JMIR Publications

ABSTRACT

Background:

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 healthcare-related mobile applications, 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 applications for dealing with depression and insomnia separately. There is now an urgent need for a systematic evaluation of mobile applications that address both depression and insomnia in order to reveal new applications and research gaps.

Objective:

To systematically review and evaluate mobile applications targeting depression and insomnia, highlighting their features, effectiveness, and gaps in the current research.

Methods:

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 included (1) focused on mobile applications 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 two 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.

Results:

Out 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 applications related to depression and insomnia were primarily utilized for early detection, assessment, and screening (5 studies), counseling and psychological support (3 studies), and Cognitive Behavioral Therapy (CBT) (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, two studies evaluated the effectiveness of mobile applications as alternative interventions for depression and sleep, further expanding the potential applications of this technology.

Conclusions:

The integration of AI and deep learning into mobile applications, particularly chatbots, is a promising avenue for personalized mental health support. Through innovative features, such as early detection, assessment, counseling, and Cognitive Behavioral Therapy (CBT), these applications significantly contribute toward improving sleep quality and addressing depression. The reviewed chatbots leveraged advanced technologies, including Natural Language Processing (NLP), machine learning, and generative dialogue, to provide intelligent and autonomous interactions. Compared to 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.