Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive‐behavior therapy for binge‐spectrum eating disorders: Protocol for a randomized controlled trial.
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| Title: | Optimizing digital health technologies to improve therapeutic skill use and acquisition alongside enhanced cognitive‐behavior therapy for binge‐spectrum eating disorders: Protocol for a randomized controlled trial. |
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| Authors: | Juarascio, Adrienne S., Presseller, Emily K., Trainor, Claire, Boda, Sneha, Manasse, Stephanie M., Srivastava, Paakhi, Forman, Evan M., Zhang, Fengqing |
| Source: | International Journal of Eating Disorders. Feb2023, Vol. 56 Issue 2, p470-477. 8p. 1 Chart. |
| Subjects: | Bulimia treatment, Evaluation of human services programs, Factorial experiment designs, Digital technology, Self-control, Digital health, Ability, Training, Randomized controlled trials, Quality assurance, Health self-care, Cognitive therapy, Longitudinal method |
| Abstract: | Objective: Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive‐behavior therapy (CBT‐E) among individuals with binge‐spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self‐monitoring and microinterventions) alongside CBT‐E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self‐regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). Method: Two hundred and sixty‐four participants with binge‐spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self‐monitoring condition (i.e., standard self‐monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just‐in‐time adaptive interventions) as an augmentation to 16 sessions of CBT‐E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. Results: Results will clarify the "active" components in mHealth interventions for binge eating. Discussion: The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT‐E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self‐regulation. Public Significance: This study will examine the relative effectiveness of commonly used components of application‐based interventions as an augmentation to cognitive‐behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Eating Disorders is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Objective: Adjunctive mobile health (mHealth) technologies offer promise for improving treatment response to enhanced cognitive‐behavior therapy (CBT‐E) among individuals with binge‐spectrum eating disorders, but research on the key "active" components of these technologies has been very limited. The present study will use a full factorial design to (1) evaluate the optimal combination of complexity of two commonly used mHealth components (i.e., self‐monitoring and microinterventions) alongside CBT‐E and (2) test whether the optimal complexity level of these interventions is moderated by baseline self‐regulation. Secondary aims of the present study include evaluating target engagement associated with each level of these intervention components and quantifying the component interaction effects (i.e., partially additive, fully additive, or synergistic effects). Method: Two hundred and sixty‐four participants with binge‐spectrum eating disorders will be randomized to six treatment conditions determined by the combination of self‐monitoring condition (i.e., standard self‐monitoring or skills monitoring) and microinterventions condition (i.e., no microinterventions, automated microinterventions, or just‐in‐time adaptive interventions) as an augmentation to 16 sessions of CBT‐E. Treatment outcomes will be measured using the Eating Disorder Examination and compared by treatment condition using multilevel models. Results: Results will clarify the "active" components in mHealth interventions for binge eating. Discussion: The present study will provide critical insight into the efficacy of commonly used digital intervention components (i.e., skills monitoring and microinterventions) alongside CBT‐E. Furthermore, results of this study may inform personalization of digital intervention intensity based on patient profiles of self‐regulation. Public Significance: This study will examine the relative effectiveness of commonly used components of application‐based interventions as an augmentation to cognitive‐behavioral therapy for binge eating. Findings from this study will inform the development of an optimized digital intervention for individuals with binge eating. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02763478 |
| DOI: | 10.1002/eat.23864 |
