ReviewThe International journal of eating disorders2025
Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.
Review in The International journal of eating disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effects of Digital Interventions on Symptom Improvement and Quality of Life in Patients With Overactive Bladder: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Association between user engagement and clinical outcomes in smartphone apps for depression and anxiety: A systematic review and meta-analysis.Psychiatry research · 2026Pooled it
- Strategic Priorities for Advancing Eating Disorder Risk Reduction: A Narrative Review.The International journal of eating disorders · 2026Review
- Benchmarking Generative Artificial Intelligence Against Human Judgment in Eating Disorder Case Recognition and Treatment Recommendations.The International journal of eating disorders · 2026Article
- AI and machine learning for early identification of eating disorders: a narrative review and Indian contextual insights.Eating and weight disorders : EWD · 2026Review
- Effect of psychopharmacological combinations on psychopathological symptom burden and BMI trajectories in patients with eating disorders.Frontiers in psychiatry · 2026Article
- From Promise to Precision: Future Directions for Smartphone Application Eating Disorder Interventions-Commentary on Cruz et al. (2025).The International journal of eating disorders · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Transdiagnostic-focused apps for depression and anxiety: a meta-analysis.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectiveSmartphone 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.
methodWe 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.
resultsEvidence 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.
conclusionThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.