Clinical moderators and predictors of <scp>cognitive‐behavioral</scp> therapy by <scp>guided‐self‐help</scp> versus therapist‐led for binge‐eating disorder: Analysis of aggregated clinical trials

Grilo, Carlos M.; Thompson‐Brenner, Heather; Shingleton, Rebecca; Thompson, Douglas; Franko, Debra L.

doi:10.1002/eat.23601

Cited by 9 publications

(9 citation statements)

References 40 publications

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“…Likewise, interpersonal deficits are integral to IPT 153 and to a lesser degree in DBT 154 and CBT-E 152 . Regarding moderators and predictors of treatment outcome in BED; there is a need for a specific synthesis in this area and findings have been hard to replicate, however, features associated with a better outcome are an early response to therapy (reduction of binge-eating within the first weeks), an absence of substance use disorder, lower age and BMI, and good premorbid interpersonal functioning 157,162 , and recent data from CBT trials identified low weight concern as a predictor for remission 163 and a history of trauma as negative predictor of treatment success 164 .. Overall, around half of people with BED achieve abstinence from binge-eating, which is maintained at 12-month follow-up; however, longer-term outcomes are less clear 165 .…”

Section: [H2] Psychological Treatmentsmentioning

confidence: 99%

Binge eating disorder

Giel¹,

Bulik

Fernández‐Aranda

et al. 2022

Nat Rev Dis Primers

100

101

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Primer articlesNature Reviews publishes timely, authoritative articles that are of broad interest and exceptional quality. Thank you for taking the time to help us to ensure that our articles meet these high standards.Primer articles in Nature Reviews provide an overview of a disease or disorder. Primers are intended to provide an authoritative, global perspective for the benefit of biomedical scientists, putting current clinical and translational challenges into context. These overview articles are meant to be introductory and cover all aspects from epidemiology to disease mechanisms, diagnosis and treatment.Please submit your report in narrative form and provide detailed justifications for all statements. Confidential comments to the editor are welcome, but it is helpful if the main points are stated in the comments for transmission to the authors.Please note that all Primer articles follow the same set organization of the main headings. Additionally, Nature Reviews articles will be thoroughly edited before publication and all figures will be redrawn by our in-house art editors. We therefore request that you concentrate on the scientific content of the article and display items, rather than the 'order' of the content or any minor errors in language or grammar that might be present in the draft version. Please consider and comment on the following points when reviewing this manuscript:• Is the article written for a broad audience, including biomedical scientists, translational researchers, medical students and clinicians?• Is the global perspective well represented?• Does the article present the current state of the art in the disease, without much emphasis on historical aspects?• Are the open research questions outlines and comprehensive?• Does the length of the article seem appropriate?• Are the ideas logically presented and discussed?• Does the article provide a balanced overview of the literature? Please bear in mind that it may not be possible to cover all aspects of a field within such a concise article.• Is the discussion fair and accurate? Although our authors are encouraged to be opinionated, they should not ignore alternative points of view. • Do the figures, boxes and tables provide clear and accurate information? Are there any additional or alternative display items that you think that the authors should include?• Are the references appropriate and up-to-date? Do they reflect the scope of the article?• Are you aware of any undeclared conflicts of interest that might affect the balance, or perceived balance, of the article?

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Section: [H2] Psychological Treatmentsmentioning

confidence: 99%

Binge eating disorder

Giel¹,

Bulik

Fernández‐Aranda

et al. 2022

Nat Rev Dis Primers

100

101

View full text Add to dashboard Cite

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“…A number of patient variables have been evaluated as predictors, including – but not limited to – various eating-disorder psychopathology scales/measures as well as specific features such as overvaluation of shape/weight, self-control, depression and negative affect, and psychiatric comorbidity (e.g. Anderson et al, 2020; Grilo, Masheb, & Crosby, 2012a; Grilo, Thompson-Brenner, Shingleton, Thompson, & Franko, 2021; Lydecker & Grilo, in press; see online Supplementary Materials). Research has also tested treatment parameters (Thompson-Brenner et al, 2013) and processes such as rapid response to treatment (⩾65% reduction in binge-eating episodes within the first month of treatment; Grilo, White, Masheb, & Gueorguieva, 2015).…”

Section: Introductionmentioning

confidence: 99%

Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

2021

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Background While effective treatments exist for binge-eating disorder (BED), prediction of treatment outcomes has proven difficult, and few reliable predictors have been identified. Machine learning is a promising method for improving the accuracy of difficult-to-predict outcomes. We compared the accuracy of traditional and machine-learning approaches for predicting BED treatment outcomes. Methods Participants were 191 adults with BED in a randomized controlled trial testing 6-month behavioral and stepped-care treatments. Outcomes, determined by independent assessors, were binge-eating (% reduction, abstinence), eating-disorder psychopathology, and weight loss (% loss, ⩾5% loss). Predictors included treatment condition, demographic information, and baseline clinical characteristics. Traditional models were logistic/linear regressions. Machine-learning models were elastic net regressions and random forests. Predictive accuracy was indicated by the area under receiver operator characteristic curve (AUC), root mean square error (RMSE), and R2. Confidence intervals were used to compare accuracy across models. Results Across outcomes, AUC ranged from very poor to fair (0.49–0.73) for logistic regressions, elastic nets, and random forests, with few significant differences across model types. RMSE was significantly lower for elastic nets and random forests v. linear regressions but R2 values were low (0.01–0.23). Conclusions Different analytic approaches revealed some predictors of key treatment outcomes, but accuracy was limited. Machine-learning models with unbiased resampling methods provided a minimal advantage over traditional models in predictive accuracy for treatment outcomes.

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“…We offer, however, some cautionary considerations for non‐responders to initial treatments, for whom guided self‐help CBT might not be the ideal next step (i.e., rather than therapist‐led CBT). A recent re‐analysis of aggregated data from controlled trials evaluating CBT interventions for BED revealed that after statistical adjustment for demographic and clinical characteristics, therapist‐led CBT was associated with significantly better outcomes than guided self‐help methods (Grilo et al, 2021). Relatedly, in a stepped care trial for BED, when patients who did not have a rapid response to initial treatment with BWL were switched to guided self‐help CBT, little additional benefit was observed (Grilo et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Cognitive‐behavioral therapy for binge‐eating disorder for non‐responders to initial acute treatments: Randomized controlled trial

Grilo

Lydecker

Gueorguieva

2023

Intl J Eating Disorders

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ObjectiveCertain treatments have demonstrated acute efficacy for binge‐eating disorder (BED) but many patients who receive “evidence‐based” interventions do not derive sufficient benefit. Given the dearth of controlled research examining treatments for patients who fail to respond to initial interventions, this study tested the efficacy of cognitive‐behavioral therapy (CBT) for patients with BED who do not respond to initial acute treatments.MethodsProspective randomized double‐blind placebo‐controlled single‐site trial, conducted August 2017–December 2021, tested 16‐weeks of therapist‐led CBT for non‐responders to initial treatment (naltrexone/bupropion and/or behavioral therapy) for BED with obesity. Thirty‐one patients (mean age 46.3 years, 77.4% women, 80.6% White, mean BMI 38.99 kg/m2) who were non‐responders to initial acute treatments were randomized to CBT (N = 18) or no‐CBT (N = 13), in addition to continuing double‐blinded pharmacotherapy. Independent assessments were performed at baseline, throughout treatment, and posttreatment; 83.9% completed posttreatment assessments.ResultsIntention‐to‐treat remission rates were significantly higher for CBT (61.1%; N = 11/18) than no‐CBT (7.7%; N = 1/13). Mixed models of binge‐eating frequency (assessed using complementary methods) converged revealing a significant interaction between CBT and time and a significant main effect of CBT. Binge‐eating frequency decreased significantly with CBT but did not change significantly with no‐CBT. Since only four patients received behavioral treatment during the acute treatments, we performed “sensitivity‐type” analyses restricted to the 27 patients who received pharmacotherapy during the acute treatment and found the same pattern of findings for CBT versus no‐CBT.ConclusionsAdult patients with BED who fail to respond to initial pharmacological treatments should be offered CBT.Public SignificanceEven with leading evidence‐based treatments for binge‐eating disorder, many patients do not derive sufficient benefit. Almost no controlled research has examined treatments for patients who fail to respond to initial interventions. This study found that that cognitive‐behavioral therapy was effective for patients with binge‐eating disorder who did not respond to initial interventions, with 61% achieving abstinence.

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Clinical moderators and predictors of cognitive‐behavioral therapy by guided‐self‐help versus therapist‐led for binge‐eating disorder: Analysis of aggregated clinical trials

Cited by 9 publications

References 40 publications

Binge eating disorder

Binge eating disorder

Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

Cognitive‐behavioral therapy for binge‐eating disorder for non‐responders to initial acute treatments: Randomized controlled trial

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