The development of a World Health Organization transdiagnostic chatbot intervention for distressed adolescents and young adults.

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-05-19 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1528580
Dharani Keyan, Jennifer Hall, Stewart Jordan, Sarah Watts, Teresa Au, Katie S Dawson, Rajiah Abu Sway, Joy Crawford, Katherine Sorsdahl, Nagendra P Luitel, Anne M de Graaff, Heba Ghalayini, Rand Habashneh, Hafsa El-Dardery, Sarah Fanatseh, Aiysha Malik, Chiara Servili, Muhannad Faroun, Adnan Abualhaija, Ibrahim Said Aqel, Syed Usman Hamdani, Latefa Dardas, Aemal Akhtar, Richard A Bryant, Kenneth Carswell
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引用次数: 0

Abstract

Background: Common mental disorders are prevalent in young people in low- and middle-income countries (LMICs). Digitally delivered interventions have the potential to overcome many structural and psychosocial barriers to mental health care. Chatbots have been proposed as one potentially acceptable and feasible method that may increase engagement. Yet, there is currently limited evidence for their efficacy in reducing psychological distress. This paper summarises the development of a World Health Organization digital psychological intervention for young people experiencing impairing psychological distress, developed in line with Human Centred Design (HCD) principles.

Objective: This study refined and adapted a chatbot intervention initially developed for adolescents aged 15-18 years that was completed in consultation with end-users in this age group (N = 236), community members (N = 73), and psychology intervention experts (N = 9) across varied settings. The purpose was to create an adaptation fit for use by young adults aged 18-21 years experiencing psychological distress in Jordan.

Methods: The current study followed a limited user-centred design process involving focus groups and key informant interviews with stakeholders including young adults aged 18-21 years (N = 33), community members (N = 13), and psychology intervention experts (N = 11). Iterative design development occurred throughout the cultural adaptation and refinement process.

Results: There was a clear preference for a chatbot based intervention that included interactions with fictional characters with relatable problems. The chatbot content followed a transdiagnostic model that addressed common problems including low mood, stress and anger with reference to vocational, familial and interpersonal stressors that the target population commonly faced. It followed a non-AI decision tree format with multiple sessions and was designed to be adaptable for use in different countries with different populations and software systems. Prototype versions of the chatbot were well-received by adolescents (15-18-year-old) and young adults (18-21-year-old).

Conclusions: This is the first report of the development of a chatbot intervention for adolescents and young adults in LMICs that was designed using a HCD framework. Systematic end-user engagement through all phases of the research aimed to make this intervention acceptable and useable for adolescents and young adults in a wide variety of settings. The chatbot is currently being tested in randomised controlled trials in Jordan and Lithuania.

发展世界卫生组织的跨诊断聊天机器人干预心疼的青少年和年轻人。
背景:常见精神障碍在低收入和中等收入国家(LMICs)的年轻人中普遍存在。数字化提供的干预措施有可能克服精神卫生保健方面的许多结构性和社会心理障碍。聊天机器人被认为是一种潜在的可接受和可行的方法,可以提高参与度。然而,目前关于它们在减少心理困扰方面的功效的证据有限。本文总结了世界卫生组织根据以人为本的设计(HCD)原则为经历有害心理困扰的年轻人制定的数字心理干预措施的发展情况。目的:本研究对最初为15-18岁青少年开发的聊天机器人干预进行了改进和调整,该干预是在咨询了该年龄组的最终用户(N = 236)、社区成员(N = 73)和心理干预专家(N = 9)之后完成的。目的是为约旦18-21岁经历心理困扰的年轻人创造一种适应方式。方法:本研究遵循有限的以用户为中心的设计过程,包括焦点小组和关键信息提供者访谈,包括18-21岁的年轻人(N = 33)、社区成员(N = 13)和心理干预专家(N = 11)。迭代式设计开发贯穿于文化适应和改进过程。结果:有一个明显的偏好是基于聊天机器人的干预,包括与虚构人物互动,解决相关问题。聊天机器人的内容遵循一种跨诊断模型,针对目标人群通常面临的职业、家庭和人际压力源,解决包括情绪低落、压力和愤怒在内的常见问题。它遵循非人工智能决策树格式,包含多个会议,旨在适应不同国家不同人口和软件系统的使用。聊天机器人的原型深受青少年(15-18岁)和年轻人(18-21岁)的欢迎。结论:这是第一份使用HCD框架设计的针对低收入国家青少年和年轻人的聊天机器人干预发展的报告。通过研究的所有阶段,系统的终端用户参与旨在使这种干预措施在各种环境中为青少年和年轻人所接受和使用。这款聊天机器人目前正在约旦和立陶宛进行随机对照试验。
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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来源期刊
CiteScore
4.20
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0.00%
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审稿时长
13 weeks
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