A demographic, clinical, and behavioral typology of obesity in the United States: an analysis of National Health and Nutrition Examination Survey 2011–2012

Jiménez, Marcia P.; Green, Mark A.; Subramanian, S. V.; Razak, Fahad

doi:10.1016/j.annepidem.2018.01.001

Cited by 9 publications

(9 citation statements)

References 41 publications

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“…Obesity represents a heterogeneous condition according to demographic, clinical, and behavioral factors. Based on a study of the demographic, clinical, and behavioral typology of obesity in the United States,8 we paid particular attention to the content that differences in the demographic characteristics, clinical conditions, and behavioral factors create distinct subtypes of obesity. Hence, stratifying obesity by subtypes or by assessing different risks across distinct groups may benefit weight management.…”

Section: Methodsmentioning

confidence: 99%

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Development and Usability of a Life-Logging Behavior Monitoring Application for Obese Patients

Lee

Kim

You³

et al. 2019

JOMES

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BackgroundObesity is a global epidemic. Behavior change monitoring using a smartphone application (app) can support weight management in obese patients. These apps must undergo usability testing, which is an important step in mobile healthcare app development. The current study aimed to develop a mobile app for behavioral monitoring and to test its usability including technical effectiveness, user efficiency, and user satisfaction for obese adults.MethodsDevelopment of the Dr. Youth app components included information on behavioral monitoring indicators and their obesity subtypes. The usability of the app was tested with 50 obese adults in a university hospital. Participants were asked to complete eight tasks for evaluating the technical effectiveness of the app. The time to complete each task was measured to test user efficiency. To explore user satisfaction, each participant completed the System Usability Scale (SUS). Descriptive statistics were used to examine the mean user efficiency and SUS scores.ResultsFifty adults (14 men and 36 women, aged 20–59 years) who are obese (body mass index ≥25 kg/m2) were recruited. The mean age of participants was 42.6 years (standard deviation [SD], 10.8 years); their mean body mass index was 29.6 kg/m2 (SD, 5.7 kg/m2). The tasks were completed with a 99% success rate. The overall mean SUS score was 76.65 (SD, 15.43).ConclusionThe Dr. Youth app shows acceptable technical effectiveness, user efficiency, and user satisfaction. Future study is warranted to establish the app’s clinical efficacy.

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Section: Methodsmentioning

confidence: 99%

“…We selected behavioral monitoring indicators based on previous studies and reports 8,14. Categories of behavioral monitoring indicators include regular meal intake, regular sleep time, frequency of alcohol drinking, frequency of critical foods intake, body weight, exercise frequency, and stress levels according to several obesity subtypes.…”

Section: Methodsmentioning

confidence: 99%

Development and Usability of a Life-Logging Behavior Monitoring Application for Obese Patients

Lee

Kim

You³

et al. 2019

JOMES

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“…Obesity is a known contributor towards cardiovascular illness and premature mortality (Peeters et al 2003;Jimenez et al 2018). The occurrence of obesity The haplotype is combined with MC4R rs17782313-rs476828-rs12970134.…”

Section: Discussionmentioning

confidence: 99%

“…Obesity is a known contributor towards cardiovascular illness and premature mortality (Peeters et al 2003 ; Jimenez et al 2018 ). The occurrence of obesity is the product of interaction between a variety of environmental factors, such as diet, unhealthy lifestyle, lack of exercise as well as genetic factors (Xi et al 2011 ; Unamuno et al 2018 ; Teixeira et al 2016 ).…”

Section: Discussionmentioning

confidence: 99%

The MC4R SNPs, their haplotypes and gene-environment interactions on the risk of obesity

Wei

Yin

Liu

et al. 2020

Mol Med

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Background: Little is known about the correlation between the melanocortin 4 receptor gene (MC4R) single nucleotide polymorphisms (SNPs) and the risk of obesity. This research sought to test the MC4R rs17782313, rs476828 and rs12970134 SNPs, their haplotypes and gene-environment interactions on the risk of obesity in the Maonan ethnic group, an isolated minority in China. Methods: A case-control study comprised of 1836 participants (obesity group, 858; and control group, 978) was conducted. Genotypes of the three SNPs were determined by the next-generation sequencing (NGS) technology. Results: The genotypic frequencies of the three SNPs were different between the obesity and control groups (P < 0.05 for all). The minor allelic frequency of the MC4R rs17782313C, rs476828C and rs12970134A was higher in obesity than in control groups (13.8% vs. 8.3%, P < 0.001, 17.1% vs. 10.9%, P < 0.001; and 15.5% vs. 11.5%, P < 0.001; respectively). Additionally, the dominant model of rs17782313 and rs476828 SNPs revealed an increased morbidity function on the risk of obesity (P < 0.05). A correlation between SNP-environment and the risk of obesity was also observed. The rs17782313C-rs476828C-rs12970134A haplotype was associated with high risk of obesity (OR = 1.796, 95% CI = 1.447-2.229), whereas the rs17782313T-rs476828T-rs12970134G and rs17782313T-rs476828T-rs12970134A haplotypes were associated with low risk of obesity (OR = 0.699, 95% CI = 0.586-0.834 and OR = 0.620, 95% CI = 0.416-0.925; respectively). The interactions between haplotype and waist circumference on the risk of obesity were also noted. Conclusions: We discovered that the MC4R rs17782313, rs476828 and rs12970134 SNPs and their haplotypes were associated with the risk of obesity in the Chinese Maonan population.

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“…Obesity has been associated with higher incidence of chronic non-communicable diseases (NCDs) such as type 2 diabetes mellitus, ischemic heart disease, and related cardiometabolic complications and, in turn, increased risk of hospital admission, more frequent readmissions, and longer hospital stays [5][6][7][8]. At the same time, obesity is known to affect populations heterogeneously across clinical, sociodemographic, and behavioural characteristics [9][10][11]. Despite pervasive arguments that obesity is a key driver of growing healthcare expenditures for associated diseases, few studies have examined trends and determinants of hospital resource use and costs among persons living with obesity, especially ones including consideration of modifiable risk factors and drawing on nationally representative samples [8,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Beyond weight: examining the association of obesity with cardiometabolic related inpatient costs among Canadian adults using linked population based survey and hospital administrative data

Gupta

Sheng

2021

BMC Health Serv Res

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Background The global population has transitioned to one where more adults are living with obesity than are underweight. Obesity is associated with the development of cardiometabolic diseases and widely attributed to increased hospital resource use; however, empirical evidence is limited regarding obesity prevention to support hospital cost containment. This study aims to test for obesity in predicting hospitalization costs for cardiometabolic conditions among the Canadian population aged 45 and over. Methods Data from the 2007−2011 Canadian Community Health Survey were linked to eight years of hospital discharge records. A cohort was identified of inpatients admitted for diabetes, hypertension, and other cardiometabolic diseases. Multiple linear regressions were used to investigate the association between obesity status and inpatient costs, controlling for sociodemographic and behavioural factors. Results The target cohort included 23,295 admissions for cardiometabolic diseases. Although inflation-adjusted inpatient costs generally increased over time, compared with the non-obese group, living with obesity was not a significant predictor of differences in cardiometabolic-related resource use (0.972 [95% CI: 0.926–1.021]). Being female and rural residence were found to be protective factors. Conclusions Obesity was not found in this study to be independently linked to higher cardiometabolic hospitalization costs, suggesting that actions to mitigate disease progression in the population may be more beneficial than simply promoting weight loss. Results amplified the need to consider gender and urbanization when formulating which levers are most amenable to adoption of healthy lifestyles to reduce impacts of obesogenic environments to the healthcare system.

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A demographic, clinical, and behavioral typology of obesity in the United States: an analysis of National Health and Nutrition Examination Survey 2011–2012

Cited by 9 publications

References 41 publications

Development and Usability of a Life-Logging Behavior Monitoring Application for Obese Patients

Development and Usability of a Life-Logging Behavior Monitoring Application for Obese Patients

The MC4R SNPs, their haplotypes and gene-environment interactions on the risk of obesity

Beyond weight: examining the association of obesity with cardiometabolic related inpatient costs among Canadian adults using linked population based survey and hospital administrative data

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