Excessive body weight and its contributing factors in visually impaired patients in northwest Ethiopia, 2024

Abstract

Background:

Excessive body weight are global health challenge affecting individuals across all age groups. They result from an imbalance between calorie intake and expenditure. Individuals with visual impairment are particularly vulnerable due to reduced physical activity, leading to higher rates of excessive body weight. This study assessed the prevalence and determinants of excessive body weight among visually impaired patients in northwest Ethiopia in 2024.

Method:

A multi-centered institution-based cross-sectional study was done with 384 visually impaired patients selected using a simple random sample technique. An interviewer-administered structured questionnaire and physical assessments were used to gather data. The crude and adjusted odds ratios, as well as the 95% confidence intervals, were calculated. Bivariate and multivariate logistic regression analyses were performed. Variables having a p-value < 0.05 were considered substantially associated with excessive body weight.

Results:

The prevalence of excessive body weight was 27.9% (95% CI: 23.2–32.8). Factors significantly associated with excessive body weight included being aged 65 or older (AOR = 5.43, 95% CI: 1.22–24.1), urban residency (AOR = 4.84, 95% CI: 2.95–7.95), and having visual impairment for five or more years (AOR = 3.33, 95% CI: 1.88–5.89).

Conclusion:

Excessive body weight affects nearly one-third of visually impaired patients in northwest Ethiopia, with significant associations found for older age, urban residence, and long-term visual impairment. Integrating weight management programs, physical activity promotion, nutritional education, and community support is essential to improve health outcomes. Future studies using prospective cohort designs are recommended to explore causal relationships.

Introduction

Visual impairments often lead to reduced physical activity and difficulty managing food (1), contributing to unhealthy eating habits (2). Additionally, psychological issues such as depression, anxiety, and chronic stress can trigger hormonal changes and emotional eating, increasing their risk of excessive body weight (3)

Excessive body weight, defined by a BMI ≥ 25 kg/m² (4), encompasses both overweight and obesity and is characterized by an abnormal accumulation of body fat, significantly increasing health risks for individuals of all age groups (5–8). Excessive body weight arises mainly from an energy imbalance between calorie intake and expenditure (9). Contributing factors include reduced physical activity (10), insufficient sleep (11), abnormal physiological conditions (hypothyroidism, Cushing’s syndrome, growth hormone deficiency, eating disorders) (12–14), medications (like insulin, steroids, sulfonylureas, antidepressants, and hormonal contraceptives) (15), Genetic predisposition (14), and late-age pregnancy (16, 17).

These conditions are major risk factors for a range of non-communicable diseases, including cardiovascular diseases (heart disease and stroke), type 2 diabetes, musculoskeletal disorders like osteoarthritis, and certain cancers (e.g., endometrial, breast, and colon cancers) (18–20).

Globally, the prevalence of excessive body weight is rising. In 2022, the WHO reported that 2.5 billion adults were overweight, and 890 million were obese, with this trend evident in both developing and developed countries (16, 19). In Africa, overweight and obesity prevalence among adults ranges from 13.6% to 31%, while among children and adolescents, it ranges from 5% to 16.5% (16, 21). In Ethiopia, the prevalence of overweight and obesity among adults was reported at 20.4% and 5.4%, respectively (22).

Among adults with visual impairments, the prevalence of excessive body weight varies significantly by country, 22.2% in Korea (23), 36% in the United States (24), and 38% in India (25). However, no studies have investigated the prevalence of excessive body weight among visually impaired adults in Ethiopia.

Excessive body weight significantly affects individuals with visual impairments and their families (26). These conditions can reduce the quality of life (QOL) (27), and contribute to physical inactivity, resulting in low socioeconomic status (28). They also increase the need for self-care assistance, adversely affecting mental health, social functioning, employment, and educational opportunities (27, 29).

Evidence indicates that excessive body weight is associated with various factors including family history (30), age (31), sex (32), income (33), educational attainment (34), dietary type (35), length of sleep in a day (36), cigarette smoking (37), and alcohol consumption (38), khat chewing (39).

Despite these associations, no studies have explored the prevalence and risk factors of excessive body weight among visually impaired adults in Northwest Ethiopia. This study aims to fill that gap by investigating the prevalence and determinants of excessive body weight among visually impaired individuals attending follow-up visits in hospitals in Northwest Ethiopia. The findings emphasize the need to address excessive body weight within this population and implement targeted preventative strategies.

Methods and materials

Study setting and period

This study was conducted in hospitals located in Northwest Ethiopia from February 1 to March 30, 2024. These hospitals provide comprehensive care and support services to a population exceeding 25 million, including approximately 5,000 visually impaired patients who receive follow-ups and comprehensive healthcare services (unpublished data).

Study design and population

An institutional-based cross-sectional study was conducted among visually impaired patients attending hospitals in Northwest Ethiopia. The source population comprised all visually impaired patients on follow-up in the selected hospitals, while the study population included visually impaired patients on follow-up during the study period at randomly selected hospitals.

Inclusion and exclusion criteria

Inclusion criteria

• Adults aged 18 years and above with documented visual impairment.

• Residents of Northwest Ethiopia for at least six months.

• Individuals who provided informed consent.

Exclusion criteria

• Patients with severe illnesses, pregnancy, or mobility disabilities.

• Individuals with chronic conditions such as hypothyroidism, Cushing’s syndrome, or other infections that could interfere with body weight.

• Patients with severe cognitive impairments, acute psychiatric conditions, or those on weight-altering psychiatric medications.

• Individuals with hearing impairments or those taking medications affecting body weight

Sample size determination

The sample size was determined using a single population proportion formula.

Assumption:

n = sample size, P = proportion of excessive body weight = 50% since the study on excessive body weight among visually impaired patients was not conducted in the study area, d = Margin of sampling error tolerated- 5% (0.05), α = Critical value at 95% confidence interval of certainty (1.96).

Sampling procedure

A combination of simple and systematic random sampling techniques was used to select participants. Three hospitals (Finote Selam General Hospital, Debre Markos Comprehensive and Specialized Hospital, and Mertulemariam Primary Hospital) were randomly selected using the lottery method. From the ophthalmic clinic records, a sampling frame of visually impaired patients was established. Systematic random sampling was applied, with every 2^nd patient selected during data collection (sampling interval K=7, calculated by dividing the total number of visually impaired patients during the study period [2,500] by the sample size [384]). The starting point (patient number 2) was determined through the lottery method. A proportional number of participants were selected from each hospital to ensure representativeness.

Study variables

Dependent variable

• Excessive body weight (Yes/No).

Independent variables

• Sociodemographic factors: Age, gender, residence, income, employment, education, marital status.

• Health-related factors: Blood pressure, family history of diabetes, diabetes mellitus

• Behavioral factors: Khat chewing, smoking, alcohol consumption, sleep duration, dietary habits, level of visual impairment

Operational definitions

Excessive body weight: BMI ≥ 25 kg/m² (4)

Diabetes Mellitus: a random plasma glucose level of ≥200 mg/dL (40).

Visual impairment: Having presenting visual acuity less than 6/12 in the better eye (41).

Mild visual impairment: Having visual acuity worse than 6/12 but better than 6/18 in the better eye (41).

Moderate visual impairment: Presenting distance visual acuity (VA) worse than 6/18 but better than 6/60 in the better eye (42).

Severe visual impairment: Presenting distance VA worse than 6/60 but better than 3/60 in the better eye (42).

Blindness: Presenting VA worse than 3/60 (43).

Current smoker: An adult who has smoked 100 cigarettes in his or her lifetime and who currently smokes cigarettes (44).

Ever alcohol user: Use of alcohol, at least once in an individual’s lifetime (45).

Current alcohol user: A person who consumed alcohol at least once within the last 30 days (45).

Duration of sleep: a short length of sleep of ≤5 hours and long sleep of ≥9 hours (46).

Data collection procedure and tools

Data collection occurred from February 1 to March 30, 2024, using a structured, interviewer-administered questionnaire developed from various validated sources (47, 48). The questionnaire collected data on medical assessments, substance use, and sociodemographic characteristics, while blood pressure and body mass index (BMI) measurements were taken for patients with visual impairment. Two BSc clinical nurses were recruited at each hospital to collect and document data.

Examinations and measurements

Anthropometric and blood pressure measurements

Weight and Height: Participants’ weight was measured using a standard balance scale, and height was recorded with a stadiometer. BMI was calculated by dividing weight (kg) by height squared (m²). Blood Pressure: Blood pressure was measured using a calibrated sphygmomanometer, ensuring standard protocol adherence.

Data analysis procedure

The collected data were checked for completeness and entered Epi data version 4.6. Then it was exported into SPSS version 26 for analysis. The crude and adjusted odds ratios were used to measure the association between study variables, and 95% confidence intervals were calculated. Descriptive measures such as median, interquartile range, and frequencies were calculated. Multicollinearity among selected independent variables was checked, and the variance inflation factor was found to be acceptable (less than 2) and model fitness was checked by the Hosmer and Lemeshow test at p-value >0.05 (p=0.965). Both bivariable and multivariable binary logistic regression models were used to identify associated factors of excessive body weight. Those variables having a p-value of <0.25 in the bivariable binary logistic regression analysis were selected for multivariable binary logistic regression. Those variables with a p-value of ≤ 0.05 in multivariable binary logistic regression were declared as having a statistically significant association with excessive body weight. Results were organized and presented by using frequency tables, graphs, and charts.

Data quality management

To ensure the data quality, high prominence was given in designing the data collection instrument. The questionnaire was pre-tested in a setup having similar socio-cultural characteristics with the 20 study participants at Bichena primary hospitals before the actual study begins. It helps to check its wording and sort out language barriers and contextual variations on the structured questionnaire. Training for both data collectors and supervisors regarding the study objectives, ethical considerations, data collection tools, interview techniques, measurement techniques, and strategies to ensure data quality and consistency was given for one day. Throughout the data collection, data collectors were supervised.

Results

Socio-demographic, clinical, substance use, and behavioral characteristics of study participants

The study involved 384 visually impaired patients, achieving a 100% response rate. Participants ranged in age from 18 to 90 years, with a median age of 59 years (± 17) and an interquartile range (IQR) of 49–69 years. Among the participants, 225 (58.6%) were male, 243 (63.3%) were married, 203 (52.9%) resided in rural areas, and 289 (75.3%) were illiterate (Table 1).

Of the participants, 244 (63.5%) had experienced visual impairment for less than five years. About half of the respondents (51.8%) reported sleeping between 5 and 8 hours per night. The majority, 232 (60.4%), were non-vegetarians. Additionally, 331 participants (86.2%) had consumed alcohol at some point in their lives, but only 47 (12.2%) reported drinking alcohol within the past 30 days, primarily traditional beverages such as tela or tej. Among the respondents, 63% had elevated systolic blood pressure, while 48.2% showed elevated diastolic blood pressure. Of the visually impaired respondents, 17.7% had diabetes mellitus (Table 2).

Prevalence of excessive body weight

The overall prevalence of excessive body weight among visually impaired patients was 27.9% (95% CI: 23.2- 32.8) (Figure 1). Among the respondents, 13.28% with mild visual impairment, 8.6% with moderate visual impairment, and 5.99% with severe visual impairment were found to have excessive body weight (Table 3).

Figure 1

Determinant factors of diabetes mellitus among hypertensive patients

In the bivariate analysis, factors such as age, marital status, family size, residence, education level, occupation, duration of visual impairment, dietary habits, current alcohol consumption, and isolated diastolic blood pressure were associated with excessive body weight. However, the multivariate analysis, which adjusts for potential confounders, revealed that age (65 years or older), residence, and the duration of visual impairment were significantly associated with excessive body weight.

Respondents aged 65 years or older were significantly more likely to have excessive body weight, with 5.43 times higher odds compared to those under 40 years (AOR = 5.43, 95% CI: 1.22–12.1). Similarly, individuals residing in urban areas had 4.84 times higher odds of excessive body weight compared to those living in rural areas (AOR = 4.84, 95% CI: 2.95–7.95). Furthermore, individuals with visual impairment lasting five or more years were 3.33 times more likely to have excessive body weight compared to those with a shorter duration of impairment (AOR = 3.33, 95% CI: 1.88–5.89) (Table 4).

Discussion

This study aimed to assess the prevalence of excessive body weight and its associated factors among visually impaired patients in hospitals in northwest Ethiopia in 2024. The prevalence of excessive body weight among these patients was found to be 27.9%, which is higher than the 22.2% reported in a Korean population-based study (23). This discrepancy might be due to this study having a smaller sample size compared to the Korean study, which included 8,435 participants. Additionally, the higher illiteracy rate among participants in this study likely contributed to lower levels of physical activity, potentially leading to an overestimation of excessive body weight prevalence. Furthermore, differences in study design may have influenced the results. While the Korean study employed a population-based prospective cohort design, this research used an institution-based cross-sectional approach. The hospital-based nature of this study might also have inflated the prevalence estimate compared to the broader, community-based Korean survey.

In contrast, the prevalence of excessive body weight in this study was lower than findings from studies conducted in the United States (36%) (24) and India (38%) (25). This discrepancy may stem from variations in participant age ranges. While both the U.S. and Indian studies focused on individuals aged 60 years and older, this study included participants aged 18 and above. Aging is often linked to decreased physical activity, resulting in reduced total energy expenditure and disturbances in energy balance, both of which can contribute to weight gain (49). Lifestyle differences between study populations may explain the observed variation. In the U.S. study, most participants were current alcohol drinkers and smokers, while in this study; the majority did not consume alcohol or smoke. Alcohol consumption, particularly binge drinking, significantly contributes to excessive body weight due to its high caloric content (7 calories per gram) (38). It reduces fat oxidation, promotes fat storage (especially abdominal), disrupts appetite-regulating hormones (leptin and ghrelin), impairs sleep, and lowers physical activity due to fatigue and hangovers (50, 51, 52–54). Smoking contributes to weight gain through metabolic dysregulation, including insulin resistance and altered fat metabolism, especially abdominal fat accumulation (55, 56). It disrupts appetite regulation via dopamine changes, increases cravings for high-calorie foods, and can lead to weight gain during cessation due to increased hunger and slower metabolism (57–61). Smoking is further linked to leptin resistance and chronic inflammation, exacerbating fat accumulation (62).

Our results revealed that 13.28%, 8.6%, and 5.99% of those with mild, moderate, and severe visual impairment respectively had excessive body weight. This discrepancy might be due to persons with severe visual impairment experiencing mobility difficulties that limit access to high-calorie diets, while reduced physical activity in mild cases may encourage weight gain (63). Additionally, individuals with advanced visual loss may adopt healthier diets due to caregiver support, while socioeconomic constraints might further reduce caloric intake (64).

Older adults (aged 65 and above) are significantly more likely to have excessive body weight, as supported by studies conducted in Ethiopia (65), Tanzania (66), Malaysia (67), and Norway (68). This association may be attributed to the close link between aging, vision impairment, and excessive body weight, which creates a cycle that negatively impacts health and quality of life in older individuals (69). Aging contributes to weight gain through a combination of metabolic, hormonal, and lifestyle changes (70). Sarcopenia, the age-related loss of muscle mass, lowers the basal metabolic rate, reducing calorie expenditure and increasing fat storage (71). Hormonal changes, such as reduced levels of growth hormone, testosterone, and estrogen, further promote fat accumulation and increase insulin resistance, exacerbating weight gain (72). Additionally, physical activity often declines with age due to mobility challenges, leading to fewer calories burned (73). Meanwhile, many older adults maintain their existing eating habits, often preferring high-calorie, low-nutrient foods, which further elevates the risk of weight gain (74).

Living in urban areas was significantly linked to excessive body weight, as observed in studies from Switzerland (75), Algeria (76), Bangladesh (77), and Ethiopia (78). Living in cities may contribute to excessive body weight due to several reasons (75). Urban dwellers often lead sedentary lifestyles, with jobs that require long periods of sitting and the use of cars or public transportation, which limits opportunities for physical activity (79). additionally, urban areas tend to have fewer safe spaces for exercise (80),, and the easy availability of unhealthy, high-calorie foods from fast food outlets and convenience stores encourages poor dietary habits (81). Collectively, these factors increase the likelihood of weight gain and make weight loss more challenging, contributing to higher rates of excessive body weight among urban populations.

A duration of visual impairment lasting five years or more is associated with excessive body weight. This could be attributed to the long-term effects of visual impairment, particularly when it persists for over five years, leading to an increase in body weight. Reduced physical activity often results from mobility issues, safety concerns, and reliance on others for assistance, which limit opportunities for exercise (82). This, in turn, fosters a more sedentary lifestyle, with individuals spending more time indoors and engaging in less physical movement. Emotional eating may also arise from stress, anxiety, or sadness linked to visual impairment, and difficulties in assessing food quantities or preparing meals can result in poor nutritional choices, typically favoring convenient or calorie-dense foods (83). These changes in physical activity and diet, combined with reduced overall energy expenditure, contribute to the risk of weight gain. Furthermore, limited access to healthcare resources and exercise programs tailored for individuals with visual impairments can make it challenging to effectively manage weight (84).

In this study, many of the questions in the data collection instrument relied on participants’ recall, which may introduce recall bias. Additionally, as a cross-sectional study, it does not allow for the establishment of a cause-and-effect relationship. Given that this study was conducted in a hospital setting, its findings on the prevalence of excessive body weight cannot be generalized to the broader community.

Conclusions and recommendations

This study found a 27.9% prevalence of excessive body weight among visually impaired patients in northwest Ethiopia. Factors such as older age, urban residency, and long-term visual impairment were significantly associated with higher rates of excessive body weight. To manage excessive body weight, promoting physical activity, nutritional education, specialized healthcare, weight monitoring, lifestyle counseling, and emotional support are crucial. Additionally, conducting a prospective cohort study would be valuable in establishing a cause-and-effect relationship. Future studies should also ensure the inclusion of individuals with mobility disabilities. Furthermore, the causes of visual impairment and obesity-related complications in patients with visual impairment such as insulin resistance, T2DM, lipid disorders, and hyperuricemia, should be included in future studies.

References

• 1

  SweetingJMeromDAstutiPASAntounMEdwardsKDingD. Physical activity interventions for visually impaired adults: a systematic review and meta-analysis. BMJ Open. (2020) 10:e034036. doi: 10.1136/bmjopen-2019-034036

  • CrossRef
  • Google Scholar

• 2

  MorleyJEArgilesJMEvansWJBhasinSCellaDDeutzNEet al. Nutritional recommendations for the management of sarcopenia. J Am Med Directors Assoc. (2010) 11:391–6. doi: 10.1016/j.jamda.2010.04.014

  • CrossRef
  • Google Scholar

• 3

  DarkuEDDiyaoluCO. The role of stress, sleep, and mental health in obesity and weight gain. Int Res J Modern Eng Technol Sci. (2024) 6. doi: 10.56726/IRJMETS62817

  • CrossRef
  • Google Scholar

• 4

  ScherüblH. Excess body weight and gastrointestinal cancer risk. Visceral Med. (2021) 37:261–6. doi: 10.1159/000515444

  • CrossRef
  • Google Scholar

• 5

  RimmerJHYamakiKDavisBMWangEVogelLC. Obesity and overweight prevalence among adolescents with disabilities. Prev Chronic Dis. (2011) 8:A41.

  • Google Scholar

• 6

  ChenHSunXPeiLWangT. Body mass index influences age-related cataracts: an updated meta-analysis and systemic review. Arquivos Brasileiros Oftalmologia. (2024) 87. doi: 10.5935/0004-2749.2021-0382

  • CrossRef
  • Google Scholar

• 7

  OkoroUNJohnsonKA. Prevalence rate of obesity and overweight among pupils with blindness in special education schools in Akwa Ibom State Nigeria. J Res Humanit Soc Sci. (2021) 9.

  • Google Scholar

• 8

  WrzesińskaMUrzędowiczBNawaryczTMotylewskiSPawlickiL. The prevalence of abdominal obesity among pupils with visual impairment in Poland. Disability Health J. (2017) 10:559–64. doi: 10.1016/j.dhjo.2017.02.004

  • CrossRef
  • Google Scholar

• 9

  FriedmanNFanningEL. Overweight and obesity: an overview of prevalence, clinical impact, and economic impact. Dis Manage. (2004) 7:S–1-S-6. doi: 10.1089/1093507042317152

  • CrossRef
  • Google Scholar

• 10

  MiyataKYoshikawaTHaranoAUedaTOgataN. Effects of visual impairment on mobility functions in elderly: Results of Fujiwara-kyo Eye Study. PLoS One. (2021) 16:e0244997. doi: 10.1371/journal.pone.0244997

  • CrossRef
  • Google Scholar

• 11

  Al-RashedFSindhuSAl MadhounAAlghaithAAzimRAl-MullaFet al. Short sleep duration and its association with obesity and other metabolic risk factors in Kuwaiti Urban adults. Nat Sci Sleep. (2021) 13:1225–41. doi: 10.2147/NSS.S311415

  • CrossRef
  • Google Scholar

• 12

  SanyalDRaychaudhuriM. Hypothyroidism and obesity: An intriguing link. Indian J Endocrinol Metab. (2016) 20:554–7. doi: 10.4103/2230-8210.183454

  • CrossRef
  • Google Scholar

• 13

  BistaBBeckN. Cushing syndrome. Indian J Pediatrics. (2014) 81:158–64. doi: 10.1007/s12098-013-1203-8

  • CrossRef
  • Google Scholar

• 14

  HuangZHuangLWatersMJChenC. Insulin and growth hormone balance: implications for obesity. Trends Endocrinol Metab. (2020) 31:642–54. doi: 10.1016/j.tem.2020.04.005

  • CrossRef
  • Google Scholar

• 15

  PattenSBWilliamsJVALavoratoDHBrownLMcLarenLEliasziwM. Major depression, antidepressant medication and the risk of obesity. Psychother Psychosomatics. (2009) 78:182–6. doi: 10.1159/000209349

  • CrossRef
  • Google Scholar

• 16

  WHO. Overweight and obesity. (2022).

  • Google Scholar

• 17

  ChatterjeeAGerdesMWMartinezSG. Identification of risk factors associated with obesity and overweight—A machine learning overview. Sensors. (2020) 20:2734. doi: 10.3390/s20092734

  • CrossRef
  • Google Scholar

• 18

  AngelaMBellM. Jacquelyn Cafasso How Does Obesity Affect the Body? Healthline (2022).

  • Google Scholar

• 19

  FruhSM. Obesity: Risk factors, complications, and strategies for sustainable long-term weight management. J Am Assoc Nurse Practitioners. (2017) 29:S3–s14. doi: 10.1002/2327-6924.12510

  • CrossRef
  • Google Scholar

• 20

  DjalaliniaSQorbaniMPeykariNKelishadiR. Health impacts of obesity. Pakistan J Med Sci. (2015) 31:239–42. doi: 10.12669/pjms.311.7033

  • CrossRef
  • Google Scholar

• 21

  AzeezTA. Obesity in Africa: The challenges of a rising epidemic amid dwindling resources. Obes Med. (2022) 31:100397. doi: 10.1016/j.obmed.2022.100397

  • CrossRef
  • Google Scholar

• 22

  KassieAMAbateBBKassawMW. Prevalence of overweight/obesity among the adult population in Ethiopia: a systematic review and meta-analysis. BMJ Open. (2020) 10:e039200. doi: 10.1136/bmjopen-2020-039200

  • CrossRef
  • Google Scholar

• 23

  HwangICBaeJHKimJMLeeJMNguyenQD. Adult body height and age-related macular degeneration in healthy individuals: A nationwide population-based survey from Korea. PLoS One. (2020) 15:e0232593. doi: 10.1371/journal.pone.0232593

  • CrossRef
  • Google Scholar

• 24

  ChenJLiHZhouBLiXZhuYYaoY. Interaction between visual impairment and subjective cognitive complaints on physical activity impairment in U.S. older adults: NHANES 2005–2008. BMC Geriatrics. (2024) 24:167. doi: 10.1186/s12877-024-04739-2

  • CrossRef
  • Google Scholar

• 25

  JaisankarDSwaminathanGRoyRKulothunganVSharmaTRamanR. Association of obesity and age-related macular degeneration in Indian population. Indian J ophthalmology. (2018) 66:976–83. doi: 10.4103/ijo.IJO_1265_17

  • CrossRef
  • Google Scholar

• 26

  GrayLAHernandez AlavaMKellyMPCampbellMJ. Family lifestyle dynamics and childhood obesity: evidence from the millennium cohort study. BMC Public Health. (2018) 18:500. doi: 10.1186/s12889-018-5398-5

  • CrossRef
  • Google Scholar

• 27

  StephensonJSmithCMKearnsBHaywoodABissellP. The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study. BMC Public Health. (2021) 21:1990. doi: 10.1186/s12889-021-12009-8

  • CrossRef
  • Google Scholar

• 28

  SarwerDBGriloCM. Obesity: Psychosocial and behavioural aspects of a modern epidemic: Introduction to the special issue. Am Psychol. (2020) 75:135–8. doi: 10.1037/amp0000610

  • CrossRef
  • Google Scholar

• 29

  FabricatoreANWaddenTA. Psychological aspects of obesity. Clin Dermatol. (2004) 22:332–7. doi: 10.1016/j.clindermatol.2004.01.006

  • CrossRef
  • Google Scholar

• 30

  KjøllesdalMKRCarslakeDSmithGDShaikhFNæssØ. The role of family factors in the association between early adulthood BMI and risk of cardiovascular disease. An intergenerational study of BMI in early adulthood and cardiovascular mortality in parents, aunts and uncles. Int J Obes. (2022) 46:228–34. doi: 10.1038/s41366-021-00987-z

  • CrossRef
  • Google Scholar

• 31

  PalmerAKJensenMD. Metabolic changes in ageing humans: current evidence and therapeutic strategies. J Clin Invest. (2022) 132. doi: 10.1172/JCI158451

  • CrossRef
  • Google Scholar

• 32

  EthunK. Chapter 9 - sex and gender differences in body composition, lipid metabolism, and glucose regulation. In: NeighGNMitzelfeltMM, editors. Sex Differences in Physiology. Academic Press, Boston (2016). p. 145–65.

  • Google Scholar

• 33

  KimTJvon dem KnesebeckO. Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis. BMJ Open. (2018) 8:e019862. doi: 10.1136/bmjopen-2017-019862

  • CrossRef
  • Google Scholar

• 34

  CohenAKRaiMRehkopfDHAbramsB. Educational attainment and obesity: a systematic review. Obes Rev. (2013) 14:989–1005. doi: 10.1111/obr.2013.14.issue-12

  • CrossRef
  • Google Scholar

• 35

  Olariike OyindasolaK. Diet and obesity. In: AkikazuTHubertusH, editors. Psychology and Pathophysiological Outcomes of Eating. IntechOpen, Rijeka (2021). p. Ch. 13.

  • Google Scholar

• 36

  GrandnerMASchopferEASands-LincolnMJacksonNMalhotraA. Relationship between sleep duration and body mass index depends on age. Obes (Silver Spring). (2015) 23:2491–8. doi: 10.1002/oby.v23.12

  • CrossRef
  • Google Scholar

• 37

  DareSMackayDFPellJP. Relationship between smoking and obesity: a cross-sectional study of 499,504 middle-aged adults in the UK general population. PLoS One. (2015) 10:e0123579. doi: 10.1371/journal.pone.0123579

  • CrossRef
  • Google Scholar

• 38

  TraversyGChaputJP. Alcohol consumption and obesity: an update. Curr Obes Rep. (2015) 4:122–30. doi: 10.1007/s13679-014-0129-4

  • CrossRef
  • Google Scholar

• 39

  GirmaTMossieAGetuY. Association between body composition and khat chewing in Ethiopian adults. BMC Res Notes. (2015) 8:680. doi: 10.1186/s13104-015-1601-2

  • CrossRef
  • Google Scholar

• 40

  PippittKLiMGurgleHE. Diabetes mellitus: screening and diagnosis. Am Family physician. (2016) 93:103–9.

  • Google Scholar

• 41

  AshenefBDiressMYeshawYDagnewBGelaYYAkaluYet al. Visual impairment and its associated factors among hypertensive patients in Amhara Region referral hospitals, Ethiopia. Clin Ophthalmology. (2023) 17:3149–61. doi: 10.2147/OPTH.S408171

  • CrossRef
  • Google Scholar

• 42

  AbebeHWagnewFZelekeHTeferaBTesfaSFeteneT. Magnitude of visual impairment and associated factors among patients attending ophthalmic clinics of Debre Markos referral hospital, north West Ethiopia. BMC ophthalmology. (2021) 21:1–10. doi: 10.1186/s12886-021-01863-0

  • CrossRef
  • Google Scholar

• 43

  TaylorHRLivingstonPMStanislavskyYLMcCartyCA. Visual impairment in Australia: distance visual acuity, near vision, and visual field findings of the Melbourne Visual Impairment Project. Am J ophthalmology. (1997) 123:328–37. doi: 10.1016/S0002-9394(14)70128-X

  • CrossRef
  • Google Scholar

• 44

  CDC. NCHS, National Health Interview Survey, Special Topics, Adult Tobacco Use Information (2019). Available online at: https://wwwcdcgov/nchs/nhis/tobacco/tobacco_glossaryhtm (Accessed August 12, 2024).

  • Google Scholar

• 45

  CDC. NCHS NHIS, Special Topics, Adult Alcohol Use (2020). Available online at: https://archive.cdc.gov/www_cdc_gov/nchs/nhis/alcohol/alcohol_glossary.htm (Accessed August 12, 2024).

  • Google Scholar

• 46

  PeltzerKPhaswana-MafuyaN. Association between visual impairment and low vision and sleep duration and quality among older adults in South Africa. Int J Environ Res Public Health. (2017) 14. doi: 10.3390/ijerph14070811

  • CrossRef
  • Google Scholar

• 47

  KushnerRFChoiSWBurnsJL. Development of a six-factor questionnaire for use in weight management counselling. Patient Educ counselling. (2016) 99:2018–25. doi: 10.1016/j.pec.2016.06.022

  • CrossRef
  • Google Scholar

• 48

  BeechyLGalpernJPetroneADasSK. Assessment tools in obesity - psychological measures, diet, activity, and body composition. Physiol behaviour. (2012) 107:154–71. doi: 10.1016/j.physbeh.2012.04.013

  • CrossRef
  • Google Scholar

• 49

  JuraMKozakLP. Obesity and related consequences to ageing. Age (Dordrecht Netherlands). (2016) 38:23. doi: 10.1007/s11357-016-9884-3

  • CrossRef
  • Google Scholar

• 50

  ZakhariS. Overview: how is alcohol metabolized by the body? Alcohol Res health: J Natl Institute Alcohol Abuse Alcoholism. (2006) 29:245–54.

  • Google Scholar

• 51

  SteinerJLLangCH. Alcohol, adipose tissue and lipid dysregulation. Biomolecules. (2017) 7. doi: 10.3390/biom7010016

  • CrossRef
  • Google Scholar

• 52

  CalissendorffJGustafssonTHolstJJBrismarKRöjdmarkS. Alcohol intake and its effect on some appetite-regulating hormones in man: influence of gastroprotection with sucralfate. Endocrine Res. (2012) 37:154–62. doi: 10.3109/07435800.2012.662662

  • CrossRef
  • Google Scholar

• 53

  HeSHaslerBPChakravortyS. Alcohol and sleep-related problems. Curr Opin Psychol. (2019) 30:117–22. doi: 10.1016/j.copsyc.2019.03.007

  • CrossRef
  • Google Scholar

• 54

  LardierDTCoakleyKEHolladayKRAmorimFTZuhlMN. Exercise as a useful intervention to reduce alcohol consumption and improve physical fitness in individuals with alcohol use disorder: A systematic review and meta-analysis. Front Psychol. (2021) 12:675285. doi: 10.3389/fpsyg.2021.675285

  • CrossRef
  • Google Scholar

• 55

  ChioleroAFaehDPaccaudFCornuzJ. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr. (2008) 87:801–9. doi: 10.1093/ajcn/87.4.801

  • CrossRef
  • Google Scholar

• 56

  ArteseAStamfordBAMoffattRJ. Cigarette smoking: an accessory to the development of insulin resistance. Am J lifestyle Med. (2019) 13:602–5. doi: 10.1177/1559827617726516

  • CrossRef
  • Google Scholar

• 57

  AshokAHMizunoYHowesOD. Tobacco smoking and dopaminergic function in humans: a meta-analysis of molecular imaging studies. Psychopharmacology. (2019) 236:1119–29. doi: 10.1007/s00213-019-05196-1

  • CrossRef
  • Google Scholar

• 58

  BaikJH. Dopaminergic control of the feeding circuit. Endocrinol Metab (Seoul Korea). (2021) 36:229–39. doi: 10.3803/EnM.2021.979

  • CrossRef
  • Google Scholar

• 59

  WallaceCWFordahlSC. Obesity and dietary fat influence dopamine neurotransmission: exploring the convergence of metabolic state, physiological stress, and inflammation on dopaminergic control of food intake. Nutr Res Rev. (2022) 35:236–51. doi: 10.1017/S0954422421000196

  • CrossRef
  • Google Scholar

• 60

  VolkowNDWangGJBalerRD. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci. (2011) 15:37–46. doi: 10.1016/j.tics.2010.11.001

  • CrossRef
  • Google Scholar

• 61

  BushTLovejoyJCDepreyMCarpenterKM. The effect of tobacco cessation on weight gain, obesity, and diabetes risk. Obes (Silver Spring Md). (2016) 24:1834–41. doi: 10.1002/oby.21582

  • CrossRef
  • Google Scholar

• 62

  ZhouYRuiL. Leptin signaling and leptin resistance. Front Med. (2013) 7:207–22. doi: 10.1007/s11684-013-0263-5

  • CrossRef
  • Google Scholar

• 63

  JonesNBartlettH. The impact of visual impairment on nutritional status: A systematic review. Br J Visual Impairment. (2018) 36:17–30. doi: 10.1177/0264619617730860

  • CrossRef
  • Google Scholar

• 64

  JonesNBartlettHCookeR. An analysis of the impact of visual impairment on activities of daily living and vision-related quality of life in a visually impaired adult population. Br J Visual Impairment. (2018) 37:026461961881407. doi: 10.1177/0264619618814071

  • CrossRef
  • Google Scholar

• 65

  TegegneKDWagawGBGebeyehuNAYirdawLTShewangashawNEMekonenNAet al. Prevalence of central obesity and associated factors in Ethiopia: A systematic review and meta-analysis. Front endocrinology. (2022) 13:983180. doi: 10.3389/fendo.2022.983180

  • CrossRef
  • Google Scholar

• 66

  MoshaDPauloHAMwanyika-SandoMMboyaIBMadzoreraILeynaGHet al. Risk factors for overweight and obesity among women of reproductive age in Dar es Salaam, Tanzania. BMC Nutr. (2021) 7:37. doi: 10.1186/s40795-021-00445-z

  • CrossRef
  • Google Scholar

• 67

  KyawTMIsmailZSelamatMINawawiH. Obesity and its associated factors among older adults: MyHEBAT (Malaysian Health and Well-Being Assessment) study. Health Sci Rep. (2022) 5:e668. doi: 10.1002/hsr2.v5.4

  • CrossRef
  • Google Scholar

• 68

  ChatterjeeAGerdesMWMartinezSG. Identification of risk factors associated with obesity and overweight-A machine learning overview. Sensors (Basel Switzerland). (2020) 20. doi: 10.3390/s20092734

  • CrossRef
  • Google Scholar

• 69

  SwenorBKLeeMJVaradarajVWhitsonHERamuluPY. Aging with vision loss: A framework for assessing the impact of visual impairment on older adults. Gerontologist. (2020) 60:989–95. doi: 10.1093/geront/gnz117

  • CrossRef
  • Google Scholar

• 70

  PatakyMWYoungWFNairKS. Hormonal and metabolic changes of aging and the influence of lifestyle modifications. Mayo Clinic Proc. (2021) 96:788–814. doi: 10.1016/j.mayocp.2020.07.033

  • CrossRef
  • Google Scholar

• 71

  ZamboniMRubeleSRossiAP. Sarcopenia and obesity. Curr Opin Clin Nutr Metab Care. (2019) 22:13–9. doi: 10.1097/MCO.0000000000000519

  • CrossRef
  • Google Scholar

• 72

  DingSMaduCOLuY. The impact of hormonal imbalances associated with obesity on the incidence of endometrial cancer in postmenopausal women. J Cancer. (2020) 11:5456–65. doi: 10.7150/jca.47580

  • CrossRef
  • Google Scholar

• 73

  BillotMCalvaniRUrtamoASánchez-SánchezJLCiccolari-MicaldiCChangMet al. Preserving mobility in older adults with physical frailty and sarcopenia: opportunities, challenges, and recommendations for physical activity interventions. Clin Interventions Aging. (2020) 15:1675–90. doi: 10.2147/CIA.S253535

  • CrossRef
  • Google Scholar

• 74

  WhitelockEEnsaffH. On your own: older adults’ Food choice and dietary habits. Nutrients. (2018) 10. doi: 10.3390/nu10040413

  • CrossRef
  • Google Scholar

• 75

  CongdonP. Obesity and urban environments. Int J Environ Res Public Health. (2019) 16. doi: 10.3390/ijerph16030464

  • CrossRef
  • Google Scholar

• 76

  AtekMTraissacPEl AtiJLaidYAounallah-SkhiriHEymard-DuvernaySet al. Obesity and association with area of residence, gender and socio-economic factors in Algerian and Tunisian adults. PLoS One. (2013) 8:e75640. doi: 10.1371/journal.pone.0075640

  • CrossRef
  • Google Scholar

• 77

  AhmadKKhanamTKeramatSAIslamMIKabirEKhanamR. Interaction between the place of residence and wealth on the risk of overweight and obesity in Bangladeshi women. PLoS One. (2020) 15:9:e024334. doi: 10.1371/journal.pone.0243349

  • CrossRef
  • Google Scholar

• 78

  EndaleFNigussieATameneAHabteAErmiasDBeyamoAet al. Abdominal obesity and associated factors among urban adults in Southwest Ethiopia: a community-based cross-sectional study. Pan Afr Med J. (2024) 47:47. doi: 10.11604/pamj.2024.47.47.34746

  • CrossRef
  • Google Scholar

• 79

  StappersNEHBekkerMPMJansenMWJKremersSPJde VriesNKSchipperijnJet al. Effects of major urban redesign on sedentary behaviour, physical activity, active transport and health-related quality of life in adults. BMC Public Health. (2023) 23:1157. doi: 10.1186/s12889-023-16035-6

  • CrossRef
  • Google Scholar

• 80

  SallisJFCerinEConwayTLAdamsMAFrankLDPrattMet al. Physical activity about urban environments in 14 cities worldwide: a cross-sectional study. Lancet. (2016) 387:2207–17. doi: 10.1016/S0140-6736(15)01284-2

  • CrossRef
  • Google Scholar

• 81

  PinedaEStocktonJScholesSLassaleCMindellJS. Food environment and obesity: a systematic review and meta-analysis. BMJ Nutrition Prev Health. (2024):e000663. doi: 10.1136/bmjnph-2023-000663

  • CrossRef
  • Google Scholar

• 82

  OngSRCrowstonJGLoprinziPDRamuluPY. Physical activity, visual impairment, and eye disease. Eye (London England). (2018) 32:1296–303. doi: 10.1038/s41433-018-0081-8

  • CrossRef
  • Google Scholar

• 83

  DakanalisAMentzelouMPapadopoulouSKPapandreouDSpanoudakiMVasiosGKet al. The association of emotional eating with overweight/obesity, depression, anxiety/stress, and dietary patterns: A review of the current clinical evidence. Nutrients. (2023) 15. doi: 10.3390/nu15051173

  • CrossRef
  • Google Scholar

• 84

  CupplesMEHartPMJohnstonAJacksonAJ. Improving healthcare access for people with visual impairment and blindness. BMJ. (2012) 344:e542. doi: 10.1136/bmj.e542

  • CrossRef
  • Google Scholar