In today’s society, children are often exposed to many different types of stimuli, such as nature sounds and artificial ones. These sounds can have calming and relaxing effects or can create engaging environments with elevated excitement. As relaxation plays a vital role in mental health, finding effective methods to promote this is essential. This exploratory pilot feasibility study aims to measure alpha and beta brainwave activity to investigate the impact of different sound stimuli on the relaxation levels of adolescents. Four volunteers (age range: 9–15 years) participated in two trials with the tested variables: nature sounds and video game music. The procedure involved wearing an electroencephalogram (EEG) cap for approximately 30 minutes while listening to nature sounds (5 minutes) and video game music (5 minutes), followed by a 10-minute break, then switching to the opposite sound pattern and repeating the listening session for the same amount of time. Throughout the experiment, the EEG cap measured brain activity (primarily alpha waves) before and after implementing the independent variables (sound stimuli) to assess the levels of relaxation. Before listening, the EEG showed moderate alpha waves with some beta activity, indicating mild alertness. After exposure to nature sounds, all of the participants’ alpha waves became more stable and increased, while beta brainwave activity decreased, reflecting neural patterns often associated with relaxation rather than directly indicating emotional calmness. In contrast, after listening to video game music, two participants’ alpha waves showed a reduction compared to normal waves without stimulation, while beta waves remained active, and all brainwaves were generally more engaged, indicating sustained neural engagement. For one of the four participants (age 13), their alpha waves were highly reduced, and their beta waves were stimulated, showing higher alertness than the other participants. All participants reported feeling more relaxed while listening to nature sounds and more alert and excited while listening to video game music. This exploratory pilot feasibility study demonstrated the influence of sound on relaxation in adolescents, suggesting that nature sounds were associated with brainwave patterns consistent with greater calmness. However, the findings are limited by the small sample size and single-session design, highlighting the need for follow-up studies. Findings should be considered preliminary and hypothesis-generating.
Mental health disorders are often misinterpreted or misdiagnosed, resulting in insufficient support. Artificial intelligence (AI) has emerged as a promising tool to improve the accuracy of early detection of mental disorders and facilitate early intervention. This study evaluates the effectiveness of AI-driven tools, including large language model (LLM) chatbots, speech analysis, and behavior tracking in identifying mental health disorders. This proof-of-concept study aimed to determine whether AI LLM models can improve diagnostic precision and enable earlier identification of at-risk individuals. This proof-of-concept study used the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition Text Revision ( DSM-5-TR ) as the primary reference for diagnostic criteria. Owing to restricted access to public clinical records, 200 patient profiles were randomly generated by AI to simulate real-world psychiatric case data using information about mental health disorders as outlined in the DSM-5-TR . These profiles incorporated key information such as name, age, sex, health condition, lifestyle, and symptoms. Four AI LLM models, including ChatGPT, DeepSeek, Gemini, and Claude, were used to evaluate diagnostic performance. Each model’s ability to identify mental health disorders based on behavioral patterns and emotional distress was assessed using the DSM-5-TR criteria. The four AI models were evaluated using the following five-tier rating system: success (4/4), substantial success (3/4), moderate success (2/4), partial success (1/4), or failure (0/4). A rating of “substantial success” or “success” was classified as a correct diagnosis. Based on this criterion, the models achieved a combined success rate of 89.5% across the 200 simulated profiles, with ChatGPT outperforming the others in terms of accuracy, organization, and speed. AI models often accurately identified the specific DSM-5-TR disorder, but when the precise diagnosis was missed, it still recognized the broader symptom category. Among the four AI models tested, ChatGPT delivered the best results due to its accuracy, speed, and well-organized responses. AI-based tools demonstrate significant potential in improving the accuracy and timeliness of mental health disorder diagnoses. Integrating these technologies into clinical practice could help healthcare providers enhance early intervention strategies and improve patient outcomes. Future research should focus on enhancing AI models for clinical integration, particularly by improving accuracy by considering additional factors.
Interpreting complex food labels and aligning them with individual health needs remains a challenge for consumers. This pilot study evaluated the ability of large language models (LLMs) to analyze structured patient profiles and provide personalized nutritional guidance based on medical history and food ingredient data. Twenty synthetic patient profiles were generated, varying by age, sex, height, weight, and medical history. Each profile was paired with a set of 10 food items (categorized as healthy, moderate, or unhealthy). These inputs were submitted to four LLMs, including ChatGPT, Claude, Gemini, and DeepSeek. The models were instructed to assess the health impact of each food item per patient and classify it as beneficial (green), moderate (yellow), or harmful (red). All LLMs accurately flagged high-sugar or ultra-processed foods as harmful and healthy items as beneficial. However, their classification accuracy and depth of reasoning varied: ChatGPT (72.5%), Claude (67%), Gemini (63.5%), and DeepSeek (63%). Only ChatGPT consistently provided both accurate classifications and personalized, relevant alternatives. Other models lacked consistency, specificity, or justification in their responses. ChatGPT demonstrated the highest accuracy and interpretability, suggesting its potential utility in delivering personalized dietary recommendations based on individual health data. Integrating advanced LLMs into digital health tools could enhance nutrition guidance, especially for individuals with diet-sensitive conditions such as diabetes or obesity.
