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Archives of Business Review – Vol. 8, No.7

Publication Date: July 25, 2020

DOI: 10.14738/abr.87.8569.

Son, C. H. (2020). Associations of Multiple Chronic Health Conditions with Health Behavior. Archives of Business Research, 8(7). 1-21.

1

Associations of Multiple Chronic Health Conditions with Health

Behavior

Chong Hwan Son

Ph.D. Economics, Department of History, Philosophy,

Social Sciences, Hudson Valley Community College,

Troy, New York, U.S.A.

ABSTRACT

This study aimed to scrutinize the association of the number of chronic

health conditions with health behavior. The health behavior was

measured by meeting the 2008 Physical Activity Guidelines (PAGs) for

Americans for five physical activity levels for adults aged 18 years or

older in the United States using data from the 2017 Behavioral Risk

Factor Surveillance System (BRFSS). The empirical results of a

multivariate logistic regression analysis revealed that respondents

living with chronic health conditions were more likely to participate in

aerobic physical activities, but not meeting the PAGs. In the insufficient

physical activity subgroup, all of the predicted odds ratios were greater

than one and increased as the number of chronic health conditions

increased. It implied that the increase in the number of chronic

conditions was positively associated with participating in insufficient

physical activity. Respondents who reported having less than three

chronic health conditions were more likely to meet the aerobic physical

activity guidelines compared with respondents living with three or

more chronic health conditions. Importantly, respondents who

reported having 4 or more chronic health conditions had a higher

likelihood of meeting the recommendations for muscle-strengthening

activity. However, chronic health conditions would significantly

discourage respondents from participating in both aerobic and muscle- strengthening physical activities. In conclusion, this study found that

chronic health conditions played an important role in determining

regular participation in the level of physical activity for individuals

living with chronic health conditions.

Keywords: Physical Activity, Chronic Health Conditions, BRFSS, the 2008

Physical Activity Guidelines.

INTRODUCTION

It is well known that participating in regular physical activity is one of the most important things

individuals can maintain or improve their fitness and physical function, and regular physical

activity can produce a large number of immediate and long-term health benefits, both physically

and mentally (Farren, et al., 2017; Kujala, 2018). Participating in regular physical activity helps

physically to strengthen bones and muscles, control weight, maintain healthier body composition,

increase the resistance to diseases, etc. The mental health benefits of regular physical activity are

improved mental health and enhanced health-related quality of life by increasing enjoyment, self-

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Son, C. H. (2020). Associations of Multiple Chronic Health Conditions with Health Behavior. Archives of Business Research, 8(7). 1-21.

esteem, energy, and engagement in social activities (Farren, et al., 2017). These health benefits of

regular physical activity are important pillars for promoting healthy lifestyles in public health

perspectives. For improving public health through participation in regular physical activity,

physical activity recommendations have been promoted by health organizations.

Current physical activity recommendations by the 2008 Physical Activity Guidelines (PAGs) for

Americans (U.S. Department of Health and Human Services, 2008) and the World Health

Organization (WHO) were subject to the evidence relating physical fitness, physical activity, and

energy expenditure to health outcomes. The recommendations provided key guidelines that adults

should do at least 150 minutes of moderate-intensity aerobic physical activity per week, or 75

minutes of vigorous-intensity aerobic physical activity per week, or an equivalent combination of

moderate- and vigorous-intensity aerobic physical activity. The aerobic physical activity should be

performed in episodes of at least 10 minutes duration. Besides, the 2008 PAGs provided additional

guidelines that adults should also engage in muscle-strengthening activities that were of moderate- or high-intensity activities and worked for all major muscle groups at least 2 days a week. Research

has indicated that muscle-strengthening activities also provide additional health benefits, such as

cardiovascular and metabolic benefits independent of aerobic physical activity. Therefore,

regularly participating in both aerobic physical activity and muscle-strengthening physical activity

was encouraged (Carlson, et al., 2015; Farren, et al., 2017).

As an increase in the average life expectancy over the last decades in the United States – the life

expectancy at birth for males (females) had increased from 71.8 (78.8) years in 1990 to 76.1 (81.1)

years in 2016 (National Centers for Health Statistics, 2017), the proportion of the elderly

population has steadily grown in the United States (Horner and Cullen, 2016; Son, 2020). As a

result, the prevalence and burden of individuals living with multiple chronic health conditions have

been anticipated to be an upward trend. The study (Newman, et al., 2019) defined chronic health

conditions as “conditions that last a year or more and require ongoing medical attention and/or

limit activities of daily living.” Chronic health conditions are a long term illness that can reduce an

individual’s quality of life and/or his/her life expectancy. However, studies (Booth, et al., 2012;

Miyawaki, et al., 2017) advocated that the chronic health conditions can be prevented or controlled

through regular participation in physical activity and that being active was an important

component of a health-promoting lifestyle for adults with chronic diseases. For instance, studies

(Rydwik, et al., 2012; Miyawaki, et al., 2017; Hallgren, et al., 2019) found that the likelihood of

having an incidence of major depressive disorder fell as the level of physical activity rose and that

physical activity was associated with a reduction in the risk of chronic health diseases and

conditions. Their findings induced that engaging in any level of physical activity for individuals

without chronic health conditions was beneficial to them to prevent chronic health problems. At

the same time, individuals living with chronic health conditions were expected to participate more

in regular physical activity to treat or manage their chronic health conditions. For example, the

study (Zechner and Gill, 2016) reported that the number of chronic health conditions was

positively associated with the amount of self-reported exercise using 31 common chronic health

conditions.

In the end, individuals living with chronic health problems may go in search of controlling or

minimizing their affliction through a positive form of health behavior (Cockerham, et al., 2014),

such as participating in physical activity. If the individuals desire to participate in regular physical

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activity to improve the transition with chronic health conditions, they could pursue a feasible

intensity level of physical activity based on the degree of their functional limitations: some may be

able to do the more intensive physical activity than others may do. The studies (Bowles, 2012;

Pettee Gabriel, et al., 2012) defined the physical activity as “the behavior that involves human

movement, resulting in physiological attributes including increased energy expenditure and

improved physical fitness.” An important part of the definition was the addressing of physical

activity as a behavior, which brought up a deliberate character of physical activity. It was the

central idea to develop a hypothesis for the current study to investigate the association of the

number of chronic health conditions with health behavior, such as meeting the 2008 PAGs. The

number of chronic health conditions would be served as a proxy for the degree of functional

limitations in the current study. This study conducted the multivariate logistic regression for five

physical activity levels for adults aged 18 years or older in the United States using the 2017

Behavioral Risk Factor Surveillance System (BRFSS) which computed and provided an assessment

of physical activity levels. Respondents from the BRFSS were classified into five mutually exclusive

physical activity levels, such as physical inactivity, insufficient physical activity, meeting the

aerobic PAGs, meeting the muscle-strengthening PAGs, and meeting both the PAGs (Desmond, et

al., 2015; Bennie, et al., 2019a). A detailed description of the BRFSS data and the definition of the

physical activity levels are presented in section 3.

ANALYTICAL FRAMEWORK

The level of physical activity based on the 2008 PAGs was served as a dichotomous dependent

variable. Each respondent was coded as either ‘1’ if ‘meeting the recommended level of physical

activity’ or ‘0’ if ‘not meeting’. Estimates of the probability of meeting the recommended level of

physical activity could be developed as a behavioral equation, such as a general linear probability

equation:

yi =β0 + xikβk + ui (1)

where the subscript i denoted an observation, and yi was the probability of meeting the

recommended level of physical activity at the ith observation. xik was an i x k matrix of explanatory

variables where k was the number of explanatory variables. β0 was a constant term, and βk was a

coefficient matrix of the equation with a dimension of k x1. ui was the error term at each

observation i with the expected value of ui, E(ui) = 0. Thus, the conditional expected value of the

dependent variable was expressed as E(yi|xik) = β0 + xikβk. The predicted values of the dependent

variables should have a certain probability of occurrence that should be between zero and one.

However, the linear probability model had an unboundedness problem that a predicted value could

fall outside of the meaningful range of zero to one. As a result, the predicted probability could be

quite different from the observed value for each observation. The binomial logit model could avoid

the unboundedness problem and have been widely used in research dealing with a binary

dependent variable (Maddala, 1992; Kleinbaum and Klein, 2002; Son, 2014).

In the logistic regression, an unknown probability (y) could be estimated for any given linear

combination of independent variables. The dependent variable in the logistic regression follows

the Bernoulli distribution (Marshall and Olkin, 1985) which is just a case of the binomial

distribution with just one trial: success is ‘1’ and failure is ‘0’. If the probability of success is y, the

probability of failure is (1 – y). Therefore, it is required to link together the independent variables

to essentially the Bernoulli distribution. The link is called a logit. In logistic regression, the

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Son, C. H. (2020). Associations of Multiple Chronic Health Conditions with Health Behavior. Archives of Business Research, 8(7). 1-21.

probability is unknown like in the binomial (Bernoulli) distribution problems. As mentioned above,

the goal of using the logistic regression is to estimate the unknown probability (y) for a given linear

combination of independent variables. The estimate of y is y-hat, !". To tie together the linear

combination of independent variables and in an essence of the Bernoulli distribution, it is needed

of a function that linked them together or mapped the linear combination of variables that could

result in any value into the Bernoulli probability distribution with a domain from zero to one. The

natural log of the odds ratio (the logit) is the link function:

ln (odds) = ln# $

%&$

' is expressed as ‘logit (y) or ln (y) – ln (1 – y)’, such as

logit (y) = ln# $

%&$

' (2)

In equation (2), if y = 0, logit (y) would be –∞; if y = 1, logit (y) would be +∞; and if y = 0.5, logit (y)

would be zero. In the logit linked function graph, the probability (y) ranges from 0 to 1 alone the x- axis, but the probability (y) should be on the y-axis. It could be achieved by taking an inverse of the

logit function. To rewrite the logit function:

logit (y) = ln# $

%&$

', and then set ln# $

%&$

' = α

where,

α is some number,

y ranges between 0 and 1.

The inverse of the logit function becomes:

logit-1 (y) = # $

%&$

' or # $

%&$

'= eα

Solve for y,

y = # ()

%*()' (3)

This study assumes that α is a linear combination of independent variables and their coefficients

as α = β0 + xikβk in equation (3). Eventually, the probability becomes the Sigmoid function curve

(Ezeafulukwel, et al., 2018; Ezeafulukwel, et al., 2020), known as the ‘S’ curve in a range of 0 to 1

inclusive alone the y-axis. Thus, equation (3) becomes the logistic function with substituting β0 +

xikβk for α, and then the probability would be a function of independent variables (xs):

P(x) = + (,-./01,1

%*(,-./01,1

2 (4)

In equation (4), when β0 + xikβk approaches to – ∞, P(x) becomes 0:

P(x) = # (3∞

%*(3∞'; # (3∞

%*(3∞' = # %

%*(∞'; and then # %

%*(∞' ≅ 0

In equation (4), when β0 + xikβk approaches to +∞, P(x) becomes 1:

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P(x) = # (.∞

%*(.∞' = 1, and then # (.∞

%*(.∞' ≅ 1

Ultimately, the range of the logistic function in equation (4) would be between 0 and 1, regardless

of the value of α which is a linear combination of independent variables with constant coefficients.

This is the primary reason the logistic model is wildly used in research dealing with the binary

dependent variable.

To solve for logit (y) from equation (2), substitute equation (3) for y in the odds in equation (2),

and then the algebraic procedure is the following:

+ !

1 − !

2 =

:;

1 + :;

1 − :;

1 + :;

=

:;

1 + :;

1 + :;

1 + :; − :;

1 + :;

=

:;

1 + :;

1

1 + :;

= :;

where α = β0 + xikβk.

Thus, the logit(y) in equation (2) becomes:

logit (y) = ln# $

%&$

'; ln# $

%&$

' = =>( :;); =>( :;) = A; and then =>( :;) = BC + DEFBF

Thus,

logit (y) = BC + DEFBF (5)

Equation (5) indicates that the natural logarithm of the odds ratio becomes a linear function of the

independent variables. From equation (2) to equation (5), the above algebraic processes result in

that the inverse of the logit function linked to the estimated regression equation. Thus, the

estimated probability from equation (4) becomes:

!"= + (,G-./01,G1

%*(,G-./01,G1

2 (6)

This estimated probability equation links to the logistic regression. From equation (6), the odds

ratio could be derived. Suppose there is only an independent variable in the model. If the value of

an independent variable, X, changes by one, let y0 be the probability of success when X = 0; y1 be

the probability of success when X =1. If the value of X changes from 0 to 1 or any increment by one,

the odds of success for X = 0 and X = 1 are the following:

the odds of success for X = 0,# $-

%&$-

' = :HG-*IHGJ , and then:HG-*IHGJ = :HG- and the odds of success for

X = 1,# $J

%&$J

' = :HG-*IHGJ, and then :HG-*IHGJ = :HG-*HGJ

Thus, the odds ratio between X = 0 and X = 1 could be derived when the value of an independent

variable ( e.g. X) changes by one (from X = 0 to X = 1).

# KJ

J3KJ

'

# K- J3K-

' = (,G-.,GJ

(,G- ;

(,G-.,GJ

(,G- = (,G-(,GJ

(,G- ;

and then (,G-(,GJ

(,G- = :HGJ

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Son, C. H. (2020). Associations of Multiple Chronic Health Conditions with Health Behavior. Archives of Business Research, 8(7). 1-21.

Finally, the odds ratio in the logistic regression is expressed as an exponential of the estimated

coefficient (BL

F) of an independent variable shown above the algebraic procedures. In this study,

odds ratios were obtained from a multivariate logistic regression analysis.

DATA

Data were drawn from the 2017 BRFSS which was a large-scale national health survey. The BRFSS

was a collaborative project of the CDC with all 50 states and the District of Columbia in the United

States. The BRFSS was designed to conduct monthly ongoing health-related telephone surveys to

collect uniform state-specific data on health-related risk behaviors, chronic diseases, chronic

health conditions, access to health care, and use of preventive

Table 1: Definition, Mean, and Standard Deviations of Variablesa

Variables Definition Mean and SD

Physical Activitiesb

Inactive

Respondent answers “no’ to the leisure-time physical activity question or

respondent reports performing an aerobic physical activity bout for less than 10

minutes in duration or respondent reports performing a non-aerobic activity.

22.00 (41.43)

Insufficient Respondent reports greater than 10 minutes and less than or equal to 149 minutes

of aerobic activity per week. 15.70 (36.38)

Meet aerobic

Respondent reports at least 150 minutes per week of moderate-intensity activity,

or at least 75 minutes per week of vigorous-intensity activity, or an equivalent

combination of moderate-intensity and vigorous-intensity (multiplied by 2)

totaling at least 150 minutes per week.

30.82 (46.17)

Meet strengthen Respondent reports participation in muscle-strengthening activities at least 2

times per week. 10.20 (30.26)

Meet both

Respondent reports participation in muscle-strengthening activities at least 2

times per week and at least 150 minutes per week of moderate-intensity activity,

or at least 75 minutes per week of vigorous-intensity activity, or an equivalent

combination of moderate-intensity and vigorous-intensity (multiplied by 2)

totaling at least 150 minutes per week.

21.28 (40.93)

Marital Status

Married Respondent is married or a member of an unmarried couple. 58.65 (49.25)

Divorced Respondent was divorced or separated. 13.86 (34.55)

Widowed Respondent was widowed. 6.58 (24.80)

Never married Respondent is never married. 20.91 (40.67)

Income Level

Income 20 Respondent’s annual household income from all sources is less than $20,000. 15.91 (36.58)

Income 2035 Respondent’s annual household income from all sources is $20,000 to less than

$35,000. 18.33 (38.70)

Income 3550 Respondent’s annual household income from all sources is $35,000 to less than

$50,000. 13.15 (33.80)

Income 5075 Respondent’s annual household income from all sources is $50,000 to less than

$75,000. 15.20 (35.91)

Income 75+ Respondent’s annual household income from all sources is $75,000 or more. 37.40 (48.39)

Race

White, non- Hispanic Respondent is white only, non-Hispanic. 66.70 (47.13)

Black, non- Hispanic Respondent is black only, non-Hispanic. 11.50 (31.91)

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Hispanic Respondent is Hispanic. 14.02 (34.72)

Other, non- Hispanic Respondent is other race, non-white, non-black, and non-Hispanic. 7.78 (26.78)

Age

Age 18-24 Respondent aged 18-24 years. 9.14 (28.82)

Age 25-34 Respondent aged 25-34 years. 16.10 (36.75)

Age 35-49 Respondent aged 35-49 years. 25.30 (43.47)

Age 50-64 Respondent aged 50-64 years. 28.44 (45.11)

Age 65+ Respondent aged 65 years or older. 21.02 (40.75)

Education

Less high school

graduate Respondent never attended school or completed grades 1 through 11. 10.56 (30.73)

High school

graduate Respondent completed grade 12 or GED (high school graduate). 26.10 (43.92)

Some college

graduate Respondent completed college 1 year to 3 years. 32.15 (46.71)

College graduate

or more Respondent completed college 4 years or more (college graduate). 31.20 (46.33)

BMIc

Under weight BMI < 18.50 1.59 (12.51)

Normal weight 18.50 ≤ BMI < 25.00 30.77 (46.16)

Over weight 25.00 ≤ BMI < 30.00 36.06 (48.09)

Obese 30.00 ≤ BMI 31.58 (46.48)

Others

Male Respondent is male. 50.34 (50.00)

Employed Respondent is employed for wages or self-employed. 59.88 (49.01)

Current smoker Dichotomous variable that equals 1 if respondent now smokes cigarettes every

day or some days. 15.72 (36.40)

Current drinkerd

Dichotomous variable that equals 1 if respondent reported having had at least one

drink of any alcoholic beverage such as beer, wine, a malt beverage or liquor in the

past 30 days.

57.70 (49.40)

Chronic health scorese

Score 0 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 0. 30.01 (45.83)

Score 1 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 1. 24.43 (42.97)

Score 2 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 2. 17.73 (38.19)

Score 3 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 3. 11.91 (32.39)

Score 4 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 4. 7.45 (26.25)

Score 5 Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 5. 4.20 (20.06)

Score 6+ Dichotomous variable that equals 1 if the total number of CHCs respondents

reported equals 6+. 4.27 (20.22)

Notes

a. Standard deviations are in parentheses. Means (%) and standard deviations are weighted by the

BRFSS sample weight factor. All means are statistically significant using two tailed t-test with 95%

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confidence interval. b. Classify respondents as meeting the 2008 Physical Activity Guidelines for

Americans. c. Body Mass Index (BMI): weight (kg) divided by square of height (meter). d. One drink

is equivalent to a 12-ounce beer, a 5-ounce glass of wine, or a drink with one shot of liquor. e. The

total number of the chronic health conditions for each respondent from 13 chronic health

conditions: hypertension awareness module, cholesterol awareness, and 11 chronic conditions in

the chronic health condition module. CHC denotes chronic health condition.

services related to the leading causes of death and disability from the non-institutionalized adult

population aged 18 years or older residing in the United States. The BRFSS field operations were

managed by the state health department that followed the protocols provided by the states with

technical assistance provided by the CDC. Some states from the beginning stratified population

allowed them to collect region-wise data in their states. The total baseline observations for adults

aged 18 years or older from the 2017 BRFSS were 450, 016. The present study excluded

observations with ‘missing values’, ‘unanswered questions’, ‘don’t know/not sure’, ‘questions not

asked’, and ‘refusals’. 281,913 survey participants (62.65% of the total baseline observations)

answered all the questions on physical activities, socio-demographic variables, health behavior

variables, and chronic health conditions. Descriptions and summary statistics are shown in Table

1.

Physical Activity

The methodology and questionnaires employed to assess physical activity within the BRFSS have

been published elsewhere. The questionnaires have been shown both reliable and valid in

assessing physical activity behavior (Yore, et al., 2007; Loprinzi, et al., 2015). To collect data

regarding the prevalence of participation in physical activity, the BRFSS interviewers asked,

“During the past month, other than your regular job, did you participate in any physical activities

or exercises such as running, calisthenics, golf, gardening, or walking for exercise?” If the

participants answered ‘yes’, they were asked, “What type of physical activity or exercise did you

spend the most time doing during the past month?” From this question, the interviewers used a

coding list of 76 physical activities plus an “other” category to classify the activities. In that coding

protocol, examples of aerobic activities included walking, hiking, biking, swimming, and running,

while non-aerobic activities included gardening, painting, golf, and bowling (Bennie, et al., 2019a;

Bennie, et al., 2019b). For each activity, respondents were asked to respond to the following

questions to measure frequency and duration, respectively: “How many times per week or per

month did you take part in this activity during the past month?”; “And when you took part in this

activity, for how many minutes or hours did you usually keep at it?” After these questions, the

interviewers also asked respondents about the other type of physical activity: “What other types

of physical activity gave you the next most exercise during the past month?” For each activity,

respondents were asked to respond to the following questions to measure frequency and duration,

respectively: “How many times per week or per month did you take part in this activity during the

past month?”; “And when you took part in this activity, for how many minutes or hours did you

usually keep at it?” Physical activity or exercises to strengthen muscles were assessed by asking,

“During the past month, how many times per week or per month did you do physical activities or

exercises to strengthen your muscles?” Respondents were prompted: “Do not count aerobic

activities like walking, running, or bicycling. Count activities using your own body weight like yoga,

sit-ups or push-ups and those using weight machines, free weights, or elastic bands.”

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A metabolic equivalent value was assigned to each activity listed on the BRFSS. Aerobic activity

should be performed in bouts of 10 minutes or longer in its calculations of the total minutes of

moderate- or vigorous-intensity activity. Aerobic activity levels were calculated for each

respondent using the 2008 PAGs. Respondents who reported no leisure-time activity were

classified as physically inactive, and respondents who did not have any bouts of aerobic activity

longer than 10 minutes were classified as also physically inactive. Respondents who reported

bouts of aerobic activity but did not meet the guideline threshold were classified as being

insufficiently active. Respondents who reported more than 150 minutes of moderate-intensity

activities, or more than 75 minutes of vigorous activity, or an equivalent combination were

classified as being sufficiently active as meeting the recommendations for aerobic physical activity.

The BRFSS also assessed meeting the recommendations for muscle-strengthening activity.

Respondents who reported participating in muscle-strengthening activities at least two times per

week were classified as meeting the muscle-strengthening guidelines. As a consequence, five

mutually exclusive subgroups of the physical activity levels in this study were used: physically

inactive, insufficient physical activity, meeting the aerobic physical activity guidelines, meeting the

muscle-strengthening activity guidelines, and meeting both the guidelines (Carlson, et al., 2015;

Desmond, et al., 2015; Boyer, et al., 2018; Bennie, et al., 2019a; Bennie, et al., 2019b). Table 1 shows

the classification of physical activity levels.

Explanatory Variables

Explanatory variables were socio-demographic variables: marital status, income level,

race/ethnicity, age, education level, gender, and employed; health behavioral variables: Body Mass

Index (BMI), current smoker, and current drinker; and chronic health conditions. These socio- demographic and health behavior variables were widely used in the research in physical activity

and chronic health problems (Miyawaki, et al., 2017; Boyer, et al., 2018; Bennie, et al., 2019b; Li, et

al., 2019).

Socio-demographic variables

Respondents were asked about marital status: married, divorced, widowed, separated, never

married, and a member of an unmarried couple. The six categories of marital status were then

reclassified into four marital subgroups: married (married or a member of an unmarried couple),

divorced (divorced or separated), widowed, and never married. After regrouping, 58.65%, 13.86%,

6.58%, and 20.91% of survey participants were married, divorced, widowed, and never married,

respectively. The BRFSS asked participants for their annual household income from all sources.

The participants could report one of the eight categories of annual household income levels from

less than $10,000 to $75,000 or more. This study re-categorized them into five income levels.

15.91% of participants reported their annual household income of less than $20,000, and 18.33%,

13.15%, 15.20%, and 37.40% of participants reported $20,000 ≤ income < $35,000, $35,000≤

income < $50,000, $50,000 ≤ income < $75,000, and $75,000 and over, respectively. Participants

were also asked to give the races. The participants were classified into four racial categories: White,

but not Hispanic; Black, but not Hispanic; Hispanic; and other races. 66.70% of the respondents

were White, non-Hispanic, 11.50% of respondents were Black, non-Hispanic, 14.02% of

respondents were Hispanic, and 7.78% of respondents were other races. Age was categorized into

five mutually exclusive categories: 18-24, 25-34, 35-49, 50-64, and 65 and older. Education was

divided into four categories: 10.56% of respondents completed less than high school, and 26.10%,

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Table 5: Odds Ratios by Physical Activity Levela

Inactive

(95% CI)

Insufficient

(95% CI)

Meet Aerobic

(95% CI)

Meet Strengthen

(95% CI)

Meet Both

(95% CI)

Marital Status

Married 1 1 1 1 1

Divorced 0.97 (0.95-1.00) 0.93 (0.90-0.96) 0.90 (0.88-0.92) 1.22 (1.17-1.27) 1.16 (1.13-1.20)

Widowed 1.05 (1.02-1.08) 0.85 (0.82-0.89) 0.97 (0.95-1.00) 1.13 (1.07-1.19) 1.01 (0.97-1.05)

Never married 0.93 (0.90-0.96) 0.95 (0.91-0.98) 0.93 (0.90-0.96) 1.15 (1.10-1.20) 1.15 (1.11-1.19)

Income Level

Income 20 1 1 1 1 1

Income 2035 0.94 (0.91-0.97) 0.96 (0.92-0.99) 1.09 (1.06-1.12) 0.98 (0.93-1.03) 1.08 (1.04-1.12)

Income 3550 0.84 (0.81-0.87) 0.91 (0.87-0.95) 1.15 (1.11-1.18) 0.98 (0.93-1.04) 1.23 (1.18-1.28)

Income 5075 0.75 (0.73-0.78) 0.90 (0.86-0.93) 1.17 (1.13-1.21) 0.99 (0.94-1.05) 1.35 (1.30-1.41)

Income 75+ 0.59 (0.57-0.61) 0.84 (0.81-0.88) 1.18 (1.14-1.22) 1.01 (0.96-1.06) 1.63 (1.57-1.69)

Race

White, non-Hispanic 1 1 1 1 1

Black, non-Hispanic 1.08 (1.05-1.12) 1.16 (1.12-1.21) 0.71 (0.69-0.74) 1.23 (1.18-1.29) 1.13 (1.09-1.18)

Hispanic 1.06 (1.02-1.10) 1.02 (0.98-1.07) 0.85 (0.82-0.88) 1.07 (1.02-1.13) 1.17 (1.13-1.22)

Other, non-Hispanic 0.90 (0.86-0.93) 1.01 (0.97-1.06) 0.93 (0.90-0.96) 1.13 (1.07-1.19) 1.14 (1.10-1.19)

Age

Age 18-24 1 1 1 1 1

Age 25-34 1.45 (1.35-1.55) 1.08 (1.02-1.16) 1.21 (1.15-1.28) 0.81 (0.76-0.86) 0.72 (0.68-0.76)

Age 35-49 1.78 (1.66-1.90) 1.07 (1.01-1.14) 1.44 (1.37-1.52) 0.54 (0.51-0.57) 0.65 (0.62-0.69)

Age 50-64 1.93 (1.81-2.06) 0.90 (0.85-0.96) 1.73 (1.65-1.83) 0.39 (0.37-0.42) 0.64 (0.61-0.67)

Age 65+ 2.16 (2.02-2.31) 0.64 (0.60-0.68) 1.99 (1.88-2.09) 0.32 (0.30-0.34) 0.67 (0.64-0.71)

Education

Less high school 1 1 1 1 1

High school 0.84 (0.81-0.88) 0.97 (0.93-1.03) 1.11 (1.07-1.16) 1.10 (1.03-1.18) 1.30 (1.22-1.38)

Some college 0.62 (0.60-0.65) 0.99 (0.94-1.05) 1.17 (1.12-1.22) 1.28 (1.19-1.38) 1.71 (1.61-1.82)

College graduate 0.42 (0.40-0.43) 1.03 (0.98-1.09) 1.18 (1.32-1.23) 1.40 (1.30-1.50) 2.15 (2.03-2.28)

BMIb

Normal weight 1 1 1 1 1

Under weight 1.44 (1.33-1.55) 1.07 (0.97-1.18) 0.91 (0.85-0.98) 0.90 (0.80-1.01) 0.84 (0.78-0.92)

Over weight 1.18 (1.15-1.21) 1.21 (1.18-1.25) 1.07 (1.04-1.09) 0.94 (0.91-0.97) 0.74 (0.73-0.76)

Obese 1.79 (1.75-1.83) 1.45 (1.41-1.50) 0.92 (0.90-0.94) 0.83 (0.80-0.86) 0.48 (0.47-0.50)

Others

Male 0.92 (0.90-0.94) 0.79 (0.78-0.81) 0.97 (0.95-0.99) 1.29 (1.25-1.32) 1.25 (1.23-1.28)

Employed 1.23 (1.21-1.26) 1.25 (1.22-1.28) 0.86 (0.85-0.88) 1.03 (1.00-1.07) 0.84 (0.82-0.86)

Current smoker 1.61 (1.57-1.65) 0.93 (0.90-0.95) 1.00 (0.97-1.02) 0.78 (0.75-0.81) 0.66 (0.64-0.68)

Current drinkerc 0.71 (0.69-0.72) 1.01 (0.99-1.03) 1.12 (1.10-1.14) 1.04 (1.01-1.07) 1.21 (1.18-1.23)

Chronic health scoresd

Score 0 1 1 1 1 1

Score 1 1.06 (1.03-1.10) 1.09 (1.05-1.12) 1.03 (1.01-1.06) 0.95 (0.92-0.99) 0.91 (0.88-0.93)

Score 2 1.21 (1.17-1.25) 1.15 (1.11-1.19) 1.00 (0.98-1.03) 0.91 (0.87-0.95) 0.83 (0.80-0.85)

Score 3 1.35 (1.31-1.40) 1.16 (1.11-1.20) 0.96 (0.93-0.99) 0.96 (0.92-1.01) 0.74 (0.72-0.77)

Score 4 1.54 (1.48-1.60) 1.23 (1.18-1.29) 0.86 (0.83-0.89) 1.02 (0.96-1.08) 0.66 (0.63-0.69)

Score 5 1.76 (1.69-1.84) 1.23 (1.17-1.30) 0.78 (0.75-0.82) 1.03 (0.96-1.10) 0.59 (0.56-0.62)

Score 6+ 2.07 (1.98-2.16) 1.18 (1.11-1.24) 0.63 (0.60-0.66) 1.20 (1.12-1.29) 0.55 (0.52-0.58)