Research in Gerontological Nursing

The articles prior to January 2010 are part of the back file collection and are not available with a current paid subscription. To access the article, you may purchase it or purchase the complete back file collection here

Secondary Analysis: Clinical Data Set 

Exercise Among Urban-Dwelling Older Adults at Risk for Health Disparities

Eileen M. Sullivan-Marx, PhD, RN, FAAN; Cindy Lou Cuesta; Sarah J. Ratcliffe, PhD

Abstract

This study assessed factors that contribute to exercise in older adults at risk for health disparities living in a predominantly African American urban community. A local health database was used to gain an understanding of these factors, which then could be used to develop programs to improve health within a specific urban community. The sample included 112 participants (mean age = 81); the majority were women and African American. Participants were more likely to exercise if not insured by Medicaid, compared with those who did receive Medicaid. Adults with two or more limitations in instrumental activities of daily living (IADLs) were less likely to exercise. Among those who exercised, those with two or more limitations in IADLs were more likely to exercise less than adults with no such limitations, and adults with high blood pressure were more likely to exercise less than those without high blood pressure. The findings of this study will help generate discussion in both the community and outreach programs to invigorate exercise among older adults at risk for health disparities.

Abstract

This study assessed factors that contribute to exercise in older adults at risk for health disparities living in a predominantly African American urban community. A local health database was used to gain an understanding of these factors, which then could be used to develop programs to improve health within a specific urban community. The sample included 112 participants (mean age = 81); the majority were women and African American. Participants were more likely to exercise if not insured by Medicaid, compared with those who did receive Medicaid. Adults with two or more limitations in instrumental activities of daily living (IADLs) were less likely to exercise. Among those who exercised, those with two or more limitations in IADLs were more likely to exercise less than adults with no such limitations, and adults with high blood pressure were more likely to exercise less than those without high blood pressure. The findings of this study will help generate discussion in both the community and outreach programs to invigorate exercise among older adults at risk for health disparities.

Specific information about the health promotion and disease prevention activities of adults older than age 75 is needed as this population increases in the United States and around the globe (Ferrucci & Simonsick, 2006; Haber, 2003). Healthy eating and exercise are key factors for nurses and other health providers to emphasize with older adults to prevent health problems and promote the highest quality of life. Regardless, it is often difficult for older adults to incorporate systematic or individualized health promotion activities into their daily lives due to myriad environmental, behavioral, and socioeconomic factors (Ferrucci & Simonsick, 2006; Nakasato & Carnes, 2006).

Exercise is recommended for all older adults as a way to promote health, prevent falls, sustain independence in functioning, and reduce complications of comorbid conditions. Exercise is considered a standard health promotion recommendation that gerontological nurses need to discuss with patients (Jedrziewski, Lee, & Trojanowski, 2005; LIFE Study Investigators et al., 2006; Tinetti, Gordon, Sogolow, Lapin, & Bradley, 2006; U.S. Department of Health and Human Services [USDHHS], 2000). Reducing health disparities for individuals at risk, such as older adults who are underserved or from minority groups, requires a greater understanding of the factors that enhance or impede interventions to promote exercise activities. Specific knowledge of these contributing factors in older adults living in urban communities would be useful for nurses caring for older adults.

This study assessed the factors that contribute to exercise among older adults at risk for health disparities living in a predominantly African American urban community. A local health database was used to gain an understanding of these factors, which then could be used to develop programs to improve health within a specific urban community (Luck, Chang, Brown, & Lumpkin, 2006). Factors associated with exercise and the frequency of exercise per week among urban-dwelling adults age 75 and older were examined.

Background

Exercise programs, as a preventive measure and for functional enhancement, have been demonstrated in all populations of older adults and are recommended as a key strategy for health in Healthy People 2010 (USDHHS, 2000). However, African American older adults living in urban areas are less likely to exercise for reasons including family responsibilities, lack of safe environments, perceptions of little benefit from exercise, and sedentary lifestyles (Walcott-McQuigg & Prohaska, 2001).

Among older adults living in communities, those who are most frail are more likely to have higher rates of mortality than those who are “fit” (Mitnitski et al., 2005). African American adults have higher rates of mortality than do their Caucasian counterparts (Satcher et al., 2005), partially due to a greater risk for hypertension, stroke, diabetes mellitus, and pulmonary problems. In addition, preventive measures (e.g., prescriptive exercise programs) are less likely to be emphasized by clinicians, paid for by providers, and adopted by older African American women (Brady & Nies, 1999; Institute of Medicine [IOM], 2003; Shi & Singh, 2003).

Frail older residents of urban communities commonly desire greater access to relevant health services, greater involvement in the planning of services, and enhanced roles in determining the kinds of care programs available to them (Sciegaj, Capitman, & Kyriacou, 2004). In planning services, relevant data to develop programs are often elusive, especially for adults age 75 and older who live in urban communities. Surveys of community-dwelling older adults can provide needed information for health care professionals and public health advocates regarding activities to meet local community health needs (Luck et al., 2006).

The Philadelphia Health Management Corporation (PHMC) is a nonprofit public health organization committed to improving the health of the community through outreach, education, research, planning, technical assistance, and direct services. PHMC conducts semiannual household health surveys that inform local health policy planners and clinicians about health needs that can be specified by geographical regions, ZIP codes, or census tracts.

The University of Pennsylvania School of Nursing provides services to frail older adults in the western and southwestern Philadelphia community through several programs:

  • The Living Independently For Elders (LIFE) Program, a Program for All-Inclusive Care of the Elderly (PACE).
  • The Healthy in Philadelphia Initiative, a community health education outreach program.
  • The Penn Nursing Consultation Service, which provides health education at local senior citizen centers.

To better understand the factors that predict whether older adults exercise, the PHMC community database was used to examine factors associated with exercise activities of adults age 75 and older living in western and southwestern Philadelphia ZIP code areas served by the School of Nursing programs. With information from the community, it was expected that the School would be better positioned to target services and community outreach efforts relevant to older adults in the areas served.

Older residents of the western and southwestern Philadelphia community represent diverse races, ethnicities, cultures, and religions, and live in neighborhoods of “row houses,” or townhouses, built prior to 1939. Many individuals own their homes. Resources for senior services are available primarily through community aging centers and faith-based worship centers.

Method

Design

This study was a secondary analysis of 2004 data from the PHMC clinical data set. The findings are from PHMC’s Southeastern Pennsylvania Household Health Survey, a telephone survey of more than 10,000 households that examined the health and social well-being of residents in Bucks, Chester, Delaware, Montgomery, and Philadelphia counties. The survey contained information about residents’ health status, use of health services, and access to care.

Two specific research questions were addressed in this study:

  • What factors are associated with exercise among urban-dwelling older adults?
  • What factors are associated with exercise frequency per week among urban-dwelling older adults?

Sampling Procedure

As noted above, the sample for the PHMC 2004 Southeastern Pennsylvania Household Health Survey was drawn from all telephone households in five counties of the Philadelphia region and was representative of the population in each of those counties. The sample was stratified by service areas to ensure sufficient representation within smaller geographical areas. The survey oversampled adults age 75 and older to provide a sufficient number of interviews for separate analysis of the responses of individuals in this age group. Households were contacted using a computerized, random-digit dialing method so residents with unpublished telephone numbers and those who had recently moved would be included in the sample. Trained interviewers used a structured questionnaire to interview participants by telephone.

Sample

Inclusion criteria were adults age 75 and older living in an eight ZIP code geographical area served by University of Pennsylvania School of Nursing programs and immediately adjacent to the university campus. The sample for this study included 112 participants with a mean age of 81 (SD = 5.192, age range = 75 to 99). The majority of the sample were women (76.8%) who lived alone or with one other person, and most (68.4%) had completed high school. Most participants (63.6%) self-identified as African American. Only 17.9% of the sample were married. Most (90.4%) reported an annual income less than $37,000, and 29% were insured by Medicaid (Table 1). The study used de-identified data and was approved by the University of Pennsylvania Institutional Review Board.

Demographic Characteristics Associated with Exercise

Table 1: Demographic Characteristics Associated with Exercise

Measurements

Outcome Variables. Exercise was categorized based on respondent-reported participation in any physical activity for exercise during the previous month. Responses were categorized as yes if participants reported any exercise and no if they stated none.

Frequency of exercise was assessed by asking respondents to report on physical activity for exercise during the past month that lasted for at least ½ hour. Response categories were less than once per week, 1 to 2 days per week, 3 days per week, and more than 3 days per week.

Health-Related Variables. Variables that may be related to exercise outcomes included obesity, which was calculated by a body mass index (BMI) of ≥ 30 using self-reported height and weight, and presence of high blood pressure and high cholesterol, measured as yes or no by self-report. Medical visit to a health care professional within the past year was also measured as yes or no, as was whether a health care professional had advised participants within the past year to lose weight, exercise, or eat healthy. Self-reported health status was categorized as excellent, good, fair, or poor.

In the PHMC database, instrumental activities of daily living (IADLs) included indicating whether assistance was needed in using the telephone, traveling, shopping, planning and cooking meals, taking medicine, and managing bills and money. If assistance was needed, responses were categorized as having a limitation. Activities of daily living (ADLs) included feeding self, dressing, maintaining appearance, walking, getting into and out of bed, bathing or showering, and getting to the bathroom in time. Number of limitations in IADLs and ADLs were used to determine associations with exercise.

Data Analysis

Factors that influenced exercise were initially tested using Fisher’s exact or Chi-square tests (categorical variables), t tests (normally distributed variables), or Mann-Whitney U tests (ordinal or non-normal variables). Variables found to be significant at the p < 0.20 level were considered for inclusion in the regression models. Logistic regression, using a backward selection approach, was used for the first research question (factors associated with exercise), and a proportional odds model was used to estimate the effect of the covariates in the second research question (exercise frequency per week) because the outcome was ordinal. Estimates for the latter model were calculated to examine the odds of more versus fewer days of exercise per week. To address multicollinearity, only one of the variables in pairs that were highly related (e.g., BMI and obesity, Medicaid and income level) was considered for inclusion in the regression models on the basis of the results of the bivariate analyses.

Results

Factors Associated with Exercise Among Urban-Dwelling Older Adults

As shown in Tables 1 and 2, factors associated with exercise (yes or no) at the p < 0.20 level were educational level, marital status, race, insured by Medicaid, number of limitations in ADLs and IADLs, self-reported health status, and having had high blood pressure. Final results (Table 3) indicated that older adults were 3.2 times more likely to exercise if not insured by Medicaid, compared with those who did receive Medicaid (p = 0.022). Adults with two or more limitations in IADLs were 7.6 times less likely to exercise (p = 0.042). No significant difference was found between adults with one IADL limitation and no IADL limitations (p = 0.314).

Clinical Characteristics Associated with Exercise

Table 2: Clinical Characteristics Associated with Exercise

Final Logistic Regression Model for Predictors of Exercise (N = 112)

Table 3: Final Logistic Regression Model for Predictors of Exercise (N = 112)

Factors Associated with Exercise Frequency per Week Among Urban-Dwelling Older Adults

In the unadjusted (bivariate) models (Table 4), race, education, marital status, Medicaid, obesity, having a health care professional advise weight loss or healthy eating, number of limitations in ADLs and IADLs, self-reported health status, and having had high blood pressure were associated with exercise frequency at the p < 0.20 level. However, due to multicollinearity issues between the variables, only race, marital status, obesity, having had high blood pressure, and number of limitations in IADLs were considered for inclusion in the adjusted model.

Odds of More Versus Less Days of Exercise per Week for Unadjusted (bivariate) and Adjusted Proportional Odds Models (N = 112)Odds of More Versus Less Days of Exercise per Week for Unadjusted (bivariate) and Adjusted Proportional Odds Models (N = 112)

Table 4: Odds of More Versus Less Days of Exercise per Week for Unadjusted (bivariate) and Adjusted Proportional Odds Models (N = 112)

In the adjusted model, only the number of limitations in IADLs and having had high blood pressure affected the odds of more versus less exercise. Respondents with two or more limitations in IADLs were 1.3 times more likely to exercise less (versus more) than those with no limitations in IADLs (p = 0.001). Respondents who had high blood pressure were 3.2 times more likely to exercise less (versus more) than those who had never had high blood pressure (p = 0.005).

Discussion

The purpose of this study was to assess the factors that contribute to exercise among older adults at risk for health disparities living in an urban community. Factors that were associated with any exercise among the older adults in the sample were not receiving Medicaid and having fewer limitations in IADLs. A greater number of limitations in IADLs and having had high blood pressure were associated with exercising less frequently.

The PHMC community health database was useful in gaining a beginning understanding of community needs for exercise among older adults, most of whom were African American, living in a large urban community. These findings will help generate discussion in both the community and University of Pennsylvania School of Nursing outreach programs to invigorate exercise among older adults most at risk for health disparities, primarily those age 75 and older, who receive Medicaid, have limitations in IADLs, and have high blood pressure. Further research that tests health education and exercise interventions for older adults in this community who are at risk for disparities in health outcomes may be useful in identifying risk categories and specifying the most effective exercise activities. Community focus groups could help validate and enlarge these findings; they are needed to comprehensively address the goals of Healthy People 2010 (USDHHS, 2000) for physical activity among older adults.

Other research has demonstrated that communitydwelling older adults who are frail may benefit from structured exercise programs. However, there is a need for greater understanding of motivational factors and ways exercise can be incorporated into systems of health care, including payment for services related to health promotion and exercise (Ferrucci & Simonsick, 2006; LIFE Study Investigators et al., 2006). Exercise programs designed broadly for older adults who live independently in communities may not be sufficient or relevant for those with impairments in IADLs or with chronic illnesses (Ferrucci & Simonsick, 2006).

Some studies have found that functional deficits and tiredness are associated with a greater risk for inactivity that may lead to further disability (Avlund, Rantanen, & Schroll, 2006; Jerome et al., 2006). Any exercise has been shown to improve physical functioning in older adults (Brach et al., 2004), even in predominantly Caucasian and female groups with functional limitations (Topp, Boardley, Morgan, Fahlman, & McNevin, 2005), and resistance exercise has been associated with greater functional improvements (Seynnes et al., 2004; Topp et al., 2005). Most research does not sufficiently address factors associated with exercise among older adults with different racial and ethnic origins living in urban environments (IOM, 2003; Stewart et al., 2001). Although the relationship between physical activity and quality of life is clear, a greater understanding of the influence of self-efficacy and motivation is needed before effective exercise programs can be implemented (McAuley et al., 2006).

In this study, receiving assistance from Medicaid was associated with not exercising, a finding that merits further study. The IOM (2003) noted that Medicaid does not directly lead to health disparities and may, indeed, provide services for a group at risk for no health care. However, those who receive care through Medicaid benefits may not receive comprehensive services and access to specialists, nutritionists, or gerontological nurse practitioners, and may not be seen in a setting where a primary care provider can spend the time necessary to develop an exercise program that is individualized and meaningful to the older adult (IOM, 2003: Walcott-McQuigg & Prohaska, 2001).

Another factor that may contribute to a lack of exercise in urban environments is concern for safety, including adequate lighting, sidewalks, crime prevention, or fear of falling. In western and southwestern Philadelphia, homes are commonly entered from steps that may not have supportive railings; the sidewalks may be cracked and uneven; and empty buildings are interspersed with occupied homes. These factors could further impede walking, particularly if older adults have limitations in ADLs.

Although obesity was not significantly associated with exercise or frequency of exercise in the final adjusted models, it may still be an important clinical factor to consider in the development of exercise interventions, in future research, and in health policy advocacy because obesity has been associated with poorer health outcomes (Villareal, Banks, Sinacore, Siener, & Klein, 2006).

Limitations

Local community databases are useful and cost-effective ways for researchers, policy makers, and health care professionals to assess public health needs. However, they may be limited by incomplete data, unavailability of data across communities, and the need to maintain privacy at the level of specific neighborhoods (Luck et al., 2006). Although the method of the PHMC Southeastern Pennsylvania Household Health Survey community database precluded problems with missing data in this study, several issues presented limitations in conducting this secondary analysis. The kind or intensity of exercise or physical activity was unknown, as respondents were asked only to report on any exercise in general. Respondents may also have overestimated or underestimated the amount of exercise reported. In addition, the telephone survey may have eliminated individuals who were unable to answer the telephone or those without access to a telephone.

Conclusion

Nurses, in particular gerontological nurses, are concerned with individualized approaches to care, healthy environments, and promotion of health and function (Meleis, 2007). This study was a beginning effort to identify factors that could influence exercise promotion programs that are part of the University of Pennsylvania School of Nursing LIFE program, the Healthy in Philadelphia Initiative to serve community outreach, and the Penn Nursing Consultation Service focusing on older adult education in senior centers. Data from the PHMC Southeastern Pennsylvania Household Health Survey showed that older adults living in the areas served by the School of Nursing are less likely to exercise if they receive Medicaid and have limitations in IADLs. If they do exercise, they do so less frequently if they have high blood pressure and have limitations in IADLs.

An opportunity exists to further explore and validate these findings with community focus groups and to target health education programs to those who have high blood pressure and are not exercising. Extant programs of exercise at LIFE, senior centers, or faith-based centers could be modified to eliminate barriers for those who need help with using the telephone, paying bills, shopping, using transportation, and preparing meals. Information from this study will help community leaders, policy planners, and older adults adapt current programs and develop new ones to increase exercise among older adults most at risk for health disparities in urban communities.

References

  • Avlund, K, Rantanen, T & Schroll, M2006. Tiredness and subsequent disability in older adults: The role of walking limitations. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61, 1201–1205.
  • Brach, JS, Simonsick, EM, Kritchevsky, S, Yaffe, K & Newman, ABHealth, Aging Body Composition Study Research Group. 2004. The association between physical function and lifestyle activity and exercise in the Health, Aging and Body Composition Study. Journal of the American Geriatrics Society, 52, 502–509. doi:10.1111/j.1532-5415.2004.52154.x [CrossRef]
  • Brady, B & Nies, MA1999. Health-promoting lifestyles and exercise: A comparison of older African American women above and below poverty level. Journal of Holistic Nursing, 17, 197–207. doi:10.1177/089801019901700207 [CrossRef]
  • Ferrucci, L & Simonsick, EM2006. A little exercise. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61, 1154–1156.
  • Haber, D2003. Health promotion and aging: Practical applications for health professionals (3rd ed) New York: Springer.
  • Institute of Medicine. 2003. Unequal treatment: Confronting racial and ethnic disparities in health care Washington, DC: National Academies Press. Retrieved November 9, 2007, from http://www.nap.edu/catalog.php?record_id=10260
  • Jedrziewski, MK, Lee, VM-Y & Trojanowski, JQ2005. Lowering the risk of Alzheimer’s disease: Evidence-based practices emerge from new research. Alzheimer’s & Dementia, 1, 152–160. doi:10.1016/j.jalz.2005.09.007 [CrossRef]
  • Jerome, GJ, Glass, TA, Mielke, M, Xue, QL, Andersen, RE & Fried, LP2006. Physical activity participation by presence and type of functional deficits in older women: The Women’s Health and Aging Studies. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61, 1171–1176.
  • Pahor, M, Blair, SN, Espeland, M, Fielding, R & Gill, TMLIFE Study Investigators, et al. 2006. Effects of a physical activity intervention on measures of physical performance: Results of the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) study. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61, 1157–1165.
  • Luck, J, Chang, C, Brown, ER & Lumpkin, J2006. Using local health information to promote public health. Health Affairs (Project Hope), 25, 979–991. doi:10.1377/hlthaff.25.4.979 [CrossRef]
  • McAuley, E, Konopack, JF, Motl, RW, Morris, KS, Doerksen, SE & Rosengren, KR2006. Physical activity and quality of life in older adults: Influence of health status and self-efficacy. Annals of Behavioral Medicine, 31, 99–103. doi:10.1207/s15324796abm3101_14 [CrossRef]
  • Meleis, AI2007. Theoretical nursing: Development & progress (4th ed) Philadelphia: Lippincott, Williams & Wilkins.
  • Mitnitski, A, Song, X, Skoog, I, Broe, GA, Cox, JL & Grunfeld, E et al. 2005. Relative fitness and frailty of elderly men and women in developed countries and their relationship with mortality. Journal of the American Geriatrics Society, 53, 2184–2189. doi:10.1111/j.1532-5415.2005.00506.x [CrossRef]
  • Nakasato, YR & Carnes, BA2006. Health promotion in older adults: Promoting successful aging in primary care settings. Geriatrics, 614, 27–31.
  • Satcher, D, Fryer, GE Jr. , McCann, J, Troutman, A, Woolf, SH & Rust, G2005. What if we were equal? A comparison of the black-white mortality gap in 1960 and 2000. Health Affairs (Project Hope), 24, 459–464. doi:10.1377/hlthaff.24.2.459 [CrossRef]
  • Sciegaj, M, Capitman, JA & Kyriacou, CK2004. Consumer-directed community care: Race/ethnicity and individual differences in preferences for control. The Gerontologist, 44, 489–499.
  • Seynnes, O, Fiatarone Singh, MA, Hue, O, Pras, P, Legros, P & Bernard, PL2004. Physiological and functional responses to lowmoderate versus high-intensity progressive resistance training in frail elders. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59, 503–509.
  • Shi, L & Singh, DA2003. Delivering health care in America: A systems approach (3rd ed) Sudbury, MA: Jones and Bartlett.
  • Stewart, AL, Verbonocoeur, CJ, McLellan, BY, Gillis, DE, Rush, S & Mills, KM et al. 2001. Physical activity outcomes of CHAMPS II: A physical activity promotion program for older adults. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56A, M465–M470.
  • Tinetti, ME, Gordon, C, Sogolow, E, Lapin, P & Bradley, EH2006. Fall-risk evaluation and management: Challenges in adopting geriatric care practices. The Gerontologist, 46, 717–725.
  • Topp, R, Boardley, D, Morgan, AL, Fahlman, M & McNevin, N2005. Exercise and functional tasks among adults who are functionally limited. Western Journal of Nursing Research, 27, 252–270. doi:10.1177/0193945904271447 [CrossRef]
  • U.S. Department of Health and Human Services. 2000. Healthy people 2010 With understanding and improving health and objectives for improving health (2nd ed, 2 Vols) Washington, DC: U.S. Government Printing Office.
  • Villareal, DT, Banks, M, Sinacore, DR, Siener, C & Klein, S2006. Effect of weight loss and exercise on frailty in obese older adults. Archives of Internal Medicine, 166, 860–866. doi:10.1001/archinte.166.8.860 [CrossRef]
  • Walcott-McQuigg, JA & Prohaska, TR2001. Factors influencing participation of African American elders in exercise behavior. Public Health Nursing, 18, 194–203. doi:10.1046/j.1525-1446.2001.00194.x [CrossRef]

Demographic Characteristics Associated with Exercise

Characteristic Total Sample Yes Exercise No Exercise p Value
Female N = 112 n = 76 n = 36 0.340
86 (76.8%) 56 (73.7%) 30 (83.3%)
Age in years (mean ± SD) N = 112 n = 76 n = 36 0.680
81 ± 5.2 80.8 ± 5.3 81.3 ± 5.1
Education N =111 n = 76 n = 35 0.075
  Less than high school 35 (31.5%) 21 (27.6%) 14 (40%)
  High school graduate 44 (39.6%) 29 (38.2%) 15 (42.9%)
  Some college 18 (16.2%) 15 (19.7%) 3 (8.6%)
  College graduate 14 (12.6%) 11 (14.5%) 3 (8.6%)
Race N = 110 n = 74 n = 36 0.197
  Caucasian 33 (30%) 25 (33.8%) 8 (22.2%)
  African American 70 (63.6%) 43 (58.1%) 27 (75%)
  Other 7 (6.4%) 6 (8.1%) 1 (2.8%)
Currently married N = 112 n = 76 n = 36 0.111
20 (17.9%) 17 (22.4%) 3 (8.3%)
Income level (per year) N = 73 n = 49 n = 24 0.252
  Less than $15,000 35 (47.9%) 21 (41.9%) 14 (58.3%)
  $15,001 to $37,000 31 (42.5%) 23 (46.9%) 8 (33.3%)
  $37,001 to $80,000 7 (9.6%) 5 (10.2%) 2 (8.3%)
  Insured by Medicaid N = 107 n = 73 n = 34
31 (29%) 17 (23.3%) 14 (41.2%) 0.069
Number of people living in house N = 112 n = 76 n = 36 0.617
  One 70 (62.5%) 46 (60.5%) 24 (66.7%)
  Two 27 (24.1%) 20 (26.3%) 7 (19.4%)
  Three or more 15 (13.4%) 10 (13.2%) 5 (13.9%)
Number of adults living in house N = 112 n = 76 n = 36 0.528
  One 79 (70.5%) 52 (68.4%) 27 (75%)
  Two 24 (21.4%) 18 (23.7%) 6 (16.7%)
  Three or more 9 (8%) 6 (7.9%) 3 (8.3%)
Number of adults age 60 or older living in house N = 110 n = 75 n = 35 0.417
  One 91 (82.7%) 60 (80%) 31 (88.6%)
  Two or more 19 (17.3%) 15 (20%) 4 (11.4%)
Number of related adults living in house N = 112 n = 76 n = 36 0.763a
  None 82 (73.2%) 55 (72.4%) 27 (75%)
  One 23 (20.5%) 16 (21.1%) 7 (19.4%)
  Two or more 7 (6.3%) 5 (6.6%) 2 (5.6%)

Clinical Characteristics Associated with Exercise

Characteristic Total Sample Yes Exercise No Exercise p Value
Obesity N = 111 n = 75 n = 36 0.206
37 (33.3%) 22 (29.3%) 15 (41.7%)
Advised to lose weight during past year N = 111 n = 75 n = 36 0.439
20 (18%) 12 (16%) 8 (22.2%)
Advised about healthy eating or nutrition during past year N = 111 n = 75 n = 36 0.243
28 (25.2%) 16 (21.3%) 12 (33.3%)
Advised about exercise during past year N = 111 n = 75 n = 36 0.841
48 (43.2%) 33 (44%) 15 (41.7%)
Last medical visit less than 1 year ago N = 112 n = 76 n = 36 0.209
105 (93.8%) 73 (96.1%) 32 (88.9%)
Number of limitations in ADLs N = 112 n = 76 n = 36 0.002a
  None 91 (81.3%) 68 (89.5%) 23 (63.9%)
  One 15 (13.4%) 5 (6.6%) 10 (27.8%)
  Two or more 6 (5.4%) 3 (3.9%) 3 (8.3%)
Number of limitations in IADLs N = 112 n = 76 n = 36 0.001a
  None 55 (49.1%) 44 (57.9%) 11 (30.6%)
  One 21 (18.8%) 16 (21.1%) 5 (13.9%)
  Two or more 36 (32.1%) 16 (21.1%) 20 (55.6%)
Health status N = 112 n = 76 n = 36 0.014a
  Excellent 10 (8.9%) 8 (10.5%) 2 (5.6%)
  Good 47 (42%) 38 (50%) 9 (25%)
  Fair 41 (36.6%) 21 (27.6%) 20 (55.6%)
  Poor 14 (12.5%) 9 (11.8%) 5 (13.9%)
Have had high blood pressure N = 112 n = 76 n = 36 0.005
76 (67.9%) 45 (59.2%) 31 (86.1%)
Have had high cholesterol N = 111 n = 76 n = 35 0.834
42 (37.8%) 28 (36.8%) 14 (40%)
Body mass index (mean ± SD) 27.2 ± 4.7 26.8 ± 4.1 27.8 ± 5.8 0.354

Final Logistic Regression Model for Predictors of Exercise (N = 112)

Variable Odds Ratio 95% CI p Value
Insured by Medicaid
  No 3.21 1.19 to 8.69 0.022
  Yes (Ref)
Number of IADLs 0.001
  None (Ref)
  One 0.51 0.14 to 1.89 0.314
  Two or more 0.13 0.05 to 0.38 0.042

Odds of More Versus Less Days of Exercise per Week for Unadjusted (bivariate) and Adjusted Proportional Odds Models (N = 112)

Unadjusted Models Adjusted Model
Characteristic Odds Ratio 95% CI p Value Odds Ratio 95% CI p Value
Female 0.65 0.29 to 1.45 0.295
Age in years 0.99 0.92 to 1.05 0.653
Education 0.063
  Less than high school 0.80 0.36 to 1.80 0.591
  High school graduate (Ref)
  Some college 1.39 0.54 to 3.59 0.501
  College graduate 4.04 1.20 to 13.7 0.024
Race 0.139 0.361
  Caucasian 2.13 1.00 to 4.55 0.050 1.88 0.79 to 4.46 0.154
  African American (Ref)
  Other 1.52 0.41 to 5.58 0.530 1.17 0.29 to 4.71 0.828
Currently married 2.24 0.93 to 5.37 0.072 1.94 0.78 to 4.80 0.154
Income level (per year) 0.711
  Less than $15,000 (Ref)
  $15,001 to $37,000 1.40 0.58 to 3.38 0.450
  $37,001 to $80,000 1.49 0.36 to 6.20 0.583
Insured by Medicaid 0.54 0.25 to 1.18 0.122
Number of people living in house 0.843
  One (Ref)
  Two 1.19 0.54 to 2.62 0.670
  Three or more 0.86 0.32 to 2.33 0.772
Number of adults living in house 0.843
  One (Ref)
  Two 1.19 0.54 to 2.62 0.670
  Three or more 0.86 0.32 to 2.33 0.772
Number of adults age 60 or older living in house
  One (Ref)
  Two or more 1.39 0.58 to 3.35 0.461
Number of related adults living in house 0.938
  None (Ref)
  One 1.01 0.44 to 2.30 0.987
  Two or more 1.28 0.33 to 4.91 0.720
Obesity 0.45 0.22 to 0.93 0.030 0.83 0.35 to 1.94 0.663
Advised to lose weight during past year 0.50 0.21 to 1.17 0.109
Advised about healthy eating or nutrition during past year 0.46 0.21 to 1.00 0.049
Advised about exercise during past year 0.97 0.49 to 1.90 0.921
Last medical visit less than 1 year ago 1.62 0.32 to 8.24 0.562
Number of limitations in ADLs 0.001
  None (Ref)
  One 0.15 0.05 to 0.45 0.001
  Two or more 0.34 0.07 to 1.63 0.178
Number of limitations in IADLs 0.001 0.003
  None (Ref)
  One 0.70 0.29 to 1.71 0.431 0.77 0.30 to 1.93 0.570
  Two or more 0.23 0.10 to 0.51 <0.001 0.21 0.09 to 0.53 0.001
Health status 0.003
  Excellent 3.09 0.72 to 13.2 0.128
  Good (Ref)
  Fair 0.33 0.15 to 0.72 0.005
  Poor 0.55 0.19 to 1.62 0.278
Have had high blood pressure 0.29 0.14 to 0.62 0.001 0.31 0.14 to 0.71 0.005
Have had high cholesterol 0.95 0.48 to 1.90 0.892
Body mass index 1.00 1.00 to 1.00 0.682
Authors

Dr. Sullivan-Marx is Shearer Term Associate Professor for Health Community Practices and Associate Dean for Practice and Community Affairs, School of Nursing, and Ms. Cuesta is an undergraduate student, University of Pennsylvania, College of Arts and Sciences, and Dr. Ratcliffe is Assistant Professor of Biostatistics, University of Pennsylvania, School of Medicine, Philadelphia, Pennsylvania.

This study was funded by the University of Pennsylvania, Penn Institute for Urban Research, and partially supported by the Shearer Endowed Term Associate Professorship for Healthy Community Practices, University of Pennsylvania, School of Nursing. The authors acknowledge the Philadelphia Health Management Corporation for assistance in use of the database, Dr. Yu-Ru Lin for assistance in statistical analysis, and Ms. Anne B. Mitchell for research assistance.

Address correspondence to Eileen M. Sullivan-Marx, PhD, RN, FAAN, Shearer Term Associate Professor for Health Community Practices and Associate Dean for Practice and Community Affairs, University of Pennsylvania, School of Nursing, Claire Fagin Hall, 418 Curie Boulevard, Philadelphia, PA 19104-6096; e-mail: marx@nursing.upenn.edu.

10.3928/19404921-20080101-07

Sign up to receive

Journal E-contents