Danieli Morena, Ciulli Tommaso, Mousavi Seyed Mahed, Riccardi Giuseppe
Speech and Interactive Signal Lab, Department of Engineering and Computer Science, Università degli Studi di Trento, Trento, Italy.
IDEGO srl, Rome, Italy.
JMIR Form Res. 2021 Dec 1;5(12):e30053. doi: 10.2196/30053.
Mobile apps for mental health are available on the market. Although they seem to be promising for improving the accessibility of mental health care, little is known about their acceptance, design methodology, evaluation, and integration into psychotherapy protocols. This makes it difficult for health care professionals to judge whether these apps may help them and their patients.
Our aim is to describe and evaluate a protocol for the participatory design of mobile apps for mental health. In this study, participants and psychotherapists are engaged in the early phases of the design and development of the app empowered by conversational artificial intelligence (AI). The app supports interventions for stress management training based on cognitive behavioral theory.
A total of 21 participants aged 33-61 years with mild to moderate levels of stress, anxiety, and depression (assessed by administering the Italian versions of the Symptom Checklist-90-Revised, Occupational Stress Indicator, and Perceived Stress Scale) were assigned randomly to 2 groups, A and B. Both groups received stress management training sessions along with cognitive behavioral treatment, but only participants assigned to group A received support through a mobile personal health care agent, designed for mental care and empowered by AI techniques. Psychopathological outcomes were assessed at baseline (T1), after 8 weeks of treatment (T2), and 3 months after treatment (T3). Focus groups with psychotherapists who administered the therapy were held after treatment to collect their impressions and suggestions.
Although the intergroup statistical analysis showed that group B participants could rely on better coping strategies, group A participants reported significant improvements in obsessivity and compulsivity and positive distress symptom assessment. The psychotherapists' acceptance of the protocol was good. In particular, they were in favor of integrating an AI-based mental health app into their practice because they could appreciate the increased engagement of patients in pursuing their therapy goals.
The integration into practice of an AI-based mobile app for mental health was shown to be acceptable to both mental health professionals and users. Although it was not possible in this experiment to show that the integration of AI-based conversational technologies into traditional remote psychotherapy significantly decreased the participants' levels of stress and anxiety, the experimental results showed significant trends of reduction of symptoms in group A and their persistence over time. The mental health professionals involved in the experiment reported interest in, and acceptance of, the proposed technology as a promising tool to be included in a blended model of psychotherapy.
市场上有用于心理健康的移动应用程序。尽管它们似乎有望提高心理健康护理的可及性，但对于它们的接受度、设计方法、评估以及纳入心理治疗方案的情况却知之甚少。这使得医疗保健专业人员难以判断这些应用程序是否对他们及其患者有帮助。
我们的目标是描述和评估一种用于心理健康移动应用程序参与式设计的方案。在本研究中，参与者和心理治疗师参与了由对话式人工智能（AI）赋能的应用程序设计和开发的早期阶段。该应用程序支持基于认知行为理论的压力管理训练干预。
共有21名年龄在33至61岁之间、有轻度至中度压力、焦虑和抑郁（通过使用意大利语版的症状自评量表90修订版、职业压力指标和感知压力量表进行评估）的参与者被随机分为A组和B组。两组都接受了压力管理培训课程以及认知行为治疗，但只有被分配到A组的参与者通过一个为心理护理设计并由AI技术赋能的移动个人医疗保健代理获得支持。在基线（T1）、治疗8周后（T2）和治疗3个月后（T3）评估心理病理结果。在治疗后与实施治疗的心理治疗师进行焦点小组讨论，以收集他们的印象和建议。
尽管组间统计分析表明B组参与者可以依赖更好的应对策略，但A组参与者在强迫观念和强迫行为以及积极痛苦症状评估方面有显著改善。心理治疗师对该方案的接受度良好。特别是，他们赞成将基于AI的心理健康应用程序纳入他们的实践，因为他们可以认识到患者在追求治疗目标方面的参与度提高。
基于AI的心理健康移动应用程序纳入实践被证明对心理健康专业人员和用户都是可以接受的。尽管在本实验中不可能表明将基于AI的对话技术纳入传统远程心理治疗会显著降低参与者的压力和焦虑水平，但实验结果显示A组有显著的症状减轻趋势且随着时间持续存在。参与实验的心理健康专业人员对所提议的技术表示感兴趣并接受，认为它是一种有前途的工具，可纳入混合心理治疗模式。
