Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Mar 28, 2024
Date Accepted: Apr 2, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence-Based Mobile Phone Applications for Child Mental Health: A Systematic Review and Content Analysis
ABSTRACT
Background:
Mobile phone apps powered by Artificial Intelligence (AI) have emerged as powerful tools to address mental health challenges faced by children. This study aimed to comprehensively review AI-driven apps for child mental health, assessing their availability, quality, readability, characteristics, and functions.
Objective:
This study aimed to comprehensively review AI-driven apps for child mental health, assessing their availability, quality, readability, characteristics, and functions.
Methods:
Utilizing a systematic review approach, we initially identified 369 apps, which, after screening eligibility, resulted in 27 apps being included in this study. Quality was evaluated using the Mobile Application Rating Scale (MARS). A readability calculator was implemented to assess readability by utilizing the count of words, syllables, and sentences to generate a score indicative of the reading difficulty level. Content analysis was conducted to evaluate the apps’ availability, characteristics, and functionality.
Results:
Evaluation of the apps revealed three functional categories: chatbot (15 apps), journal logging (9 apps), and psychotherapeutic treatment (3 apps). A majority (74.1%) employed natural language processing technology. The average MARS score of 3.45 out of 10 demonstrated a need for quality improvement. Low readability (averaged 6.04 for the content and 9.44 for the introduction in the app store) and monotonous user interface implied inadequate child-friendly design. While 74.1% of apps used clinically validated technologies, rigorous clinical tests of the apps were often missing, with only one app undergoing a clinical trial. Concerns over response accuracy were also identified. High costs (74.1% required payment with a mean of $20.16/month) could also limit accessibility.
Conclusions:
This study highlighted an urgent need for improved quality, child-friendly design, rigorous clinical testing, and affordable pricing to maximize the benefits of these AI-based mental health apps for children.
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Copyright
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