Journal of Community Health, Vol. 31, No. 6, December 2006 (
2006) DOI: 10.1007/s10900-006-9024-6

ASSOCIATIONS OF PHYSICAL ACTIVITY AND BODY MASS INDEX WITH ACTIVITIES OF DAILY LIVING IN OLDER ADULTS Eduardo J. Simoes, MD; Rosemarie Kobau, MPH; Julie Kapp, PhD; Brian Waterman, MPH; Ali Mokdad, PhD; Lynda Anderson, PhD

ABSTRACT: Research reports about the associations of leisure-time physical activity (LPA) and Body Mass Index (BMI) with activities of daily living (ADL) - or instrumental activities of daily living (IADL)-dependent disability in older adults are inconclusive. Data were obtained from the 2000 Missouri Older Adult Needs Assessment Survey. Logistic regression was used to examine the associations of LPA and BMI with ADL-or IADLdependent disability, while controlling for factors known to be associated with LPA, BMI, ADL and IADL. ADL-or IADL dependency decreased with LPA and increased with BMI regardless of each other’s level, presence of functional limitation, education, gender, race-ethnicity, and health care coverage. Physically active individuals were less likely than inactive ones to be ADL- or IADL-dependent. BMI was modestly associated with ADL- or IADL-dependency and this relationship was confounded by LPA. If confirmed by well designed longitudinal studies, LPA and BMI independent associations with ADL- or IADL-dependent disability lends supports to a strategy for improving older adult quality of life through improved physical activity. Etiological studies on the associations between risk factors and quality of life outcomes in older adults should consider the joint confounding effect of LPA and BMI. KEY WORDS: physical activity; functional limitation; activities of daily living; older adults.

Eduardo J. Simoes, MD, Prevention Research Centers Program; Rosemarie Kobau, MPH and Lynda Anderson, PhD, Health Care and Aging Studies Branch; Ali Mokdad, PhD, Behavior Surveillance Branch, all at Coordinating Center for Health Promotion, NCCDPHP-DACH, Centers for Disease Control and Prevention, 4770 Buford Highway, N.E., MS-K45. Atlanta, GA 30341,USA; Julie Kapp, PhD and Brian Waterman, MPH, Waterman Research Solutions, 5145 Shaw Ave., St. Louis, MO 63110, USA. Requests for reprints should be addressed to Eduardo J. Simoes, MD, Prevention Research Centers ProgramCoordinating Center for Health Promotion, NCCDPHP-DACH, Centers for Disease Control and Prevention, 4770 Buford Highway, N.E., MS-K45, Atlanta, GA, 30341, USA; e-mail: [email protected]

453 0094-5145/06/1200-0453
2006 Springer Science+Business Media, Inc.

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INTRODUCTION Obesity continues to increase rapidly in the United States (U.S.) across all ages with devastating and costly consequences [1–3]. Obesity also substantially interferes with activities of daily living (ADLs) (e.g., dressing, eating, bathing) and instrumental activities of daily living (IADLs) (e.g., meal preparation, housework, shopping) [4–6] and diminishes a person’s health-related quality of life [7–9]. Physical activity confers substantial health benefits in adults who are overweight or obese [10–12]. Recent research has found a dose-response relationship between physical activity and health-related quality of life among adults [13]. While many studies have found that physical activity reduced the progression of disability in older adults [12–19], few have identified obesity as increasing disability [19], and others found little evidence for such an effect in older adults [20]. These inconsistent findings may be due to methodological issues and analytical approaches. There is scant information on the associations of body mass index, physical activity levels, functional limitation and socioenvironmental variables with, ADL-or IADL-dependency in older adults. The purpose of this study was to examine a representative sample of adults aged ‡60 years to investigate whether the prevalence of ADL- and IADL- dependent disability changed across levels of leisure-time physical activity (LPA) and body mass index (BMI).

METHODS Data from the Missouri Department of Health and Senior Services (MDHSS) 2000 Statewide Missouri Older Adult Needs Assessment Survey (MOANAS) were used to examine the relationship of BMI and LPA with functional limitation, and ADL- and IADL- dependent disability and vitality. The MOANAS is a random-digit dialed telephone survey designed to collect health, social service, and needs assessment data on Missouri adults aged 60 years and older. MDHSS oversampled residents with selected zip codes in the city of St. Louis, Kansas City, and the Bootheel region (Dunklin, Mississippi, New Madrid, Pemiscot, and Scott counties but not Stoddard County) in order to include a larger number of African Americans. The oversampled areas of St. Louis and Kansas City have more than 40% African American residents, and the Bootheel region has more than 18% according to US census data. A disproportionate stratified random sampling design was used to obtain sufficient numbers of elderly respondents. The sampling frame consisted of

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telephone exchanges in eight strata along county or city boundaries in the state. Experienced personnel conducted the MOANAS, using computer-assisted telephone interviewing software. The MOANAS response rate was 64.4% for the survey, a percentage calculated by using the Council of American Survey Research Organizations’ response rate formula [21]. The Institutional Review Board at the Missouri Department of Health approved the study. Measures Self-reported height and weight were used to calculate BMI (weight in kilograms divided by height in meters squared, [kg/m2]). For data presentation on BMI relationships, subjects were classified according to the following BMI categories: £ 18.5, underweight; 18.6 to 24.9 kg/m2, normal weight; 25 to < 30 kg/m2, overweight; and ‡30 kg/m2, obese [22]. Leisure-time physical activity (LPA) was initially assessed by two BRFSS questions: 1) ‘‘During the past month, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening or walking for exercise?’’; and 2) What type of physical activity or exercise did you spend the most time doing during the past MONTH ? Interviewers were given a list of 56 LPA options to enhance their recall for LPA [23]. In addition, interviewees were asked: 3) How many times per week or per month did you take part in this activity during the past month?; and 4) For how many minutes or hours did you usually keep at it? Responses to these questions and the recommendations by the Centers for Disease Control and National Blueprint on Physical Activity were used to classify individuals into three categories of LPA: inactive, insufficient and recommended [18, 24]. Functional limitation due to co-morbidity was assessed with the question, ‘‘Are you limited in any way in any activities because of any impairment or health problem?’’ [25]. Those who responded yes to this question were identified as having a functional limitation. Activities of daily living (ADLs) are basic activities that include bathing, dressing, toileting, transfer, continence, and feeding [26]. Persons are limited in an ADL if they are unable to perform the activity, use active help, use equipment, or require standby help [26]. This study included walking, along with the six ADLs previously mentioned. Each ADL included four questions that assessed the person’s ability to perform the activity independently, the extent of difficulty with the activity, available assistance, and need for assistance associated with the activity. Scores ranged from 0 to 5 within each activity; these scores were then summed to create a final score

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for seven ADLs that ranged from 0 to 35. Higher scores reflected an indication of self-reported limitation, difficulty and need. Instrumental activities of daily living (IADLs) are indicators of more complex tasks such as the ability to do housework (e.g., dusting, washing dishes, laundry), do heavy cleaning or yard work, use the telephone, get outdoors, shop, prepare one’s own meals, take medications, and manage money [27]. Persons may be limited in an IADL activity if they can not do the activity because of a disability, health problem, or environmental barrier. Items used on IADL scales vary [27]. The MOANAS included eight items assessing IADLs modeled after the Older Americans Resources and Services Functional Assessment (OARS) scale [28]. Scores ranged from 0 to 5 within each activity. These scores were then summed to create a final score for IADLs that ranged from 0 to 40. Higher scores reflected an indication of limitation, difficulty, and need. Both ADL and IADL questions have been tested with various rating formats and scoring procedures and have acceptable levels of reliability and validity for use in surveys [27, 29, 30]. Those who identified no limitations or indicated no need or difficulty with ADL or IADL items were considered as less dependent (i.e., ADL and IADL summed score of 0), whereas those who reported limitations, need and difficulty with one or more ADLs or IADLs were considered dependent (i.e., ADL or IADL summed score ‡1). The moderate, but significant correlations of functional limitation with ADL (n = 2996; r = 0.31, p < 0.01) and IADL (n = 2996 r = 0.45, p < 0.01) and the direction of their associations with leisure-time physical activity, BMI, and demographic variables suggest construct validity of the measures used in this study. Health care coverage was assessed with the question, ‘‘Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?’’ Those who responded yes to this question were identified as having health care coverage. All demographic items used on the MOANAS are available on the BRFSS web site: http://www.gov/brfss [25]. Persons who responded ‘‘don’t know’’ or ‘‘refuse to respond’’ on any measure were coded as having missing values. Analytic Sample MDHSS surveyed 3,202 residents (2,757 overall and 445 from oversample areas). The final analytic sample in the descriptive table consisted of 3,001 adults after exclusion of 201 respondents, including those younger than 60 years (n = 17) and those with a missing value for race (n = 34), employment (n = 2), education (n = 12), region (n = 20), physical activity (n = 13), or weight/height (n = 103). The final analytical sample for

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regression analysis consisted of 2833 respondents, after excluding 159 with missing values for one or more variables used in regression models. Data Analysis Statistical analyses were conducted with SAS and STATA to accommodate the complex sampling design [31, 32]. Data were weighted to compensate for the unequal probability of sampling selection. Race- and age-specific prevalence estimates and 95% confidence intervals (95% CI) of ADL, IADL-, categorized BMI, LPA and covariates were generated. Logistic regression analyses were used controlling for sex, race, age, education, functional limitation, and health insurance coverage, to examine whether leisure-time physical activity and BMI were significant predictors of ADLs andIADLs. Employment and sampled geographical area were evaluated as control variables and later dropped from models because they did not add to model fitness and diminish precision of estimators. Interaction between leisure-time physical activity and BMI were evaluated by the )2 likelyhood goodness of a fit test [33]. Robust regression diagnostic methods were employed to assess the fitness and adequacy of regression models [33, 34].

RESULTS The analytic sample was predominantly white (89.1%), female (58.8%), between the ages of 65 and 75 (66.2%), with a high-school diploma or higher education (76.5%), with some form of health care coverage (95.9%), practicing some leisure-time physical activity (60.2%), and overweight/obese (59.8%) (Table 1). Thirty-two percent (95% CI = 30.0– 34.2) of adults reported functional limitation, but fewer reported dependency in ADLs (5.9%) and IADLs (16%). There were no differences in functional limitation or ADL dependency by sex; however, more women (18.5%, 95% CI = 16.3–20.6) reported IADL dependency than did men (12%, 95% CI = 10.0–14.9) (data not presented). Although no significant differences were found in functional limitation by race/ethnicity, more blacks (11.5%, 95% CI = 6.8–16.3) were ADL dependent than were whites (5.3%, 95% CI = 4.3–6.3), and blacks were also more likely to be IADL dependent (24.3%, 95% CI = 18.3–30.3) than whites, (14.9%, 95% CI = 13.2–16.6) (data not presented). Significantly more of those respondents who were classified as obese reported functional limitation (43.3%) than did those classified as overweight or underweight/normal (Table 2). Obese individuals had a

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TABLE 1 Demographic characteristics, Missouri older adults and needs assessment survey—2000 Variable Sex Female Male Age £ 75 > 75 Race Black Other White Employment For wages Retired Education < High school High school diploma > High school Health insurance coverage No health insurance Health insurance Physical activity Yes No Functional limitation No Yes ADL Less dependent Dependent IADL Less dependent Dependent Body Mass Index Under and Normal weight Overweight Obese

Percentage

(95% CI)

58.8 41.2

(56.6–61.0) (39.0–43.4)

66.2 33.8

(64.2–68.2) (31.7–35.9)

9.1 1.7 89.1

(7.9–10.4) (1.1–2.4) (87.7–90.5)

27.1 72.9

(25.2–29.0) (70.9–74.7)

23.5 38.7 37.8

(21.6–25.3) (36.7–40.9) (35.6–39.9)

4.1 95.9

(3.2–4.9) (95.1–96.7)

60.2 39.8

(58.1–62.3) (37.7–41.9)

67.9 32.1

(65.8–70.0) (30.0–34.2)

94.1 5.9

(93.0–95.1) (4.9–7.0)

83.9 16.1

(82.3–85.6) (14.5–17.7)

40.2 37.7 22.2

(38.1–42.3) (35.5–39.8) (20.2–24.1)

Note. ADL, activities of daily living; IADL, instrumental activities of daily living; CI, confidence interval.

ADL, % (95% CI) Less dependent Dependent IADL, % (95% CI) Less dependent Dependent Functional limitation No Yes

Variable

78.5 (74.3–82.7) 21.5 (17.3–25.7) 56.7 (51.6–61.9) 43.3 (38.1–48.4)

86.9 (84.5–89.3) 13.1 (10.7–15.5) 69.7 (66.4–72.9) 30.3 (27.1–33.6)

84.1 (81.6–86.5) 15.9 (13.5–18.4) %, (95% CI) 72.3 (69.3–75.4) 27.7 (24.6–30.7)

92.2 (89.8–94.6) 7.8 (5.4–10.2)

Obese

94.5 (92.8–96.2) 5.5 (3.8–7.2)

Overweight

94.7 (93.2–96.2) 5.3 (3.8–6.8)

Underweight/ Normal

Body Mass Index

76.0 (73.5–78.5) 24.0 (21.5–26.5)

92.3 (90.8–93.7) 7.7 (6.3–9.2)

96.7 (95.7–97.8) 3.3 (2.2–4.3)

Yes

No

55.5 (52.1–59.0) 44.5 (41.0–47.9)

71.2 (68.0–74.4) 28.8 (25.6–32.0)

90.0 (88.0–92.0) 10.0 (8.0–12.0)

Physical Activity

Prevalence estimates of activities of daily living status (ADLs) instrumental activities of daily living status (IADLs) and functional limitation across levels of body mass index and physical activity among adults aged ‡60 years, Missouri older adults needs assessment survey—2000

TABLE 2

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higher percentage of dependency as measured by ADL (7.8%), and IADL (21.5%) than did those underweight/normal or overweight (Table 2). Compared to those who were physically active, the physically inactive had a higher percentage of dependency in ADL (10.0%), or IADL (28.8%) and functional limitation (44.5%). Compared to those who are physically inactive, the physically active at the recommended levels of LPA and those active but at less than the recommended levels are respectively 44% and 41% less likely be dependent according to the ADL scale. Those who are active at the recommended level of LPA and those active at less than recommended levels are 55% and 48% less likely to be IADL-dependent, respectively. Those persons who have BMI values of 27.5 (overweight) and 32.5 (obese) were respectively 1.34 and 1.58 more likely than those persons with BMI values of 17.5 (normal) to be dependent according to the ADL scale. Compared to those who are normal or underweight, the likelihood of IADL-dependency is 1.42 and 1.73 for those who are overweight and obese, respectively (Table 3). There is no significant interaction between leisure-time physical activity and BMI. Being female, black or other race, older than age 75, having less than a high school education and having a functional limitation due to morbid condition were all associated with increased likelihood of ADL-or IADL dependency. The fitness of the two regression models was appropriate and regression diagnostics did not reveal violations of model assumptions.

DISCUSSION The findings of this study show that leisure-time physical activity was strongly and independently associated with ADL- and IADL-dependency in older adults, regardless of the level of functional limitation due to co-morbidity, BMI levels and socio-economic factors. The findings also demonstrate modest and independent associations of obesity with ADLand IADL-dependency in older adults. These findings are important to public health programs aimed at reducing the burden of obesity and disability and improving quality of life among older Americans. Moreover, the large sample size and the sufficient representation of African Americans in the sample, a group at high risk for both obesity and disability, provide more insight into the association between leisure-time physical activity and obesity-related ADL- and IADL-dependency. Approximately 38% of our study subjects were overweight, 21% were obese, and only 60% reported engaging in some leisure-time physical activity. In 2000, 40% of U.S. adults aged 65 and older were overweight and

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TABLE 3 Adjusted odds ratio (OR) of activities of daily living (ADLs), instrumental activities of daily living (IADLs) and functional limitation by levels of covariates, Missouri older adults needs assessment survey—2000 Variable Body mass index* 18.6 (reference) 27.5 vs. 18.6 32.5 vs. 18.6 Physical activity Insufficient Recommended Inactive Function Limitation

Sex Female Male Race Black/other White Age > 75 £ 75 Health insurance coverage No health insurance Health insurance Education < HS HS > HS

ADLs > = 1 model OR (95% CI)

IADLs > = 1 model OR (95% CI)

1.00 (Referent) 1.34 (1.14–1.58) 1.58 (1.23–2.05)

1.00 (Referent) 1.42 (1.21–1.67) 1.73 (1.35–2.23)

0.44 (0.35–0.56) 0.41 (0.31–053) 1.00 (Referent)

0.55 (0.45–0.69) 0.48 (0.39–0.60) 1.00 (Referent)

8.94 (7.32 – 10.91) 1.00 (Referent)

8.11 (6.64 – 9.91) 1.00 (Referent)

1.15 (0.94–1.41) 1.00 (Referent)

2.77 (2.29–3.35) 1.00 (Referent)

1.96 (1.45–2.65) 1.00 (Referent)

1.47 (1.10–1.96) 1.00 (Referent)

1.66 (1.35–2.04) 1.00 (Referent)

2.38 (1.96–2.88) 1.00 (Referent)

0.85 (0.53–1.36) 1.00 (Referent)

1.40 (0.89–2.18) 1.00 (Referent)

1.59 (1.23–2.05) 1.24 (0.98–1.57) 1.00 (Referent)

1.74 (1.37–2.21) 1.02 (0.83–125) 1.0 (Referent)

Note. CI = Confidence Interval; HS, high school; *Body mass index levels (BMI) indicate the contrast between specific values and the reference and lowest value for a normal weight person.

18.2% were obese; 65.4% of adults aged 65 and older in the U.S. reported engaging in leisure-time physical activity [35]. The prevalence of overweight and obesity are also consistent with those found in adults aged

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‡18 years who participated in the 2000 Missouri BRFSS, among whom 36.2% and 23.1% were overweight and obese, respectively [36]. Previous studies have indicated that physical activity can be an important predictor of functional ability among older adults [4, 16]. They have shown that the onset of functional ability loss might be mitigated if an older adult, even one who is obese, remains physically active. Active obese individuals have lower morbidity and mortality than do normal weight individuals who are sedentary [10]. Our study result corroborates the findings of associations of physical activity and BMI with functional ability [14, 16] and disability in older adults [19]. However, it supports a model of greater ADL- and IADL-dependency as a function of lower physical activity levels and greater BMI in older adults, regardless of their functional limitation level. Thus, it underscores the need to promote physical activity for its beneficial health effects on disability outcomes in addition to attaining healthy weight [11, 19] Previous studies have shown that adult obesity greatly raises the risk of disability [4, 37], substantially impairs health-related quality of life [38] and moderately increases health risks [39]. To our knowledge, these studies did not examine the association between obesity and quality of life in the elderly alone. Self-regulatory theories of motivation suggest the need for messages that encourage competency for physical activity at any level of fitness. Such messages may have an advantage over messages encouraging weight loss for its own sake which can foster further inefficacy for weight loss in those plagued with low efficacy and negative expectations for weight loss [11, 40]. In the present study, the associations of BMI with ADL- and IADLdependency in the elderly were significant but become modest in the presence of physical activity. This findings and the absence of interaction between BMI and LPA on their associations with ADL- and IDL dependency indicates substantial confounding between BMI and leisure-time physical activity. This study has a few limitations. Because MOANAS respondents selfreport their height and weight, health status, and functional limitations, there is the potential for measurement error associated with recall in older adults. However, variables such as weight, height, functional limitation, and demographic items have been validated with use of data from similar telephone-based surveys and found to have adequate levels of reliability (functional limitation) and validity (weight, height, and vitality) [41–44]. Another limitation is the possible misclassification of dependency as defined by the ADL and IADL scores. Sensitivity analysis carried out with

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both ADL and IADL dependency classified in two additional ways did not change direction and magnitude of the associations with LPA, BMI and other covariates in the models. In one analysis, those who indicated little need or difficulty with ADL or IADL items were considered as less dependent (i.e., ADL and IADL summed score of 0–3), whereas those who reported limitations, need and difficulty with four or more ADLs or IADLs were considered dependent (i.e., ADL or IADL summed score ‡4). In another analysis, those with four or more limitations, need and difficulty with ADLs and IADLs (i.e., ADL or IADL summed score ‡4) were classified as dependent, those who reported no limitation were considered independent (i.e., ADL or IADL summed score of 0) and those who were less dependent (i.e., ADL and IADL summed score of 1–3) were excluded from final analysis. Moreover, it is known that the measurement properties of instruments used to examine ADL- and IADL- dependent disability and functional limitation may vary according to scoring methods [27, 45–47] In this study, two measures of disability (ADL/IADL) and one measure of functional limitation were used to discriminate between moderately and more severely impaired community-dwelling older adults. The findings suggest that only a small percentage of community-dwelling elderly in Missouri are severely disabled, with over one third of our sample reporting functional limitation and fewer reporting impairments in ADLs and IADLs. Of particular interest is the observation that the measure of functional limitation may be more sensitive, albeit less specific, as a screening item to detect the prevalence of impairments in daily activities among communitydwelling older adults. Within the Basic Disablement Model framework, individuals with functional limitation might be those with a ‘‘preclinical disability,’’ and have not yet identified difficulty or need with an ADL or IADL task [48]. This may explain the higher percentage of adults reporting functional limitation than ADL or IADL impairment. An alternative explanation is that functional limitation may identify impairments not explicitly assessed by ADL and IADL scales (e.g., vision, hearing, and social functioning). Individuals with functional limitation are nonetheless at increased risk for subsequent ADL and IADL disability and may benefit from primary prevention efforts [45]. A final limitation is that because of the cross-sectional nature of this study we are unable to determine causality between study variables. For example, it is possible that a number of individuals in the study may have become physically inactive after developing a chronic disease or condition. Nevertheless, the inverse associations of LPA with ADL- and IDL-dependency remained strong and significant even after

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controlling for FL, thus accounting for a number of co-morbidities that cause functional limitation. This study has several strengths. In a review of 78 longitudinal studies on disability related factors, it was noted that a major limitation was the lack of a uniform definition of functional status decline [15]. This study uses three outcome measures on disability, including one (i.e., functional limitation) that is a precursor to two others (i.e., ADL and IADL) in the disablement model. This study includes a comprehensive set of predictors in the regressions models such as sex, age, race-ethnicity, health care coverage, and education that tap into differences attributed to the socialenvironmental determinants such as poverty, unemployment, and non-completion of high school education—all known to be associated with health-related quality of life [49]. Finally, compared to many of the studies reviewed, this study sample size is larger and included large number African American respondents, a racial-ethnic group commonly underrepresented in epidemiological studies of the elderly. This study adds to the scant but growing evidence that promoting leisure-time physical activity and reducing obesity to improve activity of daily living among older adults, including minorities such as African Americans, should be considered as a public health strategy. Its findings indicate the need for additional longitudinal studies to further examine the relationships among these variables. Future studies on the effects of physical activity on quality of life related outcomes in the elderly should consider controlling for the confounding effect of overweight and obesity. Although practicing healthy behaviors at a younger age results in lifelong health benefits, it is never too late for adults to improve health behaviors and gain health benefits and improve health-related quality of life. It is crucial for local and state health departments to develop and implement effective programs to improve leisure-time physical activity across the life span [18, 50, 51].

ACKNOWLEDGMENTS We gratefully acknowledge the contributions of staff of the Missouri Department of Health and Senior Services Division of Chronic Disease Prevention and Health Promotion, Office of Surveillance, Research and Evaluation, particularly the Behavior Risk Factors Surveillance System telephone surveyors for their invaluable help in completing the data collection. We thank Dr. Ann Deaton, current director of the Division of

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Mental Retardation and Developmental Disabilities, Missouri Department of Mental Health, then director of the Division of Aging of the Department of Senior Services, and her staff for their support of the survey and this study. Dr. John Crews, CDC Disability Program kindly provided helpful comments on an earlier version of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

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