Department of Psychiatry, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan.
Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan.
J Med Internet Res. 2024 Oct 18;26:e51110. doi: 10.2196/51110.
The COVID-19 pandemic has profoundly affected mental health, leading to an increased prevalence of depression and insomnia. Currently, artificial intelligence (AI) and deep learning have thoroughly transformed health care-related mobile apps, offered more effective mental health support, and alleviated the psychological stress that may have emerged during the pandemic. Early reviews outlined the use of mobile apps for dealing with depression and insomnia separately. However, there is now an urgent need for a systematic evaluation of mobile apps that address both depression and insomnia to reveal new applications and research gaps.
This study aims to systematically review and evaluate mobile apps targeting depression and insomnia, highlighting their features, effectiveness, and gaps in the current research.
We systematically searched PubMed, Scopus, and Web of Science for peer-reviewed journal articles published between 2017 and 2023. The inclusion criteria were studies that (1) focused on mobile apps addressing both depression and insomnia, (2) involved young people or adult participants, and (3) provided data on treatment efficacy. Data extraction was independently conducted by 2 reviewers. Title and abstract screening, as well as full-text screening, were completed in duplicate. Data were extracted by a single reviewer and verified by a second reviewer, and risk of bias assessments were completed accordingly.
Of the initial 383 studies we found, 365 were excluded after title, abstract screening, and removal of duplicates. Eventually, 18 full-text articles met our criteria and underwent full-text screening. The analysis revealed that mobile apps related to depression and insomnia were primarily utilized for early detection, assessment, and screening (n=5 studies); counseling and psychological support (n=3 studies); and cognitive behavioral therapy (CBT; n=10 studies). Among the 10 studies related to depression, our findings showed that chatbots demonstrated significant advantages in improving depression symptoms, a promising development in the field. Additionally, 2 studies evaluated the effectiveness of mobile apps as alternative interventions for depression and sleep, further expanding the potential applications of this technology.
The integration of AI and deep learning into mobile apps, particularly chatbots, is a promising avenue for personalized mental health support. Through innovative features, such as early detection, assessment, counseling, and CBT, these apps significantly contribute toward improving sleep quality and addressing depression. The reviewed chatbots leveraged advanced technologies, including natural language processing, machine learning, and generative dialog, to provide intelligent and autonomous interactions. Compared with traditional face-to-face therapies, their feasibility, acceptability, and potential efficacy highlight their user-friendly, cost-effective, and accessible nature with the aim of enhancing sleep and mental health outcomes.
新冠疫情对心理健康产生了深远影响，导致抑郁和失眠的发病率上升。目前，人工智能（AI）和深度学习彻底改变了与健康相关的移动应用程序，为心理健康提供了更有效的支持，并缓解了疫情期间可能出现的心理压力。早期的综述分别概述了使用移动应用程序治疗抑郁和失眠的情况。然而，现在迫切需要对同时治疗抑郁和失眠的移动应用程序进行系统评估，以揭示新的应用和研究空白。
本研究旨在系统地回顾和评估针对抑郁和失眠的移动应用程序，重点介绍其功能、有效性以及当前研究中的空白。
我们系统地检索了 2017 年至 2023 年期间发表的同行评议期刊文章，包括 PubMed、Scopus 和 Web of Science。纳入标准为：（1）关注同时治疗抑郁和失眠的移动应用程序，（2）涉及年轻人或成年参与者，以及（3）提供治疗效果数据的研究。由 2 名评审员独立进行数据提取。通过重复进行标题和摘要筛选以及全文筛选，完成了研究的选择。由一名评审员提取数据，另一名评审员验证数据，相应地完成偏倚风险评估。
在最初的 383 项研究中，经过标题、摘要筛选和重复项去除后，有 365 项被排除在外。最终，有 18 篇全文文章符合我们的标准，并进行了全文筛选。分析结果显示，与抑郁和失眠相关的移动应用程序主要用于早期检测、评估和筛查（n=5 项研究）；咨询和心理支持（n=3 项研究）；以及认知行为疗法（CBT；n=10 项研究）。在 10 项与抑郁相关的研究中，我们的发现表明，聊天机器人在改善抑郁症状方面具有显著优势，这是该领域的一项有前途的发展。此外，有 2 项研究评估了将移动应用程序作为抑郁和睡眠替代干预措施的有效性，进一步扩展了这项技术的应用潜力。
将人工智能和深度学习集成到移动应用程序中，特别是聊天机器人，是个性化心理健康支持的有前途的途径。通过创新功能，如早期检测、评估、咨询和 CBT，这些应用程序显著有助于改善睡眠质量和治疗抑郁。综述的聊天机器人利用了自然语言处理、机器学习和生成式对话等先进技术，提供智能和自主交互。与传统的面对面治疗相比，它们的可行性、可接受性和潜在疗效突出了其用户友好、经济高效和可及性，旨在改善睡眠和心理健康结果。
