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Home > RNJ > 2011 > March/April > Factors Associated with Exercise Among Older Adults in a Continuing Care Retirement Community (CE)

Factors Associated with Exercise Among Older Adults in a Continuing Care Retirement Community (CE)
Barbara Resnick, PhD CRNP FAAN FAANP • Christopher D’Adamo, BA, ACE Trainer

Free CE Available

The objectives of this study were to test a model of the factors that influence exercise activities among a group of older adults living in a continuing care retirement community with a wellness center that features a pool , exercise room, and exercise classes. This was a correlational study using a one-time survey. A total of 163 residents with an average age of 86.6 years (SD = 6.1) participated in the study. Ninety (55%) of the participants exercised regularly (30 minutes daily), 88% of whom used the wellness center. Of all participating residents, 49% reported using the wellness center. Self-efficacy and negative outcome expectations directly related to exercise behavior. Marital status, resilience, health status, pain, and fear of falling were indirectly related to exercise. All of these factors explained 15% of the variance in exercise behavior. These findings support previous work and provide future direction for research regarding interventions to increase exercise among older adults. Further, access to a wellness center providing exercise opportunities may increase adherence to a regular exercise regimen among older adults.

Despite the abundant evidence showing the benefits of low-to-moderate-intensity physical activity (PA) such as walking, jogging, gentle cycling, swimming, or dancing at an energy expenditure rate of about 6 kilocalories per minute (Kelley & Kelley, 2006; Martyn-St James & Carroll, 2006; Netz, Wu, Becker, & Tennenbaum, 2005; Pang, Eng, Dawson, & Gylfadóttir, 2006; Roddy et al., 2005), fewer than one-third of older adults engage in low-to-moderate-intensity PA for 30 minutes daily at least 5 days a week as recommended in current guidelines (American College of Sports Medicine and the American Heart Association, 2008). Only 23%–35% of men engage in regular PA, and the number is lower in women, ranging between 17% and 32% (Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System, 2006). Optimizing time spent in PA among older adults has the potential to improve health and overall quality of life in this population.

Numerous factors are associated with PA in older adults (Singh, Chin, Paw, Bosscher, & van Mechelen, 2006), including demographics (age, gender, education, marital status), physical and mental health, cognitive status, psychosocial factors such as resilience and motivation, and access to opportunities to engage in PA. Increased age generally is associated with a decline in PA (Dunlop, Manheim, Song, & Chang, 2002; Waidmann & Liu, 2000), although the decline may be attributed to an increase in the number of comorbid conditions that occur with aging. Women tend to be less physically active than men (Dunlop et al., 2005; Sharkey & Branch, 2004), as are those with multiple comorbid conditions (Verbrugge, Gruber-Baldini, & Fozard, 1996).

Resilience is the ability to achieve, retain, or regain a level of physical or emotional health after devastating illness or loss (Felten & Hall, 2001) or in the face of age-related changes and chronic illnesses. Motivation is different from resilience and is defined as an inner urge that moves or prompts a person to action. Regardless of age, all people have the innate ability to be resilient and successfully return to homeostasis, transform, change, and grow (Cohen-Mansfield, Marx, & Werner, 1992). A person must summon motivation while facing adversity to be resilient. Consequently, motivation may be present independent of resilience, but resilience depends upon a person being motivated to successfully reintegrate (Harris, 2008).

Motivation often is operationalized using self-efficacy and outcome expectations as described in self-efficacy theory (Bandura, 1997). The theory of self-efficacy suggests that the cognitive control of behavior, or the motivation to engage in any activity, is based on two types of expectations: self-efficacy expectations, defined as a person’s beliefs in their capability to perform a course of action to attain a desired outcome, and outcome expectancies, which are beliefs that a certain consequence will be produced by personal action. The stronger one’s self-efficacy and outcome expectations, the more likely he or she will be to initiate and continue with an activity. It has repeatedly been shown that self-efficacy and outcome expectations for exercise among older adults are associated with time spent exercising (McAuley et al., 2006; Resnick, Orwig, Yu-Yahiro, et al., 2007).

Although access to health-promotion activities such as exercise classes is important, access alone will not improve adherence to exercise (Becker et al., 2005). Even when appropriate exercise opportunities are made accessible to older adults, exercise is not always initiated or maintained over time (Chin, Walters, Cook, & Huang, 2007; Haber, 2007). Accessibility is an important first step in trying to increase time spent exercising, however. Environments that are safe, have walkable spaces, or feature age-appropriate exercise equipment and activities help facilitate adherence to regular exercise (Humpel, Owen, & Leslie, 2002; Joseph, Szimring, Harris-Kojetin, & Kiefer, 2005; Takano, Nakamura, & Watanabe, 2002).

Figure 1Wellness Centers

Many continuing care retirement communities (CCRCs) develop wellness centers as a way to increase access to exercise opportunities. In some CCRC settings, wellness centers address the entire spectrum of healthcare services (primary medical care, specialty services, and health-promotion activities). Other wellness centers may focus exclusively on exercise activities (scheduled exercise classes, aquatic activity, and open access to age-appropriate exercise equipment).

This study tests a model (Figure 1) of the factors that influence exercise behavior among a group of older adults living in a CCRC that has a wellness center with age-appropriate exercise activities. The hypothesized model of exercise behavior was developed based on empirical findings and theory. The model incorporates the theory of self-efficacy (Bandura, 1997), which focuses on self-efficacy expectations or the belief in one’s ability to organize and execute a course of action, and outcome expectations, which include beliefs that if behaviors are performed, there will be a certain outcome. The theory states that the stronger one’s self-efficacy and outcome expectations about exercise, the more likely it is that he or she will exercise regularly. Previously supported factors that influence exercise behavior including resilience (Wagnild, 2003), martial status (Resnick & Nigg, 2003), chronic illness (Resnick & Nigg), pain (Medina-Mirapeix, Escolar-Reina, Gascón-Cánovas, Montilla-Herrador, & Collins, 2009), subjective perceptions of health (Resnick, Orwig, D’Adamo, et al., 2007), and fear of falling (Resnick, Orwig et al., 2007) were hypothesized to directly and indirectly influence time spent in exercise through self-efficacy and outcome expectations.

Methods

Study Design

This correlational study was based on data from an annual face-to-face health interview conducted with residents living in a CCRC setting. Residents living in the independent living apartments within the CCRC were eligible to participate if they scored a 24 or higher on a Mini-Mental State Exam (MMSE; Folstein, Folstein, & McHugh, 1975). The interviews followed informed consent and all were scheduled at a time that was convenient for the resident and conducted privately in his or her apartment or in the outpatient healthcare office setting. Data collection used this face-to-face approach to optimize survey completion (Resnick, Riebe, King, & Ory, 2008). The study was approved by a university-based institutional review board.

Table 1Sample

Two hundred residents lived in the facility during the 6 months in which the interviews were conducted. Among residents, 163 (82%) consented to participate. Thirty (15%) refused to participate, and the remaining 7 (3%) had MMSE scores lower than 24. As noted in Table 1, the majority of participants were widowed women and the mean age was 86.6 (SD = 6.1) years. On average, these participants had four chronic illnesses and most were white. All participants had at least some college education.

Setting

The CCRC in which the study was conducted had a strong focus on wellness and optimization of health and function. In 1996 the facility developed an exercise room/area that initially housed a few pieces of exercise equipment donated by residents such as stationary exercise bikes. Over the subsequent years, the exercise room expanded into a large wellness center with a fitness room with age-appropriate exercise equipment, an open area for exercise classes, and an indoor pool. Two full-time trainers are available in the Wellness Center during daytime hours, and daily classes focus on flexibility, balance, and endurance. A geriatric nurse practitioner convenes monthly health talks on a wide range of health topics (e.g., exercise, memory loss, congestive heart failure, anemia), and participation in exercise is encouraged across all clinical populations. Monthly attendance for each of these talks ranges between 35 and 50 residents.

Measures

Study measures included the Self-Efficacy Expectations for Exercise Scale (Resnick & Jenkins, 2000), Outcome Expectations for Exercise (OEE)-2 Scale (Resnick, 2005), Resilience (Wagnild & Young, 1993), the MMSE (Folstein et al., 1975), a single-item health status question from the 12-Item Short-Form Health Survey (SF-12; Burdine, Felix, Abel, Wiltraut, & Musselman, 2000), and the Yale Physical Activity Survey (YPAS; Dipietro, Caspersen, Ostfeld, & Nadel, 1993). Participants also were asked about their use of the wellness center, which included participation in exercise classes and use of exercise equipment or the pool. Medical information was obtained from chart review, and a sum of chronic illnesses was calculated (chart-based diagnosis of hypertension, congestive heart failure, atrial fibrillation, stroke, malignancy, degenerative joint disease, rheumatoid arthritis, osteoporosis, Parkinson’s disease, anemia, diabetes, chronic obstructive pulmonary disease, and depression).

The Self-Efficacy for Exercise Scale (SEE; Resnick & Jenkins, 2000) is a 9-item measure (mean total score range 0–10) that focuses on self-efficacy expectations related to the ability to exercise regularly in the face of barriers to exercising. Higher scores indicate stronger self-efficacy expectations. Previous use of this measure with older adults provided evidence of internal consistency and construct validity (Harnirattisai & Johnson, 2002, 2005; Resnick & Jenkins, 2000). In the current study there was evidence of internal consistency with an alpha coefficient of .80.

The OEE-2 Scale (Resnick, 2005) includes both positive (eight items with a mean total score ranging between 1 and 5) and negative (four items with a mean total score ranging between 1 and 5) outcome expectations. Previous use has provided evidence for validity based on confirmatory factor analysis, internal consistency, and model fit based on a Rasch analysis (Resnick, 2005). Higher scores are indicative of stronger positive and negative outcome expectations. There was some evidence of internal consistency of the OEE-2 scale, with an alpha coefficient of .75 for positive outcome expectations and .71 for negative outcome expectations.

The moderate-intensity exercise subscale of the YPAS (Dipietro et al., 1993) was used to measure time spent in exercise. Items reflect time (minutes per week) in moderate-level activities such as brisk walking, swimming, biking, and jogging. Prior use of the YPAS provided evidence of test-retest reliability (r = 0.63, p < .001) and construct validity (Dipietro et al.).

Resilience was measured with the 14-item Resilience Scale (Wagnild & Young, 1993). Participants responded to statements about resilience using a 1 (disagree) to 7 (agree) response format. Higher scores indicate stronger resilience. There was evidence of internal consistency (alpha coefficient of .91) and validity based on construct validity, with all items fitting the measurement model (Wagnild, 2009). There also was evidence of internal consistency of the resilience measure (alpha coefficient of .89).

Physical health was measured using the single-item question about general health from the SF-12. Participants were asked to rate their overall health as excellent, very good, good, fair, or poor (Burdine et al., 2000), with higher scores reflecting better health. Testing of this single-item measure provided evidence of test-retest reliability and construct validity (Burdine et al.). Fear of falling also was measured using a single-item measure that asked participants to rate their fear of falling on a scale of 0 (no fear) to 4 (a lot of fear; Resnick, 1998). Previous use supported evidence of test-retest reliability and construct validity (Resnick, 1998).

Data Analysis

Descriptive statistics describe the sample with regard to demographics and use of the wellness center. Model testing was done using structural equation modeling and the Amos statistical program (Arbuckle, 2006). The sample covariance matrix was used as input, and a maximum likelihood solution was sought. The chi-square statistic, the normed fit index (NFI), and Steigers Root Mean Square Error of Approximation (RMSEA) were used to estimate model fit (Bollen, 1989; Loehlin, 1998). Path significance was based on the critical ratio (CR). A p < .05 significance level was used for all analyses.

Table 2Results

Descriptive results for all study variables are provided in Table 2. Participants were confident about their ability to exercise, had strong positive and weak negative outcome expectations for exercise, were resilient (M = 6.1, SD = .74), and participated in 30 minutes of exercise daily (M = .49 hr, SD = .43). A total of 90 (55%) of the 163 participants reported they exercised regularly. Of the 163 residents in the study, 79 (49%) used the wellness center, and 88% of the residents who exercised regularly did so using the wellness center.

The full hypothesized model (Figure 1) was tested and 14 of the 30 hypothesized paths were statistically significant (Figure 2). There was a fair fit of the ¨hypothesized model to the data (χ2 = 45.56, df = 25, p = .01, ratio = 1.8, NFI = .78, and root mean square error of approximation [RMSEA] = .06). Resilience was significantly related to health status, comorbid conditions, and positive and negative outcome expectations. Those who were more resilient had fewer comorbidities and stronger positive and weaker negative outcome expectations, and believed themselves to be in better health. Health status was significantly related to fear of falling and pain to the extent that those with better health had less fear of falling and less pain. Pain and fear of falling were related to negative outcome expectations, and those who had more pain and more fear of falling had stronger negative outcome expectations for exercise.

Marital status was the only variable to influence self-efficacy expectations; those who were married had stronger self-efficacy expectations. Self-efficacy and negative outcome expectations were the only variables to directly influence exercise behavior, and those with stronger self-efficacy expectations and weaker negative outcome expectations for exercise spent more time in exercise. Marital status indirectly influenced exercise through self-efficacy expectations. Resilience, health, pain, and fear of falling indirectly influenced exercise through negative outcome expectations. Taken together, all variables in the model explained 15% of the variance in exercise.

Figure 2Discussion

Among the older adults in this community, 55% were actively engaged in regular exercise (30 minutes of exercise daily), and the majority (88%) of those who exercised used the wellness center. Despite the advanced mean age and number of comorbidities of study participants, they were more likely to report exercising regularly than older adults who report their exercise activity. In 2000 the Centers for Disease Control and Prevention (CDC) reported that 28%–34% of adults age 65–74 and 35%–44% of adults age 75 or older are inactive, meaning they engage in no leisure-time low-to-moderate-intensity exercise (CDC & National Center for Health Statistics, 2000). This lack of activity has not improved over time. In 2006 the CDC reported that fewer than one-third of older people engage in daily exercise, and there is a negative association between age and time spent in exercise (CDC Behavioral Risk Factor Surveillance System, 2006). Although it is impossible to conclude that the high percentage of residents engaging in exercise was due to the presence of the wellness center, it is likely that the availability and convenience of a variety of exercise options was a factor. In addition, ongoing education about benefits of exercise and encouragement to exercise may have contributed to the high percentage of older adults who regularly exercised.

The majority of study participants using the wellness center used the equipment during unstructured time periods rather than attending scheduled classes. It is possible that the study participants, who primarily were White and well-educated, did not need the ¨external stimulation and motivation provided by group exercise and classes to engage in regular exercise activities (Resnick, 2002; Resnick, Orwig, Zimmerman, Simpson, & Magaziner, 2005). The participants were resilient and generally had high self-efficacy for exercise, so they may have been self-motivated and determined to exercise.

This study included a one-time interview and only captured current exercise behavior. Future research in CCRC settings should consider testing the implementation of wellness centers, the introduction of new classes offered within centers, and the long-term influence of the availability of exercise opportunities on adherence to regular exercise. It is possible that the introduction of classes is one way to initiate exercise behavior, but that ongoing access to age-appropriate exercise equipment may facilitate adherence to regular exercise over time.

Marital status directly and indirectly influenced exercise through self-efficacy. We anticipate that married participants were more likely to be exposed to verbal encouragement and role modeling from their spouse, which strengthened self-efficacy and increased adherence to exercise. People without spouses may need more external encouragement than those with spouses, and they should be the focus of self-efficacy-based interventions.

Model testing also showed that negative outcome expectations and self-efficacy expectations directly influenced exercise behavior among participants. Previous research repeatedly noted an association between self-efficacy and outcome expectations and exercise (Brassington, Atienza, Perczek, DiLorenzo, & King, 2002; Estabrooks, Fox, Doerksen, Bradshaw, & King, 2005; Li, Fisher, Harmer, & McAuley, 2005; McAuley et al., 2006). Of note, however, was the finding that negative, not positive, outcome expectations directly were associated with exercise behavior. Similar findings have been reported among older adults who have sustained acute medical problems such as a hip fracture or stroke (Resnick, Orwig, D’Adamo, et al., 2007) and with regard to long-term adherence to exercise (Resnick, Luisi, & Vogel, 2008). It is possible that older adults who have sustained acute events or have multiple comorbidities may be particularly concerned about exacerbating underlying problems (e.g., reinjuring a hip) and consequently are influenced by negative outcome expectations. Interventions focused on decreasing negative outcome expectations may effectively increase adherence to regular exercise among older individuals.

Resilience, which indirectly was associated with exercise through negative outcome expectations, reflects individual tenacity with regard to overcoming physical or emotional problems (Felten & Hall, 2001). Resilient people tend to manifest adaptive behavior, especially with regard to social functioning, morale, and somatic health (Wagnild & Young, 1993), and are less likely to succumb to illness (Caplan, 1990; O’Connell & Mayo, 1998). Resilience, as a component of a person’s personality, develops and changes over time through ongoing experiences with the physical and social environment (Glantz & Johnson, 1999). It is not surprising, therefore, that resilient older individuals were less likely to have negative outcome expectations related to their confidence in exercise. Future research should continue to explore the influence of resilience and focus on ways to strengthen resilience in older adults.

This study’s findings also support previous work demonstrating that resilience is associated with motivation (i.e., self-efficacy; Fredrickson, 2001; Ingledew, Markland, & Sheppard, 2004) and that motivation serves as a mediator between resilience and outcome behaviors in older adults (Wright, Zautra, & Going, 2008). In the KNEE study, a longitudinal intervention study aimed at reducing levels of pain and disability in older adults with knee osteoarthritis, resilience was mediated by self-efficacy and indirectly was related to function and physical activity. In the current study, negative outcome expectations mediated the relationship between resilience and exercise rather than self-efficacy expectations. As noted previously, this may be due to the differences in the samples, with participants in the KNEE study including younger, healthier people who may have been less concerned about negative outcome expectations (Write, Zautra, Going, 2008).

Although there was a fair fit of the data to the model, the variables in the model only explained a small percentage of the variance of exercise behavior. It is possible that other factors such as social supports for exercise (friends, family, and trainers or healthcare professionals), institutional policies (e.g., hours of access to a wellness center), or specific mental health issues such as depression or anxiety also may be associated with exercise behavior in older adults (Bonnet et al., 2005; Jensen, 2005). Future research should consider these additional factors.

Limitations

This study was limited in that it was cross-sectional in nature and included a one-time interview and subjective data from a homogenous sample of older adults in a CCRC setting. The sample size was small (fewer than 200 participants), and it is unlikely that the model tested would be consistently replicated across other samples. Consequently, the findings cannot be generalized to other samples.

There also were limitations related to measurement. All measures were based on self-report and dependent on accurate recall, and consequently may not accurately reflect behavior. There is evidence, for example, that survey reporting by older adults of time spent in exercise generally is inflated (Resnick, Riebe et al., 2008). To strengthen the findings, future research should consider the use of actigraphy to confirm time spent in exercise. Despite the many limitations of this study, the findings supported previous research noting the relationship between self-efficacy and outcome expectations, particularly negative outcome expectations, with exercise. Other factors such as resilience, health, pain, and fear of falling indirectly influenced exercise behavior through self-efficacy and negative outcome expectations. Last, the findings suggest possible benefit of having wellness centers in CCRCs to increase adherence to regular exercise.

About the Authors

Barbara Resnick, PhD CRNP FAAN FAANP, is a professor and Sonya Ziporkin Gershowitz Chair in Gerontology at the University of Maryland School of Nursing in Baltimore, MD. Address correspondence to her at barbresnick@aol.com.

Christopher D’Adamo, BA, ACE Trainer, is an assistant professor in the department of family and community medicine at the University of Maryland School of Medicine in Baltimore, MD.

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Continuing Education

Rehabilitation Nursing is pleased to offer readers the opportunity to earn nursing contact hours for its continuing education articles by taking a posttest through the ARN Web site. The posttest consists of questions based on this article, plus several assessment questions (e.g., how long did it take you to read the article and complete the posttest?). A passing score of 80% on the posttest and completion of the assessment questions yield one nursing contact hour for each article.

To earn contact hours, go to www.rehabnurse.org/education/cearticles.html. (You may also go to www.rehabnurse.org → Education → RNJ Online CE.) Once there, you may read the article again or go directly to the posttest assessment by selecting "Purchase CE Test."

Between March 1, 2011, and April 30, 2011, contact hours for this article will be free for ARN members who complete the posttest and evaluation. After April 30, 2011, regular pricing will apply.

The Association of Rehabilitation Nurses is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation (ANCC-COA).