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This study identifies correlates of walking among postmenopausal women and tests whether neighborhood walkability moderates the influence of intrapersonal factors on walking. We used data from the Women’s Health Initiative Seattle Center and linear regression models to estimate associations and interactions. Being white and healthy, having a high school education or beyond and greater non-walking exercise were significantly associated with more walking. Neighborhood walkability was not independently associated with greater walking nor did it moderate influence of intrapersonal factors on walking. Specifying types of walking (e.g., for transportation) can elucidate the relationships among intrapersonal factors, the built environment, and walking.
Engaging in physical activity such as regular walking has health benefits for people of all ages, including post-menopausal women. Health benefits include a reduction in all cause mortality (Physical Activity Guidelines Advisory Committee, 2008); improvement in risk factors for heart disease and stroke (i.e. hypertension, insulin resistance, diabetes, dyslipidemia, obesity, and physical inactivity) and a reduction in the incidence of clinical cardiovascular events (LaMonte et al., 2000; Lee et al., 2001; Thompson et al., 2003); a reduction in the incidence of colon and breast cancer (Lee, 2003); improvement in symptoms and functioning in individuals with osteoarthritis (Bean et al., 2004; Fransen et al., 2004); and improvement in the symptoms of depression (Dunn et al., 2005) and many other benefits. Despite the recognized health benefits of physical activity, fewer than 10% of women 40 years of age and above are sufficiently active to achieve health benefits (150 minutes/week of moderate physical activity) based upon accelerometer data (Troiano et al., 2008; Tucker et al., 2011). In older adults, physical activity decreases with advancing age and at each age group women are less active than men (Evenson et al., 2012). The promotion of physical activity, in particular walking for health, is a major public health challenge. Understanding the factors that influence older, post-menopausal women’s walking for health is critical in order to develop and target effective interventions to increase the number of older post menopausal women who are sufficiently active to achieve health benefits.
Multiple domains can influence women’s physical activity including biological, behavioral, social, and environmental. Ecological models describe multiple levels of interacting factors, such as environment, social, and intrapersonal, that influence behavior (Stokols, 1992) and have been applied to describe influences on physical activity behavior (Sallis et al., 2006; Spence and Lee, 2003). Intrapersonal factors including age, prior history of physical activity, race/ethnicity, education, and socio-economic status are associated with physical activity in women (Eyler et al., 2002; King et al., 2000; Masse and Anderson, 2003). Women who are older, of a minority race/ethnicity or have lower income or educational attainment participate in less leisure time physical activity compared to women who are younger and white or have a higher income or educational attainment (Marshall et al., 2007). In postmenopausal women (above age 55), previous participation in vigorous physical activity at younger ages, lower body mass index, higher socioeconomic status, and better overall health are associated with greater levels of vigorous physical activity (Evenson et al., 2002).
Built environment walkability factors, such as street connectivity, aesthetics, access to exercise facilities or parks, proximity to destinations (shops, restaurants), and residential density are associated with physical activity (Eyler et al., 2003; Pikora et al., 2006; Saelens et al., 2003a; Saelens et al., 2003b) and with walking (Saelens and Handy, 2008). There appears to be a differential association of walkability factors on walking by race/ethnicity, with walkability factors having stronger associations among whites in one study (Frank et al., 2004) and weaker associations among Asians in another (Wen et al., 2007). Associations between walking and walkability factors also differ by age (Shigematsu et al., 2009). For example, residential density was associated with leisure walking in adults under age 65 but not in adults over age 65 (Shigematsu et al., 2009). Furthermore, there are gender differences in environmental influences on physical activity (Bengoechea et al., 2005) and walking (Foster et al., 2004). In older women, the presence of enjoyable scenery, hills and others exercising in the neighborhood are all associated with greater physical activity (King et al., 2000).
Ecological models and empirical work to date both show that specific factors can influence walking behavior either directly or in interaction with another factor. Intrapersonal factors such as race/ethnicity, age and income are all independently associated with walking behavior; these factors also appear to affect the relationship between the walkability of an environment and walking behaviors. It is therefore important to understand potential interactions of intrapersonal and environmental factors and their associations with walking. If characteristics of the built environment such as walkability moderate the influence of intrapersonal factors on walking in post menopausal women, this can inform the tailoring of built environment interventions and could have significant policy implications for the allocation of resources to promote walking for health in older women.
The interaction between intrapersonal and environmental factors and the impact on level of physical activity or walking has not been well studied in postmenopausal women. Our study addressed this gap. Our first aim was to examine associations between intrapersonal and environmental factors and weekly energy expended on walking (metabolic equivalent or METs) in postmenopausal women. The second aim was to test whether there are interaction effects between neighborhood walkability and each of these intrapersonal characteristics on the amount of walking in postmenopausal women.
This study used data from the Women’s Health Initiative (WHI) and the Walkable and Bikable Communities (WBC) project to link data on neighborhood walkability, intrapersonal characteristics, and walking.
The Women’s Health Initiative (WHI) is a set of prospective multicenter clinical trials and an observational study of postmenopausal women aged 50–79 designed to evaluate specific hormone therapy, dietary pattern and calcium/vitamin D interventions in relation to the prevention of cardiovascular disease, cancer, and osteoporotic fractures. In order to be eligible for the WHI participants needed to be postmenopausal, which was defined as no menstrual period for at least 6 months if age 55 or older and no menstrual period for at least 12 months if ages 50–54 (Hays et al., 2003). Women were enrolled between 1993 and 1998 at 40 US Clinical Centers in either the clinical trial (CT) or observational study (OS). The Seattle Clinical Center was one of the early Centers to start recruitment. Data were collected at baseline and yearly in the CT and at baseline, year 3 and then annually in the OS. The WHI study methods are described in more detail elsewhere (Anderson et al., 2003; Hays et al., 2003).
The Walkable and Bikable Communities (WBC) project (2001–04) developed an empirically-derived walkability index based on environmental factors correlated with walking a sufficient amount to achieve health benefits in 88 noncontiguous square miles located within King County Washington (Moudon et al., 2007). The index was based on 608 randomly selected men and women within randomly selected households living in walkable areas of the county. Respondents completed a 27-minute survey over the telephone, which included data on minutes of walking per week in the neighborhood, neighborhood perceptions, individual household characteristics, and attitudes about the environment. Each participant’s household was geocoded, and 200 individual-level objective environmental factors were identified from parcel level data in a Geographic Information Systems (GIS) database. Environmental measures included distance to closest walking destinations (e.g. shops, parks, restaurants) up to 3 km from each participant’s home, and count and density of neighborhood destinations within a 1 km buffer of homes. Multilevel modeling estimated the likelihood of a participant walking 150 minutes per week (the amount needed to achieve health benefits), 1–149 minutes per week, or not walking at all, using individual and household level variables from the survey and objective environmental variables of the neighborhood. The WBC walkability index provides probabilities (0 to 100) of walking sufficiently for health (>150 minutes per week) in King County. Full details on the development of this index are available elsewhere (Lee et al., 2006; Moudon et al., 2007). The index has been positively associated with walking in both older men and women but not with body mass index (Berke et al., 2007b) and it showed an inverse association with depressive symptoms in older men (Berke et al., 2007a).
We appended data from the WHI Seattle Clinical Center OS (n=1,617) and the CT (n=1743) and assigned each woman a walkability score from the WBC project based on her geocoded home address. We excluded observations of WHI participants who lived outside the WBC sample areas. The study sample thus included women who were enrolled in the WHI Seattle Clinical Center Cohort (either the CT or OS) and lived within the geographic areas with a WBC index during 2000–2002.
We matched the WBC index with the WHI data using the geocoded addresses of the WHI participants and their corresponding survey data for any matched address falling between the years 2000–2002. Thus, the exposure (walkability) and outcome (walking) are both in the years 2000–2002. The dataset represents a pooled-cross-sectional time series where individuals are represented at least once and possibly as much as four times in the resulting pooled dataset. How often an individual appears in the data depends on whether her residence matched the WBC sampling frame and her survey fell within the 2000–2002 timeframe. For example, if a woman lived within the WBC sampling frame and had completed an annual survey in 2000, 2001, and 2002 she appears as three observations in the pooled dataset; the clustering of cases due to multiple observations is accounted for in the analysis. The final sample size for this study was 1038 postmenopausal women.
Walking was measured using a questionnaire asking about frequency, intensity and duration of walking. The test–retest reliability for the walking and physical activity questionnaires in a subset of the WHI OS participants (n=1094) ranged from 0.67 to 0.71 (Langer et al., 2003). The participants were asked three questions to assess walking. “Think about the walking you do outside the home. How often do you walk outside the home for more than 10 minutes without stopping?” The response choices were: rarely or never, 1–3 times each month, 1 time each week, 2–3 times each week, 4–6 times each week, and 7 or more times each week. Second, “When you walk outside the home for more than 10 minutes without stopping, for how many minutes do you usually walk?” The response choices were: less than 20 min., 20–39 min., 40–59 min., and 1 hour or more. Third, “What is your usual speed”? The speed response choices were: casual stroll (< 2mph), average (2–3 mph), fairly fast (3–4mph), very fast (> 4 mph), and don’t know.
A standard compendium of the energy expenditure associated with physical activities (Ainsworth et al., 2000) was used to calculate the total energy expenditure at each speed of walking in metabolic equivalents (MET) hours per week. One MET is1 kcal/kg/hour of energy expended and is considered the resting metabolic rate, approximately equal to the energy expended sitting quietly (American College of Sports Medicine, 2000). METS captures the intensity, duration and frequency of physical activity. MET hours per week is a measure of energy expenditure that is independent of body weight. The recommended amount of physical activity for adults is 30 minutes of moderate intensity physical activity on 5 or more days a week (150 minutes per week), which is approximately 7.5 MET hours per week (Physical Activity Guidelines Advisory Committee, 2008). Energy expenditure calculations at each speed were calculated using the compendium and then summed across total minutes walked and number of walks to arrive at the total energy expenditure of walking for each observation.
WBC walkability score is a continuous variable (range 0–100). Age, race/ethnicity, marital status, income, education level, and prior exercise history data come from baseline WHI surveys. Since the majority of the sample for the current study was white and there were relatively small numbers in the other race/ethnicity groups, race/ethnicity was dichotomized into white and non-white. Non-white included 2% Asian/Pacific Islander, 1% African American, 1% American Indian/Alaskan Native, and 1% Latino. Marital status was also dichotomized (currently married and previously/never married). There were 8 categories for income; we calculated the mid-point value at each category and used these integer values to represent level of income in the analysis. We categorized education into 5 categories: less than high school, graduated high school, some college, graduated college, and more than college. Women were asked to give a retrospective account of their exercise habits at age 35 with the question, “Did you usually do strenuous or very hard exercise at least three times a week? This would include exercise that was long enough to make you work up a sweat and make your heart beat fast.”
We also included current physical activity as an independent variable. Although the direction of effect is unclear, the amount of exercise a woman does per week could influence her amount of walking. A woman who exercises regularly may be more likely to walk than a woman who does not exercise regularly or conversely a woman who exercises regularly may use this form of activity to substitute for walking. Physical activity was assessed with a reliable questionnaire detailing frequency, intensity (mild, moderate, vigorous) and duration of physical activity (Langer et al., 2003). The MET hours per week of total physical activity were calculated from minutes per day and number of days per week of mild, moderate, and hard exercise. We subtracted METS hours per week from walking from the total METs hours per week from all physical activity to arrive at the total METs hours per week of physical activity excluding walking.
Health status was included as an independent variable and was based upon responses to two questions in the annual WHI surveys. One question asked women to rate their general health as excellent, very good, good, fair or poor. Another asked if they experienced any limitations to walking 1 mile with choices of a) yes, limited a lot; b) yes, limited a little; or c) no, not limited. We categorized women into three health categories: 1) healthy women who rated their general health as excellent, very good or good and had no limitation to walking 1 mile; 2) semi-healthy women who rated their health as excellent, very good or good and had a little limitation in walking one mile or rated their health as fair or poor and had no limitation in walking one mile; and 3) non healthy women who rated health as fair or poor and had a lot of limitation in walking one mile.
Neighborhood income level could influence both the independent and dependent variables. We used the census tract-level median married family income reported in the 2000 US Census to control for potential confounding by neighborhood income. We labeled this variable neighborhood income.
We developed linear regression models to estimate associations among the variables of interest and our outcome, MET hours of walking per week. We used robust sandwich estimators of the standard errors to account for clustered data (repeated observations of individuals who appeared in the dataset more than once) using the SPSS Genlin routine with robust standard errors command (SPSS version 17). The first model included intrapersonal characteristics: age, health status, race/ethnicity, marital status, median family income, education level, and history of strenuous physical activity at age 35, and energy expended on physical activity excluding walking. We added walkability and neighborhood income (census-tract level median family income) in the second model. We also examined a model with just intrapersonal factors and walkability. We then examined separately interaction terms of walkability with age, race/ethnicity, marital status, median family income, education level, and prior exercise history. If we observed significant interactions between the intrapersonal variables and walkability then we would run a third model in which we would retain all significant interaction terms.
A total of 1038 individual women were matched with a WBC walkability index score. Table 1 depicts sample characteristics. Ten percent of women moved during the study period. There were 323 census tracts represented. The mean walkability score was 30 (range 3–77, SD=14) and the mean neighborhood family income was $78,500 (range 22,000–152,000, SD=19,000).
Table 2 lists the unstandardized regression coefficients and 95% confidence intervals (CIs) for Model 1 (intrapersonal factors only). Being healthy was associated with 3.386 increased MET hours of walking per week compared with being unhealthy; the impact was significant but greatly attenuated for the semi-healthy group with an increase of 0.878 MET hours per week. Women who completed high school expended 1.6 more MET hours per week walking compared with women did not complete high school. Women who had education beyond college expended 1.5 more MET hours per week walking compared with those who had less than a high school education. White women expended 1.6 more MET hours per week walking compared with non-white women. Women who reported a history of vigorous exercise at age 35 expended greater energy walking compared with women who did not, although not significant (p= 0.54). The more MET hours per week a woman exercised in non-walking forms of exercise the more MET hours per week she expended on walking.
In model 2 (intrapersonal and neighborhood factors), net of the individual characteristics, neighborhood walkability index was not significantly associated with the amount of walking per week (see table 2). Excluding women who had moved during the 2000–2002 timeframe gave similar results (data not shown). We added both neighborhood variables (walkability and neighborhood income) to the intrapersonal characteristics and neither the direction nor the strength of the relationship between these variables and amount of walking per week changed. The Wald test showed that adding neighborhood variables (walkability and neighborhood income) to the model did not improve the fit of the model. In examining the interaction terms for walkability with each intrapersonal characteristic we found no significant interactions. Therefore, constructing a third model was not warranted.
In this sample, being white and healthy and having earned a high school diploma or higher were associated with greater MET hours of walking per week, which is consistent with previous studies on intrapersonal determinants of physical activity in women (Eyler et al., 2002; King et al., 2000; Marshall et al., 2007; Masse and Anderson, 2003). Women who had a history of vigorous exercise at age 35 tended to expend greater energy on walking compared with women who did not have this exercise history, but the difference did not reach statistical significance. This tendency affirms previous research that found participation in exercise as a young woman predicted exercise engagement as an older woman (Hirvensalo et al., 2000). Taken together, this suggests that a possible avenue to increasing postmenopausal women’s amount of walking is to promote vigorous exercise in women at younger ages. In the current study, women who engaged in more total MET hours of physical activity also expended more MET hours walking. It seems that walking did not substitute for other forms of exercise among these postmenopausal women in our sample.
The neighborhood walkability index was not significantly associated with total MET hours of walking in our sample of postmenopausal women; this was unexpected. Prior studies examining the association between walking and walkability factors in older women have had mixed results. In one study of postmenopausal women, proximity to specific destinations (golf course and post office), urban form (grid street design), and low neighborhood socioeconomic status were associated with greater steps per day of physical activity (King et al., 2005). However, the number of retail establishments in close proximity was not associated with greater steps. In another study of predominantly female (70%), older adults (mean age of 74), total walking and leisure walking were not associated with the walkability factors of traffic volume, sidewalk coverage, intersection frequency, public transportation access, retail establishments, and park and green space (Nagel et al., 2008). The discordant findings may be partly due to different measures of the built environment and/or measurement of walking used in the studies.
Walkability factors appear to have differential associations by race/ethnicity. In our study of predominantly white women there was not a significant association between walkability and total walking. In another study (50% women), the association between proximity to a park or open space and walking the recommended amount of 150 minutes per week was stronger in blacks compared with whites and stronger in whites compared with Asians (Wen et al., 2007). It is not clear why there is a discrepancy between this study and our study regarding an association between walkability and walking with whites. One explanation is that this difference is reflecting a gender difference since the study by Wen and colleagues (2007) was approximately 50% women. Another possibility is that the study by Wen and colleagues (2007) used specific walkability factors and it is possible that one of these specific walkability factors is more pertinent with a certain racial/ethnic group. Differences in the association between specific walkability factors and walking by race/ethnicity were found in one study. In that study, the association between walking for transportation and intersection density and residential density was stronger with white women compared with black women (Frank et al., 2004). However, in isolating a specific walkability factor out of the context of the entire neighborhood, it is possible that other aspects of the neighborhood that might influence walking (either encourage or discourage) are not accounted for when examining the association between walkability and walking. In our study we used a walkability index, which provides a more complete picture of the neighborhood than just a specific walkability factor. Thus the different associations of walkability with walking by race/ethnicity could in part reflect differences between the neighborhoods as a whole rather than differences between racial/ethnic groups.
Different purposes for walking, such as walking for transportation or for exercise/health, have been associated with different environmental determinants (Humpel et al., 2004; Suminski et al., 2005). In a study using a sample of older adults, researchers looked at the association of walkability and walking for transportation and found that walkability was associated with walking for transportation (King et al., 2011). In another study, travel destinations (a walkability factor) were associated with transportation but not recreation walking in adults (Lee and Moudon, 2006). In another study, those in high walkability neighborhoods spent significantly more minutes walking for transportation but not for leisure than those in low walkable neighborhoods (Sundquist et al., 2011). The results from these studies suggest that walkability might be pertinent when one is walking for transportation and less pertinent when one is walking for leisure.
Measuring total walking does not take into account the purpose for walking and therefore, does not reflect the different factors that encourage or discourage different purposes for walking. One study which looked at older adults (64% female) from the same geographic area (King County, Washington) and used the same walkability index (WBC score) as our study, found a higher walkability score was associated with a greater amount of walking for exercise (Berke et al., 2007b) though total walking was not assessed. In our study, we calculated total walking and were not able to distinguish between different purposes for walking (transportation versus exercise). Taking the results of these two studies together suggests that the same walkability factors influenced walking for exercise but not total walking (which includes walking for transportation). Thus, measuring total walking may not provide the needed detail to capture the relationship between walkability and walking. Examining specific purposes for walking rather than total walking may more clearly describe the relationship between walkability and walking behavior.
Neighborhood walkability did not moderate the effect of any of the intrapersonal characteristics we examined (race/ethnicity, age, educational level, income, prior exercise history, and weekly energy expended on exercise) on total MET hours of walking per week in this sample of postmenopausal women. A recent study examined the interactions between self-efficacy and barriers to walking (intrapersonal psychosocial factors), and a walkability index on walking (both transportation and leisure) in adults over age 65. There was a significant interaction between self-efficacy and walkability and between barriers and walkability, and walking for transportation, but not walking for leisure (Carlson et al., 2012). This suggests that intrapersonal psychosocial factors, such as self-efficacy, and barriers to walking, in addition to other intrapersonal factors, such as age or prior history of exercise, are important to consider in understanding these relationships. Furthermore, the study by Carlson and colleagues suggests that measuring specific purposes for walking (leisure/exercise or transportation) might provide sufficient sensitivity to capture the moderating influence of walkability on intrapersonal factors and walking; whereas, measuring total walking may not.
Measuring specific types of walking, such as exercise or transportation, might provide greater insights into the interaction effect of walkability and intrapersonal factors on walking. Furthermore, specific purposes for walking, such as transportation, might be more strongly influenced by walkability factors. A greater understanding of how intrapersonal and walkability factors interact with specific purposes for walking (e.g. exercise, transportation) would assist in identifying walkability factors to address in policy or built environment interventions designed to promote specific purposes for walking in specific populations (such as women, elderly).
Walking location also may be important. For example, walking for transportation could occur within a home neighborhood or between destinations in an area away from the neighborhood. In a study of walking and the built environment in US adults, 52% of women reported engaging in walking for transportation, but only 32% reported engaging in walking for transportation in their own neighborhood (Suminski et al., 2005). In our study, we did not have data on the location of walking. Improving data on walking location, whether through survey data or by empirical measurement, can clarify relationships between walkability, intrapersonal factors and amount of walking.
Our results must be interpreted with the following limitations in mind. An overwhelming majority (95%) of the women in our sample were white, which limits generalizability of our findings to women of other race/ethnic backgrounds. However, even with only a 5% non-white sample, we were able to see differences in walking outcome by race/ethnicity. History of vigorous exercise at age 35 was based on retrospective self-report and might be influenced by recall bias. Additionally, walking was measured via self-report, which may be subject to recall bias. For example, walking time and/or distance might be underestimated compared with more vigorous physical activity because it is less easily recalled (Ainslie et al., 2003; Bassett et al., 2000). An underestimation of the amount of walking would tend to bias results towards the null (reduce the relationship between walking and walkability). However, we used high-quality data, including a reliable measure of walking and physical activity from the WHI, and the WBC index, a tested measure of walkability.
In summary, we found that postmenopausal women who were white, healthy, and had at least a high school diploma engaged in more MET hours per week of walking compared with postmenopausal women who were nonwhite, unhealthy and had not earned a high school diploma. Women who engaged in a greater number of MET hours per week of total exercise also expended more MET hours walking; this suggests that these women did not substitute walking for other forms of exercise. In this sample of postmenopausal women, neighborhood walkability was not associated with energy expended on total walking and it did not moderate the effects of intrapersonal factors on energy expended on total walking. Examining the interaction effects of intrapersonal and walkability with specific purposes for walking (e.g. exercise, transportation), may yield a more precise estimation of the influence of walkability on walking and the interaction effects of intrapersonal factors and walkability on walking. This, in turn, could help identify specific factors to address in interventions designed to promote walking among specific populations.
Ethan Berke is supported by the National Institute on Aging 1K23AG036934
The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C
The WHI investigators:
Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller Clinical Coordinating Center: Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg Investigators and Academic Centers: (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker
Women’s Health Initiative Memory Study:(Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker
Enormous gratitude is extended to the women from the Seattle Clinical Center who have participated in the Women’s Health Initiative since its inception, without whose dedication to furthering the understanding of factors influencing women’s health this study would not have been possible.
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