Journal of Aging and Physical Activity, 2015, 23, 180-186 http://dx.doi.org/10.1123/japa.2012-0337 © 2015 Human Kinetics, Inc.

Official Journal of ICAPA www.JAPA-Journal.com ORIGINAL RESEARCH

Aging Expectations are Associated With Physical Activity and Health Among Older Adults of Low Socioeconomic Status Shilpa Dogra, Ban Al-Sahab, James Manson, and Hala Tamim The purpose of the current study was to determine whether aging expectations (AE) are associated with physical activity participation and health among older adults of low socioeconomic status (SES). A cross-sectional analysis of a sample of 170 older adults (mean age 70.9 years) was conducted. Data on AE, physical activity, and health were collected using the 12 item Expectations Regarding Aging instrument, the Healthy Physical Activity Participation Questionnaire, and the Short Form-36, respectively. Adjusted linear regression models showed significant associations between AE and social functioning, energy/vitality, mental health, and self-rated general health, as well as physical activity. These results suggest that AE may help to better explain the established association between low SES, low physical activity uptake, and poor health outcomes among older adults. Keywords: exercise behavior, health behavior, older person, beliefs

Attitudes, beliefs, and expectations predict behavior, particularly health behaviors such as medication adherence (Horne & Weinman, 1999), healthy eating (Traill, Chambers, & Butler, 2012), and physical activity participation (Sallis et al., 1986). Through its influence on behavior, attitudes also influence outcomes such as health. The Theory of Planned Behavior (TOPB) indicates that there are three determinants of intention and behavior: attitude, subjective norm, and perceived behavioral control (Ajzen, 1991). Attitude toward behavior refers specifically to one’s beliefs about the consequences of a behavior and the positive or negative evaluation of the behavior. For example, an individual who believes smoking is bad for one’s health (belief) and finds it to be a filthy habit (negative attitude) is unlikely to engage in this behavior. The TOPB claims that for any given behavior or situation, the relative importance of the three determinants can vary (i.e., in certain circumstances attitudes might better predict behavior than subjective norm or perceived control) (Ajzen, 1991). The TOPB is greatly applicable to the adoption and maintenance of physical activity behavior. A meta-analysis of 31 studies (n = 10,621) found that intention had a large effect on behavior and that attitude was significantly more important in determining exercise behavior than subjective norms (Hausenblas, Carron, & Mack, 1997). In other words, the association between attitude and exercise behavior is very strong. A study conducted by Courneya (1995) aimed to assess the utility of TOPB and physical activity among older adults. It was found that attitude, along with perceived behavioral control, was one of the most important discriminators of physical activity behavior. It is clear that one’s attitude strongly influences one’s physical activity behavior; this in turn would influence health outcomes. Evidence suggests that attitudes toward aging, or aging expectations (AE), influence health behaviors, especially among older Dogra is with the Faculty of Health Sciences, Kinesiology, University of Ontario Institute of Technology, Oshawa, Ontario, Canada. School of Recreation Management and Kinesiology, Acadia University, Wolfville, Nova Scotia, Canada. Al-Sahab, Manson, and Tamim are with the School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada. Address author correspondence to Shilpa Dogra at [email protected]. 180

adults. These attitudes can be either positive or negative in nature. A study of adults between the ages of 50–70 years found that those who had positive AE were more likely to engage in exercise and consume a healthier diet compared with those who had conventional AE (Huy, Schneider, & Thiel, 2010). Given the strong link between lifestyle behaviors and chronic disease, it is not surprising that AE have also been associated with cardiovascular disease. A secondary analysis of the Baltimore Longitudinal Aging Study found that young adults who had negative AE were more likely to experience a cardiovascular event over the next 38 years compared with those with positive AE (Levy, Zonderman, Slade, & Ferrucci, 2009). Interestingly, negative AE may be more detrimental to health and uptake of health behavior than the benefits associated with having positive AE (Meisner, 2012). AE among older adults of low socioeconomic status (SES) are not well researched. A recent investigation by Sarkisian, Shunkwiler, Aguilar, and Moore (2006) found that there were some differences in AE among a variety of cultural groups, but that these differences disappeared once SES was accounted for. In other words, SES may be an important predictor of AE among older adults. SES has indeed been associated with attitudes and health behavior in the past. In a cross-sectional analysis conducted by Wardle and Steptoe (2003) using middle-aged and older adults, it was found that those who were of lower SES had poorer life expectations (longevity), poorer lifestyle habits, and lower belief in personal control. It was also noted that those of lower SES seldom thought about the future. This is an interesting and important point from a health and health behavior perspective, as it would be difficult to motivate those of low SES to adopt a healthier lifestyle if they do not think about their future health. Poorer health (Veenstra, 2009) and poorer lifestyle habits (Bryan, Tremblay, Pérez, Ardern, & Katzmarzyk, 2006; Dogra, Meisner, & Ardern, 2010) have been documented among those of lower SES and ethnic minorities. Evidence that AE are associated with health and health behavior is building, however, little is known about this association among older adults of low SES. It is imperative to determine whether such a relationship exists so that the appropriate health promotion tools can be used with this population. The purpose of the current study therefore was to determine whether AE are associated with

Aging Attitudes, Physical Activity, and Health   181

physical activity participation and health (physical and mental) among a sample of older adults of low SES. Based on the TOPB, it was hypothesized that lower AE would be associated with lower physical activity levels and poorer physical and mental health.

indicative of better physical activity participation. To ensure that our measure of physical activity was consistent, we conducted a reliability analysis. Cronbach’s alpha for consistency of the three items from the questionnaire was 0.597, showing acceptable internal consistency.

Methods

Physical and Mental Health.  Health was measured using the short-form 36 (SF-36). This questionnaire has been tested for validity and reliability (Brazier et al., 1992). Using a sample of 1,582 participants, Brazier et al. (1992) showed that the questionnaire had acceptable internal consistency; Cronbach’s α exceeded 0.85 and the reliability coefficients were greater than 0.75 for all dimensions except social functioning (α = 0.73, reliability = 0.74). There was also a high correlation between test-retest scores; for all dimensions, 91–98% of cases lay within the 95% confidence interval. Finally, construct validity was confirmed using a variety of outcomes such as age and chronic disease, and discriminant validity was satisfactory based on the Nottingham health profile. The questionnaire contains 36 items that measure eight dimensions (scales) of functional health and well-being. The dimensions of physical functioning, role limitation-physical, bodily pain, and general health perception are used to compute an overall score for physical health. The dimensions of role limitation-emotional, social functioning, mental health (emotional stability), and energy and vitality are used to compute an overall score of mental health. Scores range from 0 to 100, with higher scores indicating better health status.

Study Design The present study was cross-sectional in nature and was part of a larger 16-week Tai Chi intervention study for which classes were offered to participants free of charge at local centers. Communitydwelling older adults from two locations in the greater Toronto area of Ontario, Canada were targeted. These locations were Jane and Finch as well as Dundas and Spadina; areas were chosen for their diverse ethnic make-up and their low SES (Toronto Social Development, Finance & Administration, 2010).

Participants Participants were recruited via focus groups, community contacts, flyers, and friends and relatives that were currently participating in the study. Eligibility for participation was limited to males and females who were 50 years of age and older, residing in the abovementioned locations, and with the medical capability to be involved in an exercise intervention. Medical capability was measured by the Physical Activity Readiness Questionnaire and Physical Activity Readiness Medical Examination (Canadian Society for Exercise Physiology [CSEP], 2004). Three cohorts of participants were recruited during a three-year span. Data for the first cohort were collected over the months of August–December 2009 while data for cohorts 2 and 3 were collected from February–August 2011 and October 2011–April 2012, respectively. The study was approved by the ethics review committee of York University. Analysis for the current study was restricted to data gathered at baseline and to study participants who were 60 years of age and older.

All participants attended one data collection session at baseline. During this session participants were asked to complete a series of questionnaires (hard-copies only) pertaining to expectations, health, health behavior, and demographics.

Aging Expectations.  The main independent variable, AE, was measured using the Expectations Regarding Aging 12-item survey (Sarkisian, Steers, Hays, & Mangione, 2005). This questionnaire is based on the valid and reliable 38-item survey (Sarkisian, Hays, Berry, & Mangione, 2002). In the original 38-item survey, all scales (other than pain) demonstrated good internal consistency reliability (α ≥ .73) and item discrimination (≥ 0.80). Construct validity was supported by correlations with age, activities of daily living, and health questionnaires. The 12-item survey measures expectations relating to physical health, mental health, and cognitive functioning. It was validated against the 38-item survey. Internal consistency reliability estimates for all scales exceeded 0.74, and the 12 items together explained over 88% of the variance in the total score for the 38-item survey. Comparable associations of the 12-item survey with age and self-reported health measures were noted. The 12-item survey provides a continuous score ranging from 0 to 100. Higher scores represent better AE.

Physical Activity.  Physical activity was measured using the

Covariates.  Several sociodemographic and health-related vari-

Measurements

Healthy Physical Activity Participation Questionnaire; a widelyused questionnaire containing three simple questions on physical activity intensity, frequency, and perceived fitness (CSEP, 2004). This questionnaire was designed from a study conducted by Shephard and Bouchard (1994) in which physical activity questions were evaluated using clinical measures such as blood pressure, resting heart rate, lipid profiles, and more. It was found that selfreported frequency and intensity of physical activity along with self-perceived fitness were significantly associated with optimal body fat content, lipid profiles, and cardiovascular health. For example, males reporting high intensity physical activity had better blood pressure (115/71 vs. 121/76 mmHg) and body composition (waist to hip ratio of 0.91 vs. 0.95) compared with those reporting low intensity physical activity. Based on these findings, the authors developed a scoring method for the three self-reported physical activity questions in which responses to all three questions are tallied and scored on a continuous scale of 0–11; a higher score is

ables were also collected. Sociodemographic variables were age, sex, marital status, income, and education levels, while healthrelated variables were use of assistance devices for walking (cane/ Zimmer/stick/frame) and the number of self-reported chronic comorbidities. The number of chronic comorbidities was measured by adding the number of chronic medical conditions reported by each study participant. All participants were asked to report if they had any of the following medical conditions: hypertension, diabetes, cardiovascular disease, arthritis, depression, or chronic obstructive pulmonary disease.

Statistical Analysis Means and standard deviations were calculated for all sociodemographic and health-related variables for each of the three outcomes. Independent samples t tests and ANOVA were used to determine differences between groups. At the bivariate level,

182  Dogra et al.

correlation coefficients (r) and beta coefficients with standard errors were assessed using Pearson correlations and simple linear regression for AE and covariates for each of the three outcomes. AE and all other covariates were entered in multiple linear regression models for each of the outcomes. Multiple linear regression analyses were performed for each of the eight SF-36 outcomes adjusting for AE and all the other covariates. Beta coefficients and standard errors were reported for all the linear regression models. All analyses were conducted using the Statistical Package for the Social Sciences, version 19.0 (IBM Inc., Chicago, Illinois).

Results A total of 170 older adults aged 60 years and older were included in the study. The average age of the participants was 70.9 years (SD = 6.8, range 60–87). The majority were female (79%) and were single, widowed, or divorced (60%). Approximately 72% of the participants reported an income lower than $14,000 CAD/ year. The two main cultural origins of the study participants were Chinese (30%) and South American (30%). Others reported their origins from Europe (17%), Central America (8%), and South Asia (5%). Participants had 1.5 chronic conditions on average (SD = 1.1, range 0–5). The most common morbidity among older adults was hypertension (54.1%, n = 92), followed by arthritis (48.2%, n = 82), diabetes (22.9%, n = 39), depression (13.5%, n

= 23), cardiovascular disease (6.5%, n = 11), and finally, chronic obstructive pulmonary disease (4.7%, n = 8). More demographic information is available in Table 1. Of note, there were no differences in physical activity, physical health, or mental health between males and females. Further, all three outcomes were significantly different by education levels. The mean score for AE was 35.2 (SD = 21.9). The score on the cognitive, mental, and physical components of AE were 22.2 (SD = 19.1), 37.5 (SD = 20.9), and 19.7 (SD = 18.4), respectively. The average score for physical activity was 6.9 (SD = 3.0). Finally, the computed scores for overall physical health were 49.2 (SD = 8.1) and 52.5 (SD = 9.0) for overall mental health. The unadjusted associations between AE and other covariates with each of the outcomes are presented in Table 2. AE were significantly associated with physical activity (p-value < .001) and overall mental health (p-value < .001); this was not the case for overall physical health, however, the p-value was approaching statistical significance (p = .08). No significant differences in physical activity and mental health were evident with age. However, physical health declined with advancing age. Sex was not significantly associated with any of the outcomes. The number of chronic comorbidities was significantly and positively associated with physical and mental health; there was a significant but negative association with physical activity. Older adults using assistance devices had lower scores for physical activity and overall physical health than their counterparts, but there was no significant association with mental health.

Table 1  Sample Characteristics by Physical Activity, Physical Health, and Mental Health Sample N (%)

Physical Activity Mean (SD)

Partial Eta2

Physical Health Mean (SD)

Partial Eta2

Male

134 (78.8)

6.7 (3.0)

.005

48.8 (7.8)

.012

Female

36 (21.2)

7.3 (3.1)

Unmarried/widowed/divorced

98 (59.4)

6.6 (3.0)

Married or living with partner

67 (40.6)

7.2 (2.9)

Illiterate

11 (6.7)

5.1 (3.1)*$

Primary

72 (43.6)

6.7 (3.0)

50.7 (6.3)

50.9 (9.7)

Junior/senior high school

66 (40.0)

7.0 (2.9)

48.1 (9.0)

52.7 (8.8)

University

16 (9.7)

8.5 (2.4)

50.6 (6.7)

58.4 (4.3)

< 14,000

115 (76.2)

6.4 (3.0)*

14,000–30,000

27 (17.9)

8.5 (2.6)

51.5 (6.0)

54.2 (8.5)

9 (6.0)

7.2 (2.6)

47.2 (12.0)

58.2 (4.5)

No

16 (9.4)

7.1 (2.9)*

Yes

154 (90.6)

4.4 (2.9)

Characteristic

Mental Health Mean (SD)

Partial Eta2

52.7(8.5)

.002

Sex 50.9 (9.0)

51.7 (10.8)

Marital status .009

47.7 (8.2)*

.050

51.5 (7.6)

52.0 (9.5)

.008

53.6 (8.1)

Education level .052

43.9 (12.3)*$$

.055

51.5 (7.1)*$$$

.059

Income

> 30,000

.071

48.9 (7.9)

.019

51.0 (9.3)*

.045

Walking assistance .070

50.5 (7.1)*

.188

39.2 (9.3)

*P < .05 as per ANOVA or t tests for each characteristic with each outcome. $ Based on least significant difference (LSD), significance was between illiterate and university, and primary and university categories. $$ Based on LSD, significance was between illiterate and primary, illiterate and university categories. $$$ Based on LSD, significance was between primary and university, high school and university categories.

52.6 (9.1) 51.7 (8.0)

.001

Aging Attitudes, Physical Activity, and Health   183

Table 2  Crude Correlations (r) and Beta-coefficients (β) for Aging Expectations and Covariates by Physical Activity, Physical Health, and Mental Health Physical Activity Characteristic

r

Physical Health

β (SE)

p-value

r

β (SE)

Mental Health p-value

r

β (SE)

p-value

Aging expectations

.32

0.04 (0.01)

< .001*

.15

0.06 (0.03)

.08

.42

0.17 (0.03)

< .001*

Age

–.05

–0.05 (0.04)

.20

.27

–0.32 (0.09)

.001*

.04

0.03 (0.11)

.81

Income

.20

1.03 (0.43)

.02*

.03

0.38 (1.21)

.75

.21

3.43 (1.36)

.013*

Education

.21

0.83 (0.31)

.008*

.03

0.34 (0.87)

.70

.20

2.37* (0.95)

.013*

Comorbidity

.11

0.29 (0.21)

.18

.24

–0.17 (0.57)

.003*

–.23

–1.83 (0.64)

.005*

Sex^

–.07

–0.52 (0.58)

–.38

–.11

–2.14 (1.60)

.18

.05

0.98 (1.78)

.58

Marital status^^

.09

0.57 (0.48)

.24

.23

3.70 (1.33)

.006*

.09

1.60 (1.50)

.29

Walking assistance

–.27

–2.68 (0.77)

.001*

.43

–11.28 (1.94)

< .001*

–.03

–0.86 (2.39)

.72

^ Male is the reference category. ^^ Unmarried/widowed/divorced is the reference category. *p < .05.

Table 3  Adjusted Beta-coefficients for Aging Expectations and Covariates by Physical Activity, Physical Health, and Mental Health Physical Activity Characteristic

β (SE)

p-value

Physical Health β (SE)

p-value

Mental Health β (SE)

p-value

Aging expectations

0.04 (0.01)

.003*

0.06 (0.03)

.094

0.16 (0.04)

< .001*

Age

0.01 (0.05)

.981

–0.27 (0.12)

.021*

0.04 (0.14)

.792

Income

0.44 (0.49)

.371

0.82 (1.27)

.519

0.58 (1.47)

.695

Education

0.31 (0.38)

.415

–1.42 (0.96)

.139

0.85 (1.11)

.447

Comorbidity

0.67 (0.23)

.005*

–1.13 (0.60)

.061

–1.72 (0.69)

.015*

Sex^

0.09 (0.63)

.887

–1.87 (1.68)

.268

2.62 (1.95)

.180

Marital status^^

0.55 (0.60)

.360

0.14 (1.54)

.925

2.47 (1.78)

.168

Walking assistance

–2.28 (0.90)

.012*

–8.72 (2.33)

< .001*

0.21 (2.71)

.938

^ Male is the reference category. ^^ Unmarried/widowed/divorced is the reference category. *p < .05.

After adjusting for all covariates in the multivariate analysis (Table 3), AE remained significantly associated with physical activity (p-value = .003) and overall mental health (p-value < .001) and was approaching statistical significance with physical health (p = .094). For physical activity, the number of comorbidities and use of a walking assistance device explained some of the variation. For physical health, age and use of a walking assistance device were the only two significantly associated variables. Finally, the only additional variable that was significantly associated with mental health was sex. The multivariate associations between AE and the individual SF-36 components that comprised the overall physical and overall mental health scores are presented in Table 4. Older adults with higher AE scores had significantly better scores on social functioning, mental health, energy and vitality, and general health perception. The beta coefficient for bodily pain was approaching statistical significance (p = .058).

Table 4  Adjusted Beta-Coefficients for Aging Expectations by Subscales of the SF-36 Aging Expectations Domains

Subscales

Physical health

Physical functioning Role limitation-physical Bodily pain General health perception Role limitation-emotional Social functioning Mental health Energy and vitality

Mental health

β

SE

p-value

0.02 0.13 0.20 0.39 0.19 0.21 0.23 0.48

0.09 0.11 0.11 0.09 0.10 0.09 0.08 0.08

.786 .239 .058 < .001* .068 .020* .003* < .001*

Note. Adjusted for age, sex, marital status, income, education, number of comorbidities, and walking assistance. *p < .05.

184  Dogra et al.

Discussion The aim of the current study was to determine whether AE are associated with physical activity levels, physical health, and mental health among older adults of low SES. Based on the TOPB, it was hypothesized that AE, or attitudes toward the process of aging, would influence intention to participate in health behaviors such as physical activity, and that these attitudes, through their direct (physiological effects) and indirect effects (on health behaviors such as physical activity, healthcare utilization, and others), would influence physical and mental health. AE may play an important role in the link between attitude and intention thus significantly impacting behaviors such as physical activity. Whether AE would be more important than subjective norm or perceived behavioral control cannot be determined from the present data; however, studies using the TOPB in older adults suggest that attitude is indeed more important than subjective norm (Gretebeck et al., 2007; Gretebeck et al., 2013). For example, in a study conducted on walking behavior in older adults, it was noted that subjective norm did not influence walking behavior while affective and instrumental attitude exerted the greatest influence on intention to walk (Gretebeck et al., 2013). Thus, it seems that AE could be an important component of attitude in the TOPB model; however, future research should assess the importance of AE using structural equation modeling to better understand the influence on intention and behavioral outcomes. Using a sample of older adults of low SES, our results indicate that AE were positively associated with physical activity participation, such that those with better AE had better physical activity participation. AE were also positively associated with components of mental and physical health such that those with better AE had better social functioning, energy and vitality, self-rated general health, and emotional stability (mental health). These findings are novel as they are the first to show an association between AE and health or health behavior among older adults of low SES. There are mixed results on the relationship between SES and AE. While some data suggest that those of low SES have better AE (Kavirajan et al., 2011), there seems to be greater evidence to support the contrary (i.e., lower SES is associated with more negative AE) (Bryant et al., 2012; Bodner, Cohen-Fridel, &Yaretzky, 2011). For example, in a group of 126 older adults aged 64–94 years, Bodner et al. (2011) found that ageist attitudes differed significantly between community-dwelling older adults and those living in sheltered housing, such that those living in sheltered housing were more likely to hold ageist attitudes (poor AE) and had a poorer quality of life as well as poorer mental health. Further, in a study used to assess AE among older adults of diverse cultural backgrounds, Sarkisian et al. (2006) noted that AE varied considerably across different cultural groups. However, these variations could be explained by differences in education levels (i.e., SES was responsible for differences in AE). Several large reviews have assessed the association between low SES and health among adults but have failed to assess the contribution of AE in this relationship. In a series of reviews (Gallo & Matthews, 2003; Matthews, Gallo, & Taylor, 2010; Matthews & Gallo, 2011) it was noted that none of the factors previously thought to link low SES with poor health outcomes were able to fully explain this relationship; however, none of these reviews assessed the potential influence of AE. Nevertheless, negative emotion and attitudes were assessed and it was noted that negative emotions and attitudes predict health outcomes (Gallo & Matthews, 2003). Thus, it appears that AE may be the missing link in previously proposed models of low SES and poor health outcomes. In a proposed pathway (Matthews et al., 2010), it was suggested that low SES leads to a strain on one’s

reserve capacity (i.e., their need to cope with constant challenges drains their physical and mental reserves). This strain on reserve capacity, taken together with other daily challenges associated with low SES, lead to negative emotion and cognition (perhaps negative AE). Such emotions and cognitions are thought to influence health behaviors and in turn overall health. This proposed pathway lines up perfectly with the current study in that, among our sample of low SES older adults, poor AE (i.e., negative emotions) were associated with lower physical activity levels (health behavior) as well as poorer health. Based on the current findings, it is suggested that future research assess AE in greater depth within the context of the proposed pathway between SES and health. Another suggestion made in the above reviews was that negative emotions may begin in childhood and thus a lifelong approach to low SES and poor health outcomes may be required (Gallo & Matthews, 2003; Matthews et al., 2010; Matthews & Gallo, 2011). This is interesting as children and young adults are more likely to hold negative AE and poor aging attitudes (McConatha, Schnell, Volkwein, Riley, & Leach, 2003; O’Hanlon, Campa, & Osofskyb, 1993). Moreover, those of low SES are exposed to the poor health of their parents, grandparents, and other adults in the community, thus reinforcing such AE and stereotypes of older adults. It seems therefore, that among children of low SES, interventions to dispel aging stereotypes and to stop the development of negative AE may be important for ensuring future uptake of positive health behavior and thus better health outcomes. The reviews mentioned above primarily focused on physical health, however, a connection between SES and mental health has also been noted. In a study conducted by Pinquart and Sörensen (2000), older adults with higher SES (particularly income) reported greater life satisfaction, higher self-esteem, and greater happiness. In other words, higher SES may be associated with better mental health. Further, in a study of 12,247 noninstitutionalized men over the age of 50 years, it was found that the association between poor mental health and immigration can be explained at least in part by lower SES among immigrants. In other words, low SES may be a greater contributor to poor mental health than immigration or cultural background (Ladin & Reinhold, 2013). Cultural background and SES have also been linked to physical activity levels in older adults. In a study conducted by Tucker-Seeley, Subramanian, Li, and Sorensen (2009), data from the Health and Retirement Study of adults over the age of 50 years were analyzed. Results indicated an SES gradient in leisure-time physical activity such that those with higher SES had the highest physical activity levels. Similarly, in a 22-year follow-up of adults aged 30–80 years, a change in occupational status (moved upwards) over this period was associated with higher levels of leisure time physical activity (Borodulin et al., 2012). While it is clear that SES is a predictor of physical activity levels, the influence of AE are less clear. Only one other study to date has directly assessed this association. In a sample of older adults, Sarkisian, Prohaska, Wong, Hirsch, and Mangione (2005) found that those with lower AE were less physically active. While this study used a diverse group of older adults (only 50% White), they were from the Los Angeles area and had a higher SES than those in the current sample. Differences in AE have been noted between cultural groups. In a sample of older adults (mean age ranging from 61–75 years), Laditka et al. (2009) found that among Chinese and Vietnamese participants, having few health problems and being mentally alert were given great importance, while American Indians did not believe that health behaviors such as diet or physical activity were important for aging well. Another important factor for AE among

Aging Attitudes, Physical Activity, and Health   185

culturally-diverse groups may be acculturation. Using a group of Korean Americans, Kim, Jang, and Chiriboga (2012) found that those with greater levels of acculturation had more positive views on aging and better functional status. The authors suggested that a sense of independence and personal achievement may be responsible for this. While it is clear that cultural differences in AE exist, our sample was not sufficiently large for cross-cultural comparisons. Thus, it is suggested that future research assess the differential impact of AE on physical activity and health among a variety of cultural groups. A significant relationship between AE and physical health was not found; however, the p-values for AE and physical health (unadjusted and adjusted) were approaching statistical significance. Two of the subscales of physical health—self-rated general health and bodily pain—were associated with AE, although the latter was also approaching statistical significance (p = .058). Previous research has shown that there is an association between AE and physical health. In a study conducted among postmenopausal women (n = 1,151) with a mean age of 72.1 ± 7.2, Kavirajan et al. (2011) found that higher physical and mental health-related quality of life and greater resilience were associated with better AE. In a similar study of community-dwelling men and women (n = 421) aged 71.7 ± 7.9, more positive AE were associated with higher levels of satisfaction with life, better self-reported physical and mental health, and lower levels of both anxiety and depression (Bryant et al., 2012). Thus, it appears that the analysis in the current study was underpowered and that a higher sample size would have led to the expected statistically significant association between AE and physical health. The direction of the relationship between chronic comorbidities and physical activity was significant and positive in that those with a higher number of chronic comorbidities were more physically active. While it is true that physical inactivity is a risk factor for many of the most prevalent chronic conditions, once diagnosed, these individuals are educated about the benefit of physical activity and are enrolled in physical activity or rehabilitation style programs. Thus, those with chronic comorbidities may in fact be more physically active than the healthy population of older adults. Given the cross-sectional nature of the current study, this explanation can only be considered speculative.

Limitations There are several limitations of the current study that should be addressed. First, this study was cross-sectional in nature, thus reverse-causality cannot be ruled out. As such, we cannot conclude from the current data that negative AE lead to poorer health and physical activity levels as the reverse is also possible. The latter is less likely, as previous research strongly indicates that attitudes, beliefs, and expectations predict behavior and not vice versa (Horne & Weinman, 1999; Traill et al., 2012; Sallis et al., 1986). Second, the questionnaires used for AE, physical activity, and health are all based on self-report and are therefore subject to information or misclassification bias. Moreover, the physical activity questionnaire chosen was not validated or reliable and was chosen simply to provide a description of the sample (as part of the larger study). Future research should use validated physical activity questionnaires or objective measures such as accelerometry or clinical markers of disease to avoid such bias. Third, the study was subject to self-selection bias since the participants elected themselves to participate in the study. The generalizability of the study, hence, is limited. Finally, data on measures such as acculturation or immigration were not collected. This would have allowed for a greater understanding of the role of cultural background in the current analysis. Furthermore, the

sample size was not large enough for comparison between cultural groups. Future research should assess such differences, as cultural background and acculturation could significantly impact AE and thus health and health behavior. In conclusion, among culturally-diverse older adults of low SES, it appears that AE are strongly associated with mental health, self-rated general health, and physical activity participation such that those with poorer AE have poorer outcomes. Given the importance of engaging in physical activity and the impact that such behaviors have on health, public health interventions could be conducted to change AE in the aging population, particularly among those of low SES. Previous research has shown that simple, short-term educational or instructional interventions can be highly effective in improving aging attitudes among young adults (Ragan & Bowen, 2001; Snyder, 2005) and young medical professionals (Gonzales, Morrow-Howell, & Gilbert, 2010), but little research has been conducted among older populations. Given the potential impact of such interventions, future research should be conducted among older adults of low SES. Funding This project was funded by the Social Sciences and Humanities Research Council of Canada - Sport Participation Research Initiative.

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Aging expectations are associated with physical activity and health among older adults of low socioeconomic status.

The purpose of the current study was to determine whether aging expectations (AE) are associated with physical activity participation and health among...
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