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Depression and Other Antecedents of Obesity

Introduction

Decades of advances in pharmacological science have stemmed the tide of infectious diseases, given the man on the street the benefit of information about proper sanitation and better nutrition, fostered greater expertise at the primary health care level, and lengthened life spans. In highly developed economies like the UK, the United States and the original Common Market signatories, however, ever more prominent concerns about mental health and healthy eating have come to the fore alongside the drive to eradicate dread diseases. For the “silent killer” that is depression impacts quality of life, strains marriages and friendships, aggravates absenteeism, reduces national productivity and shatters self-esteem not a little. In turn, there is concern on both sides of the Atlantic about how the continuing spread of obesity up and down the generational cohorts increases the risk for hypertension, diabetes, cardiovascular disease and coronary heart disease.

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Within the UK, “social marketing” campaigns – promoting prudent choices, eschewing binge drinking, aiming to bring smoking rates down, and encouraging healthy eating, for instance – depend on consumers making rational choices. After all, advocacy campaigns by the Food Standards Agency, the NHS or counterparts in the private, not-for-profit sector count on target publics to adopt behaviour that is to their own benefit. But they have to reckon with a great inertia to human habits and addiction.

Thoughtful opinion leaders have attempted to combat the imprudent eating and more sedentary lifestyles of today that lead to obesity even among the youth and children of the next generation. This has included defensive PR by the Food and Drink Federation (FDF) dating back to 1996 themed “Join the Activaters” and “Foodfitness”. The latter urges a mix of at least moderate physical activity and healthy but appetizing eating. Partly because they have been consistent with the British Dietetic Association social marketing themes such as “5 a day”, the FDF programmes have been received well by dietitians, parents and educators.

Defeating the inertia about taking up a regular programme of sports and exercise can be a challenging goal. Hence, more advocacy campaigns focus on doing something about obesity with a more prudent diet. In the latest British Heart Foundation advert, “Look at the fat in that!” employs the device of ridicule to inform children that indulging in cakes, pastries, doughnuts and other “junk food” all year round can give them ten kilos of fat inevitably. Even retailers put up special food displays around themes of healthy living and healthy eating because they have come to realize it is both good business and sensible corporate social responsibility.

Finally, it is worth noting that the National Heart Foundation charity seeks to prevent a catastrophic increase in avoidable coronary disease, given the prevalence of obesity among UK adults and children both. In pursuit of this goal, the NHF relies on a three-pronged campaign of better nutrition, more regular physical activity and quitting smoking.

Given the opportunity to study a subset of the AHIMA Health Assessment Survey database, we hypothesize that obesity is a function of depressive symptoms and lack of regular physical activity. This is expressed as the null hypothesis:

H0: BMI values of 25 or higher are strongly related to lack of physical activity; low weekly frequency of aerobic workouts; a comparatively high incidence of poor mental health that interfered with work, social activities, leisure and other personal pursuits; and greater alcohol consumption.

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Literature Review

Methods

  1.  Study Design, Type of Analysis and Time Frame (DATA SOURCE COULD NOT BE FOUND)
  1.  Phenomena Studied

The “Health Assessment Survey” covered a comprehensive list of criterion variables around physical wellbeing and a handful of mental health matters. These included physical activity, debility (physical weakness) and insomnia, the height and weight information needed to calculate Body Mass Index, dieting attempts, healthy eating, the extent to which emotional problems prompted withdrawal from work and social involvement, indulging in tobacco and alcoholic beverages, health insurance coverage, going for regular medical and dental check-up’s, self-assessment of health, morbidity for common diseases and degenerative disorders, interest in health promotions and subsidised health screening, occupational safety and health issues, and some peripheral issues like safe driving habits.

  1. Sample and Descriptive Statistics

The Health Assessment Survey (HAS) covered a sample of 400 over what appeared to be a cross-section of a U.S. subpopulation spanning from under 24 years of age to the elderly, 65 and over; both genders; working full-, part-time or not; across income classes, and of varied marital status.

Owing partly to the fact that the current research focused on the two lowest age groups in the HAS, there is no question about the study being generalizable to the American population at large. The youthful skew of the subsample means the findings are more applicable to young singles (Table 4) 25 to 34 years old (Table 2). As well, there is a 2:1 male-female ratio and a similarly unaccountable bias toward mainstream Whites (Table 3). On the other hand, the education profile is about what one would expect of the U.S. population at large: around half completed secondary education at most.

Table 1
Gender % #
Male 68.1 49
Female 31.9 23
TOTAL 100.0 72
Table 2
Age Class % #
24 or younger 25.0 18
25 to 34 75.0 54
TOTAL 100.0 72
Table 3
Race % #
White or Caucasian 98.6 71
Hispanic or Latino 1.4 1
TOTAL 100.0 72
Table 4
Marital Status % #
Married 27.8 20
Member of an unmarried couple 9.7 7
Separated 4.2 3
Single, never married 51.4 37
Divorced 6.9 5
TOTAL 100.0 72
Table 5
Highest Educational Attainment % #
Grade 11 or less. 4.2 3
Grade 12 or GED certificate 45.8 33
College/Tech school, 1 year to 3 years 33.3 24
College, 4 years or more 13.9 10
Post graduate 2.8 2
TOTAL 100.0 72
Table 6
Annual Family Income % #
Less than $19,999 11.1 8
$20,000 to $29,999 30.6 22
$30,000 to $39,999 16.7 12
$40,000 to $49,999 19.4 14
$50,000 to $59,999 4.2 3
Over $60,000 11.1 8
DK/NR 6.9 5
TOTAL 100.0 72
N.B. DK/NR = Refuse to answer or don’t know

Upper-middle income families ($60,000+ annual income) may be over-represented (Table 6) but that is probably due to the fact that college degree holders or college students are more numerous in these young age cohorts.

  1. Intervention NOT APPLICABLE
  1. Outcome Measures

Table 7

BMI Score % #
Underweight = <18.5 1.4 1
Normal = 18.5-24.9 41.7 30
Overweight = 25-29.9 34.7 25
Obese = BMI of 30 or greater 22.2 16
TOTAL 100.0 72
Median 25.1
Average 26.3
Mode 23.0

The DV, obesity, is a fact of life even among the youth covered by the AHIMA Study. The median of the BMI score distribution is in the “slightly overweight” class of the BMI standard, the mean is still higher (suggesting the effect of outliers with “morbidly obese” scores) and more than one-fifth of the subjects show BMI’s of 30 or greater.

Returning to the working hypothesis that the antecedents (or IV’s) of obesity comprise depressive symptoms and lack of regular physical activity, the current research operationalizes these as follows:

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  • Depression – Items III-5, -6, -7 and the alcoholic beverage consumption item II-10
  • Physical Activity – Item II-19 and the created categorical variable on incidence of engaging in physical activity.

Subsequently, examining the data distribution for each IV permits decisions on suitability for correlation and regression analysis (see Tables 8-12 and Figure 1).

Marital status and educational attainment are arbitrarily included as dummy variables in the correlation and regression tests.

Results

Table 13. Correlates of BMI based on Pearson’s R

CORRELATES OF BMI BASED ON PEARSON’S R
Depression Items Others – Physical Activity
mental health limit work social activity 0.069 vigorous aerobic activity (0.031)
mental health not good (0.152) physical activity 0.022
mental health keep you from usual activities (0.094) Others – Dummy Variables
alcoholic drink/wk 0.088 marital status 0.127
high grade 0.111

Based purely on the available dataset for the youth subpopulation of the HAS, the evidence for a relationship between obesity and depression among the young is weak. At best, the computed correlation comes to 0.07 for BMI and the frequency with which mental health problems (like anxiety, depression and stress) detract from social activity or being able to attend to work (real or academic). For the two other depression-linked IV’s, the correlation, such as it is, is even in the inverse direction. As well, the correlation between obesity and moderate physical activity is virtually non-existent. Not even the 0.09 correlation with weekly alcohol consumption bears investigating.

The major reason for these counter-intuitive results is not the small size of the youth subpopulation per se but the fact that, after setting aside the 75% or 80% who aver no depressive symptoms, a subsample base of 72 yields just 17 to 23 subjects who admitted to crippling mental health problems recently (see Tables 8 to 10). This falls short of the threshold for parametric tests. In fact, the 20-25% self-reported incidence of mental health problems is high compared to an 8 percent morbidity for frank depressive disorders and an additional 4 percent evincing generalized anxiety from a recent report (by Gwynn et al. 2008, p. 642, fieldwork done in 2004) of the “first community-specific Health and Nutrition Examination Survey in the United States” ran in New York City. At that, just one in seven depressives reported significant disability from their condition.

  1.  Statistical Test Employed

In addition to the correlations, linear multiple regression was carried out in SPSS. The highlights:

Table 14

ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 263.664 7 37.666 1.124 .359a
Residual 2144.944 64 33.515
Total 2408.608 71
a. Predictors: (Constant), Highest educational attainment, Affects work and social life, Vigorous aerobic activity per week, Marital status, Physically active, Mental health not good, Mental health prevents usual activities
b. Dependent Variable: Body mass index

Table 15

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Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 20.121 3.427 5.872 .000
Affects work and social life 2.551 1.392 .291 1.832 .072
Mental health not good -.274 .183 -.220 -1.500 .138
Mental health prevents usual activities -.426 .377 -.196 -1.130 .263
Vigorous aerobic activity per week -.361 .760 -.058 -.476 .636
Physically active .182 1.526 .015 .119 .905
Marital status .672 .476 .178 1.413 .163
Highest educational attainment .763 .829 .115 .921 .361
a. Dependent Variable: Body mass index

Given the low correlations to begin with, attempting to model five IV’s and two dummy variables using linear multiple regression suggests that:

  • The short set of variables (ergo, also the condensed dataset) employed explains only a small part of the variation in BMI.
  • None of the derived beta coefficients are significant at α = 0.05. Only the variable admitting that mental health issues get in the way of work (or studies) and social life comes closest.
  • Though not statistically significant, beta coefficients for the two other mental health IV’s are in a not-unexpected direction: the greater the frequency of crippling distress, the likelier that BMI drops. This is a reminder that severe depression is characterized by appetite loss, not weight gain.
  • The inclusion of the two dummy variables, marital status and educational attainment, did not prove statistically significant either. Marital status comes closer. Because SPSS designates a reference category in the case of nominal variables such as marital status, all the coefficient points to is that any change away from being “divorced” (the last category and which has low incidence among the youth to begin with) may contribute a 0.18 change in BMI. The confounding variable, which bears testing, is that married men tend to gain weight in the medium and long term, a phenomenon that has nothing to do with depression.

Discussion

While more than half of the youth sub-segment in the HAS dataset were slightly or grossly obese, the statistical tests did not yield enough evidence to reject the null hypothesis and accept the alternative that lack of physical activity or crippling mental problems are truly antecedents of raised BMI. This finding is somewhat at odds with published research and this must be attributed primarily to an inadequate sample size of 72 subjects. By contrast, related studies on the subject have scrutinized 847, 3,101, and up to 4,743 American adolescents (Latty et al., 2007; Richardson et al., 2006; and Swallen et al., 2005 respectively) and as many as 12,376 in just one Canadian province (Dragan and Danesh, 2007). There is also the question, encountered repeatedly in the literature, as to whether depression ought to be the DV after all.

Considering that the present subsample turned out to be preponderantly male and if one assumes that inadequate sample size did not materially affect reliability, one explanation may be found in the finding of Richardson et al. (2006) that the odds ratio for obesity leading to depression dropped among males in late adolescence. However, the implication that young adult males transition to being less prone to overeat in reaction to severe depression or generalized anxiety is not validly borne out by the current study.

Ultimately, serious attempts to explain BMI must include measures of overindulging in high-fat food and a sugary diet. This has been the bane of two generations of Americans now and it may well be that emotional disorders must take the backseat to imprudent eating as an antecedent to obesity.

Works Cited

Gwynn, R. C., McQuistion, H. L., McVeigh, K. H., Garg, R. K., Frieden, T. R., Thorpe, L. E. (2008) “Prevalence, Diagnosis, And Treatment Of Depression And Generalized Anxiety Disorder In A Diverse Urban Community.” Psychiatric Services, 59(6), 641-7.

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StudyCorgi. (2021, October 22). Depression and Other Antecedents of Obesity. Retrieved from https://studycorgi.com/depression-and-other-antecedents-of-obesity/

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StudyCorgi. (2021, October 22). Depression and Other Antecedents of Obesity. https://studycorgi.com/depression-and-other-antecedents-of-obesity/

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StudyCorgi. (2021) 'Depression and Other Antecedents of Obesity'. 22 October.

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