Associations between meal patterns, binge eating, and weight for Latinas

Weight status is associated with clinical characteristics among individuals with bulimia nervosa

Recent studies have found increasing rates of overweight and obesity in bulimia nervosa (BN). However, the relationships between body mass index (BMI) and BN symptoms and other clinically relevant constructs are unknown. Participants (N = 152 adults with BN) were assigned to three groups by BMI: group with no overweight or obesity (NOW-BN; BMI <25; N = 32), group with overweight (OW-BN; BMI ≥25 and <30; N = 66), and group with obesity (O-BN; BMI ≥30; N = 54). We compared the groups on demographics, diet and weight histories, body esteem, BN symptoms, and depression using chi square, analysis of variance, analysis of covariance, and Poisson regression models. The O-BN group was older (d = 0.57) and OW-BN and O-BN groups had greater proportions of race/ethnic minorities than NOW-BN group. The O-BN group was significantly younger at first diet (d = 0.41) and demonstrated significantly higher cognitive dietary restraint (d = 0.31). Compared to NOW-BN, O-BN participants had lower incidence of objective binge eating (incidence rate ratio [IRR] = 4.86) and driven exercise (IRR = 7.13), and greater incidence of vomiting (IRR = 9.30), laxative misuse (IRR = 4.01), and diuretic misuse (d = 2.08). O-BN participants also experienced higher shape (d = 0.41) and weight (d = 0.42) concerns than NOW-BN and OW-BN, although NOW-BN experienced higher shape (d = 0.44) and weight (d = 0.39) concerns than OW-BN. Groups did not differ on depression scores. These results were replicated when examining BMI as a continuous predictor across the full sample, with the exception of objective binge eating and driven exercise, which were not significantly associated with BMI. Individuals with BN and comorbid obesity have distinct clinical characteristics. Existing interventions may need to be adapted to meet clinical needs of these individuals.

A Call to Reconceptualize Obesity Treatment in Service of Health Equity: Review of Evidence and Future Directions

PURPOSE OF REVIEW

Rates of obesity and associated comorbidities are higher among Black and Latino adults compared to white adults. We sought to provide an overview of both structural and individual factors contributing to obesity inequities and synthesize available evidence regarding treatment outcomes in Black and Latino adults, with an eye towards informing future directions.

RECENT FINDINGS

Obesity disparities are influenced by myriad systemic issues, yet the vast majority of interventions target individual-level factors only, and most behavioral treatments fail to target drivers beyond eating and physical activity. Extant treatments are not equally accessible, affordable, or effective among Black and Latino adults compared with white counterparts. Asset-based, culturally relevant interventions that target the root causes of obesity and address intersectional stress-designed in partnership with intended beneficiaries-are urgently needed. Treatment trials must improve enrollment of Black and Latino adults and report treatment outcomes by race and ethnicity.

A matter of time: A systematic scoping review on a potential role of the circadian system in binge eating behavior

Background

Emerging research suggests that food intake timing, eating behavior and food preference are associated with aspects of the circadian system function but the role that the circadian system may play in binge eating (BE) behavior in humans remains unclear.

Objective

To systematically evaluate the evidence for circadian system involvement in BE behavior.

Methods

Systematic searches of PubMed, EMBASE, and Scopus were performed for reports published from inception until May 2020 (PROSPERO Registration CRD42020186325). Searches were conducted by combining Medical Subject Headings related to the circadian system, BE behavior, and/or interventions. Observational and interventional studies in humans with BE behavior published in peer-review journals in the English language were included. Studies were assessed using quality and risk of bias tools (AXIS, ROB 2.0, or ROBINS).

Results

The search produced 660 articles, 51 of which were included in this review. Of these articles, 46 were observational studies and 5 were interventional trials. Evidence from these studies suggests that individuals with BE behavior tend to have more food intake, more binge cravings, and more BE episodes later in the day. Hormonal and day/night locomotor activity rhythm disturbances may be associated with BE behavior. Furthermore, late diurnal preference ("eveningness") was associated with BE behavior and chronobiological interventions that shift the circadian clock earlier (e.g., morning bright light therapy) were found to possibly decrease BE behavior. Substantive clinical overlap exists between BE and night eating behavior. However, there is a significant knowledge gap regarding their potential relationship with the circadian system. Limitations include the lack of studies that use best-established techniques to assess the chronobiology of BE behavior, heterogeneity of participants, diagnostic criteria, and study design, which preclude a meta-analytic approach.

Conclusion

Current evidence, although limited, suggests that the circadian system may play a role in the etiology of BE behavior. Further mechanistic studies are needed to fully characterize a potential role of the circadian system in BE behavior. A chronobiological approach to studying BE behavior may lead to identification of its neurobiological components and development of novel therapeutic interventions.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020186325], identifier [CRD42020186325].

On computing critical factors based healthy behavior index for behavior assessment

OBJECTIVE

Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

MATERIALS AND METHODS

To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines.

RESULTS

The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants.

CONCLUSIONS

The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation.