{"title":"Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.","authors":"Jake Linardon, John Torous","doi":"10.1002/eat.24468","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Smartphone technology presents a promising path toward expanding access to evidence-based eating disorder assessment and treatment. Despite rapid technological advances, research has yet to harness these systems in ways that make personalized digital health care a clinical reality. In this forum, we review extant research testing smartphone intervention and monitoring tools for eating disorders and explore innovative ways integrating this technology with AI can enhance assessment, symptom detection, and intervention efforts.</p><p><strong>Method: </strong>We highlight three capabilities of smartphones that hold promise for delivering personalized and maximally effective digital health tools: (1) passive sensing and digital phenotyping; (2) natural language processing of reflections from in-app homework tasks; and (3) closed-loop adaptive interventions. We discuss how these capabilities can augment current assessment and treatment efforts and draw on literature from other fields to inform research questions for the eating disorder field.</p><p><strong>Results: </strong>Evidence from other fields demonstrates the feasibility of constructing data-driven models from smartphone sensor data and textual input from in-app CBT activities to predict clinical outcomes. These models may inform closed-loop interventions, enabling apps to deliver timely, personalized support in response to real-time changes in a user's needs.</p><p><strong>Conclusion: </strong>The eating disorder field can draw on lessons from other fields to evaluate smartphone technology that leverages AI to enhance personalization. Realizing the potential of these tools will require addressing challenges related to engagement, trust, data governance, and clinical integration. The testable research questions presented here offer a roadmap to guide future large-scale, collaborative efforts aimed at transforming eating disorder care.</p>","PeriodicalId":51067,"journal":{"name":"International Journal of Eating Disorders","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Eating Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/eat.24468","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: Smartphone technology presents a promising path toward expanding access to evidence-based eating disorder assessment and treatment. Despite rapid technological advances, research has yet to harness these systems in ways that make personalized digital health care a clinical reality. In this forum, we review extant research testing smartphone intervention and monitoring tools for eating disorders and explore innovative ways integrating this technology with AI can enhance assessment, symptom detection, and intervention efforts.
Method: We highlight three capabilities of smartphones that hold promise for delivering personalized and maximally effective digital health tools: (1) passive sensing and digital phenotyping; (2) natural language processing of reflections from in-app homework tasks; and (3) closed-loop adaptive interventions. We discuss how these capabilities can augment current assessment and treatment efforts and draw on literature from other fields to inform research questions for the eating disorder field.
Results: Evidence from other fields demonstrates the feasibility of constructing data-driven models from smartphone sensor data and textual input from in-app CBT activities to predict clinical outcomes. These models may inform closed-loop interventions, enabling apps to deliver timely, personalized support in response to real-time changes in a user's needs.
Conclusion: The eating disorder field can draw on lessons from other fields to evaluate smartphone technology that leverages AI to enhance personalization. Realizing the potential of these tools will require addressing challenges related to engagement, trust, data governance, and clinical integration. The testable research questions presented here offer a roadmap to guide future large-scale, collaborative efforts aimed at transforming eating disorder care.
期刊介绍：
Articles featured in the journal describe state-of-the-art scientific research on theory, methodology, etiology, clinical practice, and policy related to eating disorders, as well as contributions that facilitate scholarly critique and discussion of science and practice in the field. Theoretical and empirical work on obesity or healthy eating falls within the journal’s scope inasmuch as it facilitates the advancement of efforts to describe and understand, prevent, or treat eating disorders. IJED welcomes submissions from all regions of the world and representing all levels of inquiry (including basic science, clinical trials, implementation research, and dissemination studies), and across a full range of scientific methods, disciplines, and approaches.
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