https://orcid.org/0000-0002-5741-8913
Figures
Abstract
Background
The prevalence of obesity and depression shows an accelerating trend with increased risk of morbidity and disability. The exact underlying relationship between them is unclear. We aimed to evaluate the prevalence of body mass index (BMI) and depression and their associations in a large sample of Saudi adults.
Methods
We administered a nationwide cross-sectional web-based survey using a snowball sampling method among Saudi adults aged 18–60 years. We used a validated Arabic version of Beck’s Depression Inventory-II (BDI-II) for depression assessment. We classified BMI into underweight, normal weight, overweight, and obesity. We used logistic regression analysis to determine the factors associated with depression.
Results
Among 4,683 Saudi adults, different grades of depression were present in 43.3%, most (25.2%) with a mild condition. Overweight and obesity were present in 26.4% and 21%, respectively. We found a positive association between BMI and BDI-II score (ρ = 0.14, p = 0.006). BMI was significantly higher among those who were older, males, married, living in the Eastern region of Saudi Arabia, educated at a pre-university level, employed, at high family-income levels, smokers, and people with chronic diseases. Depression score was significantly higher among married, non-employees, non-smokers, people with chronic diseases, and those with BMI ≥ 25 kg/m2. Non-smoking, presence of chronic diseases, and being overweight or obese were significantly associated with depression.
Conclusions
Saudi adults were suffering from different grades of depression, overweight, and obesity. A positive association between BMI and BDI-II score was observed. Depression score did not differ by age, sex, geographical region, educational level, or family income. Non-smoking, presence of chronic diseases, and being overweight or obese were significantly associated with depression. Further longitudinal research is required to understand the factors underpinning causal relationships between BMI and depression, the subgroups’ variation, and mediating strategies.
Citation: Nour MO, Hafiz TA, Alharbi KK (2023) Measuring the relationship between body mass index and depression among Saudi adult population: A nationwide cross-sectional study. PLoS ONE 18(11): e0293799. https://doi.org/10.1371/journal.pone.0293799
Editor: Amin Nakhostin-Ansari, Tehran University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN
Received: January 4, 2023; Accepted: October 19, 2023; Published: November 16, 2023
Copyright: © 2023 Nour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This research was funded by the Deanship of Scientific Research at Umm Al-Qura University, Kingdom of Saudi Arabia by Grant Code: (22UQU4331203DSR01).
Competing interests: The authors have declared that no competing interests exist.
Introduction
The prevalence of both obesity and depression shows an accelerating trend across all ethnic groups and among adults of both sexes [1]. Overweight and obesity, according to the World Health Organization (WHO), are defined as “abnormal or excessive fat accumulation that may impair health.” Body mass index (BMI) is a simple weight-for-height index that is used to classify obesity and overweight in adults. However, it should be used only as a general guide because it may not reflect the same level of obesity in various people [2].
Depression is described as “a mental condition characterized by chronic melancholy, lack of interest in, desire for, and enjoyment of formerly enjoyable activities, as well as possible sleep and eating issues” [3]. Inability to care for oneself, incapacity, and suicide are primarily brought on by depression. Furthermore, both obesity and depression are associated with short- and long-term consequences for population health and economy including increased risk of disability, chronic diseases, mortality rates, and health-care expenses, and a decline in quality of life, life expectancy, and productivity [4, 5].
WHO has estimated that there are about 1.3 billion overweight adults, over 600 million obese persons, and 350 million people from all age groups who suffer from depression [6, 7]. So far, research has revealed an association between obesity and depression, as the prevalence of depression among obese people is more than twice the prevalence of depression in persons of normal weight [8, 9]. Some researchers have argued that obesity leads to depression [10, 11], and others have claimed that depression leads to weight gain and obesity [12]. Taken together, the research suggests a bidirectional relationship, and sociodemographic characteristics may modulate the association between BMI and depression [13].
The precise underlying relationship between obesity and depression is uncertain. Three hypotheses have been proposed: positive association (higher depression is correlated with more obesity) [14, 15], negative association (higher depression is correlated with lower obesity) [16, 17], and an absence of association [18, 19]. In addition, a nonlinear (U-curved) association—meaning that both obesity and underweight are associated with depression—can be expected [20, 21]. To our knowledge, there are no research studies at the national level in Saudi Arabia that measured the relationship between BMI and depression or that investigated whether the trend was linear or nonlinear. Therefore, we aimed to measure the prevalence of obesity and depression at a national level, and to explore their associations in a large sample of Saudi adults.
Materials and methods
Sampling and sample size
The study used a snowball sampling method and calculated the sample size with the Raosoft sample size calculator (http://www.raosoft.com/samplesize.html). The minimum required sample size was 1,037 based on the following factors: a 99% confidence interval, a standard deviation set at 1.96, a 4% margin of error, an expected response of 50%, and a total Saudi-adult population of 13,110,901 (aged 18–60 years, representing about 70% of the total population) (according to issue number 55 of the Statistical Yearbook published by General Authority for Statistics, 2019 (https://www.stats.gov.sa/ar/1007-0) and the updated mid-2020 report (https://www.stats.gov.sa/en/43)]. To avoid bias, all eligible respondents with complete responses during the survey period from January through April 2022 were enrolled.
Population and inclusion criteria
The study included members of the general adult Saudi population, aged 18–60 years, of both sexes, residing in all administrative districts within the Kingdom (Western, Northern, Southern, Central, and Eastern regions), not suffering from chronic mental or psychological illness, and not having BMI below 14 or above 60 (considered outliers). In a trial to achieve a proportional nationwide representative sample, we considered weighing based on different age groups, gender, and population distribution within different regions. This approach aimed to align with data obtained from the General Authority for Statistics (with accepted ±5% variation for each subgroup). In total, 5,896 participants took part in the survey, of which 4,683 (79.4%) matched the criteria for inclusion.
Preparing the study instrument
We used an Arabic version of the validated Beck’s Depression Inventory-II (BDI-II) for depression assessment. The Arabic version has been previously validated for use in Arab populations [22–24]. The BDI-II is self-administered and takes about 5–10 minutes; the recall period is 2 weeks. It includes twenty-one items on a 4-point scale ranging from zero (no symptoms) to 3 (severe symptoms). Scoring is calculated by adding the highest ratings for all twenty-one items. The minimum and maximum scores are zero and 63, respectively. Higher scores reflect greater symptom severity. In non-clinical subjects, scores of zero–13 indicate no depression and 14–19, mild depression. Scores above 20 indicate depression: 20–28 (moderate depression) and 29–63 (severe depression). The validity and reliability of BDI-II across different populations and countries have been examined in many studies, revealing good internal consistency (Cronbach’s alpha 0.91) and high test-retest reliability (Pearson r = 0.93) [25, 26].
We measured BMI by dividing weight in kilograms (kg) by height in meters squared (m2). Weight and height were both self-reported. According to WHO criteria, we divided weight into four categories: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2), and obesity (BMI > 30.0 kg/m2) [27].
The questionnaire was self-reported and included the following:
- ■ Sociodemographic characteristics such as age, gender, geographical district, nationality, social status, occupation, education, and income.
- ■ Smoking habit and presence of chronic illness such as high blood pressure, diabetes, cardiovascular disease, asthma, chronic kidney disease, chronic liver disease, high cholesterol, cancer, and others.
- ■ Presence of chronic mental/psychological illness (they were 22 subjects, accounted for less than 0.5%, and excluded from analysis).
- ■ Weight in kilograms and height in centimeters (to calculate the BMI, and those with BMI below 14 (two subjects) or above 60 (three subjects) were excluded from analysis).
- ■ The twenty-one items of BDI-II.
We distributed the questionnaire via various means of communication on the internet and social media platforms such as Twitter, WhatsApp, and Telegram. Participants were encouraged to share the survey link using a snowball sampling technique. In addition, personal communications contributed to rapid distribution of the survey. The decision to participate was completely voluntary, and respondents were not provided with any form of incentives for participation or sharing links to the survey. By clicking the relevant link, every participant had the opportunity to access the questionnaire and answer the questions.
Ethical considerations
Ethical approval was obtained from the Bioethics Committee at Umm Al-Qura University (HAPO-02-K-012-2022-03-1000). Prior to commencing the online survey, participants were provided with an initial cover page containing an information letter. This letter emphasized the study’s objectives and the basic items involved, ensuring clarity for the participants. Written informed consent was obtained electronically from all participants on the cover page of the questionnaire, indicating their voluntary participation and the option to withdraw from the study at any time without providing a reason. So, each participant would only be able to start the survey after clicking on the “Accept to participate” icon on the cover page. It is important to note that this study strictly adhered to the inclusion of adult participants only (aged 18–60 years), and no minors were involved.
Statistical analysis
We used the software program SPSS (IBM SPSS, V 25.0, Armonk, NY: IBM Corp., USA). For descriptive statistics: we used the mean ± SD and median (interquartile range [IQR]) for quantitative variables and frequency and percentage for qualitative variables. We examined numerical data for normality using the Shapiro–Wilk test. We used the Mann-Whitney or Kruskal Wallis tests for continuous non-normal distribution variables (BMI and depression score). We used the Spearman correlation coefficient to correlate both BMI and depression score. The specific association between BMI and depression was further explored. Both crude and adjusted Odds ratios (ORs) with 95% confidence intervals (95% CIs) were considered using three models; Model 1 (referred to as unadjusted) including the total sample, Model 2 (adjusted for age, gender, and socio–demographics (marital status, educational level, occupation, family income), and Model 3 (adjusted for all covariates to rule out the confounding effects of population heterogeneity; age, gender, socio–demographics (marital status, educational level, occupation, family income), lifestyle factors (smoking habit), and presence of chronic diseases, referred to as “adjusted for all factors”). Factors associated with depression (dependent variable) (score ≥ 14) were tested by logistic regression analysis where OR and 95% CI were calculated for each independent variable. A significant level of p < 0.05 (two-tailed) was considered.
Results
The study included 4,683 adult Saudi participants filling the inclusion criteria with mean age 29.9 ± 11.1 years, ranging from 18–60 years, and the majority (62.7%) were from the 18–30 age group. More than half of participants (56.8%) were males, 40.5% were married, and 42.4%, 22.2%, 20.5%, 7.9% and 7% were living in the Western, Southern, Central, Northern, and Eastern regions of Saudi Arabia, respectively. Only 3.2% had primary or middle school education, while the majority (70%) were university graduates; 6% had postgraduate studies, 42.4% were students, 24.1% were working for the government, and 29.9% had family income less than 5,000 riyals per month. A total of 643 (13.7%) were smokers, and 727 (15.5%) had chronic diseases. The most common chronic conditions, either individually or in combination with others, included asthma, 266 (5.7%), hypertension, 240 (5.1%), and diabetes, 221 (4.7%) (Table 1).
The mean BMI of participants was 25.6 ± 7.4, ranging from 14–60 kg/m2, and the median (IQR) was 24.6 (21.1–29) kg/m2. A total of 505 (10.8%) participants were underweight, 1,957 (41.8%) were normal weight, and 1,237 (26.4%) were overweight, while 984 (21%) suffered from different grades of obesity (Fig 1). The mean depression score of participants according to BDI-II was 11.3 ± 10.3 and the median (IQR) was 9 (3–17). A total of 2,029 (43.3%) were suffering from different grades of depression ranging from 1,179 (25.2%) with mild depression, 576 (12.3%) with moderate depression, and 274 (5.8%) with severe depression (Fig 2). We found a significant positive correlation between BMI and depression score (Spearman’s rho = 0.14, p = 0.006) (Fig 3).
Both BMI and depression score were tested against different study variables (that were dichotomized when appropriate). BMI was significantly higher among participants who were older, male, married, living in the Eastern region, working, and smokers, in addition to those with higher family income, pre-university education, and chronic diseases. Depression score was significantly higher among those who had chronic diseases and were married, non-workers, non-smokers, and those suffering from overweight/obesity with BMI ≥ 25 kg/m2. Depression score did not differ by age, sex, geographical region, educational level, or family income (Table 2).
The association between BMI and depression was explored. First, BMI was entered into the model as a continuous variable and then as a categorical variable (considering subjects with normal weight as reference group) using both unadjusted and adjusted models. The ORs showed significant association between BMI and depression in the model adjusted for all important confounders with each 5 unit increase in BMI was associated with an OR of 1.32 (1.25–1.43, P = 0.003). Further, the specific association between BMI categories and depression showed that the ORs for depression in underweight, overweight, and obesity groups were 1.39 (1.19–1.63), 1.73 (1.49–2.00), and 1.91 (1.56–2.34) respectively. After adjustment of all factors in Model 3, the ORs for depression in underweight, overweight, and obesity groups were 1.49 (1.26–1.76), 1.76 (1.51–2.05), and 1.92 (1.56–3.36) respectively with higher ORs for depression among obese then overweight participants and the trend was significant in all (P < 0.05). (Table 3)
We further dichotomized the depression score into no depression (score zero–13) and depression (score ≥ 14). We analyzed variables in univariate analysis that significantly affect depression score with a multinomial logistic regression to explore independent variables associated with depression. Non-smoking (OR = 1.76; CI: 1.46–2.11), presence of chronic diseases (OR = 1.6; CI: 1.34–1.88), and BMI ≥ 25 kg/m2 (overweight/obesity) (OR = 1.41; CI: 1.24–1.6) were significantly associated with depression among Saudi adults (Table 4).
Discussion
The study aimed to determine the prevalence of BMI categories and depression, and to explore their associations in a large sample of Saudi adults. We found that 43.3% of Saudi adults were suffering from different grades of depression ranging from mild (25.2%), moderate (12.3%), and severe (5.8%). These results are comparable to those reported by Al-Qadhi et al., in which 49.9% of Saudi adults who routinely visited primary care centers exhibited depressive symptoms. of which 31%, 13.4%, 4.4%, and 1% were mild, moderate, moderate-severe, and severe, respectively [28]. However, the prevalence of depression in our study was higher than that reported among the general population in the Jazan region in Saudi Arabia during the COVID-19 pandemic, with a rate of nearly 26% [29] and higher than that reported by Alamri et al., in which 28.9% of the general Saudi population reported experiencing any depressive symptoms, of which 11.8% were mild, 10.1% were moderate, and 7% were severe [30].
Our findings related to the prevalence of depression were higher than those reported by Alzahrani et al., who investigated the prevalence of common mental disorders, including depression related to the COVID-19 pandemic, among the Saudi general population by a systematic review and meta-analysis. They found that the overall prevalence of depression was 30% (95% CI: 22% to 38%, I2 = 99.58%) [31]. The variation in prevalence could be attributed to different population characteristics, the variable consequences of the COVID-19 pandemic, and the use of different depression screening scales in the studies.
Slightly less than half of our participants (47.4%) suffered from being overweight and obese (26.4% and 21%, respectively), while about 10.8% were underweight. The overall national-level prevalence of overweight/obesity among Saudi adults in the last two decades showed a decreasing trend, with 36.9% overweight and 35.6% obesity between 1995 and 2000 [32], 28.7% obesity in 2013 [33], and 24.7% obesity in 2020 [34]. However, the international figure reported by the 2019 World Health Survey found the overall prevalence of overweight/obesity among Saudi adults to be 58.4% [35]. The patterns of Saudi food consumption and physical activity are among factors contributing to prevalence variations [36].
Both overweight/obesity and depression can be costly to the individual and the health-care system. Thus, there is a need to understand the causal relationships between these two disorders to enhance implementation of preventive measures. To the extent that some individuals have depression and obesity, obesity prevention efforts could help reduce the prevalence of depression, and programs targeting depression could have the added benefit of reducing the prevalence of obesity [37].
Our findings add to the growing literature suggesting that obesity/overweight is associated with depression among adults, and they revealed a significant positive association between depression and BMI (more severe depression is associated with increased BMI), which is consistent with other studies [14, 15, 38, 39] but inconsistent with other studies that reported negative association [16, 17], no association [18, 19], or a nonlinear (U-curved) association [20, 21].
Although most of the studies reported a relationship between both depression and obesity, understanding the meaning of this relationship is complex and unclear, and a significant relationship cannot necessarily reflect causality. Several factors can affect this relationship, such as sex, ethnicity, genetics, biology, culture, unhealthy lifestyle, poor self-esteem, the stigma attached to obesity, emotional eating, the presence of associated chronic conditions, and the use of psychiatric medication [13, 38–43]. The occurrence of obesity as a consequence of chronic illnesses is another concern that may affect the relationship. Improving patients’ quality of life, lifestyle, emotional management, and psychological support can be useful in both obesity and depression intervention.
Almarhoon et al. recently investigated the relation between depression and obesity among adults in the Eastern region of Saudi Arabia. They found that the association was most prominent in young adults (18 to 25 years), in being single, in the presence of associated chronic or psychiatric conditions, in having a BMI > 30 for 10 years or more, and in taking regular medications [44].
According to our findings, not smoking, having a chronic disease, and being overweight or obese were significantly associated with depression among Saudi adults. Other studies have shown that the association between depression and BMI is gender dependent [39], and others have confirmed the role of socioeconomic status [45].
In summary, there is a consensus that obesity/overweight is associated with increased depression. However, further longitudinal research is required to better understand the factors that underlie the causal relationships between them, the subgroups among whom they exist, and mediating strategies.
To our knowledge, this is the first nationwide study in Saudi Arabia that has measured the relationship between depression and BMI. The findings of our study underscore the importance of addressing obesity as a significant public health concern. The prevalence of obesity and its associated negative consequences, including increased risk for depression, highlight the need for comprehensive and more targeted health education programs on obesity. When developing such programs, it is essential to consider the cultural diversity within the local community and the influence of negative behaviors and false beliefs that affect the ability of society to maintain physical and psychological well-being. These factors have led to an increase in the prevalence of depression and a delay in taking the necessary measures and timely medical interventions for cases of weight disorders, which may pose a threat to individuals and society.
Study limitations
Our findings should be viewed with caution as the study has several limitations. (1) Causal inferences and the reciprocal effects are difficult to assess in cross-sectional studies. (2) The snowball non-random method carries a possible sampling bias. (3) There is a potential selection bias when using a web-based survey (being accessible to web users only with possible overrepresentation of health-oriented and more interested individuals). (4) There may be recall bias of self-reported data and social desirability bias especially for weight and height measurements, presence of chronic illness, and being suffering from chronic mental/psychological illness. The study employed self-reporting as a practical and efficient method to identify and exclude individuals with chronic mental or psychological illness from the analysis. It is important to note that the study did not specifically investigate the relationship between depression and individual chronic diseases but instead focused on the overall relationship between depression and chronic diseases as a collective category. These limitations should be taken into consideration when interpreting the findings of the study. (5) Non-Saudi residents within the Kingdom and the older age group were not included. In addition, a related concern here is the overrepresentation of young adults and those with a university degree that might affect the generalizability of findings. (6) BDI-II is valid for measuring depressive symptoms but cannot be used as a diagnostic instrument for clinical depression. (7) Categorization of continuous variables, namely BMI and depression, is a concern that affects our findings with possible bias, loss of power, missing non-linear relationship, and incomplete correction for confounding factors. Also, possible over-adjustment in multi-variable adjusted models may be a concern (8) Depression episodes may occur during anyone’s life span, so lifetime prevalence is needed to study the association. (9) Residual confounding caused by non-measured covariates might have occurred, such as the concurrent impacts of COVID-19 and other psychological, behavior, dietary, and lifestyle factors. (10) Variability of geographical, cultural, and ethnic backgrounds of other populations may affect the findings. Furthermore, cross-cultural differences in the symptomatology and experience of depression should not be neglected.
Conclusions
A remarkable percentage of Saudi adults were suffering from different grades of depression (43.3%), overweight (26.4%), and obesity (21%). A positive association was found between BMI and depression score. Depression score did not differ by age, sex, geographical region, educational level, or family income. Non-smoking, having chronic diseases, and being overweight/obese were significantly associated with depression. Further longitudinal research is required to better understand the factors underpinning the causal relationships between BMI and depression, the subgroups among whom they exist, and mediating strategies.
Acknowledgments
The authors would like to thank all participants for their help completing the survey.
References
- 1. Gu JK, Charles LE, Bang KM, et al. Prevalence of obesity by occupation among US workers: the National Health Interview Survey 2004–2011. J Occup Environ Med. 2014; 56(5):516–28. pmid:24682108
- 2. World Health Organization. Obesity and overweight. Available at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (Accessed November 4, 2022).
- 3. World Health Organization. Depression. Available at: https://www.who.int/health-topics/depression#tab=tab_1 (Accessed November 4, 2022).
- 4. Engel L, Chen G, Richardson J, Mihalopoulos C. The impact of depression on health-related quality of life and wellbeing: identifying important dimensions and assessing their inclusion in multi-attribute utility instruments. Qual Life Res. 2018; 27(11):2873–84. pmid:30006664
- 5.
Alluhidan M, Alsukait RF, Alghaith T, et al. Overweight and Obesity in Saudi Arabia: Consequences and Solutions. International Development in Focus, Washington, DC: World Bank. 2022. Available at: https://openknowledge.worldbank.org/bitstream/handle/10986/37723/9781464818288.pdf?sequence=1&isAllowed=y
- 6. Özkaya İ, Tunçkale A. Neck Circumference Positively Related with Central Obesity and Overweight in Turkish University Students: A Preliminary Study. Cent Eur J Public Health. 2016; 24(2):91–4. pmid:27119655
- 7. Marcus M, Yasamy MT, Ommeren MV, et al. Depression: a global public health concern. 2012. https://doi.org/10.1037/e517532013-004 Available at: https://psycnet.apa.org/record/517532013-004?doi=1
- 8. Kotsis V, Jordan J, Micic D, et al. Obesity and cardiovascular risk: a call for action from the European Society of Hypertension Working Group of Obesity, Diabetes and the High-risk Patient and European Association for the Study of Obesity: part A: mechanisms of obesity induced hypertension, diabetes and dyslipidemia and practice guidelines for treatment. J Hypertens. 2018; 36(7):1427–1440. pmid:29634663
- 9. Pereira-Miranda E, Costa PRF, Queiroz VAO, et al. Overweight and Obesity Associated with Higher Depression Prevalence in Adults: A Systematic Review and Meta-Analysis. J Am Coll Nutr. 2017; 36(3):223–33. pmid:28394727
- 10. Nigatu YT, Bültmann U, Reijneveld SA. The prospective association between obesity and major depression in the general population: does single or recurrent episode matter? BMC Public Health 2015; 15:350. pmid:25880736
- 11. Ul-Haq Z, Smith DJ, Nicholl BI, et al. Gender differences in the association between adiposity and probable major depression: a cross-sectional study of 140,564 UK Biobank participants. BMC Psychiatry. 2014; 14:153. pmid:24884621
- 12. Kiecolt-Glaser JK, Habash DL, Fagundes CP, et al. Daily stressors, past depression, and metabolic responses to high-fat meals: a novel path to obesity. Biol Psychiatry. 2015; 77(7):653–60. pmid:25034950
- 13. Luppino FS, de Wit LM, Bouvy PF, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010; 67(3):220–9. pmid:20194822
- 14. de Wit L, Luppino F, van Straten A, et al. Depression and obesity: a meta-analysis of community-based studies. Psychiatry Res. 2010; 178(2):230–5. pmid:20462641
- 15. Baumeister H, Härter M. Mental disorders in patients with obesity in comparison with healthy probands. Int J Obes (Lond). 2007; 31(7):1155–64. pmid:17264844
- 16. Zhou Q, Wang T, Basu K. Negative association between BMI and depressive symptoms in middle aged and elderly Chinese: Results from a national household survey. Psychiatry Res. 2018; 269:571–8. pmid:30199699
- 17. Yim G, Ahn Y, Cho J, et al. The “jolly fat” effect in middle-aged Korean women. J Womens Health (Larchmt). 2017; 26(11):1236–43. pmid:28922089
- 18. Askari J, Hassanbeigi A, Khosravi HM, et al. The Relationship Between Obesity and Depression. Procedia Soc Behav Sci. 2013; 84: 796–800.
- 19. Rivenes A, Harvey S, Mykletun A. The relationship between abdominal fat, obesity, and common mental disorders: results from the HUNT Study. J Psychosom Res. 2009; 66(4):269–75. pmid:19302883
- 20. Noh JW, Kwon YD, Park J, Kim J. Body mass index and depressive symptoms in middle aged and older adults. BMC Public Health 2015; 15:310. pmid:25884564
- 21. de Wit LM, van Straten A, van Herten M, et al. Depression and body mass index, a u-shaped association. BMC Public Health. 2009; 9:14. pmid:19144098
- 22. West J. An Arabic validation of a depression inventory. Int J Soc Psychiatry. 1985; 31(4):282–9. pmid:4077428
- 23. Abdel-Khalek AM. Internal consistency of an Arabic Adaptation of the Beck Depression Inventory in four Arab countries. Psychol Rep. 1998; 82(1):264–6. pmid:9520563
- 24. Al-Musawi NM. Psychometric properties of the beck depression inventory-II with university students in Bahrain. J Pers Assess. 2001; 77(3):568–79. pmid:11781039
- 25. Wang Y, Gorenstein C. Psychometric properties of the Beck depression inventory-II: a comprehensive review. Revista Brasileira de Psiquiatria. 2013; 35(4): 416–31. pmid:24402217
- 26. García-Batista ZE, Guerra-Peña K, Cano-Vindel A, et al. Validity and reliability of the Beck Depression Inventory (BDI-II) in general and hospital population of Dominican Republic. PLoS One. 2018; 13(6): e0199750. pmid:29958268
- 27. World Health Organization. A healthy lifestyle—WHO recommendations. Available at: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle—who-recommendations (Accessed November 4, 2022).
- 28. Al-Qadhi W, Ur Rahman S, Ferwana MS, Abdulmajeed IA. Adult depression screening in Saudi primary care: prevalence, instrument and cost. BMC Psychiatry. 2014; 14:190. pmid:24992932
- 29. Alharbi A. The Prevalence of Depression and Related Factors During the COVID-19 Pandemic Among the General Population of the Jazan Region of Saudi Arabia. Cureus. 2022; 14(2): e21965. pmid:35155046
- 30. Alamri HS, Algarni A, Shehata SF, et al. Prevalence of Depression, Anxiety, and Stress among the General Population in Saudi Arabia during Covid-19 Pandemic. Int J Environ Res Public Health. 2020; 17(24):9183. pmid:33316900
- 31. Alzahrani F, Alshahrani NZ, Abu Sabah A, et al. Prevalence and factors associated with mental health problems in Saudi general population during the coronavirus disease 2019 pandemic: A systematic review and meta-analysis. Psych J. 2022; 11(1):18–29. pmid:34986503
- 32. Al-Nozha MM, Al-Mazrou YY, Al-Maatouq MA, et al. Obesity in Saudi Arabia. Saudi Med J. 2005; 26(5):824–9. pmid:15951877
- 33. Memish ZA, El Bcheraoui C, Tuffaha M, et al. Obesity and associated factors—Kingdom of Saudi Arabia, 2013. Prev Chronic Dis. 2014; 11: E174. pmid:25299980
- 34. Althumiri NA, Basyouni MH, AlMousa N, et al. Obesity in Saudi Arabia in 2020: Prevalence, Distribution, and Its Current Association with Various Health Conditions. Healthcare (Basel). 2021; 9(3):311. pmid:33799725
- 35.
Ministry of Health. World Health Survey Saudi Arabia (KSAWHS) 2019 Final Report. 2020. Ministry of Health Riyadh, Kingdom of Saudi Arabia. Available at: https://www.moh.gov.sa/en/Ministry/Statistics/Population-Health-Indicators/Documents/World-Health-Survey-Saudi-Arabia.pdf
- 36. Althumiri NA, Alammari NS, Almubark RA, et al. The National Survey of Health, Diet, Physical Activity and Supplements among Adults in Saudi Arabia. Food Drug Regul Sci J. 2018; 1:1.
- 37. Faith MS, Butryn M, Wadden TA, et al. Evidence for prospective associations among depression and obesity in population-based studies. Obes Rev. 2011; 12(5): e438–53. pmid:21414128
- 38. Blasco BV, García-Jiménez J, Bodoano I, Gutiérrez-Rojas L. Obesity and Depression: Its Prevalence and Influence as a Prognostic Factor: A Systematic Review. Psychiatry Investig. 2020; 17(8):715–24. pmid:32777922
- 39. Dragan A, Akhtar-Danesh N. Relation between body mass index and depression: a structural equation modeling approach. BMC Med Res Methodol. 2007; 7:17. pmid:17470289
- 40. Heidari-Beni M, Azizi-Soleiman F, Afshar H, et al. Relationship between obesity and depression, anxiety and psychological distress among Iranian health-care staff. East Mediterr Health J. 2021; 27(4):327–35. pmid:33955528
- 41. Konttinen H, van Strien T, Männistö S, et al. Depression, emotional eating and long-term weight changes: a population-based prospective study. Int J Behav Nutr Phys Act. 2019; 16(1):28. pmid:30894189
- 42. Amiri H, Borjali A, Sohrabi F, Pazouki A. The comparison of depression and quality of life in the obese women with different body mass indices (BMI) with the normal women residing in Tehran. RJMS 2015; 21 (127): 102–12. Available at: http://rjms.iums.ac.ir/article-1-3485-en.html
- 43. Simon G, Ludman E, Linde J, et al. Association between obesity and depression in middle-aged women. Gen Hosp Psychiatry. 2008; 30(1):32–9. pmid:18164938
- 44. Almarhoon FH, Almubarak KA, Alramdhan ZA, et al. The Association Between Depression and Obesity Among Adults in the Eastern Province, Saudi Arabia. Cureus. 2021; 13(10): e18794. pmid:34804660
- 45. Morrison KM, Shin S, Tarnopolsky M, Taylor VH. Association of depression & health related quality of life with body composition in children and youth with obesity. J Affect Disord. 2015; 172:18–23. pmid:25451390