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Association Between Physical Activity, Depression, and Diabetes in Urban-Dwelling People Living with HIV
Accepted Manuscript Association between physical activity, depression, and diabetes in urban dwelling people living with HIV Norberto N. Quiles, EdD, Joseph T. Ciccolo, PhD, Carol Ewing Garber, PhD, FACSM PII:
S1055-3290(17)30155-3
DOI:
10.1016/j.jana.2017.06.015
Reference:
JANA 936
To appear in:
Journal of the Association of Nurses in AIDS Care
Received Date: 14 March 2017 Accepted Date: 27 June 2017
Please cite this article as: Quiles N.N., Ciccolo J.T. & Garber C.E., Association between physical activity, depression, and diabetes in urban dwelling people living with HIV, Journal of the Association of Nurses in AIDS Care (2017), doi: 10.1016/j.jana.2017.06.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Association between physical activity, depression, and diabetes in urban dwelling people living with HIV
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Norberto N. Quiles, EdD Joseph T. Ciccolo, PhD
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Carol Ewing Garber, PhD, FACSM
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Norberto N. Quiles*, EdD, is Assistant Professor, Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, New York, USA ([email protected]). Joseph T. Ciccolo, PhD, is Assistant Professor, Department of Biobehavioral Sciences, Teachers College Columbia University, New York, New York, USA. Carol Ewing Garber, PhD, FACSM, is Professor, Department of Biobehavioral Sciences, Teachers College Columbia University, New York, New York, USA.
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Corresponding author: Norberto Quiles: [email protected]
Disclosures
The authors report no real or perceived vested interests that relate to this article that could
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be construed as a conflict of interest.
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Acknowledgements
This project was supported in part by the Teachers College Deans Grant for student Research. All authors declare no financial interests or potential conflicts of interest. We thank Abdulaziz Alnafesah for his assistance conducting this study.
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Abstract Physical activity (PA) may improve physical and mental health in people living with HIV (PLWH). However, the associations between PA participation and physical and mental
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health problems of PLWH in urban settings remain largely unknown. Our objective was to determine the relationships between PA and physical and mental health in urban-dwelling PLWH. 289 adult PLWH responded to an electronic survey including questions on PA and
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current physical and mental health. The associations between physical and mental health and PA were investigated using linear and logistic regression. A large proportion of
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participants met recommended volumes of PA. 83% of respondents reported symptoms of severe mental distress. Diabetes mellitus was associated with a lower total volume of PA (p = 0.035). Similarly, depression was negatively associated with muscle strengthening exercise participation (p = .030). Sufficient amounts of aerobic activity and/or muscle
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strengthening exercise are associated with better physical and mental health.
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Key Words. cardiovascular disease, exercise, mental health, metabolic disease
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Association between physical activity, depression, and diabetes in urban dwelling people living with HIV People living with HIV (PLWH) often experience physical and mental health
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complications in association with the progression of the disease and the antiretroviral therapy (ART) used to treat HIV. Among these complications are depression (Arseniou, Arvaniti & Samakouri, 2014) and cardio-metabolic diseases such as hypertension (De Socio
which can increase the risk of premature death.
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et al., 2013, coronary heart disease, and diabetes mellitus (Willig & Overton, 2014), all of
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Physical activity produces many health-related benefits for PLWH; these consist of greater life satisfaction and quality of life (Ramírez-Marrero, Smith, Meléndez-Brau, & Santana-Bagur, 2004), less depression (Blashill et al., 2013), better lipid profiles (Gavrila et al., 2003), and healthier body composition (Justina, Luiz, Maurici, & Schuelter-Trevisol,
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2014). However, the majority of studies have used small sample sizes and/or selective recruitment from clinics and community center settings (Fillipas, Cicuttini, Holland, & Cherry, 2013; Ramirez-Marrero et al., 2004; Segatto et al., 2011), so the results have not
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been generalizable to larger populations of PLWH. Physical activity behaviors in PLWH
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residing in major urban settings, where the prevalence of HIV infection is disproportionately higher, is largely unknown. Data in the population show large variations in physical activity across neighborhoods in urban areas, and the neighborhoods with the highest poverty levels tend to have the lowest levels of physical activity (Wyker et al., 2013). HIV infection is concentrated in people in lower socioeconomic statuses, with many living in lower-resourced neighborhoods where there may be safety concerns, poorer public transportation, and fewer parks and attractive environments conducive to
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physical activity (Pellowski, Kalichman, Matthews, & Adler, 2013). Thus, it is possible that PLWH in urban areas may be less physically active when compared to their uninfected counterparts. However, to date, no studies have investigated physical activity behaviors of
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PLWH living in an urban setting. Furthermore, the relationships between physical activity and physical and mental health problems routinely occurring in PLWH have not been
characterized in urban-dwelling PLWH. We hypothesized that there would be a positive
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relationship between physical activity, including muscular strengthening exercise, and physical and mental health in PLWH.
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The specific aims of our study were to (a) describe the leisure and non-leisure physical activity participation behaviors in urban-dwelling adult PLWH and (b) examine the associations between physical activity and physical and mental health in PLWH. Methods
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Study Design and Population
We used a cross-sectional observational design. Four hundred and eighty-nine urban dwelling men and women who were 18 years of age or older and living with HIV
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responded to a 10- to 15-minute survey through announcements made available online
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from April 2015 to April 2016. Classified advertisements were placed on websites (e.g., Craigslist© and Backpage©), online fora and blogs for PLWH, social media (e.g., Facebook©, Twitter©, and LinkedIn©), and other online sites. Links to the survey were made available in 25 metropolitan areas of more than 500,000 residents with the highest rates of HIV as reported by the Centers for Disease Control and Prevention (CDC, 2013b). Survey Data Collection Participant inclusion criteria included being previously diagnosed with HIV, at least
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18 years of age, living in an urban setting, and being willing and able to fill out questionnaires electronically. Eligible individuals who were interested in participating in the anonymous survey were directed to the SurveyMonkey® survey system
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(SurveyMonkey©, Inc., Palo Alto, CA; www.surveymonkey.com). After participants accessed the online survey, they provided informed consent before answering any of the survey questions as per the policies and procedures of the Teachers College, Columbia University
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Institutional Review Board, which approved the study. The respondent then answered 2 eligibility questions: Have you previously tested positive for the HIV virus? and Are you an
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adult (i.e., ≥ 18 years old)?. If the response was yes to each, the participant continued the anonymous survey; if no to one or both questions, the respondent was thanked and was ineligible to continue with the survey. Links to the survey were available for English and Spanish versions of the survey. Participants were eligible to participate in a drawing for 1
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of 15 gift certificates worth $20 USD at the end of the study, if they provided an email that was not linked to the survey itself to protect anonymity. Sociodemographics and Health
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Participants provided information about sociodemographics, health-related
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lifestyle, and diagnosed diseases by self-report. Sociodemographic variables included age, gender, race, level of education, and household income. Race was categorized as White or Caucasian; Black or African American; and Hispanic or Latino; the remaining races or ethnicities were pooled into a category called other races or ethnicities. The question on education came from the 2013 Behavioral Risk Factor Surveillance System questionnaire (BRFSS; CDC, 2013a), and was further trichotomized into high school education or less, some college, or college graduate, to have sufficient subjects per group for analytic
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purposes. Household income was dichotomized into two categories with a cut-off point consistent with current poverty limits for a family of three (U.S. Department of Health and Human Services, 2016). Questions about physician-diagnosed diseases of diabetes,
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hypertension, stroke, myocardial infarction, and coronary artery disease or angina came from the BRFSS questionnaire (CDC, 2013a). Diagnoses of hypertension, myocardial
infarction, stroke, and coronary artery disease or angina were collapsed into one category,
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renamed cardiovascular disease (CVD), because of small numbers of respondents reporting these diseases. Mental health was evaluated using the BRFSS question (CDC, 2013a) for
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diagnosed depression. The validity of these questions has been established (Cossman et al., 2008; Fahimi, Link, Schwartz, Levy, & Mokdad, 2008; Pierannunzi, Hu, & Balluz, 2013). The Kessler Psychological Distress (K10) Scale (Kessler et al., 2002) was used to assess mental health symptoms. This instrument has been used and validated in PLWH (Spies et al.,
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2009). Participants were also asked about time since HIV diagnosis, viral load, CD4+ T cell count, and exposure to antiretroviral therapy (ART; i.e., current use and duration of ART). Physical Activity and Muscle Strengthening
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Physical activity and muscle-strengthening were assessed using questions about
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muscle strengthening exercise, taken from the BRFSS questionnaire (CDC, 2013a), and leisure and non-leisure physical activity were assessed with the International Physical Activity Questionnaire (IPAQ)-Long Form (IPAQ Research Committee, 2005). Consistent with CDC definitions, those performing at least 2 days of muscle strengthening exercises were classified as meeting muscle strengthening targets, while performing fewer than 2 days of muscle strengthening was classified as not meeting recommended levels of participation (Physical Activity Guidelines Advisory Committee, 2008). The validity and
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reliability of the muscle strengthening question has been determined compared to physical activity logs (Yore et al., 2007). Total (overall) physical activity and subscales representing transportation, occupational, domestic, and leisure time physical activity were determined
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using the self-reported IPAQ-Long Form. This instrument has shown moderate but
significant correlation with objectively measured physical activity (i.e., accelerometry) in PLWH (Fillipas et al., 2013), which is similar to what has been found in the general
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population. Transportation-related physical activity was calculated from the total minutes per day and days per week of walking and/or cycling for transportation purposes.
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Occupational physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity activity at work, as well as walking at work. Domestic physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity outside chores, as well as moderate intensity
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indoor chores. Leisure-time physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity activity, and walking during leisure time. The IPAQ criteria for physical activity levels (IPAQ Research Committee, 2005) were
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used to classify participants’ physical activity levels as low, moderate, or high.
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Statistical Analysis
Descriptive statistics are presented as means and standard deviations for
continuous variables, and as absolute and relative frequencies for categorical variables. Physical activity volume (MET·min·wk-1) is presented in median and interquartile ranges given that these data were not normally distributed. Gender differences were analyzed using Chi Square or Fisher exact tests for categorical variables, and independent t-tests or Mann-Whitney U tests, as appropriate, for continuous variables. Logistic regression
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analyses were used to examine relationships between muscle strengthening participation, as a categorical variable, with chronic disease and mental health related outcomes. Simple and multiple linear regression analyses examined the relationships between physical
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activity volumes and chronic disease and mental health related variables. Age, gender, race, income level, smoking, education level, CD4+ T cell count, years living with HIV, and years
were not included in the final models. Results
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Sociodemographics
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taking ART were explored as covariates and, because none were significant, these variables
Participant characteristics are summarized by gender in Table 1. Most of our respondents were men ranging in age between 19 and 76 years. They were, on whole, highly educated (i.e., some college or more), but a high proportion reported low income.
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The proportion of female participants in our study (28.9%) was substantially lower than the 51.4% reported in the most recent census (U.S. Census Bureau, 2015) for the cities that were sampled in this study. Approximately 49% of our sample identified as White or
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Caucasian, which was similar to the 48.5% Whites reported in the Census. In our sample,
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approximately 37% reported having a bachelor’s degree or higher, which was also similar to the 34.7% reported in the most recent census data. More than 67% of our sample fell below the median income (i.e., $46,657 USD) for the cities sampled in our study. Physical Activity and Muscle Strengthening Complete data on physical activity were available for 220 respondents. Most participants were moderately active or greater, when total physical activity was considered (Table 2). Domestic physical activity was the largest proportion of the reported volume of
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physical activity, followed by leisure time, transportation, and occupational physical activity. Data on muscle strengthening exercise participation were available for 283 respondents. More than half of our respondents reported not meeting muscle
(Physical Activity Guidelines Advisory Committee, 2008). Physical and Mental Health
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strengthening recommendations at a level consistent with the guidelines for Americans
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Figure 1 shows the percentage of respondents who had been told by a health care provider that they had a physical or mental health condition. Approximately 34% of
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respondents reported no physical or mental health conditions. However, close to 60% had one or two diagnosed physical or mental health conditions, while 6% had three or more physical or mental health conditions. Hypertension was common, while fewer had diabetes mellitus. About half of the respondents reported a diagnosis of depression. However, the
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results from the K10 Psychological Distress Scale (Figure 2) showed a preponderance of symptoms of severe mental health disorder amongst respondents. In fact, 43.3% of respondents who had severe mental distress symptoms based on the K10 did not have a
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formal diagnosis of depression.
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Association Between Physical Activity and Chronic Diseases Table 3 shows the results of a series of univariate linear regression analyses to
examine associations between chronic diseases and total physical activity. Having a diabetes mellitus diagnosis was associated with a lower total physical activity volume in PLWH [F(1, 202) = 4.492, p = 0.035]. No significant associations were found between total physical activity volume and the presence or absence of a diagnosis of high blood pressure, cardiovascular disease, or depression.
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Table 4 shows the univariate logistic regression models to evaluate chronic diseases as correlates of meeting muscle-strengthening recommendations. There was a significant association between meeting muscle strengthening recommendations and depression
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diagnosis [OR 1.712, 95% CI: 1.051 to 2.790; p = .030]. There were greater rates of
diagnosed depression reported by respondents who did not meet muscle-strengthening recommendations compared to those who did meet muscle-strengthening
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recommendations. No significant associations were uncovered between meeting muscle strengthening recommendations and the presence of cardiovascular disease, diabetes
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mellitus, or a high blood pressure diagnosis. Associations by Gender
When examining the data by gender there were several gender-related differences. Men were older on average than were women and more men were Black or African
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American compared to women respondents. There were comparative differences in education and income between men and women, with a greater proportion of women reporting lower education attainment and very low income, compared to men. No gender-
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related differences were found in rates of diagnosed diseases, years living with HIV, CD4+
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T-cell count, having an undetectable viral load, years taking ART, current treatment with ART, or smoking. In addition, there were no gender differences in physical activity levels, total volume of physical activity, volume of physical activity by domain (e.g., domestic, leisure-time, transportation and occupational physical activity), meeting muscle strengthening guidelines, or sitting time. Discussion Some respondents had been living with HIV for many years and were receiving
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treatment for the disease, and most reported well-controlled disease as evidenced by mean CD4+ T cell count within the normal range. The respondents reported several chronic health conditions, with the most prevalent being depression, hypertension, and diabetes
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mellitus. Most worrying was our finding that most respondents indicated they had
symptoms of psychological distress, suggesting the presence of a severe mental health disorder. Interestingly, approximately 43% of respondents who had severe symptoms of
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mental distress based on the K10, did not have a diagnosis of depression from a primary care provider, which suggested an under diagnosis of mental health issues in this
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population. These results are particularly problematic in PLWH, because physical and mental health conditions have been linked to a higher risk of CVD in the general population (Hare, Toukhsati, Johansson, & Jaarsma, 2013). Furthermore, PLWH who are treated with ART have been shown to be twice as likely to develop CVD compared to the general
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population (Islam, Wu, Jansson, & Wilson, 2012). Some of the increased risk of CVD in PLWH is likely due to the direct effects of ART and some due to more frequent incidence of risk factors (Islam et al., 2012; Willig & Overton, 2014). In our study, we were unable to
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assess CVD risk due to the cross sectional observational design of the study. We examined
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the possibility of confounding variables in our analyses, but no variables were significant covariates, so we did not adjust for any variables. It is notable that we were unable to detect any associations between ART use and physical activity and the presence of any physical or mental health conditions in our sample. These findings do not preclude the possibility that there were confounding variables that may have affected the associations between variables, but our results suggest that these interactions were modest at best. More than half of our participants reported being White or Caucasian, nearly one
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third reported being African American, and a small proportion self-reported being Hispanic or Latino. These results were different from CDC (2014) data that reported the largest percentage of HIV cases in Black or African Americans, followed by Hispanic or Latino, and
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White or Caucasians. However, it is worth noting that our data were from 25 metropolitan areas and not the entire United States. As reported by the CDC (2014), the largest
proportion of PLWH are found in the 45- to 49-years-of-age range, which was consistent
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with our mean age of 46.5 years.
Nearly half of our respondents reported low incomes. However, the great majority
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had some college education or more, which presented an unexpected disparity between income and education. It is possible that some respondents with higher education were unable to work due to health, possibly related to disease progression. However, we did not ask questions to determine this relationship. There were some observed differences in
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education and income between men and women in our sample. Overall, women were less educated and more economically disadvantaged than male respondents. Our survey respondents reported high levels of leisure and non-leisure physical
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activity, including muscle strengthening exercise. Surprisingly, approximately 89% of our
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sample met general physical activity recommendations, a proportion considerably higher than reported in the general population (CDC, 2014). Although it is notable that, when examining the physical activity subdomains that contributed to this high level of physical activity, a substantial contribution was made by domestic and leisure-time physical activity and a smaller proportion by occupational physical activity. The IPAQ long form used in our study provided details of physical activity across four domains that included not only intentional exercise but also physical activities performed during active transportation,
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household, and occupational tasks. Most other reports on physical activity in PLWH have reported solely on leisure-time physical activity, which consisted mostly of intentional exercise (Schuelter-Trevisol et al., 2012). Other studies that have found higher rates of
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physical activity in PLWH compared to the general population add further credibility to our results concerning physical activity. Studies reporting on physical activity
recommendations in more select samples of PLWH in the United States have found a
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prevalence of sufficient self-reported physical activity ranging from 53% to 81%
(Clingerman, 2003; Ramirez-Marrero et al., 2008). Notably, these studies had much smaller
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sample sizes and participants were selected from clinic or community center settings, rather than from the urban community.
Moderate to high participation in muscle strengthening exercise in PLWH has been reported in a few other studies (Clingerman, 2003; Gavrila et al., 2003). Nearly half of our
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sample engaged in muscle strengthening exercises on at least 2 days per week, which was similar to the findings of a smaller scale study by Clingerman (2003). Another small study by Gavrila and colleagues (2003) reported weight training participation in nearly 40% of
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participants. However, these studies measured resistance (weight) training rather than
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overall muscle strengthening exercise participation as in our study. The question on muscle strengthening that we used asked about muscular strengthening activities such as yoga, resistance band exercises, calisthenics, and the like, in addition to resistance (weight) exercise training. Other studies reporting on resistance exercise training participation in PLWH showed considerably lower rates of participation than did our study. It is possible that differences in participant characteristics, in addition to differences in the questions asked, may partly explain these differences. For example, participants in the studies by
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Tang et al. (2010) and Smit et al. (2006) were intravenous drug users. Median sitting time in our sample was about 6 hours on a weekday and 5 hours on a weekend day, which was considerably higher than reported in surveys of the general U.S.
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population. For example, a study using the IPAQ, reported a median sitting time for
Americans of 240 minutes per day (Bauman et al. 2011). When considering that our sample included many with low income, the higher sitting time could reflect
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unemployment. Alternatively, the high prevalence of symptoms of psychological distress in our sample may have also factored into the long sitting times. Comparisons with other
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studies in PLWH in the United States were difficult to make, given the wide variety of instruments and definitions used to measure sedentary behaviors in the few available studies (Schuelter-Trevisol et al., 2012). At least one study reported a TV watching time of 5.4 hours for Hispanic adults living with HIV, which was similar to our sitting time results
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(Ramirez-Marrero et al., 2004). Nonetheless, our findings clearly indicated that time spent in sedentary pursuits was excessive in many individuals living with HIV in our study. Given the data showing a dose-response relationship between sitting time, mortality, and CVD
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risk in the general population (Katzmarzyk, Church, Craig, & Bouchard, 2009) as well as in
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regularly active individuals (Healy et al., 2008), combined with the fact that PLWH were already twice as likely to develop CVD when compared to the general population (Islam et al., 2012), these findings are of concern. A diagnosis of diabetes mellitus was associated with the total volume of overall
physical activity in our sample, so that PLWH who had diabetes engaged in less physical activity than those without a diabetes diagnosis. These results were consistent with a study by Codella, Guffanti, Castagna, Lazzarin, and Luzi, (2005) who described low levels of
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leisure time physical activity in PLWH who also had diabetes. Our results showed that all types of physical activities were reduced in PLWH who had diabetes. Co-morbidities and complications of diabetes such as fatigue, peripheral neuropathy, nephropathies, and
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retinopathy can play a role in limiting physical activity participation in people with
diabetes (Korkiakangas, Alahuhta, & Laitinen, 2009) and this could very well be the case for PLWH who have diabetes, such as some of our study respondents. On the other hand,
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regular physical activity is important to control diabetes mellitus and is recommended (Colberg et al., 2010).
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Our data showed that PLWH who did not meet muscle strengthening targets were more likely to present with depression than those who met such guidelines. In fact, respondents who had been diagnosed with depression were 71% less likely to meet muscle strengthening recommendations than those without such a diagnosis. While insufficient
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physical activity has been associated with depression in PLWH (Blashill et al., 2013), no studies have reported on relationships between muscle strengthening and depression in this population. In apparently healthy people, resistance exercise interventions have been
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shown to be effective in the treatment of depression (Pilu et al., 2007). However,
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depression has been consistently associated with low levels of physical activity in the general population (Roshanaei-Moghaddam, Katon, & Russo, 2009) and it seems, based on our study, that this relationship also holds true in PLWH. The anonymous nature of our study was a strength because anonymity can enhance
the truthfulness of responses (Ong & Weiss, 2000). Our participants were not asked for any self-identifying information and were made aware that the study was conducted under full anonymity. However, our study was not without limitations. Self-report measurements
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have some inherent shortcomings. Although self-report is a widely used and accepted method of retrieving health and physical activity data, it is possible that some overreporting of physical activity occurred in our study because a large number of individuals
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were classified as physically active. Discrepancies between more objective and subjective measures of physical activity also may reflect discordance between perception and actual behavior, problems with validity, and biases (Ramirez-Marrero et al., 2008; Fillipas,
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Cicuttini, Holland, & Cherry, 2010). Approximately 40% of respondents did not complete the entire survey. It is possible that the large number of items in the survey (i.e., 59) could
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have played a role in the incomplete surveys. However, care was taken in the study design process to decrease the possibility of non-completions, including progress bars and motivational statements throughout the survey.
Summary and Conclusion
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Our study showed that many urban dwelling adult PLWH have relatively high levels of physical activity, including muscle-strengthening exercise. High rates of daily sitting time were also observed. Chronic diseases such as hypertension, diabetes, and especially
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depression were common in our sample, although only diabetes and depression were
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associated with poorer participation in all types of physical activity and muscle strengthening, respectively. In conclusion, while a majority of respondents met targets for physical activity and muscle-strengthening exercise, this was offset by elevated sitting time. Furthermore, there was a high prevalence of co-morbid physical and mental health conditions, all of which can increase the risk of CVD mortality and morbidity. Our results showed that PLWH could benefit from primary and secondary prevention strategies, including reducing sedentary time and engaging in sufficient amounts of physical activity
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Healy, G. N., Dunstan, D. W., Salmon, J. O., Shaw, J. E., Zimmet, P. Z., & Owen, N. (2008). Television time and continuous metabolic Risk in physically active adults. Medicine and Science in Sports and Exercise, 40(4), 639. doi:10.1249/MSS.0b013e3181607421 International Physical Activity Questionnaire Research Committee. (2005). Guidelines for
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data processing and analysis of the International Physical Activity Questionnaire (IPAQ). Retrieved from http://www.ipaq.ki.se/scoring.pdf Islam, F. M., Wu, J., Jansson, J., & Wilson, D. P. (2012). Relative risk of cardiovascular disease
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among people living with HIV: A systematic review and meta-analysis. HIV Medicine, 13(8), 453-468. doi:10.1111/j.1468-1293.2012.00996.x
Katzmarzyk, P. T., Church, T. S., Craig, C. L., & Bouchard, C. (2009). Sitting time and
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mortality from all causes, cardiovascular disease, and cancer. Medicine and Science in Sports and Exercise, 41(5), 998-1005. doi:10.1249/MSS.0b013e3181930355
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Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L., & Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(06), 959-976. doi:10.1002/mpr.310
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Korkiakangas, E. E., Alahuhta, M. A., & Laitinen, J. H. (2009). Barriers to regular exercise among adults at high risk or diagnosed with type 2 diabetes: A systematic review. Health Promotion International, 24(4), 416-427.
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Ong, A. D., & Weiss, D. J. (2000). The impact of anonymity on responses to sensitive questions. Journal of Applied Social Psychology, 30(8), 1691-1708. doi:10.1111/j.1559-1816.2000.tb02462.x
Pellowski, J. A., Kalichman, S. C., Matthews, K. A., & Adler, N. (2013). A pandemic of the poor: Social disadvantage and the US HIV epidemic. American Psychologist, 68(4), 197. doi:10.1037/a0032694 Pierannunzi, C., Hu, S. S., & Balluz, L. (2013). A systematic review of publications assessing
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reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011. BMC Medical Research Methodology, 13(1), 49. doi:10.1186/1471-228813-49
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Pilu, A., Sorba, M., Hardoy, M. C., Floris, A. L., Mannu, F., Seruis, M. L., & Carta, M. G. (2007). Efficacy of physical activity in the adjunctive treatment of major depressive
Health, 3(1), 8. doi:10.1186/1471-2288-13-49
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disorders: Preliminary results. Clinical Practice and Epidemiology in Mental
Physical Activity Guidelines Advisory Committee. (2008). Physical Activity Guidelines
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Advisory Committee report, 2008. Retrieved from
https://health.gov/paguidelines/report/pdf/committeereport.pdf. Ramírez-Marrero, F. A., Smith, B. A., Meléndez-Brau, N., & Santana-Bagur, J. L. (2004). Physical and leisure activity, body composition, and life satisfaction in HIV-positive
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Hispanics in Puerto Rico. Journal of the Association of Nurses in AIDS Care, 15(4), 6877. doi:10.1177/1055329003261966
Ramírez-Marrero, F. A., Rivera-Brown, A. M., Nazario, C. M., Rodríguez-Orengo, J. F., Smit, E.,
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& Smith, B. A. (2008). Self-reported physical activity in Hispanic adults living with
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HIV: Comparison with accelerometer and pedometer. Journal of the Association of Nurses in AIDS Care, 19(4), 283-294. doi:10.1016/j.jana.2008.04.003
Roshanaei-Moghaddam, B., Katon, W. J., & Russo, J. (2009). The longitudinal effects of depression on physical activity. General Hospital Psychiatry, 31(4), 306-315.
doi:10.1016/j.genhosppsych.2009.04.002 Schuelter-Trevisol, F., Wolff, F.H., Alencastro, P.R., Grigoletti, S., Ikeda, M.L., Brandao, A.B., … Fuchs, S.C. (2012). Physical activity: Do patients infected with HIV practice? How
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much? A systematic review. Current HIV Research, 10(6), 487-497. doi:10.2174/157016212802429794 Segatto, A. F. M., Freitas Junior, I. F., Santos, V. R. D., Alves, K. C. P., Barbosa, D. A., Portelinha
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Filho, A. M., & Monteiro, H. L. (2011). Lipodystrophy in HIV/AIDS patients with different levels of physical activity while on antiretroviral therapy. Revista da
Sociedade Brasileira de Medicina Tropical, 44(4), 420-424. doi:10.1590/S0037-
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86822011000400004
Smit, E., Crespo, C. J., Semba, R. D., Jaworowicz, D., Vlahov, D., Ricketts, E. P., … Tang, A. M.
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(2006). Physical activity in a cohort of HIV-positive and HIV-negative injection drug users. AIDS Care, 18(8), 1040-1045. doi:10.1080/09540120600580926 Spies, G., Kader, K., Kidd, M., Smit, J., Myer, L., Stein, D. J., & Seedat, S. (2009). Validity of the K-10 in detecting DSM-IV-defined depression and anxiety disorders among HIV-
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infected individuals. AIDS Care, 21(9), 1163-1168. doi:10.1080/09540120902729965
Tang, A. M., Forrester, J. E., Spiegelman, D., Flanigan, T., Dobs, A., Skinner, S., & Wanke, C.
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(2010). Heavy injection drug use is associated with lower percent body fat in a
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multi-ethnic cohort of HIV-positive and HIV-negative drug users from three US cities. American Journal of Drug and Alcohol Abuse, 36(1), 78-86.
doi:10.3109/00952990903544851
U.S. Census Bureau. (2015). Selected race, sex, education and income characteristics, 20112015 . Retrieved from https://www.census.gov/quickfacts/table/PST045216/00 U.S. Department of Health and Human Services. (2016). Annual update of the Health and Human Services Poverty Guidelines. Retrieved from
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https://www.federalregister.gov/documents/2016/01/25/2016-01450/annualupdate-of-the-hhs-poverty-guidelines Willig, A. L., & Overton, E. T. (2014). Metabolic consequences of HIV: Pathogenic
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insights. Current HIV/AIDS Reports, 11(1), 35-44. doi:10.1007/s11904-013-0191-7 Wyker, B., Bartley, K., Holder-Hayes, E., Immerwahr, S., Eisenhower, D., & Harris, T. G.
(2013). Self-reported and accelerometer-measured physical activity: A comparison in
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New York City. Retrieved from
http://www1.nyc.gov/assets/doh/downloads/pdf/epi/epiresearch-
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pa_measures.pdf
Yore, M. M., Ham, S. A., Ainsworth, B. E., Kruger, J., Reis, J. P., Kohl 3rd, H. W., & Macera, C. A. (2007). Reliability and validity of the instrument used in BRFSS to assess physical activity. Medicine and Science in Sports and Exercise, 39(8), 1267-1274.
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doi:10.1249/mss.0b013e3180618bbe
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Key Considerations Engaging in physical activity may help reduce the risk of PLWH developing diabetes.
•
Clinicians should counsel PLWH on ways to increase daily physical activity, especially given the high sedentary time found in this population.
•
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•
Performing muscle strengthening activities such as resistance training, calisthenics, yoga, and other activities can help with depression in PLWH.
Clients living with HIV should be provided with guidance about starting a muscle-
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•
PLWH may suffer from mental health symptoms, even when a clinical diagnosis has not been documented. Clinicians should carefully evaluate the mental health of PLWH to
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ensure proper diagnosis and treatment.
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•
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strengthening program.
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Table 1 Descriptive Characteristics of 289 Urban Dwelling Men and Women Respondents Living With HIV Females n (%)
Total n (%)
P
47.4 ± 11.7
44.4 ± 10.3
48.5 ±10.3
.028
White or Caucasian
104 (52)
32 (40)
136 (49)
.053
Black or African American
50 (25)
31 (38)
81 (29)
.028
Hispanic or Latino
34 (17)
11 (14)
45 (16)
.469
Other
10 (6)
6 (8)
16 (6)
.436
29 (36)
60 (21)
Age, years
Education
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Ethnicity
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Males n (%)
31 (16)
Some College
86 (43)
32 (39)
118 (42)
College Graduate
82 (41)
20 (25)
102 (37)
< $19,999
73 (39)
46 (59)
119 (45)
$20,000
112 (61)
32 (41)
144 (55)
14.8 ± 10.1
14.3 ± 8.9
14.9 ± 9.8
.636
652.1 ± 290.9
650.5 ± 326.6
651.7 ± 299.5
.976
143 (78)
49 (68)
192(75)
.076
11.9 ± 8.7
13.1 ± 7.8
12.2 ± 7.8
.227
175 (91)
67 (88)
242 (90)
.456
96 (52)
43 (52)
139 (52)
.345
High Blood Pressure
60 (32)
24 (29)
84 (31)
.345
Diabetes Mellitus
16 (9)
10 (12)
26 (10)
.225
Any Cardiovascular Disease
14 (7)
6 (7)
20 (7)
.858
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High School or less
Years Living with HIV
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Income (USD)
CD4+ T Cell Count, cells/µL Undetectable Viral Load
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Years Taking ART
Current treatment with ART
<.001
.004
Diagnosed Diseases
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Depression
Note. Table values are means ± standard deviations or frequencies (%); USD = U.S. dollars; ART = antiretroviral therapy; P value refers to comparison between men and women.
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Table 2 Physical Activity Participation and Sedentary Time in Urban Dwelling Men and Women Living With HIV Females n (%)
Total n (%)
High
99 (66)
40 (69)
146 (66)
Moderate
34 (23)
14 (24)
Low
16 (11)
4 (7)
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Meeting Muscle-Strengthening Recommendations
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Physical Activity Levels
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Males n (%)
51 (23)
P
.702
23 (11)
Yes
87 (45)
32 (42)
124 (44)
No
107 (55)
47 (58)
159 (56)
Week Day
360 (265)
360 (405)
360 (318)
.572
Weekend Day
300 (300)
360 (420)
300 (300)
.297
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Sitting Time, minutes
Note. Table values are means ± standard deviations or frequencies (%); sitting time in minutes are median values and interquartile ranges (IQR); P value refers to comparison between men and women; PLWH =
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people living with HIV infection.
.662
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Table 3 Univariate Linear Regression Models of Diagnosed Chronic Diseases as Correlates of Total
B
Standardized B
P
Diabetes Mellitus
204
-2553.273
-.147
0.035*
High Blood Pressure
204
-1320.863
-.112
0.110
Cardiovascular Diseases
206
2047.285
.093
0.185
Depression
200
-292.313
-.027
0.701
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n
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Disease
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Physical Activity (MET·min·wk-1) in Urban Dwelling Men and Women Living with HIV
Note. Cardiovascular diseases include stroke, angina pectoris, coronary heart disease, and myocardial infarction; B = Unstandardized Beta weights. *Significant (p < 0.05) association between physical activity
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(MET·min·wk-1) participation and a diabetes mellitus diagnosis.
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Table 4 Univariate Linear Regression Models of Diagnosed Chronic Diseases as Correlates of Muscle Strengthening in Urban Dwelling Men and Women Living with HIV
Depression
266
1.712
Diabetes Mellitus
271
0.528
High Blood Pressure
269
0.801
Cardiovascular Diseases
271
0.841
95% Confidence Interval
P
1.051 to 2.790
0.031*
0.221 to 1.260
0.138
0.474 to 1.356
0.408
0.332 to 2.128
0.713
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OR
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n
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Disease
Note. Cardiovascular diseases includes stroke, angina, coronary heart disease and myocardial infarction.
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Significant (p < 0.05) association between muscle strengthening participation and a depression diagnosis
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*
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60%
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Percentage of PLWH
45%
30%
0% HBP
Angina or CAD
MI
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15%
Stroke
CVD
Diabetes Depression
Figure 1. Prevalence of physician-diagnosed physical and mental health conditions among 289 urban dwelling people living with HIV.
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Note. PLWH = people living with HIV infection; HBP = high blood pressure; CAD = coronary artery disease; MI = myocardial infarction; CVD = cardiovascular diseases
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includes stroke, angina, CAD, and myocardial infarction.
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60%
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Percentage of PLWH
80%
40%
0% Mild
Moderate
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Well
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20%
Severe
Figure 2. Prevalence of having a likely mental health disorder based on the K10 criteria among 275 urban dwelling PLWH.
Note. PLWH = people living with HIV infection; Well = likely to be well; Mild = likely
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to have mild mental disorder; Moderate = likely to have moderate mental disorder;
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Severe = likely to have severe mental disorder.
S1055-3290(17)30155-3
DOI:
10.1016/j.jana.2017.06.015
Reference:
JANA 936
To appear in:
Journal of the Association of Nurses in AIDS Care
Received Date: 14 March 2017 Accepted Date: 27 June 2017
Please cite this article as: Quiles N.N., Ciccolo J.T. & Garber C.E., Association between physical activity, depression, and diabetes in urban dwelling people living with HIV, Journal of the Association of Nurses in AIDS Care (2017), doi: 10.1016/j.jana.2017.06.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Association between physical activity, depression, and diabetes in urban dwelling people living with HIV
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Norberto N. Quiles, EdD Joseph T. Ciccolo, PhD
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Carol Ewing Garber, PhD, FACSM
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Norberto N. Quiles*, EdD, is Assistant Professor, Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, New York, USA ([email protected]). Joseph T. Ciccolo, PhD, is Assistant Professor, Department of Biobehavioral Sciences, Teachers College Columbia University, New York, New York, USA. Carol Ewing Garber, PhD, FACSM, is Professor, Department of Biobehavioral Sciences, Teachers College Columbia University, New York, New York, USA.
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Corresponding author: Norberto Quiles: [email protected]
Disclosures
The authors report no real or perceived vested interests that relate to this article that could
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be construed as a conflict of interest.
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Acknowledgements
This project was supported in part by the Teachers College Deans Grant for student Research. All authors declare no financial interests or potential conflicts of interest. We thank Abdulaziz Alnafesah for his assistance conducting this study.
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Abstract Physical activity (PA) may improve physical and mental health in people living with HIV (PLWH). However, the associations between PA participation and physical and mental
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health problems of PLWH in urban settings remain largely unknown. Our objective was to determine the relationships between PA and physical and mental health in urban-dwelling PLWH. 289 adult PLWH responded to an electronic survey including questions on PA and
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current physical and mental health. The associations between physical and mental health and PA were investigated using linear and logistic regression. A large proportion of
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participants met recommended volumes of PA. 83% of respondents reported symptoms of severe mental distress. Diabetes mellitus was associated with a lower total volume of PA (p = 0.035). Similarly, depression was negatively associated with muscle strengthening exercise participation (p = .030). Sufficient amounts of aerobic activity and/or muscle
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strengthening exercise are associated with better physical and mental health.
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Key Words. cardiovascular disease, exercise, mental health, metabolic disease
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Association between physical activity, depression, and diabetes in urban dwelling people living with HIV People living with HIV (PLWH) often experience physical and mental health
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complications in association with the progression of the disease and the antiretroviral therapy (ART) used to treat HIV. Among these complications are depression (Arseniou, Arvaniti & Samakouri, 2014) and cardio-metabolic diseases such as hypertension (De Socio
which can increase the risk of premature death.
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et al., 2013, coronary heart disease, and diabetes mellitus (Willig & Overton, 2014), all of
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Physical activity produces many health-related benefits for PLWH; these consist of greater life satisfaction and quality of life (Ramírez-Marrero, Smith, Meléndez-Brau, & Santana-Bagur, 2004), less depression (Blashill et al., 2013), better lipid profiles (Gavrila et al., 2003), and healthier body composition (Justina, Luiz, Maurici, & Schuelter-Trevisol,
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2014). However, the majority of studies have used small sample sizes and/or selective recruitment from clinics and community center settings (Fillipas, Cicuttini, Holland, & Cherry, 2013; Ramirez-Marrero et al., 2004; Segatto et al., 2011), so the results have not
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been generalizable to larger populations of PLWH. Physical activity behaviors in PLWH
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residing in major urban settings, where the prevalence of HIV infection is disproportionately higher, is largely unknown. Data in the population show large variations in physical activity across neighborhoods in urban areas, and the neighborhoods with the highest poverty levels tend to have the lowest levels of physical activity (Wyker et al., 2013). HIV infection is concentrated in people in lower socioeconomic statuses, with many living in lower-resourced neighborhoods where there may be safety concerns, poorer public transportation, and fewer parks and attractive environments conducive to
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physical activity (Pellowski, Kalichman, Matthews, & Adler, 2013). Thus, it is possible that PLWH in urban areas may be less physically active when compared to their uninfected counterparts. However, to date, no studies have investigated physical activity behaviors of
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PLWH living in an urban setting. Furthermore, the relationships between physical activity and physical and mental health problems routinely occurring in PLWH have not been
characterized in urban-dwelling PLWH. We hypothesized that there would be a positive
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relationship between physical activity, including muscular strengthening exercise, and physical and mental health in PLWH.
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The specific aims of our study were to (a) describe the leisure and non-leisure physical activity participation behaviors in urban-dwelling adult PLWH and (b) examine the associations between physical activity and physical and mental health in PLWH. Methods
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Study Design and Population
We used a cross-sectional observational design. Four hundred and eighty-nine urban dwelling men and women who were 18 years of age or older and living with HIV
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responded to a 10- to 15-minute survey through announcements made available online
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from April 2015 to April 2016. Classified advertisements were placed on websites (e.g., Craigslist© and Backpage©), online fora and blogs for PLWH, social media (e.g., Facebook©, Twitter©, and LinkedIn©), and other online sites. Links to the survey were made available in 25 metropolitan areas of more than 500,000 residents with the highest rates of HIV as reported by the Centers for Disease Control and Prevention (CDC, 2013b). Survey Data Collection Participant inclusion criteria included being previously diagnosed with HIV, at least
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18 years of age, living in an urban setting, and being willing and able to fill out questionnaires electronically. Eligible individuals who were interested in participating in the anonymous survey were directed to the SurveyMonkey® survey system
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(SurveyMonkey©, Inc., Palo Alto, CA; www.surveymonkey.com). After participants accessed the online survey, they provided informed consent before answering any of the survey questions as per the policies and procedures of the Teachers College, Columbia University
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Institutional Review Board, which approved the study. The respondent then answered 2 eligibility questions: Have you previously tested positive for the HIV virus? and Are you an
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adult (i.e., ≥ 18 years old)?. If the response was yes to each, the participant continued the anonymous survey; if no to one or both questions, the respondent was thanked and was ineligible to continue with the survey. Links to the survey were available for English and Spanish versions of the survey. Participants were eligible to participate in a drawing for 1
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of 15 gift certificates worth $20 USD at the end of the study, if they provided an email that was not linked to the survey itself to protect anonymity. Sociodemographics and Health
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Participants provided information about sociodemographics, health-related
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lifestyle, and diagnosed diseases by self-report. Sociodemographic variables included age, gender, race, level of education, and household income. Race was categorized as White or Caucasian; Black or African American; and Hispanic or Latino; the remaining races or ethnicities were pooled into a category called other races or ethnicities. The question on education came from the 2013 Behavioral Risk Factor Surveillance System questionnaire (BRFSS; CDC, 2013a), and was further trichotomized into high school education or less, some college, or college graduate, to have sufficient subjects per group for analytic
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purposes. Household income was dichotomized into two categories with a cut-off point consistent with current poverty limits for a family of three (U.S. Department of Health and Human Services, 2016). Questions about physician-diagnosed diseases of diabetes,
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hypertension, stroke, myocardial infarction, and coronary artery disease or angina came from the BRFSS questionnaire (CDC, 2013a). Diagnoses of hypertension, myocardial
infarction, stroke, and coronary artery disease or angina were collapsed into one category,
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renamed cardiovascular disease (CVD), because of small numbers of respondents reporting these diseases. Mental health was evaluated using the BRFSS question (CDC, 2013a) for
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diagnosed depression. The validity of these questions has been established (Cossman et al., 2008; Fahimi, Link, Schwartz, Levy, & Mokdad, 2008; Pierannunzi, Hu, & Balluz, 2013). The Kessler Psychological Distress (K10) Scale (Kessler et al., 2002) was used to assess mental health symptoms. This instrument has been used and validated in PLWH (Spies et al.,
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2009). Participants were also asked about time since HIV diagnosis, viral load, CD4+ T cell count, and exposure to antiretroviral therapy (ART; i.e., current use and duration of ART). Physical Activity and Muscle Strengthening
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Physical activity and muscle-strengthening were assessed using questions about
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muscle strengthening exercise, taken from the BRFSS questionnaire (CDC, 2013a), and leisure and non-leisure physical activity were assessed with the International Physical Activity Questionnaire (IPAQ)-Long Form (IPAQ Research Committee, 2005). Consistent with CDC definitions, those performing at least 2 days of muscle strengthening exercises were classified as meeting muscle strengthening targets, while performing fewer than 2 days of muscle strengthening was classified as not meeting recommended levels of participation (Physical Activity Guidelines Advisory Committee, 2008). The validity and
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reliability of the muscle strengthening question has been determined compared to physical activity logs (Yore et al., 2007). Total (overall) physical activity and subscales representing transportation, occupational, domestic, and leisure time physical activity were determined
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using the self-reported IPAQ-Long Form. This instrument has shown moderate but
significant correlation with objectively measured physical activity (i.e., accelerometry) in PLWH (Fillipas et al., 2013), which is similar to what has been found in the general
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population. Transportation-related physical activity was calculated from the total minutes per day and days per week of walking and/or cycling for transportation purposes.
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Occupational physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity activity at work, as well as walking at work. Domestic physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity outside chores, as well as moderate intensity
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indoor chores. Leisure-time physical activity was calculated from the total minutes per day and days per week of moderate and vigorous intensity activity, and walking during leisure time. The IPAQ criteria for physical activity levels (IPAQ Research Committee, 2005) were
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used to classify participants’ physical activity levels as low, moderate, or high.
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Statistical Analysis
Descriptive statistics are presented as means and standard deviations for
continuous variables, and as absolute and relative frequencies for categorical variables. Physical activity volume (MET·min·wk-1) is presented in median and interquartile ranges given that these data were not normally distributed. Gender differences were analyzed using Chi Square or Fisher exact tests for categorical variables, and independent t-tests or Mann-Whitney U tests, as appropriate, for continuous variables. Logistic regression
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analyses were used to examine relationships between muscle strengthening participation, as a categorical variable, with chronic disease and mental health related outcomes. Simple and multiple linear regression analyses examined the relationships between physical
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activity volumes and chronic disease and mental health related variables. Age, gender, race, income level, smoking, education level, CD4+ T cell count, years living with HIV, and years
were not included in the final models. Results
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Sociodemographics
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taking ART were explored as covariates and, because none were significant, these variables
Participant characteristics are summarized by gender in Table 1. Most of our respondents were men ranging in age between 19 and 76 years. They were, on whole, highly educated (i.e., some college or more), but a high proportion reported low income.
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The proportion of female participants in our study (28.9%) was substantially lower than the 51.4% reported in the most recent census (U.S. Census Bureau, 2015) for the cities that were sampled in this study. Approximately 49% of our sample identified as White or
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Caucasian, which was similar to the 48.5% Whites reported in the Census. In our sample,
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approximately 37% reported having a bachelor’s degree or higher, which was also similar to the 34.7% reported in the most recent census data. More than 67% of our sample fell below the median income (i.e., $46,657 USD) for the cities sampled in our study. Physical Activity and Muscle Strengthening Complete data on physical activity were available for 220 respondents. Most participants were moderately active or greater, when total physical activity was considered (Table 2). Domestic physical activity was the largest proportion of the reported volume of
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physical activity, followed by leisure time, transportation, and occupational physical activity. Data on muscle strengthening exercise participation were available for 283 respondents. More than half of our respondents reported not meeting muscle
(Physical Activity Guidelines Advisory Committee, 2008). Physical and Mental Health
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strengthening recommendations at a level consistent with the guidelines for Americans
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Figure 1 shows the percentage of respondents who had been told by a health care provider that they had a physical or mental health condition. Approximately 34% of
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respondents reported no physical or mental health conditions. However, close to 60% had one or two diagnosed physical or mental health conditions, while 6% had three or more physical or mental health conditions. Hypertension was common, while fewer had diabetes mellitus. About half of the respondents reported a diagnosis of depression. However, the
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results from the K10 Psychological Distress Scale (Figure 2) showed a preponderance of symptoms of severe mental health disorder amongst respondents. In fact, 43.3% of respondents who had severe mental distress symptoms based on the K10 did not have a
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formal diagnosis of depression.
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Association Between Physical Activity and Chronic Diseases Table 3 shows the results of a series of univariate linear regression analyses to
examine associations between chronic diseases and total physical activity. Having a diabetes mellitus diagnosis was associated with a lower total physical activity volume in PLWH [F(1, 202) = 4.492, p = 0.035]. No significant associations were found between total physical activity volume and the presence or absence of a diagnosis of high blood pressure, cardiovascular disease, or depression.
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Table 4 shows the univariate logistic regression models to evaluate chronic diseases as correlates of meeting muscle-strengthening recommendations. There was a significant association between meeting muscle strengthening recommendations and depression
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diagnosis [OR 1.712, 95% CI: 1.051 to 2.790; p = .030]. There were greater rates of
diagnosed depression reported by respondents who did not meet muscle-strengthening recommendations compared to those who did meet muscle-strengthening
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recommendations. No significant associations were uncovered between meeting muscle strengthening recommendations and the presence of cardiovascular disease, diabetes
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mellitus, or a high blood pressure diagnosis. Associations by Gender
When examining the data by gender there were several gender-related differences. Men were older on average than were women and more men were Black or African
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American compared to women respondents. There were comparative differences in education and income between men and women, with a greater proportion of women reporting lower education attainment and very low income, compared to men. No gender-
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related differences were found in rates of diagnosed diseases, years living with HIV, CD4+
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T-cell count, having an undetectable viral load, years taking ART, current treatment with ART, or smoking. In addition, there were no gender differences in physical activity levels, total volume of physical activity, volume of physical activity by domain (e.g., domestic, leisure-time, transportation and occupational physical activity), meeting muscle strengthening guidelines, or sitting time. Discussion Some respondents had been living with HIV for many years and were receiving
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treatment for the disease, and most reported well-controlled disease as evidenced by mean CD4+ T cell count within the normal range. The respondents reported several chronic health conditions, with the most prevalent being depression, hypertension, and diabetes
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mellitus. Most worrying was our finding that most respondents indicated they had
symptoms of psychological distress, suggesting the presence of a severe mental health disorder. Interestingly, approximately 43% of respondents who had severe symptoms of
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mental distress based on the K10, did not have a diagnosis of depression from a primary care provider, which suggested an under diagnosis of mental health issues in this
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population. These results are particularly problematic in PLWH, because physical and mental health conditions have been linked to a higher risk of CVD in the general population (Hare, Toukhsati, Johansson, & Jaarsma, 2013). Furthermore, PLWH who are treated with ART have been shown to be twice as likely to develop CVD compared to the general
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population (Islam, Wu, Jansson, & Wilson, 2012). Some of the increased risk of CVD in PLWH is likely due to the direct effects of ART and some due to more frequent incidence of risk factors (Islam et al., 2012; Willig & Overton, 2014). In our study, we were unable to
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assess CVD risk due to the cross sectional observational design of the study. We examined
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the possibility of confounding variables in our analyses, but no variables were significant covariates, so we did not adjust for any variables. It is notable that we were unable to detect any associations between ART use and physical activity and the presence of any physical or mental health conditions in our sample. These findings do not preclude the possibility that there were confounding variables that may have affected the associations between variables, but our results suggest that these interactions were modest at best. More than half of our participants reported being White or Caucasian, nearly one
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third reported being African American, and a small proportion self-reported being Hispanic or Latino. These results were different from CDC (2014) data that reported the largest percentage of HIV cases in Black or African Americans, followed by Hispanic or Latino, and
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White or Caucasians. However, it is worth noting that our data were from 25 metropolitan areas and not the entire United States. As reported by the CDC (2014), the largest
proportion of PLWH are found in the 45- to 49-years-of-age range, which was consistent
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with our mean age of 46.5 years.
Nearly half of our respondents reported low incomes. However, the great majority
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had some college education or more, which presented an unexpected disparity between income and education. It is possible that some respondents with higher education were unable to work due to health, possibly related to disease progression. However, we did not ask questions to determine this relationship. There were some observed differences in
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education and income between men and women in our sample. Overall, women were less educated and more economically disadvantaged than male respondents. Our survey respondents reported high levels of leisure and non-leisure physical
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activity, including muscle strengthening exercise. Surprisingly, approximately 89% of our
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sample met general physical activity recommendations, a proportion considerably higher than reported in the general population (CDC, 2014). Although it is notable that, when examining the physical activity subdomains that contributed to this high level of physical activity, a substantial contribution was made by domestic and leisure-time physical activity and a smaller proportion by occupational physical activity. The IPAQ long form used in our study provided details of physical activity across four domains that included not only intentional exercise but also physical activities performed during active transportation,
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household, and occupational tasks. Most other reports on physical activity in PLWH have reported solely on leisure-time physical activity, which consisted mostly of intentional exercise (Schuelter-Trevisol et al., 2012). Other studies that have found higher rates of
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physical activity in PLWH compared to the general population add further credibility to our results concerning physical activity. Studies reporting on physical activity
recommendations in more select samples of PLWH in the United States have found a
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prevalence of sufficient self-reported physical activity ranging from 53% to 81%
(Clingerman, 2003; Ramirez-Marrero et al., 2008). Notably, these studies had much smaller
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sample sizes and participants were selected from clinic or community center settings, rather than from the urban community.
Moderate to high participation in muscle strengthening exercise in PLWH has been reported in a few other studies (Clingerman, 2003; Gavrila et al., 2003). Nearly half of our
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sample engaged in muscle strengthening exercises on at least 2 days per week, which was similar to the findings of a smaller scale study by Clingerman (2003). Another small study by Gavrila and colleagues (2003) reported weight training participation in nearly 40% of
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participants. However, these studies measured resistance (weight) training rather than
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overall muscle strengthening exercise participation as in our study. The question on muscle strengthening that we used asked about muscular strengthening activities such as yoga, resistance band exercises, calisthenics, and the like, in addition to resistance (weight) exercise training. Other studies reporting on resistance exercise training participation in PLWH showed considerably lower rates of participation than did our study. It is possible that differences in participant characteristics, in addition to differences in the questions asked, may partly explain these differences. For example, participants in the studies by
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Tang et al. (2010) and Smit et al. (2006) were intravenous drug users. Median sitting time in our sample was about 6 hours on a weekday and 5 hours on a weekend day, which was considerably higher than reported in surveys of the general U.S.
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population. For example, a study using the IPAQ, reported a median sitting time for
Americans of 240 minutes per day (Bauman et al. 2011). When considering that our sample included many with low income, the higher sitting time could reflect
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unemployment. Alternatively, the high prevalence of symptoms of psychological distress in our sample may have also factored into the long sitting times. Comparisons with other
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studies in PLWH in the United States were difficult to make, given the wide variety of instruments and definitions used to measure sedentary behaviors in the few available studies (Schuelter-Trevisol et al., 2012). At least one study reported a TV watching time of 5.4 hours for Hispanic adults living with HIV, which was similar to our sitting time results
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(Ramirez-Marrero et al., 2004). Nonetheless, our findings clearly indicated that time spent in sedentary pursuits was excessive in many individuals living with HIV in our study. Given the data showing a dose-response relationship between sitting time, mortality, and CVD
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risk in the general population (Katzmarzyk, Church, Craig, & Bouchard, 2009) as well as in
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regularly active individuals (Healy et al., 2008), combined with the fact that PLWH were already twice as likely to develop CVD when compared to the general population (Islam et al., 2012), these findings are of concern. A diagnosis of diabetes mellitus was associated with the total volume of overall
physical activity in our sample, so that PLWH who had diabetes engaged in less physical activity than those without a diabetes diagnosis. These results were consistent with a study by Codella, Guffanti, Castagna, Lazzarin, and Luzi, (2005) who described low levels of
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leisure time physical activity in PLWH who also had diabetes. Our results showed that all types of physical activities were reduced in PLWH who had diabetes. Co-morbidities and complications of diabetes such as fatigue, peripheral neuropathy, nephropathies, and
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retinopathy can play a role in limiting physical activity participation in people with
diabetes (Korkiakangas, Alahuhta, & Laitinen, 2009) and this could very well be the case for PLWH who have diabetes, such as some of our study respondents. On the other hand,
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regular physical activity is important to control diabetes mellitus and is recommended (Colberg et al., 2010).
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Our data showed that PLWH who did not meet muscle strengthening targets were more likely to present with depression than those who met such guidelines. In fact, respondents who had been diagnosed with depression were 71% less likely to meet muscle strengthening recommendations than those without such a diagnosis. While insufficient
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physical activity has been associated with depression in PLWH (Blashill et al., 2013), no studies have reported on relationships between muscle strengthening and depression in this population. In apparently healthy people, resistance exercise interventions have been
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shown to be effective in the treatment of depression (Pilu et al., 2007). However,
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depression has been consistently associated with low levels of physical activity in the general population (Roshanaei-Moghaddam, Katon, & Russo, 2009) and it seems, based on our study, that this relationship also holds true in PLWH. The anonymous nature of our study was a strength because anonymity can enhance
the truthfulness of responses (Ong & Weiss, 2000). Our participants were not asked for any self-identifying information and were made aware that the study was conducted under full anonymity. However, our study was not without limitations. Self-report measurements
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have some inherent shortcomings. Although self-report is a widely used and accepted method of retrieving health and physical activity data, it is possible that some overreporting of physical activity occurred in our study because a large number of individuals
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were classified as physically active. Discrepancies between more objective and subjective measures of physical activity also may reflect discordance between perception and actual behavior, problems with validity, and biases (Ramirez-Marrero et al., 2008; Fillipas,
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Cicuttini, Holland, & Cherry, 2010). Approximately 40% of respondents did not complete the entire survey. It is possible that the large number of items in the survey (i.e., 59) could
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have played a role in the incomplete surveys. However, care was taken in the study design process to decrease the possibility of non-completions, including progress bars and motivational statements throughout the survey.
Summary and Conclusion
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Our study showed that many urban dwelling adult PLWH have relatively high levels of physical activity, including muscle-strengthening exercise. High rates of daily sitting time were also observed. Chronic diseases such as hypertension, diabetes, and especially
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depression were common in our sample, although only diabetes and depression were
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associated with poorer participation in all types of physical activity and muscle strengthening, respectively. In conclusion, while a majority of respondents met targets for physical activity and muscle-strengthening exercise, this was offset by elevated sitting time. Furthermore, there was a high prevalence of co-morbid physical and mental health conditions, all of which can increase the risk of CVD mortality and morbidity. Our results showed that PLWH could benefit from primary and secondary prevention strategies, including reducing sedentary time and engaging in sufficient amounts of physical activity
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and muscle strengthening exercise.
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https://www.federalregister.gov/documents/2016/01/25/2016-01450/annualupdate-of-the-hhs-poverty-guidelines Willig, A. L., & Overton, E. T. (2014). Metabolic consequences of HIV: Pathogenic
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insights. Current HIV/AIDS Reports, 11(1), 35-44. doi:10.1007/s11904-013-0191-7 Wyker, B., Bartley, K., Holder-Hayes, E., Immerwahr, S., Eisenhower, D., & Harris, T. G.
(2013). Self-reported and accelerometer-measured physical activity: A comparison in
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pa_measures.pdf
Yore, M. M., Ham, S. A., Ainsworth, B. E., Kruger, J., Reis, J. P., Kohl 3rd, H. W., & Macera, C. A. (2007). Reliability and validity of the instrument used in BRFSS to assess physical activity. Medicine and Science in Sports and Exercise, 39(8), 1267-1274.
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doi:10.1249/mss.0b013e3180618bbe
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Key Considerations Engaging in physical activity may help reduce the risk of PLWH developing diabetes.
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Clinicians should counsel PLWH on ways to increase daily physical activity, especially given the high sedentary time found in this population.
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Performing muscle strengthening activities such as resistance training, calisthenics, yoga, and other activities can help with depression in PLWH.
Clients living with HIV should be provided with guidance about starting a muscle-
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PLWH may suffer from mental health symptoms, even when a clinical diagnosis has not been documented. Clinicians should carefully evaluate the mental health of PLWH to
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strengthening program.
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Table 1 Descriptive Characteristics of 289 Urban Dwelling Men and Women Respondents Living With HIV Females n (%)
Total n (%)
P
47.4 ± 11.7
44.4 ± 10.3
48.5 ±10.3
.028
White or Caucasian
104 (52)
32 (40)
136 (49)
.053
Black or African American
50 (25)
31 (38)
81 (29)
.028
Hispanic or Latino
34 (17)
11 (14)
45 (16)
.469
Other
10 (6)
6 (8)
16 (6)
.436
29 (36)
60 (21)
Age, years
Education
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Ethnicity
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Males n (%)
31 (16)
Some College
86 (43)
32 (39)
118 (42)
College Graduate
82 (41)
20 (25)
102 (37)
< $19,999
73 (39)
46 (59)
119 (45)
$20,000
112 (61)
32 (41)
144 (55)
14.8 ± 10.1
14.3 ± 8.9
14.9 ± 9.8
.636
652.1 ± 290.9
650.5 ± 326.6
651.7 ± 299.5
.976
143 (78)
49 (68)
192(75)
.076
11.9 ± 8.7
13.1 ± 7.8
12.2 ± 7.8
.227
175 (91)
67 (88)
242 (90)
.456
96 (52)
43 (52)
139 (52)
.345
High Blood Pressure
60 (32)
24 (29)
84 (31)
.345
Diabetes Mellitus
16 (9)
10 (12)
26 (10)
.225
Any Cardiovascular Disease
14 (7)
6 (7)
20 (7)
.858
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High School or less
Years Living with HIV
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Income (USD)
CD4+ T Cell Count, cells/µL Undetectable Viral Load
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Years Taking ART
Current treatment with ART
<.001
.004
Diagnosed Diseases
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Depression
Note. Table values are means ± standard deviations or frequencies (%); USD = U.S. dollars; ART = antiretroviral therapy; P value refers to comparison between men and women.
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Table 2 Physical Activity Participation and Sedentary Time in Urban Dwelling Men and Women Living With HIV Females n (%)
Total n (%)
High
99 (66)
40 (69)
146 (66)
Moderate
34 (23)
14 (24)
Low
16 (11)
4 (7)
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Meeting Muscle-Strengthening Recommendations
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Physical Activity Levels
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Males n (%)
51 (23)
P
.702
23 (11)
Yes
87 (45)
32 (42)
124 (44)
No
107 (55)
47 (58)
159 (56)
Week Day
360 (265)
360 (405)
360 (318)
.572
Weekend Day
300 (300)
360 (420)
300 (300)
.297
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Sitting Time, minutes
Note. Table values are means ± standard deviations or frequencies (%); sitting time in minutes are median values and interquartile ranges (IQR); P value refers to comparison between men and women; PLWH =
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people living with HIV infection.
.662
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Table 3 Univariate Linear Regression Models of Diagnosed Chronic Diseases as Correlates of Total
B
Standardized B
P
Diabetes Mellitus
204
-2553.273
-.147
0.035*
High Blood Pressure
204
-1320.863
-.112
0.110
Cardiovascular Diseases
206
2047.285
.093
0.185
Depression
200
-292.313
-.027
0.701
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n
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Disease
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Physical Activity (MET·min·wk-1) in Urban Dwelling Men and Women Living with HIV
Note. Cardiovascular diseases include stroke, angina pectoris, coronary heart disease, and myocardial infarction; B = Unstandardized Beta weights. *Significant (p < 0.05) association between physical activity
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(MET·min·wk-1) participation and a diabetes mellitus diagnosis.
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Table 4 Univariate Linear Regression Models of Diagnosed Chronic Diseases as Correlates of Muscle Strengthening in Urban Dwelling Men and Women Living with HIV
Depression
266
1.712
Diabetes Mellitus
271
0.528
High Blood Pressure
269
0.801
Cardiovascular Diseases
271
0.841
95% Confidence Interval
P
1.051 to 2.790
0.031*
0.221 to 1.260
0.138
0.474 to 1.356
0.408
0.332 to 2.128
0.713
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OR
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n
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Disease
Note. Cardiovascular diseases includes stroke, angina, coronary heart disease and myocardial infarction.
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Significant (p < 0.05) association between muscle strengthening participation and a depression diagnosis
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*
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60%
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Percentage of PLWH
45%
30%
0% HBP
Angina or CAD
MI
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15%
Stroke
CVD
Diabetes Depression
Figure 1. Prevalence of physician-diagnosed physical and mental health conditions among 289 urban dwelling people living with HIV.
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Note. PLWH = people living with HIV infection; HBP = high blood pressure; CAD = coronary artery disease; MI = myocardial infarction; CVD = cardiovascular diseases
AC C
EP
includes stroke, angina, CAD, and myocardial infarction.
ACCEPTED MANUSCRIPT
60%
RI PT
Percentage of PLWH
80%
40%
0% Mild
Moderate
M AN U
Well
SC
20%
Severe
Figure 2. Prevalence of having a likely mental health disorder based on the K10 criteria among 275 urban dwelling PLWH.
Note. PLWH = people living with HIV infection; Well = likely to be well; Mild = likely
TE D
to have mild mental disorder; Moderate = likely to have moderate mental disorder;
AC C
EP
Severe = likely to have severe mental disorder.