An Examination of Recurrent Binge Eating and Body Image Dissatisfaction in Preoperative Bariatric Surgery Patients Enrolled in The Longitudinal Assessment of Bariatric Surgery
Notice bibliographique
Résumé
Obesity is often a serious medical condition with many biopsychosocial risk factors and co-occurring conditions. Published data suggests that rates of obesity are increasing in the United States and around the globe. Obese individuals often have multiple medical conditions that place them at increased risk of premature death. These conditions include cardiac disease, pulmonary disease, diabetes mellitus-type II and others. In addition, obese individuals are at increased risk of eating, mood and anxiety disorders. The most effective treatment for reducing weight in obese individuals and maintaining weight loss over time is bariatric surgery. While individuals undergoing this treatment can expect to lose 25-30% of their original body weight and experience other biopsychosocial benefits, 20-30% will not. This subset of patients will not experience significant weight loss, may regain lost weight, and often report lower quality of life and treatment satisfaction. Understanding the relationships between biopsychosocial risk factors common to presurgical bariatric patients may improve post-surgical outcomes.\nThe first aim of the current study was to examine the relationships between recurrent binge eating behavior and BMI, eating symptomatology, body image dissatisfaction, and depression. The second aim was to examine differences between two models of the Eating Disorder Examination interview. The first model being the original four-factor model commonly utilized to assess eating pathology and proposed by Fairburn and Cooper (1993); the second model is a three-factor model proposed by Grilo, Henderson, Bell and Crosby (2013). It was hypothesized there would be significant differences between sample groups (i.e., individuals endorsing recurrent binge eating and those that did not), across all biopsychosocial variables noted in the study. It was also predicted that there would be differences between groups using the different EDE models. Study participants included patients enrolled in the LABS-2 project at Oregon Health and Sciences University (OHSU) medical center in Portland, Oregon between 2007-2010. Participants (N = 59) included both female (n = 45) and male (n = 14) patients. Of these, 57 (96.6%) identified as Caucasian/White, one (1.8%) identified as French Canadian/Native American, and one (1.8%) identified as “mixed” (i.e., race/ethnicity).\nResults of the study indicated medium to large effect sizes associated with group differences on many dependent variables, EDE original 4-factor model: Eating Concern (d = .58), Shape Concern (d = .57), EDE 3-factor Global Score (d = .51), BSQ (d = .46), ASI-R (d = .52), Composite BID score (d = .51). However, only the Global Score from the original EDE four-factor model produced a significant difference between groups, (d = .77, u = 381.5, z = 1.98, p < .05). This suggests meaningful differences on eating symptomatology for those engaging in recurrent binge eating compared to those that do not. More generally, results suggest that the small sample size may have limited the statistical power of the study. Results further suggest the possibility of significant differences between individuals that endorse and do not endorse recurrent binge eating behaviors on eating symptomatology and body image dissatisfaction, but not BMI and depression. As such, screening for these patient characteristics may inform pre- and post-surgical treatment intended to promote favorable surgical outcomes. Future research may include a larger sample size to increase statistical power along with recruitment and participation of broader demographic profiles. Research may examine specific sub-constructs of each dependent variable to refine targets of treatment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».