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Obesity and health-related quality of life: a cross-sectional analysis of the US population

Abstract

OBJECTIVE: To examine the relationship between body mass index (BMI) and health-related quality-of-life (HRQL), in the presence of dietary controls and/or exercise in a national sample in the United States.

METHODS: BMI and its association with HRQL domains (physical, mental and activity limitations) were examined using the Centers' for Disease Control and Prevention's 2000 Behavioral Risk Factor Surveillence System (BRFSS) data, after adjusting for various sociodemographic factors, self-reported health-status, and diet/exercise behavior.

RESULTS: Based on World Health Organization's (WHO) classification of obesity, the study sample (N=182 372) included approximately 43.7% nonoverweight, 36% overweight, 14% obese, and 7% severely obese respondents. Exercise and dietary modifications were used by 17.5% of overweight, 15.2% of obese, and 12.5% of severely obese individuals. Logistic regression results using nonoverweight BMI as the reference category showed that severely obese (OR=1.87, 95% CI 1.64–2.12) and obese (OR=1.21, 95% CI 1.09–1.33) were more likely to experience greater than 14 unhealthy days affecting the physical health domain. Severely obese (OR=1.41, 95% CI 1.26–1.59) and obese (OR=1.17, 95% CI 1.07–1.28) were also more likely to experience greater than 14 unhealthy days affecting the mental health domain. Similarly, severely obese (OR=1.73, 95% CI 1.50–1.99) and obese (OR=1.22, 95% CI 1.08–1.37) were more likely to experience greater than 14 days with activity limitations. Exercise and dietary controls were associated with better HRQL across all three domains.

CONCLUSION: The study highlights the relationship between BMI and HRQL in the United States. The study also underlines the positive correlation of exercise and dietary modifications with HRQL.

Introduction

Obesity has reached epidemic proportions in the United States, affecting approximately 97 million adults.1 Health-related quality of life (HRQL) is emerging as an important outcome in obesity studies. HRQL refers to the self-reported effects of a medical condition on physical and mental functioning and well-being of patients.2 Obesity is associated with increased health risks and pain that can impair physical health status and impose limitations on daily activities. Reduced physical health as well as stigmatization and discrimination associated with obesity can contribute to impaired mental well-being.3,4

Literature provides evidence that obesity influences overall HRQL.4 It has been reported that physical problems in obese individuals affect HRQL more than the mental problems.5 Although underlying comorbidities in obese individuals act as major confounders impacting HRQL, research has shown that obesity itself can also significantly impact HRQL.5 Previous studies have usually focused on obese individuals seeking treatment.5,6 There is relatively less information on the impact of obesity on HRQL in the general population. Also, more attention needs to be paid to outcomes related to changes in functional status (activity limitations) in obese individuals. Regular physical activity and exercise, especially when used in combination with dietary therapy, has been shown to improve various HRQL indicators, such as physical and social functioning, mood and self-esteem in obese individuals.7

This paper reports the prevalence of obesity in the US based on World Health Organization's (WHO) classification of obesity. It also reports the prevalence of individuals in the general population who use dietary modifications and/or exercise to lose weight. The main objective of this paper is to assess the relationship between obesity status, HRQL, dietary modifications, and exercise. We hypothesize that obese individuals have lower HRQL scores, and that behavioral interventions such as dietary modifications and exercise are associated with improved HRQL.

Methods

Data source

The Centers for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance Survey (BRFSS) data from 2000 were used to accomplish the study objectives. The BRFSS collects information on health behaviors and risk factors for various diseases by employing random-digit dialing telephone survey techniques. Each state selects a probability sample of the noninstitutionalized adult population ≥18 y of age. One adult resident is randomly chosen from each selected household. BRFSS data are weighted to the age-, race-, and sex-specific distributions found in each state, along with each respondent's probability of selection. The BRFSS survey consists of core questions and state-added optional modules. Core questions are asked by all 50 states and District of Columbia every year whereas optional modules are chosen by some states and communities to be included in their surveys. The core questionnaire consists of four questions that can be used for surveillance of health status and HRQL in the nationwide population. Owing to its large and random population-based coverage, the BRFSS survey has high generalizibility. Thus, it can be used to provide valid statewide as well as nationwide estimates of HRQL.8 Various studies have utilized BRFSS measures to examine HRQL in different disease states.8,9

HRQL measurement

The BRFSS HRQL questionnaire utilizes self-reported measures of healthy and unhealthy days as indicators of HRQL. These measures have been proven to be cost-effective, easy to administer, reliable and valid.8,10 We used the four HRQL questions from the core-section of 2000 BRFSS survey. These questions obtain information on: (1) general health status, (2) number of days during the 30 days preceding the survey when physical health was not good, (3) number of days during the 30 days preceding the survey when mental health was not good, and (4) number of days during the 30 days preceding the survey when activity was limited as a result of poor physical or mental health (Appendix A). The general health status question measures self-reported health status on a scale of poor to excellent and has been found to be a predictor of mortality and chronic disease conditions.8,11 Responses to questions 2 and 3 are measures of recent physical symptoms, and mental or emotional distress, respectively. Recent activity limitations reported in question 4 indicate perceived disability and lost productivity.8

Obesity classification

Body mass index (BMI) was based on self-reported weight in kilograms divided by height in meters-square. According to the WHO's classification of obesity, respondents were classified into nonoverweight (BMI less than 24.9 kg/m2), overweight (BMI between 25.0 and 29.9 kg/m2), obese (BMI between 30.0 and 34.9 kg/m2) and severely obese (BMI above 35.0 kg/m2).

Statistical analysis

The 2000 BRFSS data contained a total of 184 450 respondents. Self-reported pregnant women were excluded for the purpose of the analysis, yielding 182 372 respondents. Logistic regression procedures were employed to study the impact of obesity status on HRQL measures. Responses to the HRQL questions were used as dependent variables in the logistic regression analysis by dichotomizing the number of reported days with poor physical health, poor mental health, or activity limitations into ≤14 days and >14 days. A similar cutoff of ≤14 days and >14 days has been used in a previous study to get more meaningful ramifications of physical and mental unhealthy days as well as of days with activity limitations.12 The regression analysis controlled for various demographic factors, smoking, exercise and dietary controls, and self-perceived general health status. Respondents who had smoked ≥100 cigarettes in their lifetime ‘and’ were currently smoking were considered as current smokers. Respondents who were involved in physical activity for 30 or more minutes, five or more times per week, regardless of intensity were considered as exercising. Respondents who were eating less to lose weight were considered to be dieting. The responses to overall self-rated health status question were dichotomized into poor (poor or fair health) and good (good, very good, or excellent health) and served as a proxy measure for acute and chronic health conditions impacting overall health. Descriptive statistics were run for the variables of interest. χ2, t-tests, and one-way ANOVA were used to measure statistical significance. Statistical analysis was carried out using STATA® for Windows. This statistical package was used as it adjusts for the nonrandom cluster sampling methodology used by the BRFSS and gives more generalizable results.

Results

Based on their BMI, 43.7% of respondents were nonoverweight, 35.9% were overweight, 13.8% were obese, and 6.6% of the populations were severely obese. Survey respondents were predominantly white (85%), female (57.7%), and reported excellent (22.2%) or very good (33.1%) health status. Approximately 54.9% tried to reduce weight through diet controls, 6.2% tried to reduce weight through exercise, and 17.2% of the enrollees used diet controls as well as exercise. A distribution of participant characteristics by obesity class is given in Table 1.

Table 1 Characteristics of respondents by obesity status

Univariate analyses

BMI obesity and HRQL scores

Scores in all three HRQL domains deteriorated with increasing BMI. Physical health worsened as BMI increased. The mean number of physical unhealthy days was highest in severely obese (6.5 days), followed by obese (4.3 days), overweight (3.3 days), and nonoverweight (3.0 days) respondents. For mental health, severely obese experienced significantly more unhealthy days (5.2 days) as compared to 3.6, 2.9, and 3.2 days for obese, overweight, and nonoverweight respondents, respectively (P<0.001). Severely obese respondents experienced 7.2 days with activity limitations as compared to 6.6, 6.5, and 6.1 days for obese, overweight, and nonoverweight respondents, respectively (P<0.001).

Logistic regression analyses

Obesity and physical health (Table 2)

Table 2 Logistic regression models determining factors associated with HRQL domains (2000 BRFSS)

The likelihood of experiencing greater than 14 days of poor physical health was 87% higher in severely obese, and 21% higher in obese respondents, as compared to respondents who were nonoverweight. Respondents who were on diet as well as exercised were 32% less likely to experience greater than 14 unhealthy days than respondents who used neither diet nor exercise. Smokers were 52% more likely to experience greater than 14 unhealthy days than nonsmokers. Other factors that significantly impacted the likelihood of experiencing greater than 14 unhealthy days included self-perceived health status, income, African-American race, gender, and age.

Obesity and mental health (Table 2)

The likelihood of experiencing greater than 14 days of poor mental health was 41% higher in severely obese, and 17% higher in obese respondents, as compared to respondents who were nonoverweight. Respondents who were on diet as well as exercise were 13% less likely to experience greater than 14 unhealthy days than respondents who used neither diet nor exercise. Smoking was significantly associated with mental health as smokers were 110% more likely to experience greater than 14 unhealthy days than nonsmokers. Female subjects were 69% more likely to experience greater than 14 unhealthy days than male subjects. Other factors that significantly impacted the likelihood of experiencing greater than 14 unhealthy days included self-perceived health status, income, and age.

Obesity and activity limitations (Table 2)

As compared to respondents who were nonoverweight, the likelihood of experiencing greater than 14 unhealthy days of poor health due to activity limitations was 73% higher in severely obese, and 22% higher in obese respondents. Respondents who were on a diet as well as exercised were 25% less likely to experience greater than 14 unhealthy days. Smokers were 75% more likely to experience greater than 14 unhealthy days than nonsmokers. Other factors that significantly impacted the likelihood of experiencing greater than 14 unhealthy days included self-perceived health status, income, gender, and age.

Discussion

Our study assessed the association between obesity, weight-loss measures, and HRQL using a large, nationally representative data set. Being obese and severely obese was associated with a significant deterioration in all three domains of HRQL. However, being overweight did not significantly affect HRQL scores. Diet and exercise were associated with better HRQL scores, whereas smoking was associated with deterioration in HRQL.

Approximately one-fifth of the study respondents were either obese or severely obese. The high prevalence of obesity found in this study is consistent with findings from other nationally representative data.13,14 The trend data from the National Health and Nutrition Examination Surveys (NHANES) reported an increase in the percentage of obese respondents (BMI ≥30 kg/m2) from 14.5% in NHANES II (1976–1980) to 22.5% in NHANES III (1988–1994).13 Mokdad et al's14 analysis of BRFSS data also found an increasing prevalence of obesity, from 12.0 % in 1991 to 17.7% in 1998. Ford et al12 analyzed the 1996 BRFSS data and reported 11.2 % respondents as obese (BMI ≥30 kg/m2) and 4.5% respondents as severely obese (BMI ≥35 kg/m2). Thus, the prevalence of obesity has increased alarmingly over time.

The results show that more than 60% of the population was trying to lose weight using various weight-loss strategies like diet controls and exercise. Levy and Heaton15 reported that 71% of women and 62% of men were attempting to lose weight through diet and exercise. French et al16 reported that more than 70% of their study population had tried different weight-loss strategies such as increased exercise, decreased fat intake, reduced food amount, and reduced calories.16 A large proportion (40%) of respondents in our study used dietary controls to manage their weight. Effectiveness of adding exercise to their dietary regimen should be emphasized in this population. Most of the severely obese respondents (70.2%) used only diet controls to manage weight, whereas a significant proportion (15.5%) used neither diet nor exercise to lose weight. Various complications of obesity that make exercise difficult could be one of the reasons for the low prevalence of exercise in severely obese population.17

Obese respondents in our study reported significantly higher unhealthy days for physical and mental health, as well as for activity limitations as compared to nonobese respondents. This was consistent with findings from other studies.3,5,18 Katz et al3 used the SF-36 to show lower physical health scores in obese as compared to nonobese. They also reported that among obese individuals, women and African-Americans have lower HRQL scores than men and whites, respectively.3 Fontaine et al2 reported significant impairments in various HRQL domains, especially vitality and body pain, in obese individuals as compared to nonobese individuals. Ford et al12 found that people with increased BMI have greater odds of reporting poor or fair health. Also, physical functioning was affected more than mental functioning in obese individuals.12 However, in our study we found that the obese respondents showed similar odds ratios for unhealthy physical and mental health days. Also, severely obese respondents had higher odds ratios for the physical health and activity limitations domain than for the mental health domain. This indicates that severe obesity has a stronger positive correlation with physical health and activity limitations than mental health. Our study adjusted for the use of diet as well as exercise, whereas the study by Ford et al12 did not. Poor HRQL in obese individuals reported in various studies is not surprising and can be due to many reasons. Various comorbidities and functional limitations associated with obesity can adversely affect physical quality of life. Dissatisfaction with body image, low self-esteem, poor health, depression, and employment-related as well as other forms of social discrimination can add to the psychological distress in obese individuals.3,4,19

We investigated whether lifestyle modifications used by obese individuals to lose weight were associated with HRQL scores. We found that individuals who used both diet and exercise to lose weight reported better HRQL scores. Individuals who used exercise alone reported better HRQL scores than individuals who used dietary modifications alone or did not attempt to lose weight. Thus, it is possible that individuals who used diet and exercise did experience better HRQL; or, individuals with better HRQL were able to exercise and were motivated to use dietary controls. If the former were true, then it is possible that various psychological changes such as an improvement in confidence, self-perception and self-efficacy associated with exercise may have influenced these scores.20 Reports of lower HRQL scores in obese who were only dieting to reduce weight are similar to the findings in the literature.21 The process of dieting involves preoccupation with dietary foods, constant efforts to adhere to the dietary regimen, and guilt feelings on failing to comply with dietary goals, which can potentially impact perceived HRQL. Also, a low calorie diet has been shown to be associated with depression.21

Our study has certain limitations. First, due to the cross-sectional nature of our study, causality cannot be inferred. Thus, obesity itself affected HRQL, or HRQL was affected by other factors that also led to obesity. Similarly, it is possible that diet and exercise improved HRQL, or HRQL influenced diet and exercise behavior. Second, the survey did not collect any information on the duration of obesity, and the duration and intensity of lifestyle modifications to reduce weight. Therefore, these factors could not be included in the analysis. Only noninstitutionalized persons with telephones were included in the BRFSS survey. Since a high proportion of US residents own telephones, telephone coverage bias is less likely to occur. Also, some possibility of bias exists due to the use of self-reported measures of height and weight to calculate BMI. However, several studies have established the psychometric properties of the BRFSS survey in terms of reliability and validity for height, weight, and BMI measurements.22,23 The BRFSS core questionnaire did not contain adequate information about comorbid conditions. Our study results therefore depict an association between obesity and HRQL using health status as a proxy measure for comorbid conditions.

In conclusion, obesity has become a well-recognized national problem. Reducing the health impact of obesity and encouraging appropriate diet and physical activity are among the top priorities of the Healthy People 2010 objectives.24 The BRFSS data provide a large population-based data set that can help in monitoring impact of obesity on HRQL, in the presence of diet and/or exercise. This study highlights the increasing prevalence of obesity in the nation and the relationship of obesity with HRQL. It also reports the association between weight control activities like dieting and physical activity and HRQL. Future studies can be directed towards providing a better understanding of this association and its implications on compliance and effectiveness of weight control measures.

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Acknowledgements

The authors express their gratitude to the Centers for Disease Control and Prevention in Atlanta, Georgia for furnishing the BRFSS data.

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Correspondence to M K Hassan.

Appendix A: BRFSS HRQL core module

Appendix A: BRFSS HRQL core module

(A) General health

Would you say that in general your health is:

1. Excellent

2. Very good

3. Good

4. Fair

5. Poor

(B) Number of days physical health not good

Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?

(C) Number of days mental health not good

Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?

(D) Activity limitations

During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?

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Hassan, M., Joshi, A., Madhavan, S. et al. Obesity and health-related quality of life: a cross-sectional analysis of the US population. Int J Obes 27, 1227–1232 (2003). https://doi.org/10.1038/sj.ijo.0802396

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Keywords

  • body mass index
  • health-related quality of life
  • diet
  • exercise

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