https://doi.org/10.17795/ijpbs-3458

Psychosocial Correlates of Body Mass Index in the United States: Intersection of Race, Gender and Age

authors:

avatar Shervin Assari 1 , 2 , *

Department of Psychiatry, University of Michigan, Ann Arbor, USA
Center for Research on Ethnicity, Culture and Health (CRECH), School of Public Health, University of Michigan, Ann Arbor, USA
Iranian Journal of Psychiatry and Behavioral Sciences: Vol.10, issue 2; e3458
published online: May 16, 2016
article type: Original Article
received: January 13, 2015
revised: April 11, 2015
accepted: April 28, 2016

How To Cite Assari S. Psychosocial Correlates of Body Mass Index in the United States: Intersection of Race, Gender and Age. Iran J Psychiatry Behav Sci. 2016;10(2):e3458. https://doi.org/10.17795/ijpbs-3458.

Abstract

Background:

Studies on the association between psychosocial factors and obesity have provided mixed findings.

Objectives:

Current study used a nationally representative sample of U.S. adults to investigate how intersection of race, gender and age alters the psychosocial correlates of body mass index (BMI).

Materials and Methods:

Data came from the national health measurement study (NHMS), a cross-sectional study with nationally representative sample of Black and White (n = 3,648) adults ages 35 - 89. We tested bivariate correlations between BMI and physical and mental quality of life, purpose in life, number of chronic medical conditions, and discrimination across race × gender × age groups.

Results:

Higher purpose in life was associated with lower BMI among middle aged and older White women, middle aged Black men, middle aged Black women, and older Black women but not middle aged or older White men and older Black men. There was a positive association between multi-morbidity and BMI in all groups other than older White men. High BMI was associated with poor mental quality of life among older White women, older Black men and women, but not any of middle aged groups, and older White men. High BMI was associated with poor physical quality of life among all groups. Everyday discrimination was positively associated with BMI among older White women but not any other group. Lifetime discrimination was not associated with BMI among any of the groups.

Conclusions:

Race, gender, and age shape psychosocial and health related correlates of BMI. There is a need for further research on group differences in psychosocial correlates of obesity.

1. Background

Studies on the association between psychosocial factors and obesity have provided mixed findings (1). As a result, there has been an increasing interest in understanding factors that may moderate the above link. For instance, the link between mental health and obesity may vary across populations (2). Some of the inconsistent findings of this literature may be due to race (3), ethnicity (3, 4), and gender (3, 5) that modify factors that are associated with obesity.

There is not enough knowledge on race, ethnic and gender differences in psychological factors that predict obesity (3, 6). Gavin et al. analyzed the Comprehensive psychiatric epidemiology surveys (CPES) data, and showed that Blacks and Whites may differ in the association between 12-month major depressive disorder (MDD) and obesity (3). Using the national survey of American life, Assari showed that magnitude and direction of the associations between MDD and body mass index among Blacks varies based on gender and ethnicity (6).

2. Objectives

In line with the empirical evidence and theories suggesting race, ethnicity, gender, and place may have contextual effects on shaping causes and consequences of health and well-being (6-31), current study was conducted to investigate if groups based on the intersection of race, gender, and age differ in the association between purpose in life, physical and mental health, multi-morbidity, and discrimination with BMI. In this study we compared six race × gender × age groups; middle age (age between 35 and 65 years) and older (age between 66 and 89 years) Black and White men and women. As the sample was representative of the US, our results are believed to be generalizable to US adults.

3. Materials and Methods

3.1. Design and Setting

With a cross-sectional design, we used data of the national health measurement study (NHMS), 2005 - 2006. The NHMS enrolled a nationally representative sample of adults who aged between 35 and 89 years. The study sample is believed to represent the older half of the U.S. population in 2005 - 06.

3.2. Participants and Sampling

The study used a random digit dialed (RDD) surveys use random telephone numbers from non-cellular exchanges to sample households. The study over-sampled Black Americans and persons aged 65 to 89, using standard survey sampling methods. Over sampling of Blacks was done by using a factor of 2 for probability of calling in geographically scattered phone exchanges in areas with high proportions of Black population. Older individuals were also over-sampled, by a factor of 3 in sampling. Based on the above proportions, and using above described pre-determined probabilities, sampling weights were calculated for each respondent. This weight was then used to estimate results representative of the U.S. population aged 35 - 89, using standard survey methods.

3.3. Ethics

For about 40% of households, street addresses were available from reverse directories. For these households, advance letters with a full explanation of the study purpose were sent. Respondent were contacted via telephone and asked to participate in the survey. At the first step, all respondents were provided full explanations about the purpose of the study. Then participants also provided informed consent. All respondents were offered financial incentive for completion of the survey. All data was collected anonymously. Consent form was mailed as data was collected via telephone interview. The study protocol was approved by the institute review board of the University of Wisconsin.

3.4. Process and Interviews

All data was collected through computer assisted telephone interview (CATI) conducted by trained interviewers at the University of Wisconsin Survey Center. Data collection started June 2005 and lasted up to August 2006.

At the beginning of the telephone interviews, the interviewer determined the number of adults in the householder with age 35 to 89. CATI software assigned persons to three age ranges, 35 - 44, 45 - 64, and 65 - 89, and sampled among the age groups using pre-determined probabilities favoring the oldest age group. Troldahl-Carter-Bryant technique was used to select eligible respondents if more than one adult were living in the householder in the selected age group (32).

3.5. Measures

Outcome: BMI was calculated based on self-reported data on height and weight. Based on this information, BMI was classified as underweight (BMI < 18.5), normal weight (18.5 - 24.9 kg/m2), overweight (25 - 29.9 kg/m2), obese (30 - 50 kg/m2), and server obese (BMI > 50.0) (33).

A 15-item version of Ryff’s Psychological Well-being scale was used. The scale had the following seven items on purpose of life: 1) living one day at a time, 2) sense of direction, 3) no sense of what trying to accomplish, 4) activities seem trivial and unimportant, 5) enjoy making future plans, 6) active in carrying out plans, 7) do not wander aimlessly through life, and 7) done all there is to do in life. Five item scale likert was used for response options, ranging from ‘strongly disagree’ to ‘strongly agree’. As our scale comprised both positive and negative item content. Prior to analysis, items with negative content were reverse coded. We computed a score in which higher score reflected higher purpose in life (34-43) (α = 0.89).

Questionnaires were administered in an order randomized across respondents by the CATI software. Later sections of the interview elicited a wide range of other information including socio-economic characteristics. For 734 individuals, interview could not be completed for collection of all data. In all these cases, a call-back was arranged for completion of data collection. In most cases, call-backs were within a few days after first call.

3.6. Statistical Analysis

We used Stata 13.0 for data analysis to account for the complex sampling design of the study. Taylor series approximation technique was used for calculating the standard errors. We recalculated standard errors based on the study's complex design. P less than 0.05 were considered statistically significant. Adjusted odds ratio (OR) and a 95% confidence interval (CI) were reported. Pearson test was used to calculate correlations

4. Results

4.1. Middle Aged or Older White Men

Among middle aged and older aged White men, purpose in life was not associated with BMI. Among both groups, BMI was associated with physical but not mental quality of life. Among middle aged White men, BMI was also associated with comorbidities. BMI was not associated with discrimination in neither of groups (Table 1).

Table 1.

Correlation Matrix Among Middle Age (Age Between 35 and 65 Years) and Older (Age Between 66 and 89 Years) White Mena

1234567
1Body mass index1-0.0670.0230.022-0.282b0.0550.203b
2Purpose in life-0.0351-0.197b-0.218b0.250b0.510b-0.216b
3Everyday discrimination0.083-0.07910.380b-0.118b-0.281b0.063
4Lifetime discrimination0.0580.0090.180b1-0.225b-0.291b0.176b
5Physical quality of life-0.101c0.244b0.026-0.02810.141b-0.595b
6Mental quality of life-0.0320.335b-0.083-0.0430.104c1-0.167b
7Comorbidity0.052-0.126b0.0400.020-0.506b-0.127b1

aIndividuals younger than 65; up diagonal. Individuals older than 65 years; low diagonal.

bCorrelation is significant at the 0.01 level (2-tailed).

cCorrelation is significant at the 0.05 level (2-tailed).

4.2. Middle Aged or Older White Women

Among middle aged and older White women, higher purpose in life was associated with lower BMI. Among both groups, BMI was associated with physical quality of life, and comorbidities. BMI was not associated with lifetime discrimination in neither of groups. Among older White women, BMI was associated with mental quality of life and everyday discrimination (Table 2).

Table 2.

Correlation Matrix Among Middle Age (Age Between 35 and 65 Years) and Older (Age Between 66 and 89 Years) White Womena

1234567
1Body mass index1-0.211b0.0560.058-0.393b-0.0410.286b
2Purpose in life-0.178b1-0.324b-0.177b0.333b0.565b-0.301b
3Everyday discrimination0.100c-0.166b10.345b-0.170b-0.340b0.164b
4Lifetime discrimination0.0600.0280.322b1-0.190b-0.143b0.281b
5Physical quality of life-0.307b0.359b-0.0600.04010.171b-0.599b
6Mental quality of life-0.115b0.443b-0.164b-0.080c0.183b1-0.302b
7Comorbidity0.237b-0.213b0.0550.049-0.558b-0.200b1

aIndividuals younger than 65; up diagonal. Individuals older than 65 years; low diagonal.

bCorrelation is significant at the 0.01 level (2-tailed).

cCorrelation is significant at the 0.05 level (2-tailed).

4.3. Middle Aged or Older Black Men

Among middle aged Black men, higher purpose in life was associated with lower BMI. Among older Black men, purpose in life was not associated with lower BMI. Among both groups, BMI was associated with physical quality of life, and comorbidities. BMI was not associated with discrimination in neither of groups (Table 3).

Table 3.

Correlation Matrix Among Middle Age (Age Between 35 and 65 Years) and Older (Age Between 66 and 89 Years) Black Mena

1234567
1Body mass index1-0.132b0.056-0.002-0.181c-0.0290.286c
2Purpose in life0.0341-0.226c-0.1260.346c0.619c-0.293c
3Everyday discrimination0.128-0.08110.436c-0.196c-0.346c0.174c
4Lifetime discrimination0.0640.0630.416c1-0.158b-0.241c0.143b
5Physical quality of life-0.316c0.278c-0.105-0.08310.311c-0.664c
6Mental quality of life-0.178b0.381c-0.183b-0.0390.423c1-0.321c
7Comorbidity0.343c-0.0890.288c0.223c-0.460c-0.322c1

aIndividuals younger than 65; up diagonal. Individuals older than 65 years; low diagonal.

bCorrelation is significant at the 0.05 level (2-tailed).

cCorrelation is significant at the 0.01 level (2-tailed).

4.4. Middle Aged or Older Black Women

As Table 4 depicts, among both middle aged and older Black women, higher purpose in life was associated with lower BMI. Among both groups, BMI was associated with physical quality of life, and comorbidities. BMI was not associated with discrimination in neither of groups. Among older Black women, BMI was associated with mental quality of life.

Table 4.

Correlation Matrix Among Middle Age (Age Between 35 and 65 Years) and Older (Age Between 66 and 89 Years) Black Womena

1234567
1Body mass index1-0.178b0.083-0.084-0.326b-0.0840.308b
2Purpose in life-0.119c1-0.338b-0.0390.375b0.536b-0.389b
3Everyday discrimination0.035-0.123c10.386b-0.118c-0.342b0.122c
4Lifetime discrimination-0.034-0.0120.324b1-0.017-0.182b0.024
5Physical quality of life-0.217b0.191b-0.0420.07510.323b-0.650b
6Mental quality of life-0.128c0.385b-0.328b-0.0030.148c1-0.334b
7Comorbidity0.124c-0.202b0.0850.033-0.562b-0.296b1

aIndividuals younger than 65; up diagonal. Individuals older than 65 years; low diagonal.

bCorrelation is significant at the 0.01 level (2-tailed).

cCorrelation is significant at the 0.05 level (2-tailed).

5. Discussion

In the current study we aimed to explore how middle aged and older White and Black men and women are different in psychosocial correlates of BMI. Findings of our study suggest that groups based on race × gender × age are very different in how their BMI is linked to purpose in life, mental health, multi-morbidity, and discrimination. This finding is in line with the empirical evidence suggesting that race, ethnicity, gender, and place shape causes and consequences of health and well-being including BMI (6-31).

Higher purpose in life was associated with lower BMI among middle aged and older White women, middle aged and older Black women, and middle aged Black men, but not middle aged or older White men and older Black men. Although the association between BMI and low physical quality of life was consistent among all groups, the association between BMI and mental quality of life was only present among older White women, older Black men, and older Black women, but not any of middle aged groups, and older White men. Association between multi-morbidity and BMI was consistent, with an exception of older White men. While lifetime discrimination was not associated with BMI among any of the groups, everyday discrimination was only positively associated with BMI among older White women.

Purpose in life has been defined as “having goals in life and a sense of directedness, a feeling that there is meaning to present and past life, harbouring a belief that gives life purpose, and having aims and objectives for living” (44). Purpose in life is being interchangeably used by the concept meaning in life defined as, “the cognizance of order, coherence, and purpose in one’s existence, the pursuit and attainment of worthwhile goals, and an accompanying sense of fulfilment” (44, 45). Based on a multidimensional model developed by Ryff in 1995, purpose in life is a major dimension of the psychological well-being of humans (42) and changes over aging (38, 46, 47). Purpose in life is a personal characteristic that varies among populations (38, 39). However, it is not known how purpose in life is linked to obesity.

Purpose in life may be one of many possible mechanisms by which quality of life (48, 49), hopelessness (50), depression and other mental health conditions (3) are linked to obesity. Although several review studies have confirmed the association between mental health and obesity (51, 52), the existing knowledge is very limited about group differences in role of purpose in life, physical and mental health, and discrimination on obesity.

The literature is limited about the moderating effect of race/ethnicity on the link between psychological well-being and obesity (53). Bentley et al. used data of the national health measurement study (NHMS) to compare the link between various measures of quality of life and BMI levels. The study showed that quality of life decreases as BMI increases, however, this may depend on domain of quality of life, gender, and race. The study suggested that mental health summary score of the SF36 may be an exception in direction of association with BMI. Obese individuals had lower score on physical quality of life than those with normal weight, however, for mental health summary score, scores were lower for obese than normal-weight only among women but not men. Overweight Blacks had better quality of life than Blacks in other BMI categories. The study suggested that in general, obesity and overweight are associated with lower quality of life than those with normal BMI, however, in both genders, the association may be driven primarily by physical health, although mental health seems to be an important factor only among women. Blacks may have best quality of life when overweight (33).

There are only very few available studies that have tested the effects of race, gender and age on mental health correlates of obesity. Gavin et al. suggested that among women with obesity, prevalence of 12-month MDD is considerably lower among Blacks than Whites. That said, the study showed a stronger association between obesity and MDD among Whites than Blacks (3). In 2013, Assari showed that direction of association between MDD and BMI among Blacks were reversed among men and women. Among Black men and women, there were positive and negative associations between BMI and MDD, respectively. The gradient effect of BMI on MDD was significant among African American men (6). Sachs-Ericsson et al. reported a larger influence of BMI on depressive symptoms among Blacks than Whites (52). Analysis of the national comorbidity survey replication (NCS-R) reported a stronger association between obesity and MDD for Whites than non-Whites (54). Gariepy et al. showed that baseline obesity predicts subsequent major depression episode among men but not women (55).

Considerable gender differences have been also shown in the associations between anxiety, depression and obesity. Among men, BMI was linked to depression symptoms and negative affect, however, the difference among men was only between the underweight and normal weight groups. Depression and negative affect were higher among underweight men. Unexpectedly, obese and overweight men had less depressive symptoms and negative affect. Among women, BMI was associated with anxiety, depression and negative affect. Among women, those underweight had more depression and negative affect than those normal weight. Obese women, however, had better mental health, both for anxiety and negative affect. Among women, compared to those with normal weight, those who were overweight had lower anxiety, depression and negative affect (56).

Our findings may have implications for prevention of race, gender and age disparities in depression-related obesity in the U.S. Our finding about the pattern of the links between well-being, life purpose, and obesity is essential for explaining gender and racial differences in the link between depression and obesity. Results may also help with the design of interventions which address depression-related obesity in the community (3). Clinicians may benefit from considering differential role of purpose in life, physical and mental quality of life, and multi-morbidity on obesity across populations.

Current study had a few limitations. Although the association between BMI and psychological outcomes is not limited to linear associations (57, 58), current analysis is limited to modeling linear association between BMI and our factors. Compared to the simplistic linear (gradient) effects, threshold effects may better explain these complex links. Finally, BMI was calculated based on self-reported weight and height, which is prone to measurement error. In addition, psychiatric comorbidities were not included in the analysis.

5.1. Conclusions

Race, gender and age groups differ in the pattern of associations between purpose in life, mental quality of life, physical quality of life, multimorbidity, and BMI.

Acknowledgements

References

  • 1.

    Stunkard AJ, Faith MS, Allison KC. Depression and obesity. Biol Psychiatr. 2003;54(3):330-7.

  • 2.

    Lykouras L. Psychological profile of obese patients. Dig Dis. 2008;26(1):36-9. [PubMed ID: 18600013]. https://doi.org/10.1159/000109384.

  • 3.

    Gavin AR, Rue T, Takeuchi D. Racial/ethnic differences in the association between obesity and major depressive disorder: findings from the Comprehensive Psychiatric Epidemiology Surveys. Public Health Rep. 2010;125(5):698-708. [PubMed ID: 20873286].

  • 4.

    Blazer DG, Moody-Ayers S, Craft-Morgan J, Burchett B. Depression in diabetes and obesity: racial/ethnic/gender issues in older adults. J Psychosom Res. 2002;53(4):913-6. [PubMed ID: 12377303].

  • 5.

    Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health. 2000;90(2):251-7. [PubMed ID: 10667187].

  • 6.

    Assari S. Association between obesity and depression among American Blacks: Role of ethnicity and gender. J Racial Ethnic Health Disparities. 2014;1(1):36-44.

  • 7.

    Assari S. Race and Ethnicity, Religion Involvement, Church-based Social Support and Subjective Health in United States: A Case of Moderated Mediation. Int J Prev Med. 2013;4(2):208-17. [PubMed ID: 23543791].

  • 8.

    Assari S. The link between mental health and obesity: role of individual and contextual factors. Int J Prev Med. 2014;5(3):247-9. [PubMed ID: 24829706].

  • 9.

    Assari S. Additive Effects of Anxiety and Depression on Body Mass Index among Blacks: Role of Ethnicity and Gender. Int Cardiovasc Res J. 2014;8(2):44-51. [PubMed ID: 24936480].

  • 10.

    Assari S. Chronic Medical Conditions and Major Depressive Disorder: Differential Role of Positive Religious Coping among African Americans, Caribbean Blacks and Non-Hispanic Whites. Int J Prev Med. 2014;5(4):405-13. [PubMed ID: 24829727].

  • 11.

    Assari S. Separate and Combined Effects of Anxiety, Depression and Problem Drinking on Subjective Health among Black, Hispanic and Non-Hispanic White Men. Int J Prev Med. 2014;5(3):269-79. [PubMed ID: 24829710].

  • 12.

    Assari S. Cross-country variation in additive effects of socio-economics, health behaviors, and comorbidities on subjective health of patients with diabetes. J Diabetes Metab Disord. 2014;13(1):36. [PubMed ID: 24559091]. https://doi.org/10.1186/2251-6581-13-36.

  • 13.

    Assari S, Ahmadi K, Kazemi Saleh D. Gender Differences in the Association between Lipid Profile and Sexual Function among Patients with Coronary Artery Disease. Int Cardiovasc Res J. 2014;8(1):9-14. [PubMed ID: 24757645].

  • 14.

    Assari S, Caldwell CH, Zimmerman MA. Sex differences in the association between testosterone and violent behaviors. Trauma Mon. 2014;19(3). e18040. [PubMed ID: 25337519]. https://doi.org/10.5812/traumamon.18040.

  • 15.

    Assari S, Lankarani MM, Lankarani RM. Ethnicity Modifies the Additive Effects of Anxiety and Drug Use Disorders on Suicidal Ideation among Black Adults in the United States. Int J Prev Med. 2013;4(11):1251-7. [PubMed ID: 24404358].

  • 16.

    Assari S, Lankarani RM, Lankarani MM. Cross-country differences in the association between diabetes and disability. J Diabetes Metab Disord. 2014;13(1):3. [PubMed ID: 24393171]. https://doi.org/10.1186/2251-6581-13-3.

  • 17.

    Assari S, Moghani Lankarani M, Ahmadi K. Comorbidity influences multiple aspects of well-being of patients with ischemic heart disease. Int Cardiovasc Res J. 2013;7(4):118-23. [PubMed ID: 24757635].

  • 18.

    Assari S, Moghani Lankarani M, Kazemi Saleh D, Ahmadi K. Gender modifies the effects of education and income on sleep quality of the patients with coronary artery disease. Int Cardiovasc Res J. 2013;7(4):141-6. [PubMed ID: 24757639].

  • 19.

    Bayat N, Alishiri GH, Salimzadeh A, Izadi M, Saleh DK, Lankarani MM, et al. Symptoms of anxiety and depression: A comparison among patients with different chronic conditions. J Res Med Sci. 2011;16(11):1441-7. [PubMed ID: 22973345].

  • 20.

    Dejman M, Forouzan A, Assari S, Malekafzali H, Nohesara S, Khatibzadeh N, et al. An Explanatory Model of Depression among Female Patients in Fars, Kurds, Turks Ethnic Groups of Iran. Iran J Public Health. 2011;40(3):79-88. [PubMed ID: 23113089].

  • 21.

    Dejman M, Forouzan AS, Assari S, Rasoulian M, Jazayery A, Malekafzali H, et al. How Iranian lay people in three ethnic groups conceptualize a case of a depressed woman: an explanatory model. Ethn Health. 2010;15(5):475-93. [PubMed ID: 20694866]. https://doi.org/10.1080/13557858.2010.488262.

  • 22.

    Hajibeigi B, Azarkeyvan A, Alavian SM, Lankarani MM, Assari S. Anxiety and depression affects life and sleep quality in adults with beta-thalassemia. Indian J Hematol Blood Transfus. 2009;25(2):59-65. [PubMed ID: 23100977]. https://doi.org/10.1007/s12288-009-0015-5.

  • 23.

    Kazemi-Saleh D, Pishgoo B, Farrokhi F, Fotros A, Assari S. Sexual function and psychological status among males and females with ischemic heart disease. J Sex Med. 2008;5(10):2330-7. [PubMed ID: 18331261]. https://doi.org/10.1111/j.1743-6109.2008.00788.x.

  • 24.

    Kazemi-Saleh D, Pishgou B, Farrokhi F, Assari S, Fotros A, Naseri H. Gender impact on the correlation between sexuality and marital relation quality in patients with coronary artery disease. J Sex Med. 2008;5(9):2100-6. [PubMed ID: 18221280]. https://doi.org/10.1111/j.1743-6109.2007.00724.x.

  • 25.

    Khooshabi K, Ameneh-Forouzan S, Ghassabian A, Assari S. Is there a gender difference in associates of adolescents' lifetime illicit drug use in Tehran, Iran? Arch Med Sci. 2010;6(3):399-406. [PubMed ID: 22371778]. https://doi.org/10.5114/aoms.2010.14263.

  • 26.

    Mohammadkhani P, Forouzan AS, Khooshabi KS, Assari S, Lankarani MM. Are the predictors of sexual violence the same as those of nonsexual violence? A gender analysis. J Sex Med. 2009;6(8):2215-23. [PubMed ID: 19493281]. https://doi.org/10.1111/j.1743-6109.2009.01338.x.

  • 27.

    Mohammadkhani P, Khooshabi KS, Forouzan AS, Azadmehr H, Assari S, Lankarani MM. Associations between coerced anal sex and psychopathology, marital distress and non-sexual violence. J Sex Med. 2009;6(7):1938-46. [PubMed ID: 19453876]. https://doi.org/10.1111/j.1743-6109.2009.01273.x.

  • 28.

    Noohi S, Khaghani-Zadeh M, Javadipour M, Assari S, Najafi M, Ebrahiminia M, et al. Anxiety and depression are correlated with higher morbidity after kidney transplantation. Transplant Proc. 2007;39(4):1074-8. [PubMed ID: 17524895]. https://doi.org/10.1016/j.transproceed.2007.04.002.

  • 29.

    Saleh DK, Nouhi S, Zandi H, Lankarani MM, Assari S, Pishgou B. The quality of sleep in coronary artery disease patients with and without anxiety and depressive symptoms. Indian Heart J. 2008;60(4):309-12. [PubMed ID: 19242007].

  • 30.

    Tavallaii SA, Fathi-Ashtiani A, Nasiri M, Assari S, Maleki P, Einollahi B. Correlation between sexual function and postrenal transplant quality of life: does gender matter? J Sex Med. 2007;4(6):1610-8. [PubMed ID: 17672846]. https://doi.org/10.1111/j.1743-6109.2007.00565.x.

  • 31.

    Tavallaii SA, Mirzamani M, Heshmatzade Behzadi A, Assari S, Khoddami Vishteh HR, Hajarizadeh B, et al. Sexual function: a comparison between male renal transplant recipients and hemodialysis patients. J Sex Med. 2009;6(1):142-8. [PubMed ID: 19170845]. https://doi.org/10.1111/j.1743-6109.2008.01047.x.

  • 32.

    Kessler RC, Wittchen HU, Abelson JM, Mcgonagle K, Schwarz N, Kendler KS, et al. Methodological studies of the Composite International Diagnostic Interview (CIDI) in the US national comorbidity survey (NCS). Int J Methods Psychiatr Res. 1998;7(1):33-55.

  • 33.

    Bentley TG, Palta M, Paulsen AJ, Cherepanov D, Dunham NC, Feeny D, et al. Race and gender associations between obesity and nine health-related quality-of-life measures. Qual Life Res. 2011;20(5):665-74. [PubMed ID: 21547358]. https://doi.org/10.1007/s11136-011-9878-7.

  • 34.

    Abbott RA, Ploubidis GB, Huppert FA, Kuh D, Croudace TJ. An Evaluation of the Precision of Measurement of Ryff's Psychological Well-Being Scales in a Population Sample. Soc Indic Res. 2010;97(3):357-73. [PubMed ID: 20543875]. https://doi.org/10.1007/s11205-009-9506-x.

  • 35.

    Abbott RA, Ploubidis GB, Huppert FA, Kuh D, Wadsworth ME, Croudace TJ. Psychometric evaluation and predictive validity of Ryff's psychological well-being items in a UK birth cohort sample of women. Health Qual Life Outcomes. 2006;4:76. [PubMed ID: 17020614]. https://doi.org/10.1186/1477-7525-4-76.

  • 36.

    Fernandes HM, Vasconcelos-Raposo J, Teixeira CM. Preliminary analysis of the psychometric properties of Ryff's scales of psychological well-being in Portuguese adolescents. Span J Psychol. 2010;13(2):1032-43. [PubMed ID: 20977050].

  • 37.

    Keyes CL, Shmotkin D, Ryff CD. Optimizing well-being: the empirical encounter of two traditions. J Pers Soc Psychol. 2002;82(6):1007-22. [PubMed ID: 12051575].

  • 38.

    Ryff CD. Beyond Ponce de Leon and life satisfaction: New directions in quest of successful ageing. Int J Behav Dev. 1989;12(1):35-55.

  • 39.

    Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Psychol. 1989;57(6):1069.

  • 40.

    Ryff CD. Psychological well-being in adult life. Curr Direct Psychol Sci. 1995;4(4):99-104.

  • 41.

    Ryff CD. Possible selves in adulthood and old age: a tale of shifting horizons. Psychol Aging. 1991;6(2):286-95. [PubMed ID: 1863398].

  • 42.

    Ryff CD, Keyes CLM. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69(4):719.

  • 43.

    Villar F, Triado C, Celdran M, Sole C. Measuring well-being among Spanish older adults: development of a simplified version of Ryff's Scales of Psychological Well-being. Psychol Rep. 2010;107(1):265-80. [PubMed ID: 20923072]. https://doi.org/10.2466/02.07.08.10.21.PR0.107.4.265-280.

  • 44.

    Makola S, Van den Berg H. Values systems: What differentiates students with a “high” from those with a “low” sense of meaning? Int J Existent Psychol Psychother. 2009;2(2).

  • 45.

    Reker GTW. Aging as an individual process: Toward a theory of personal meaning. In: Birren JE, Bengston VL, editors. Emergent theories of Aging. New York: Springer; 1988.

  • 46.

    An JS, Cooney TM. Psychological well-being in mid to late life: The role of generativity development and parent–child relationships across the lifespan. Int J Behav Dev. 2006;30(5):410-21.

  • 47.

    Clarke P, Marshall V, Black SE, Colantonio A. Well-being after stroke in Canadian seniors: findings from the Canadian Study of Health and Aging. Stroke. 2002;33(4):1016-21. [PubMed ID: 11935054].

  • 48.

    Rosemann T, Grol R, Herman K, Wensing M, Szecsenyi J. Association between obesity, quality of life, physical activity and health service utilization in primary care patients with osteoarthritis. Int J Behav Nutr Phys Act. 2008;5:4. [PubMed ID: 18226211]. https://doi.org/10.1186/1479-5868-5-4.

  • 49.

    Fontaine KR, Barofsky I. Obesity and health‐related quality of life. ObesRev. 2001;2(3):173-82.

  • 50.

    Phillips WM. Purpose in life, depression, and locus of control. J Clin Psychol. 1980;36(3):661-7. [PubMed ID: 7410563].

  • 51.

    Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67(3):220-9. [PubMed ID: 20194822]. https://doi.org/10.1001/archgenpsychiatry.2010.2.

  • 52.

    Sachs-Ericsson N, Burns AB, Gordon KH, Eckel LA, Wonderlich SA, Crosby RD, et al. Body mass index and depressive symptoms in older adults: the moderating roles of race, sex, and socioeconomic status. Am J Geriatr Psychiatry. 2007;15(9):815-25. [PubMed ID: 17804833]. https://doi.org/10.1097/JGP.0b013e3180a725d6.

  • 53.

    Mizell CA. Life course influences on African American men's depression: Adolescent parental composition, self-concept, and adult earnings. J Black Stud. 1999;29(4):467-90.

  • 54.

    Simon GE, Von Korff M, Saunders K, Miglioretti DL, Crane PK, van Belle G, et al. Association between obesity and psychiatric disorders in the US adult population. Arch Gen Psychiatry. 2006;63(7):824-30. [PubMed ID: 16818872]. https://doi.org/10.1001/archpsyc.63.7.824.

  • 55.

    Gariepy G, Wang J, Lesage AD, Schmitz N. The longitudinal association from obesity to depression: results from the 12-year National Population Health Survey. Obesity (Silver Spring). 2010;18(5):1033-8. [PubMed ID: 19816409]. https://doi.org/10.1038/oby.2009.333.

  • 56.

    Jorm AF, Korten AE, Christensen H, Jacomb PA, Rodgers B, Parslow RA. Association of obesity with anxiety, depression and emotional well-being: a community survey. Aust N Z J Public Health. 2003;27(4):434-40. [PubMed ID: 14705308].

  • 57.

    Andersen JR, Aasprang A, Bergsholm P, Sletteskog N, Vage V, Natvig GK. Anxiety and depression in association with morbid obesity: changes with improved physical health after duodenal switch. Health Qual Life Outcomes. 2010;8:52. [PubMed ID: 20492663]. https://doi.org/10.1186/1477-7525-8-52.

  • 58.

    Fryback DG. United States National Health Measurement Study, 2005-2006. ICPSR23263-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. 2009 6 23. Available from: http://doi.org/10.3886ICPSR23263.v1/.