Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report.

JMIRx med Pub Date : 2025-09-05 DOI:10.2196/70960
Junichi Fujita, Yuichiro Yano, Satoru Shinoda, Noriko Sho, Masaki Otsuki, Akira Suda, Mizuho Takayama, Tomoko Moroga, Hiroyuki Yamaguchi, Mio Ishii, Tomoyuki Miyazaki
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Abstract

Background: The mental health of children and adolescents is a growing public health concern, with increasing rates of depression and anxiety impacting their emotional, social, and academic well-being. In Japan, access to timely psychiatric care is limited, leading to extended waiting periods that can range from 3 months to a year. Artificial intelligence (AI)-driven chatbots, such as emol (Emol Inc) that integrates acceptance and commitment therapy, show potential as digital solutions to support young patients during these waiting times. The AI chatbot emol was selected based on a comprehensive review of Japanese mental health technology apps, including in-person evaluations with company representatives.

Objective: This exploratory parallel-group randomized controlled trial examined the feasibility of using an AI chatbot emol with pediatric and adolescent individuals on psychiatric waiting lists.

Methods: Participants aged 12-18 years were recruited from 4 hospitals in Kanagawa Prefecture and randomly assigned to either an intervention group, receiving 8 weekly chatbot sessions, or a control group, receiving standard mental health information. The primary outcome was the change in scores on the 9-item Patient Health Questionnaire from pre- to postintervention. Secondary assessments, such as voice and writing pressure analysis, provided additional engagement metrics, with data collected at baseline, during the intervention, and at week 9.

Results: Of the 96 eligible individuals on psychiatric waiting lists, 8 expressed interest and 3 provided initial consent. However, all participants subsequently withdrew or were excluded, resulting in no evaluable data for analysis. Low engagement may have been influenced by the perceived irrelevance of digital tools, complex protocols, and privacy concerns.

Conclusions: Significant barriers to engagement suggest that digital interventions may need simpler protocols and trusted environments to improve feasibility. Future studies could test these interventions in supportive settings, like schools or community centers, to enhance accessibility and participation among youth.

实施移动AI聊天机器人干预精神科候诊青少年抑郁症的挑战:随机对照研究终止报告。
背景:儿童和青少年的心理健康是一个日益受到关注的公共卫生问题,抑郁症和焦虑症的发病率不断上升,影响着他们的情感、社交和学业健康。在日本,及时获得精神科护理的机会有限,导致等待时间延长,从3个月到一年不等。人工智能(AI)驱动的聊天机器人,如整合了接受和承诺治疗的emol (emol Inc),显示出作为数字解决方案的潜力,可以在这些等待时间为年轻患者提供支持。人工智能聊天机器人模型的选择是基于对日本心理健康技术应用程序的全面审查,包括与公司代表的亲自评估。目的:本探索性平行组随机对照试验检验了在精神病等候名单上的儿童和青少年中使用AI聊天机器人模型的可行性。方法:从神奈川县的4家医院招募年龄在12-18岁的参与者,并随机分配到干预组和对照组,干预组每周接受8次聊天机器人会话,对照组接受标准的心理健康信息。主要结果是干预前到干预后9项患者健康问卷得分的变化。二次评估,如声音和写作压力分析,提供了额外的参与指标,数据收集于基线、干预期间和第9周。结果:96名符合条件的精神病患者中,8人表示有兴趣,3人初步同意。然而,所有参与者随后退出或被排除,导致没有可评估的数据用于分析。低参与度可能受到数字工具的不相关性、复杂协议和隐私问题的影响。结论:参与的重大障碍表明,数字干预可能需要更简单的协议和可信的环境来提高可行性。未来的研究可以在学校或社区中心等支持性环境中测试这些干预措施,以提高青少年的可及性和参与度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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