Year 6. In our next model, gender was entered as a Level 2 predictor of the intercept and slopes for time and time2. Results revealed a significant main effect of gender (p < .001), but no significant gender by time (p = .256) or gender by time2 (p = .270) interaction, suggesting that the effect of time on the log odds of engaging in physical activity did not vary by gender (see Model 2). Because there was no evidence of a significant gender by time interaction, gender was removed as a Level 2 predictor of the slopes for time and time2 and the model was re-run. Exponentiating the coefficient for gender in the
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McLaughlin et al. Table 1. Sample Characteristics (n = 1,857).
Characteristic Age (years) Gender Men Women Education Less than high school High school Some college or higher Race-ethnicity Hispanic Non-Hispanic Black Non-Hispanic White Wealth quartiles Lowest Second lowest Second highest Highest Cognitive score Chronic conditions None One Two Three or more ≥4 Depressive symptoms Mobility limitations None One Two Three Four Five Physically active
Overall
Men
Women
M (SE) or % (n)
M (SE) or % (n)
M (SE) or % (n)
74.1 (.2)
73.9 (.3)
74.2 (.3)
—
—
46.7 (868) 53.3 (989)
p for gender differences .292 —
32.1 (658) 51.2 (908) 16.8 (291)
27.6 (264) 50.0 (422) 22.4 (182)
35.9 (394) 52.2 (486) 11.9 (109)
