CLINICAL SCHOLARSHIP
Gender Differences in the Predictors of Physical Activity Among Assisted Living Residents Yuh-Min Chen, PhD, RN1 , Yueh-Ping Li, PhD, RN2 , & Min-Ling Yen, MSN, RN3 1 Lambda Beta-at-Large, Associate professor, School of Nursing, China Medical University, and Supervisor, Department of Nursing, China Medical University Hospital, Taiwan 2 Assistant Professor, Department of Nursing, Chung Hwa University of Medical Technology, Taiwan 3 Supervisor, Department of Nursing, Kuang Tien General Hospital, Taiwan
Key words Assisted living facilities, gender differences, older adults, physical activity Correspondence Dr. Yuh-Min Chen, No. 91, Hsueh Shih Rd, Taichung, Taiwan 40402, ROC. E-mail: [email protected] Accepted: January 27, 2015 doi: 10.1111/jnu.12132
Abstract Purpose: To explore gender differences in the predictors of physical activity (PA) among assisted living residents. Design and Methods: A cross-sectional design was adopted. A convenience sample of 304 older adults was recruited from four assisted living facilities in Taiwan. Two separate simultaneous multiple regression analyses were conducted to identify the predictors of PA for older men and women. Independent variables entered into the regression models were age, marital status, educational level, past regular exercise participation, number of chronic diseases, functional status, self-rated health, depression, and self-efficacy expectations. Findings: In older men, a junior high school or higher educational level, past regular exercise participation, better functional status, better self-rated health, and higher self-efficacy expectations predicted more PA, accounting for 61.3% of the total variance in PA. In older women, better self-rated health, lower depression, and higher self-efficacy expectations predicted more PA, accounting for 50% of the total variance in PA. Conclusions: Predictors of PA differed between the two genders. The results have crucial implications for developing gender-specific PA interventions. Clinical Relevance: Through a clearer understanding of gender-specific predictors, healthcare providers can implement gender-sensitive PA-enhancing interventions to assist older residents in performing sufficient PA.
In Taiwan, the population of adults 65 years of age and older is rapidly increasing, which compares to the increase in life expectancy worldwide. By 2025, the older population is expected to constitute 20% of the general population (National Development Council, 2012). The aging processes increase an individual’s vulnerability to chronic diseases and functional disabilities, which in turn decrease quality of life and increase healthcare costs. About 81.1% of Taiwanese older adults have one or more chronic diseases, and 20.8% have functional disabilities (Ministry of Health and Welfare, 2014). Improving the health of older adults is a critical healthcare concern. Because of the rapidly increasing older population, the need for long-term care has increased (Hirschfeld,
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2009). Approximately 1.6% of Taiwanese older adults reside in assisted living facilities (Ministry of Health and Welfare, 2015). Compared with older adults in skilled nursing homes, those in assisted living facilities have greater health potential. However, the care needs of older adults will change as their health and functional status change. The World Health Organization (WHO; 2014) urges the implementation of population-based strategies and programs to increase physical activity (PA) levels worldwide. PA is any bodily movement produced by skeletal muscles that requires energy expenditure (WHO, 2014). The health benefits of PA for older adults have been widely recognized. PA is beneficial to physical, mental,
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and psychosocial health (Archer, Paluch, Shook, & Blair, 2013; WHO, 2014). PA has a protective effect in preventing the decline of physical functioning and maximizing functional independence (Archer et al., 2013; Elsawy & Higgins, 2010), particularly in frail older adults (Rabaglietti, Liubicich, & Ciairano, 2011). However, the levels ¨ of PA decrease with increasing age (Paivi, Mirja, & Terttu, 2010) and tend to be lower in institutionalized older adults than in their community-dwelling counter´ ´ ´ ´ parts (Krol-Zieli nska, Kusy, Zielinski, & Osinski, 2010; ´ Salguero, Mart´ınez-Garc´ıa, Molinero, & Marquez, 2011). Older adults tend to reduce their PA after institutionalization because of various barriers, such as health problems and environmental constraints (Chen, 2010; ´ ´ Krol-Zieli nska et al., 2010). However, physical inactivity can accelerate the rate of physical and functional decline (Archer et al., 2013). Further loss in functional status resulting from inactivity not only increases the intensity and cost of care but also negatively affects quality of life. Thus, assisted living residents should maintain and increase their level of PA to prevent the negative consequences of inactivity. Previous studies on PA in older adults indicated that several factors relate to activity participation. Among these factors, health status, depression, and self-efficacy expectations have been identified as major determinants of PA among older adults. Studies have shown that poor health and functional limitations are negatively related to PA (Cohen-Mansfield, Shmotkin, & Goldberg, 2010; Haley & Andel, 2010). Depression has also been found to predict declining PA and is a leading cause of disability in older adults (Perrino, Mason, Brown, & Szapocznik, 2010). It is noteworthy that the prevalence rate of depression among institutionalized older adults is even higher than that among community-dwelling older adults (Anstey, von Sanden, Sargent-Cox, & Luszcz, 2007; Salguero et al., 2011). Self-efficacy is the belief that one can successfully perform a behavior that is necessary to yield a particular outcome (Bandura, 1997). Previous research has consistently shown that self-efficacy expectations are a significant predictor of PA for older adults (Vagetti et al., 2014). Because of the frail characteristics and high prevalence rate of depression among institutionalized older adults, these three factors particularly must be noted when studying assisted living residents. However, the extent to which these factors predict PA of assisted living residents remains unclear. Gender differences should be considered in exploring PA of older adults. Older men and women suffer from different types of chronic diseases (Centers for Disease Control and Prevention [CDC], 2007). In addition, older women encounter more obstacles to PA, such as a lower educational level, poorer health, a higher 212
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prevalence of functional limitations, and more depressive symptoms than older men (CDC, 2007; WHO, 2014). Moreover, previous studies have repeatedly shown that older women engage in a lower level of PA than older men (Haley & Andel, 2010; Lin, Yeh, Chen, & Huang, 2010). Despite a growing research interest in PA among community-dwelling older adults, few studies on this topic have addressed assisted living residents, not to mention gender differences. Given the great health potential of assisted living residents, conducting cross-gender comparisons of PA is imperative. The findings will enable healthcare providers to apply empirical evidence in practice. Therefore, the purpose of this study was to explore gender differences in the predictors of PA among assisted living residents. The predictors explored were demographic characteristics, health status, depression, and self-efficacy expectations. Through a clearer understanding of gender-specific predictors, healthcare providers will be able to implement gender-sensitive PA-enhancing interventions that foster physical independence among older residents.
Methods Sample and Data Collection A cross-sectional design was adopted for this study. A convenience sample of 304 older adults was recruited from four assisted living facilities in the middle region of Taiwan. Eligibility criteria were: (a) 65 years of age or older, (b) residence in an assisted living facility for at least 3 months, (c) ability to communicate verbally in Mandarin or Taiwanese, and (d) absence of cognitive problems in understanding the questionnaires. Formal ethical approval was obtained from the research ethics committee. The manager of each facility helped identify eligible residents. The investigators then approached eligible residents individually and invited them to participate in this study. They were provided information about the research purpose and process. In addition, the residents were assured of confidentiality and anonymity in the use of data. They were also informed of their right to withdraw from the study at any time. After obtaining the informed consent, a one-on-one interview was conducted with each participant.
Measures The research instruments comprised five parts: a demographic data sheet, a health status profile, the Geriatric Depression Scale Short-Form (GDS-SF), the Self-Efficacy for Physical Activity Scale (SEPA), and the Seven-day Physical Activity Recall (7-day PAR). Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
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Demographic data sheet. The collected demographic data were age, gender, educational level, marital status, and past regular exercise participation. Past regular exercise participation was measured using the following question: “Did you participate in regular exercise for at least 3 consecutive months before entering this facility?” Regular exercise was explained to participants as participating in at least 20 min of continuous PA three times per week for at least 3 months (Resnick, 2000).
Health status profile. Measures of health status included the number of chronic diseases, functional status, and self-rated health. The data on the total number of chronic diseases were collected using participants’ selfreports of physician-diagnosed chronic diseases and then validated by performing chart review. Functional status was measured using the Barthel Index (BI). The BI assesses an individual’s degree of independence in performing 10 basic activities of daily living (Mahoney & Barthel, 1965). Total scores range from “totally dependent” (0) to “totally independent” (100). Self-rated health was measured using a single item. Participants rated their current health from “very poor” (1) to “very good” (5). Higher scores indicate better perceived health.
GDS-SF. Depression was measured using the GDS-SF developed by Sheikh and Yesavage (1986). The GDS-SF consists of 15 items relating to depressed mood over the past week. Participants were asked to respond to each statement with a “yes” (1) or “no” (0) answer. The scores range from 0 to 15. The Cronbach’s α for this study was .86.
SEPA. Self-efficacy expectations were measured using a nine-item Self-Efficacy for PA Scale modified from the Self Efficacy for Exercise Scale (SEE; Resnick & Jenkins, 2000). The SEE is a nine-item scale for measuring self-efficacy expectations related to the ability to continue exercising in the face of barriers to exercising (Resnick & Jenkins, 2000). In the SEPA, the term “exercise” in the SEE is replaced with “physical activity.” In addition, to fit the living situations of institutionalized older adults, three items were reworded. Participants responded to each item on an 11-point scale ranging from “not very confident” (0) to “very confident” (10). The scale is scored by summing the total score for each response and dividing the total score by the number of items. Higher scores indicate greater confidence. In the current study, Cronbach’s α for the SEPA was .95. The 2-week testretest reliability of 30 participants was .91. Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
7-day PAR. PA was measured using the 7-day PAR (Sallis et al., 1985). Each participant was asked to recall the time they spent in sleep, and performing moderate, hard, and very hard activities over the previous 7 days. Moderate activities are as strenuous to an individual as walking at a normal pace and very hard activities are as strenuous as running. The intensity of hard activities is in between walking and running. Hours spent in performing light activity were determined as the remaining time in 24 hr that was not spent engaging in the aforementioned activities. Total time in each level of activity per day was added up and multiplied by the appropriate mean metabolic equivalents (METs) and then summed to obtain a total MET score. One MET is approximately equal to 1 kcal/kg/hr in sleep, and 1.5, 4.0, 6.0, and 10.0 were assigned to light, moderate, hard, and very hard activities, respectively. This yields an estimation of the kilocalories per kilogram used per day (kcal/kg/day). In this study, the 2-week test-retest reliability of 30 participants was .95.
Data Analysis The SPSS for Windows version 17.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data. Descriptive statistics, including percentages, means, and standard deviations, were used to describe the sample and study variables. The chi-square and independent t test were used to examine gender differences in demographic characteristics, health status, depression, self-efficacy expectations, and PA. The Pearson product-moment correlation coefficient, independent t test, and one-way analysis of variance (ANOVA) were used to examine the relationships between demographic characteristics, health status, depression, self-efficacy expectations, and PA. Finally, two separate simultaneous multiple regression analyses were conducted to identify the predictors of PA for older men and women.
Results Description of Sample Characteristics and Study Variables The average age of the 304 participants was 79.4 years (SD = 6.69, range = 65–102), of which 55.9% were male. Compared with men, women were older (average age = 80.59 vs. 78.41). The majority of women (67.9%) were widowed, while the majority of men (51.6%) were single or divorced. No significant gender differences were found in the educational level, past regular exercise participation, number of chronic diseases, functional status, selfrated health, depression, and self-efficacy expectations 213
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Table 1. Gender Differences in Demographic Characteristics, Health Status, Depression, and Self-Efficacy Expectations (N = 304) Men (n = 170) Variables
Women (n = 134)
n
%
n
%
35 87 48
20.2 51.6 28.2
28 15 91
20.9 11.2 67.9
122 29 19
71.8 17.1 11.1
104 15 15
77.6 11.2 11.2
68 102
40.0 60.0
43 91
32.1 67.9
p 1
F = 2.99
30.88 ± 3.04 31.63 ± 1.40 32.92 ± 1.54
30.50 ± 2.56 31.17 ± 2.59 32.12 ± 1.77 t = −3.13∗∗
t = −.98
32.03 ± 2.91 30.71 ± 2.54
31.07 ± 2.64 30.61 ± 2.48
Note. t/F = t value of independent-sample t test or F value of analysis of variance. ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001. Table 4. Gender Differences in Predictors of Physical Activity (N = 304) Men (n = 170) p
B
SE
β
p
.010
.843
−.036
.025
−.102
.145
.386 .405
.100 .044
.154 .510
.415 .318
.612 .444
.052 .059
.499 .475
.378 .471 .289 .151 .008 .215 .059 .076
.038 .116 .156 −.079 .335 .125 −.090 .239
.462 .032 .003 .157 .001 .047 .406 .001
.953 .941 .085 −.128 .010 .516 −.144 .210
.551 .544 .357 .181 .008 .246 .070 .088
.119 .118 .016 −.054 .147 .173 −.265 .212
.086 .086 .813 .482 .230 .038 .041 .019
Variables
B
Age Marital status (married as reference group) Single/divorced Widowed Educational level (no formal education as reference group) Elementary school Junior high school/higher Past regular exercise experience Number of chronic diseases Functional status Self-rated health Depression Self-efficacy expectations R2
.005
.023
.553 .267 .279 1.018 .875 −.215 .026 .430 −.049 .247
junior high school or higher educational level (β = .116, p = .032), past regular exercise participation (β = .156, p = .003), better functional status (β = .335, p = .001), better self-rated health (β = .125, p = .047), and higher self-efficacy expectations (β = .239, p = .001) predicted more PA. All of these predictors explained 61.3% of the total variance in PA. In older women, better self-rated health (β = .173, p = .038), lower depression (β = -.265, p = .041), and higher Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
β
Women (n = 134)
SE
.613
.500
self-efficacy expectations (β = .212, p = .019) predicted more PA, accounting for 50% of the total variance in PA (Table 4).
Discussion There was no gender difference in total PA. However, older men engaged in a significantly higher amount of hard activities than older women. Previous research also 215
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indicated that older women engaged in lower intensity of PA than older men (Lin et al., 2010; Moschny, Platen, Klaaßen-Mielke, Trampisch, & Hinrichs, 2011). One possible explanation is that older women had a worse functional status than older men although the difference was not statistically significant. In addition, possibly because of Taiwanese cultural expectations, these older women took care of family members and handled household chores when they were young. Therefore, they probably had fewer opportunities and less time to develop requisite skills of PA or become acquainted with PA, particularly leisure-time PA. Although PA need not be strenuous to obtain health benefits, growing old undermines the caloric expenditure of PA among older women more than among older men (Lin et al., 2010). Therefore, efforts focusing on preventing the decrease in the level of PA among older women and enabling them to perform the desired level of PA to obtain health benefits are important. Further longitudinal research is suggested to explore gender differences regarding the course of PA changes and the influential factors among assisted living residents. The findings will provide useful information for guiding the design of programs for preventing PA decline over time. Similarities and differences existed between older men and women regarding predictors of PA. In older men, a junior high school or higher educational level, past regular exercise participation, better functional status, better self-rated health, and higher self-efficacy expectations predicted more PA. In older women, better self-rated health, lower depression, and higher self-efficacy expectations predicted more PA. The result indicated that older men with a junior high school or higher educational level engaged in more PA than older men with no formal education. It might be that older men with a higher educational level possibly have more opportunities to find or use resources for PA. In addition, better educated older adults also tend to have more knowledge regarding the benefits of being physically active, which motivates them to perform more PA (Haley & Andel, 2010). Healthcare providers must pay more attention to the PA status of older men with a lower educational level. Past regular exercise participation was another significant predictor of PA in older men. Previous qualitative research has also shown that past PA experience is an important influencing factor for PA in institutionalized older adults (Chen & Li, 2014; Phillips & Flesner, 2013). Previously cultivated habits regarding PA play an influential role in current PA performance (Archer et al., 2013). On the other hand, a past sedentary lifestyle poses a barrier to PA (Chen, 2010). This finding suggests that healthcare providers should assess and consider residents’ previous 216
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exercise habits to facilitate PA engagement or to modify their habitual sedentary lifestyle. Functional status was also a significant predictor of PA in older men. In older women, although functional status was significantly correlated with PA in the bivariate analysis, it was not a significant predictor of PA. It appears that functional status plays a more crucial role in PA for men than for women. This result was similar with the finding from Haley and Andel (2010). One possible explanation is that men generally perform higher intensity of PA than women (Lin et al., 2010; Moschny et al., 2011). Therefore, poor functional status causes more difficulties in PA engagement in men. This implies the need to take the individual functional condition into consideration in planning PA programs and in providing advice on activities suitable for each resident’s level of function. Self-rated health was a significant predictor of PA in both older men and women. Better self-rated health predicted a higher amount of PA. Self-rated health has been consistently identified as a predictor of PA in older adults (Dogra, 2011). In addition, self-rated health has been reported to be a significant predictor of health outcomes (Mavaddat, Van der Linde, Savva, Brayne, & Mant, 2013). PA might play a mediating role between self-rated health and health outcomes. Further research can explore this issue. Depression was a significant negative predictor of PA in older women. Older women with more depressive symptoms tended to perform less PA. Although depression is not a significant predictor of PA in older men, it was significantly correlated with PA in the bivariate analysis. This result is similar to those of previous studies (Perrino et al., 2010; Wassink-Vossen et al., 2014). Depressive symptoms limit PA engagement. Especially, more older women than men suffer from depressive symptoms (Ku, Fox, & Chen, 2009; Salguero et al., 2011; WHO, 2014). Thus, proper identification of depression is necessary to prevent physical inactivity and the subsequent decline in functional ability, particularly among older women. Self-efficacy expectations are a significant predictor of PA for both older men and women. Stronger self-efficacy expectations predicted a higher amount of PA. Selfefficacy has been consistently reported to be a significant ´ predictor of PA for older adults (Phillips, Wojcicki, & McAuley, 2013; Sniehotta et al., 2013). To the best of our knowledge, there is a lack of studies exploring the relationship between PA and self-efficacy expectations in Taiwanese older population. The finding of this study supports the practical importance of self-efficacy expectations on PA in different cultures. Self-efficacy expectations are amenable to change. Therefore, it is suggested that healthcare providers can apply four Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
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major sources of self-efficacy, including enactive mastery experience, vicarious experience, verbal persuasion, and physiological and affective states (Bandura, 1997), to enhance self-efficacy beliefs for PA to bolster older residents’ confidence in PA participation.
Limitations Some limitations of this study need to be mentioned. First, this study used a convenience sample from four assisted living facilities in Taiwan. Therefore, generalizing the results to other long-term care residents within and outside Taiwan is limited. Second, the use of crosssectional design restricts the inference of causality. Third, the use of the self-reported 7-day PAR to measure PA might introduce the problem due to recall bias or social desirability. To validate these findings, future research can include an objective measure of PA, such as accelerometer data, at the same time.
Conclusions The results of this study showed that predictors of PA are inconsistent between the two genders. The findings supplement the scant knowledge on PA in assisted living residents. The results have crucial implications for developing gender-specific PA interventions. Strategies responsive to the differences between older men and women would facilitate planning and implementing effective interventions for assisting older adults in engaging in sufficient PA. Moreover, further research is warranted to develop and examine the effectiveness of genderspecific PA programs.
Acknowledgments This study was funded by a 2013 grant from the China Medical University (CMU 102-S-20).
Clinical Resources
r r r
Physical Activity Guidelines for Older Adults, American Academy of Family Physicians: http:// www.aafp.org/afp/2010/0101/p55.html Physical Activity and Older Adults, World Health Organization: http://www.who.int/diet physicalactivity/factsheet_olderadutls/en Physical Activity in Older Americans, American Heart Association: http://www.heart.org/HEART ORG/GettingHealthy/PhysicalActivity/FitnessBasics/ Physical-Activity-in-Older-Americans_UCM_ 308039_Article.jsp
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References Anstey, K. J., von Sanden, C., Sargent-Cox, K. H., & Luszcz, M. A. (2007). Prevalence and risk factors for depression in a longitudinal, population-based study including individuals in the community and residential care. American Journal of Geriatric Psychiatry, 15(6), 497–505. doi:10.1097/JGP.0b013e31802e21d8 Archer, E., Paluch, A. E., Shook, R. P., & Blair, S. N. (2013). Physical activity and the science of successful aging. Kinesiology Review, 2(1), 29–38. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company. Centers for Disease Control and Prevention. (2007). Trends in health status and health care use among older women. Retrieved from http://www.cdc.gov/nchs/data/ahcd/agingtrends/ 07olderwomen.pdf Chen, Y. M. (2010). Perceived barriers to physical activity among older adults residing in long-term care institutions. Journal of Clinical Nursing, 19(3–4), 432–439. doi:10.1111/j.1365-2702.2009.02990.x Chen, Y. M., & Li, Y. P. (2014). Motivators for physical activity among ambulatory nursing home older residents. Scientific World Journal, Article No. 329397, 1–7. doi:10.1155/2014/329397 Cohen-Mansfield, J., Shmotkin, D., & Goldberg, S. (2010). Predictors of longitudinal changes in older adults’ physical activity engagement. Journal of Aging and Physical Activity, 18(2), 141–157. Dogra, S. (2011). Better self-perceived health is associated with lower odds of physical inactivity in older adults with chronic disease. Journal of Aging and Physical Activity, 19(4), 322–335. Elsawy, B., & Higgins, K. E. (2010). Physical activity guidelines for older adults. American Family Physician, 81(1), 55–59. Haley, C., & Andel, R. (2010). Correlates of physical activity participation in community-dwelling older adults. Journal of Aging and Physical Activity, 18(4), 375–389. Hirschfeld, M. J. (2009). Accepting responsibility for long-term care—A paradox in times of a global nursing shortage? Journal of Nursing Scholarship, 41(1), 104–111. doi:10.1111/j.1547-5069.2009.01257.x ´ ´ ´ ´ Krol-Zieli nska, M., Kusy, K., Zielinski, J., & Osinski, W. (2010). Physical activity and functional fitness in institutionalized vs. independently living elderly: A comparison of 70–80-year-old city-dwellers Archives of Gerontology and Geriatrics, 53(1), e10–e16. doi:10.1016/j.archger.2010.07.013 Ku, P. W., Fox, K. R., & Chen, L. J. (2009). Physical activity and depressive symptoms in Taiwanese older adults: A seven-year follow-up study. Preventive Medicine, 48(3), 250–255. doi:10.1016/j.ypmed.2009.01.006 Lin, Y. C., Yeh, M. C., Chen, Y. M., & Huang, L. H. (2010). Physical activity status and gender differences in 217
Gender Differences of Physical Activity
community-dwelling older adults with chronic diseases. Journal of Nursing Research, 18(2), 88–97. doi:10.1097/JNR.0b013e3181dda6d8 Mahoney, F. I., & Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14(2), 56–61. Mavaddat, N., Van der Linde, R., Savva, G. M., Brayne, C., & Mant, J. (2013). What determines the self-rated health of older individuals with stroke compared to other older individuals? A cross-sectional analysis of the medical research council cognitive function and aging study. BMC Geriatrics, 13, 85. doi:10.1186/1471-2318-13-85 Ministry of Health and Welfare. (2014). Report of the senior citizen condition survey. Retrieved from http://www.mohw. gov.tw/cht/DOS/Statistic.aspx?f_list_no=312&fod_list_no= 4695 Ministry of Health and Welfare. (2015). Health and welfare statistics. Retrieved from http://www.mohw.gov.tw/cht/ DOS/Statistic.aspx?f_list_no=312&fod_list_no=4181 Moschny, A., Platen, P., Klaaßen-Mielke, R., Trampisch, U., & Hinrichs, T. (2011). Physical activity patterns in older men and women in Germany: A cross-sectional study. BMC Public Health, 11, 559. doi:10.1186/1471-2458-11-559 National Development Council. (2012). Population projections for R. O. C. (Taiwan): 20142060. Taipei, Taiwan: Author. ¨ Paivi, M., Mirja, H., & Terttu, P. (2010). Changes in physical activity involvement and attitude to physical activity in a 16-year follow-up study among the elderly. Journal of Aging Research, Article No. 174290, 1–7. doi:10.4061/2010/174290 Perrino, T., Mason, C. A., Brown, S. C., & Szapocznik, J. (2010). The relationship between depressive symptoms and walking among Hispanic older adults: A longitudinal, cross-lagged panel analysis. Aging & Mental Health, 14(2), 211–219. doi:10.1080/13607860903191374 Phillips, L. J., & Flesner, M. (2013). Perspectives and experiences related to physical activity of elders in long-term-care settings. Journal of Aging and Physical Activity, 21(1), 33–50. ´ Phillips, S. M., Wojcicki, T. R., & McAuley, E. (2013). Physical activity and quality of life in older adults: An 18-month panel analysis. Quality of Life Research, 22(7), 1647–1654. doi:10.1007/s11136-012-0319-z Rabaglietti, E., Liubicich, E., & Ciairano, S. (2011). Gender differences in the relationships between physical activity and the psychological and physical self-reported condition
218
Chen et al.
of the elderly in a residential care facility. Psychology, 2(1), 35–41. doi:10.4236/psych.2011.21006 Resnick, B. (2000). Functional performance and exercise of older adults in long-term care settings. Journal of Gerontological Nursing, 26(3), 7–16. Resnick, B., & Jenkins, L. B. (2000). Testing the reliability and validity of the self-efficacy for exercise scale. Nursing Research, 49(3), 154–159. doi:10.1097/00006199-200005000-00007 Saillis, J. F., Haskell, W. L., Wood, P. D., Fortmann, S. P., Rogers, T., Blair, S. N., & Paffenbarger, R. (1985). Physical activity assessment methodology in the five-city project. American Journal of Epidemiology, 121(1), 91–106. ´ Salguero, A., Mart´ınez-Garc´ıa, R., Molinero, O., & Marquez, S. (2011). Physical activity, quality of life and symptoms of depression in community-dwelling and institutionalized older adults. Archives of Gerontology and Geriatrics, 53(2), 152–157. doi:10.1016/j.archger.2010.10.005 Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. In T. L. Brink (Ed.), Clinical gerontology: A guide to assessment and intervention (pp. 165–173). New York, NY: Haworth. Sniehotta, F. F., Gellert, P., Witham, M. D., Donnan, P. T., Crombie, I. K., & McMurdo, M. E. T. (2013). Psychological theory in an interdisciplinary context: Psychological, demographic, health-related, social, and environmental correlates of physical activity in a representative cohort of community-dwelling older adults. International Journal of Behavioral Nutrition and Physical Activity, 10, 106–117. doi:10.1186/1479-5868-10-106 Vagetti, G. C., Filho, V. C. B., Moreira, N. B., de Oliveira, V., Mazzardo, O., & de Campos, W. (2014). Association between physical activity and quality of life in the elderly: A systematic review, 2000–2012. Revista Brasileira de Psiquiatria, 36(1), 76–88. doi:10.1590/1516-44462012-0895 Wassink-Vossen, S., Collard, R. M., Voshaar, R. C. O., Comijs, H. C., de Vocht, H. M., & Naarding, P. (2014). Physical (in)activity and depression in older people. Journal of Affective Disorders, 161, 65–72. doi:10.1016/j.jad. 2014.03.001 World Health Organization. (2014). Global strategy on diet, physical activity and health. Retrieved from http://www.who.int/dietphysicalactivity/pa/en/
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Gender Differences in the Predictors of Physical Activity Among Assisted Living Residents Yuh-Min Chen, PhD, RN1 , Yueh-Ping Li, PhD, RN2 , & Min-Ling Yen, MSN, RN3 1 Lambda Beta-at-Large, Associate professor, School of Nursing, China Medical University, and Supervisor, Department of Nursing, China Medical University Hospital, Taiwan 2 Assistant Professor, Department of Nursing, Chung Hwa University of Medical Technology, Taiwan 3 Supervisor, Department of Nursing, Kuang Tien General Hospital, Taiwan
Key words Assisted living facilities, gender differences, older adults, physical activity Correspondence Dr. Yuh-Min Chen, No. 91, Hsueh Shih Rd, Taichung, Taiwan 40402, ROC. E-mail: [email protected] Accepted: January 27, 2015 doi: 10.1111/jnu.12132
Abstract Purpose: To explore gender differences in the predictors of physical activity (PA) among assisted living residents. Design and Methods: A cross-sectional design was adopted. A convenience sample of 304 older adults was recruited from four assisted living facilities in Taiwan. Two separate simultaneous multiple regression analyses were conducted to identify the predictors of PA for older men and women. Independent variables entered into the regression models were age, marital status, educational level, past regular exercise participation, number of chronic diseases, functional status, self-rated health, depression, and self-efficacy expectations. Findings: In older men, a junior high school or higher educational level, past regular exercise participation, better functional status, better self-rated health, and higher self-efficacy expectations predicted more PA, accounting for 61.3% of the total variance in PA. In older women, better self-rated health, lower depression, and higher self-efficacy expectations predicted more PA, accounting for 50% of the total variance in PA. Conclusions: Predictors of PA differed between the two genders. The results have crucial implications for developing gender-specific PA interventions. Clinical Relevance: Through a clearer understanding of gender-specific predictors, healthcare providers can implement gender-sensitive PA-enhancing interventions to assist older residents in performing sufficient PA.
In Taiwan, the population of adults 65 years of age and older is rapidly increasing, which compares to the increase in life expectancy worldwide. By 2025, the older population is expected to constitute 20% of the general population (National Development Council, 2012). The aging processes increase an individual’s vulnerability to chronic diseases and functional disabilities, which in turn decrease quality of life and increase healthcare costs. About 81.1% of Taiwanese older adults have one or more chronic diseases, and 20.8% have functional disabilities (Ministry of Health and Welfare, 2014). Improving the health of older adults is a critical healthcare concern. Because of the rapidly increasing older population, the need for long-term care has increased (Hirschfeld,
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2009). Approximately 1.6% of Taiwanese older adults reside in assisted living facilities (Ministry of Health and Welfare, 2015). Compared with older adults in skilled nursing homes, those in assisted living facilities have greater health potential. However, the care needs of older adults will change as their health and functional status change. The World Health Organization (WHO; 2014) urges the implementation of population-based strategies and programs to increase physical activity (PA) levels worldwide. PA is any bodily movement produced by skeletal muscles that requires energy expenditure (WHO, 2014). The health benefits of PA for older adults have been widely recognized. PA is beneficial to physical, mental,
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and psychosocial health (Archer, Paluch, Shook, & Blair, 2013; WHO, 2014). PA has a protective effect in preventing the decline of physical functioning and maximizing functional independence (Archer et al., 2013; Elsawy & Higgins, 2010), particularly in frail older adults (Rabaglietti, Liubicich, & Ciairano, 2011). However, the levels ¨ of PA decrease with increasing age (Paivi, Mirja, & Terttu, 2010) and tend to be lower in institutionalized older adults than in their community-dwelling counter´ ´ ´ ´ parts (Krol-Zieli nska, Kusy, Zielinski, & Osinski, 2010; ´ Salguero, Mart´ınez-Garc´ıa, Molinero, & Marquez, 2011). Older adults tend to reduce their PA after institutionalization because of various barriers, such as health problems and environmental constraints (Chen, 2010; ´ ´ Krol-Zieli nska et al., 2010). However, physical inactivity can accelerate the rate of physical and functional decline (Archer et al., 2013). Further loss in functional status resulting from inactivity not only increases the intensity and cost of care but also negatively affects quality of life. Thus, assisted living residents should maintain and increase their level of PA to prevent the negative consequences of inactivity. Previous studies on PA in older adults indicated that several factors relate to activity participation. Among these factors, health status, depression, and self-efficacy expectations have been identified as major determinants of PA among older adults. Studies have shown that poor health and functional limitations are negatively related to PA (Cohen-Mansfield, Shmotkin, & Goldberg, 2010; Haley & Andel, 2010). Depression has also been found to predict declining PA and is a leading cause of disability in older adults (Perrino, Mason, Brown, & Szapocznik, 2010). It is noteworthy that the prevalence rate of depression among institutionalized older adults is even higher than that among community-dwelling older adults (Anstey, von Sanden, Sargent-Cox, & Luszcz, 2007; Salguero et al., 2011). Self-efficacy is the belief that one can successfully perform a behavior that is necessary to yield a particular outcome (Bandura, 1997). Previous research has consistently shown that self-efficacy expectations are a significant predictor of PA for older adults (Vagetti et al., 2014). Because of the frail characteristics and high prevalence rate of depression among institutionalized older adults, these three factors particularly must be noted when studying assisted living residents. However, the extent to which these factors predict PA of assisted living residents remains unclear. Gender differences should be considered in exploring PA of older adults. Older men and women suffer from different types of chronic diseases (Centers for Disease Control and Prevention [CDC], 2007). In addition, older women encounter more obstacles to PA, such as a lower educational level, poorer health, a higher 212
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prevalence of functional limitations, and more depressive symptoms than older men (CDC, 2007; WHO, 2014). Moreover, previous studies have repeatedly shown that older women engage in a lower level of PA than older men (Haley & Andel, 2010; Lin, Yeh, Chen, & Huang, 2010). Despite a growing research interest in PA among community-dwelling older adults, few studies on this topic have addressed assisted living residents, not to mention gender differences. Given the great health potential of assisted living residents, conducting cross-gender comparisons of PA is imperative. The findings will enable healthcare providers to apply empirical evidence in practice. Therefore, the purpose of this study was to explore gender differences in the predictors of PA among assisted living residents. The predictors explored were demographic characteristics, health status, depression, and self-efficacy expectations. Through a clearer understanding of gender-specific predictors, healthcare providers will be able to implement gender-sensitive PA-enhancing interventions that foster physical independence among older residents.
Methods Sample and Data Collection A cross-sectional design was adopted for this study. A convenience sample of 304 older adults was recruited from four assisted living facilities in the middle region of Taiwan. Eligibility criteria were: (a) 65 years of age or older, (b) residence in an assisted living facility for at least 3 months, (c) ability to communicate verbally in Mandarin or Taiwanese, and (d) absence of cognitive problems in understanding the questionnaires. Formal ethical approval was obtained from the research ethics committee. The manager of each facility helped identify eligible residents. The investigators then approached eligible residents individually and invited them to participate in this study. They were provided information about the research purpose and process. In addition, the residents were assured of confidentiality and anonymity in the use of data. They were also informed of their right to withdraw from the study at any time. After obtaining the informed consent, a one-on-one interview was conducted with each participant.
Measures The research instruments comprised five parts: a demographic data sheet, a health status profile, the Geriatric Depression Scale Short-Form (GDS-SF), the Self-Efficacy for Physical Activity Scale (SEPA), and the Seven-day Physical Activity Recall (7-day PAR). Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
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Demographic data sheet. The collected demographic data were age, gender, educational level, marital status, and past regular exercise participation. Past regular exercise participation was measured using the following question: “Did you participate in regular exercise for at least 3 consecutive months before entering this facility?” Regular exercise was explained to participants as participating in at least 20 min of continuous PA three times per week for at least 3 months (Resnick, 2000).
Health status profile. Measures of health status included the number of chronic diseases, functional status, and self-rated health. The data on the total number of chronic diseases were collected using participants’ selfreports of physician-diagnosed chronic diseases and then validated by performing chart review. Functional status was measured using the Barthel Index (BI). The BI assesses an individual’s degree of independence in performing 10 basic activities of daily living (Mahoney & Barthel, 1965). Total scores range from “totally dependent” (0) to “totally independent” (100). Self-rated health was measured using a single item. Participants rated their current health from “very poor” (1) to “very good” (5). Higher scores indicate better perceived health.
GDS-SF. Depression was measured using the GDS-SF developed by Sheikh and Yesavage (1986). The GDS-SF consists of 15 items relating to depressed mood over the past week. Participants were asked to respond to each statement with a “yes” (1) or “no” (0) answer. The scores range from 0 to 15. The Cronbach’s α for this study was .86.
SEPA. Self-efficacy expectations were measured using a nine-item Self-Efficacy for PA Scale modified from the Self Efficacy for Exercise Scale (SEE; Resnick & Jenkins, 2000). The SEE is a nine-item scale for measuring self-efficacy expectations related to the ability to continue exercising in the face of barriers to exercising (Resnick & Jenkins, 2000). In the SEPA, the term “exercise” in the SEE is replaced with “physical activity.” In addition, to fit the living situations of institutionalized older adults, three items were reworded. Participants responded to each item on an 11-point scale ranging from “not very confident” (0) to “very confident” (10). The scale is scored by summing the total score for each response and dividing the total score by the number of items. Higher scores indicate greater confidence. In the current study, Cronbach’s α for the SEPA was .95. The 2-week testretest reliability of 30 participants was .91. Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
7-day PAR. PA was measured using the 7-day PAR (Sallis et al., 1985). Each participant was asked to recall the time they spent in sleep, and performing moderate, hard, and very hard activities over the previous 7 days. Moderate activities are as strenuous to an individual as walking at a normal pace and very hard activities are as strenuous as running. The intensity of hard activities is in between walking and running. Hours spent in performing light activity were determined as the remaining time in 24 hr that was not spent engaging in the aforementioned activities. Total time in each level of activity per day was added up and multiplied by the appropriate mean metabolic equivalents (METs) and then summed to obtain a total MET score. One MET is approximately equal to 1 kcal/kg/hr in sleep, and 1.5, 4.0, 6.0, and 10.0 were assigned to light, moderate, hard, and very hard activities, respectively. This yields an estimation of the kilocalories per kilogram used per day (kcal/kg/day). In this study, the 2-week test-retest reliability of 30 participants was .95.
Data Analysis The SPSS for Windows version 17.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data. Descriptive statistics, including percentages, means, and standard deviations, were used to describe the sample and study variables. The chi-square and independent t test were used to examine gender differences in demographic characteristics, health status, depression, self-efficacy expectations, and PA. The Pearson product-moment correlation coefficient, independent t test, and one-way analysis of variance (ANOVA) were used to examine the relationships between demographic characteristics, health status, depression, self-efficacy expectations, and PA. Finally, two separate simultaneous multiple regression analyses were conducted to identify the predictors of PA for older men and women.
Results Description of Sample Characteristics and Study Variables The average age of the 304 participants was 79.4 years (SD = 6.69, range = 65–102), of which 55.9% were male. Compared with men, women were older (average age = 80.59 vs. 78.41). The majority of women (67.9%) were widowed, while the majority of men (51.6%) were single or divorced. No significant gender differences were found in the educational level, past regular exercise participation, number of chronic diseases, functional status, selfrated health, depression, and self-efficacy expectations 213
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Table 1. Gender Differences in Demographic Characteristics, Health Status, Depression, and Self-Efficacy Expectations (N = 304) Men (n = 170) Variables
Women (n = 134)
n
%
n
%
35 87 48
20.2 51.6 28.2
28 15 91
20.9 11.2 67.9
122 29 19
71.8 17.1 11.1
104 15 15
77.6 11.2 11.2
68 102
40.0 60.0
43 91
32.1 67.9
p 1
F = 2.99
30.88 ± 3.04 31.63 ± 1.40 32.92 ± 1.54
30.50 ± 2.56 31.17 ± 2.59 32.12 ± 1.77 t = −3.13∗∗
t = −.98
32.03 ± 2.91 30.71 ± 2.54
31.07 ± 2.64 30.61 ± 2.48
Note. t/F = t value of independent-sample t test or F value of analysis of variance. ∗ p < .05; ∗∗ p < .01; ∗∗∗ p < .001. Table 4. Gender Differences in Predictors of Physical Activity (N = 304) Men (n = 170) p
B
SE
β
p
.010
.843
−.036
.025
−.102
.145
.386 .405
.100 .044
.154 .510
.415 .318
.612 .444
.052 .059
.499 .475
.378 .471 .289 .151 .008 .215 .059 .076
.038 .116 .156 −.079 .335 .125 −.090 .239
.462 .032 .003 .157 .001 .047 .406 .001
.953 .941 .085 −.128 .010 .516 −.144 .210
.551 .544 .357 .181 .008 .246 .070 .088
.119 .118 .016 −.054 .147 .173 −.265 .212
.086 .086 .813 .482 .230 .038 .041 .019
Variables
B
Age Marital status (married as reference group) Single/divorced Widowed Educational level (no formal education as reference group) Elementary school Junior high school/higher Past regular exercise experience Number of chronic diseases Functional status Self-rated health Depression Self-efficacy expectations R2
.005
.023
.553 .267 .279 1.018 .875 −.215 .026 .430 −.049 .247
junior high school or higher educational level (β = .116, p = .032), past regular exercise participation (β = .156, p = .003), better functional status (β = .335, p = .001), better self-rated health (β = .125, p = .047), and higher self-efficacy expectations (β = .239, p = .001) predicted more PA. All of these predictors explained 61.3% of the total variance in PA. In older women, better self-rated health (β = .173, p = .038), lower depression (β = -.265, p = .041), and higher Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
β
Women (n = 134)
SE
.613
.500
self-efficacy expectations (β = .212, p = .019) predicted more PA, accounting for 50% of the total variance in PA (Table 4).
Discussion There was no gender difference in total PA. However, older men engaged in a significantly higher amount of hard activities than older women. Previous research also 215
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indicated that older women engaged in lower intensity of PA than older men (Lin et al., 2010; Moschny, Platen, Klaaßen-Mielke, Trampisch, & Hinrichs, 2011). One possible explanation is that older women had a worse functional status than older men although the difference was not statistically significant. In addition, possibly because of Taiwanese cultural expectations, these older women took care of family members and handled household chores when they were young. Therefore, they probably had fewer opportunities and less time to develop requisite skills of PA or become acquainted with PA, particularly leisure-time PA. Although PA need not be strenuous to obtain health benefits, growing old undermines the caloric expenditure of PA among older women more than among older men (Lin et al., 2010). Therefore, efforts focusing on preventing the decrease in the level of PA among older women and enabling them to perform the desired level of PA to obtain health benefits are important. Further longitudinal research is suggested to explore gender differences regarding the course of PA changes and the influential factors among assisted living residents. The findings will provide useful information for guiding the design of programs for preventing PA decline over time. Similarities and differences existed between older men and women regarding predictors of PA. In older men, a junior high school or higher educational level, past regular exercise participation, better functional status, better self-rated health, and higher self-efficacy expectations predicted more PA. In older women, better self-rated health, lower depression, and higher self-efficacy expectations predicted more PA. The result indicated that older men with a junior high school or higher educational level engaged in more PA than older men with no formal education. It might be that older men with a higher educational level possibly have more opportunities to find or use resources for PA. In addition, better educated older adults also tend to have more knowledge regarding the benefits of being physically active, which motivates them to perform more PA (Haley & Andel, 2010). Healthcare providers must pay more attention to the PA status of older men with a lower educational level. Past regular exercise participation was another significant predictor of PA in older men. Previous qualitative research has also shown that past PA experience is an important influencing factor for PA in institutionalized older adults (Chen & Li, 2014; Phillips & Flesner, 2013). Previously cultivated habits regarding PA play an influential role in current PA performance (Archer et al., 2013). On the other hand, a past sedentary lifestyle poses a barrier to PA (Chen, 2010). This finding suggests that healthcare providers should assess and consider residents’ previous 216
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exercise habits to facilitate PA engagement or to modify their habitual sedentary lifestyle. Functional status was also a significant predictor of PA in older men. In older women, although functional status was significantly correlated with PA in the bivariate analysis, it was not a significant predictor of PA. It appears that functional status plays a more crucial role in PA for men than for women. This result was similar with the finding from Haley and Andel (2010). One possible explanation is that men generally perform higher intensity of PA than women (Lin et al., 2010; Moschny et al., 2011). Therefore, poor functional status causes more difficulties in PA engagement in men. This implies the need to take the individual functional condition into consideration in planning PA programs and in providing advice on activities suitable for each resident’s level of function. Self-rated health was a significant predictor of PA in both older men and women. Better self-rated health predicted a higher amount of PA. Self-rated health has been consistently identified as a predictor of PA in older adults (Dogra, 2011). In addition, self-rated health has been reported to be a significant predictor of health outcomes (Mavaddat, Van der Linde, Savva, Brayne, & Mant, 2013). PA might play a mediating role between self-rated health and health outcomes. Further research can explore this issue. Depression was a significant negative predictor of PA in older women. Older women with more depressive symptoms tended to perform less PA. Although depression is not a significant predictor of PA in older men, it was significantly correlated with PA in the bivariate analysis. This result is similar to those of previous studies (Perrino et al., 2010; Wassink-Vossen et al., 2014). Depressive symptoms limit PA engagement. Especially, more older women than men suffer from depressive symptoms (Ku, Fox, & Chen, 2009; Salguero et al., 2011; WHO, 2014). Thus, proper identification of depression is necessary to prevent physical inactivity and the subsequent decline in functional ability, particularly among older women. Self-efficacy expectations are a significant predictor of PA for both older men and women. Stronger self-efficacy expectations predicted a higher amount of PA. Selfefficacy has been consistently reported to be a significant ´ predictor of PA for older adults (Phillips, Wojcicki, & McAuley, 2013; Sniehotta et al., 2013). To the best of our knowledge, there is a lack of studies exploring the relationship between PA and self-efficacy expectations in Taiwanese older population. The finding of this study supports the practical importance of self-efficacy expectations on PA in different cultures. Self-efficacy expectations are amenable to change. Therefore, it is suggested that healthcare providers can apply four Journal of Nursing Scholarship, 2015; 47:3, 211–218. C 2015 Sigma Theta Tau International
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major sources of self-efficacy, including enactive mastery experience, vicarious experience, verbal persuasion, and physiological and affective states (Bandura, 1997), to enhance self-efficacy beliefs for PA to bolster older residents’ confidence in PA participation.
Limitations Some limitations of this study need to be mentioned. First, this study used a convenience sample from four assisted living facilities in Taiwan. Therefore, generalizing the results to other long-term care residents within and outside Taiwan is limited. Second, the use of crosssectional design restricts the inference of causality. Third, the use of the self-reported 7-day PAR to measure PA might introduce the problem due to recall bias or social desirability. To validate these findings, future research can include an objective measure of PA, such as accelerometer data, at the same time.
Conclusions The results of this study showed that predictors of PA are inconsistent between the two genders. The findings supplement the scant knowledge on PA in assisted living residents. The results have crucial implications for developing gender-specific PA interventions. Strategies responsive to the differences between older men and women would facilitate planning and implementing effective interventions for assisting older adults in engaging in sufficient PA. Moreover, further research is warranted to develop and examine the effectiveness of genderspecific PA programs.
Acknowledgments This study was funded by a 2013 grant from the China Medical University (CMU 102-S-20).
Clinical Resources
r r r
Physical Activity Guidelines for Older Adults, American Academy of Family Physicians: http:// www.aafp.org/afp/2010/0101/p55.html Physical Activity and Older Adults, World Health Organization: http://www.who.int/diet physicalactivity/factsheet_olderadutls/en Physical Activity in Older Americans, American Heart Association: http://www.heart.org/HEART ORG/GettingHealthy/PhysicalActivity/FitnessBasics/ Physical-Activity-in-Older-Americans_UCM_ 308039_Article.jsp
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References Anstey, K. J., von Sanden, C., Sargent-Cox, K. H., & Luszcz, M. A. (2007). Prevalence and risk factors for depression in a longitudinal, population-based study including individuals in the community and residential care. American Journal of Geriatric Psychiatry, 15(6), 497–505. doi:10.1097/JGP.0b013e31802e21d8 Archer, E., Paluch, A. E., Shook, R. P., & Blair, S. N. (2013). Physical activity and the science of successful aging. Kinesiology Review, 2(1), 29–38. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company. Centers for Disease Control and Prevention. (2007). Trends in health status and health care use among older women. Retrieved from http://www.cdc.gov/nchs/data/ahcd/agingtrends/ 07olderwomen.pdf Chen, Y. M. (2010). Perceived barriers to physical activity among older adults residing in long-term care institutions. Journal of Clinical Nursing, 19(3–4), 432–439. doi:10.1111/j.1365-2702.2009.02990.x Chen, Y. M., & Li, Y. P. (2014). Motivators for physical activity among ambulatory nursing home older residents. Scientific World Journal, Article No. 329397, 1–7. doi:10.1155/2014/329397 Cohen-Mansfield, J., Shmotkin, D., & Goldberg, S. (2010). Predictors of longitudinal changes in older adults’ physical activity engagement. Journal of Aging and Physical Activity, 18(2), 141–157. Dogra, S. (2011). Better self-perceived health is associated with lower odds of physical inactivity in older adults with chronic disease. Journal of Aging and Physical Activity, 19(4), 322–335. Elsawy, B., & Higgins, K. E. (2010). Physical activity guidelines for older adults. American Family Physician, 81(1), 55–59. Haley, C., & Andel, R. (2010). Correlates of physical activity participation in community-dwelling older adults. Journal of Aging and Physical Activity, 18(4), 375–389. Hirschfeld, M. J. (2009). Accepting responsibility for long-term care—A paradox in times of a global nursing shortage? Journal of Nursing Scholarship, 41(1), 104–111. doi:10.1111/j.1547-5069.2009.01257.x ´ ´ ´ ´ Krol-Zieli nska, M., Kusy, K., Zielinski, J., & Osinski, W. (2010). Physical activity and functional fitness in institutionalized vs. independently living elderly: A comparison of 70–80-year-old city-dwellers Archives of Gerontology and Geriatrics, 53(1), e10–e16. doi:10.1016/j.archger.2010.07.013 Ku, P. W., Fox, K. R., & Chen, L. J. (2009). Physical activity and depressive symptoms in Taiwanese older adults: A seven-year follow-up study. Preventive Medicine, 48(3), 250–255. doi:10.1016/j.ypmed.2009.01.006 Lin, Y. C., Yeh, M. C., Chen, Y. M., & Huang, L. H. (2010). Physical activity status and gender differences in 217
Gender Differences of Physical Activity
community-dwelling older adults with chronic diseases. Journal of Nursing Research, 18(2), 88–97. doi:10.1097/JNR.0b013e3181dda6d8 Mahoney, F. I., & Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14(2), 56–61. Mavaddat, N., Van der Linde, R., Savva, G. M., Brayne, C., & Mant, J. (2013). What determines the self-rated health of older individuals with stroke compared to other older individuals? A cross-sectional analysis of the medical research council cognitive function and aging study. BMC Geriatrics, 13, 85. doi:10.1186/1471-2318-13-85 Ministry of Health and Welfare. (2014). Report of the senior citizen condition survey. Retrieved from http://www.mohw. gov.tw/cht/DOS/Statistic.aspx?f_list_no=312&fod_list_no= 4695 Ministry of Health and Welfare. (2015). Health and welfare statistics. Retrieved from http://www.mohw.gov.tw/cht/ DOS/Statistic.aspx?f_list_no=312&fod_list_no=4181 Moschny, A., Platen, P., Klaaßen-Mielke, R., Trampisch, U., & Hinrichs, T. (2011). Physical activity patterns in older men and women in Germany: A cross-sectional study. BMC Public Health, 11, 559. doi:10.1186/1471-2458-11-559 National Development Council. (2012). Population projections for R. O. C. (Taiwan): 20142060. Taipei, Taiwan: Author. ¨ Paivi, M., Mirja, H., & Terttu, P. (2010). Changes in physical activity involvement and attitude to physical activity in a 16-year follow-up study among the elderly. Journal of Aging Research, Article No. 174290, 1–7. doi:10.4061/2010/174290 Perrino, T., Mason, C. A., Brown, S. C., & Szapocznik, J. (2010). The relationship between depressive symptoms and walking among Hispanic older adults: A longitudinal, cross-lagged panel analysis. Aging & Mental Health, 14(2), 211–219. doi:10.1080/13607860903191374 Phillips, L. J., & Flesner, M. (2013). Perspectives and experiences related to physical activity of elders in long-term-care settings. Journal of Aging and Physical Activity, 21(1), 33–50. ´ Phillips, S. M., Wojcicki, T. R., & McAuley, E. (2013). Physical activity and quality of life in older adults: An 18-month panel analysis. Quality of Life Research, 22(7), 1647–1654. doi:10.1007/s11136-012-0319-z Rabaglietti, E., Liubicich, E., & Ciairano, S. (2011). Gender differences in the relationships between physical activity and the psychological and physical self-reported condition
218
Chen et al.
of the elderly in a residential care facility. Psychology, 2(1), 35–41. doi:10.4236/psych.2011.21006 Resnick, B. (2000). Functional performance and exercise of older adults in long-term care settings. Journal of Gerontological Nursing, 26(3), 7–16. Resnick, B., & Jenkins, L. B. (2000). Testing the reliability and validity of the self-efficacy for exercise scale. Nursing Research, 49(3), 154–159. doi:10.1097/00006199-200005000-00007 Saillis, J. F., Haskell, W. L., Wood, P. D., Fortmann, S. P., Rogers, T., Blair, S. N., & Paffenbarger, R. (1985). Physical activity assessment methodology in the five-city project. American Journal of Epidemiology, 121(1), 91–106. ´ Salguero, A., Mart´ınez-Garc´ıa, R., Molinero, O., & Marquez, S. (2011). Physical activity, quality of life and symptoms of depression in community-dwelling and institutionalized older adults. Archives of Gerontology and Geriatrics, 53(2), 152–157. doi:10.1016/j.archger.2010.10.005 Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. In T. L. Brink (Ed.), Clinical gerontology: A guide to assessment and intervention (pp. 165–173). New York, NY: Haworth. Sniehotta, F. F., Gellert, P., Witham, M. D., Donnan, P. T., Crombie, I. K., & McMurdo, M. E. T. (2013). Psychological theory in an interdisciplinary context: Psychological, demographic, health-related, social, and environmental correlates of physical activity in a representative cohort of community-dwelling older adults. International Journal of Behavioral Nutrition and Physical Activity, 10, 106–117. doi:10.1186/1479-5868-10-106 Vagetti, G. C., Filho, V. C. B., Moreira, N. B., de Oliveira, V., Mazzardo, O., & de Campos, W. (2014). Association between physical activity and quality of life in the elderly: A systematic review, 2000–2012. Revista Brasileira de Psiquiatria, 36(1), 76–88. doi:10.1590/1516-44462012-0895 Wassink-Vossen, S., Collard, R. M., Voshaar, R. C. O., Comijs, H. C., de Vocht, H. M., & Naarding, P. (2014). Physical (in)activity and depression in older people. Journal of Affective Disorders, 161, 65–72. doi:10.1016/j.jad. 2014.03.001 World Health Organization. (2014). Global strategy on diet, physical activity and health. Retrieved from http://www.who.int/dietphysicalactivity/pa/en/
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