Development and Usability Testing of a Chatbot to Promote Mental Health Services Use Among Individuals With Eating Disorders Following Screening
Notice bibliographique
Résumé
Background Eating disorders (EDs) are complex mental illnesses with debilitating, pervasive psychological and physiological consequences when left untreated. Unfortunately, patients may face barriers to receiving treatment, such as stereotypes surrounding EDs, denial of illness severity, lack of motivation for treatment, and lack of knowledge about treatment resources. Barriers such as these result in a large treatment gap: only 20% of those with EDs will ever receive treatment. Digital tools like chatbots show potential to disseminate mental health–related interventions to large populations while offering a user-friendly, cost-effective, accessible, and anonymous means of tackling patient concerns. Objective This study developed and evaluated the usability of a chatbot designed for pairing with online ED screening. The tool aimed to promote mental health service utilization by improving motivation for treatment and self-efficacy among individuals with EDs. Methods A chatbot prototype, Alex, was designed using decision trees and theoretically informed components: psychoeducation, motivational interviewing, personalized recommendations, and repeated administration. Usability testing was conducted over 4 iterative cycles, with user feedback informing refinements to the next iteration. Postintervention, participants (N=21) completed the System Usability Scale (SUS), the Usefulness, Satisfaction, and Ease of Use Questionnaire (USE), and a semistructured interview. This process aimed to create an optimized chatbot by the final cycle for use in a randomized trial. Results Interview feedback detailed chatbot aspects participants enjoyed and aspects necessitating improvement. Feedback converged on four themes: user experience, chatbot qualities, chatbot content, and ease of use. Following refinements, users described Alex as humanlike, supportive, and encouraging. Content was perceived as novel and personally relevant. USE scores across domains were generally above average (~5 out of 7), and SUS scores indicated “good” to “excellent” usability across cycles, with the final iteration receiving the highest average SUS score. Conclusions Overall, participants responded well in interactions with Alex, including the initial version. Refinements between cycles further improved user experiences. This study provides preliminary evidence of the feasibility and acceptance of a chatbot designed to promote motivation for and use of services among individuals with EDs. Alex is the first chatbot designed for pairing with an ED or other mental health–related online screen, with the goal of ultimately increasing service utilization. Acknowledgments This research was supported by K08 MH120341 from the National Institute of Mental Health. Availability of Data, Materials, and Code The data will be made available by reasonable request to the corresponding author. Authors’ Contributions EEFC conceptualized and designed the study. OL and BD conducted the investigation process. BD and JS assisted with data curation and conducted formal thematic analyses. JS conducted formal statistical analyses. JS wrote the original manuscript, with contribution from BD. EEFC, CBT, DEW, and SSS designed the data collection instruments, and coordinated and supervised data collection, in addition to reviewing and editing the manuscript with LS, LMF, LAF, and LD. Conflicts of Interest None declared.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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 ».