Objectives: The purpose of this study was to classify the users of a university counseling center and define their characteristics using the information from the service application written by them as one of the approaches to improve the efficiency of the center. Methods: The participants were a total of 300 users of the university counseling and affiliated psychiatry center in the local science and technology institute. The service application form used in this study contained questions on personal information, overall satisfaction with university life and interpersonal relationships, problem areas and symptoms, depression, anxiety, sleep, family history, suicidal thoughts, experiences of suicide/self-harm attempts, and a functional impairment questionnaire. First, a latent class analysis was performed to classify the layers and define them. Also, a tree-based model was used to confirm the characteristics of the service applications of the suicide/self-harm attempt experience group. Results: A total of 5 layers were found to be the most suitable fit. They were named the ‘health concern type,’ ‘relation problem type,’ ‘depression-sleep problem combined type,’ ‘complex problem type,’ and ‘sleep problem type.’ The variable that best discriminates the suicide/selfharm attempt experience group was question 5 of the Patient Health Questionnaire (PHQ-9). The frequency of selection in the impulsivity domain among the problem types and question 9 of the PHQ-9 was followed. Conclusion The results of this study indicate that five types of discriminatory interventions are possible based on the characteristics of the service application. The application can thus be used as basic data for increasing service efficiency in university counseling.

The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.

The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.

The coronavirus disease 2019 pandemic has exacerbated global mental health challenges, underscoring the urgent need for advancements in digital healthcare technologies. Digital therapeutics have demonstrated clinical utility across various domains; however, their development and implementation in the mental health remain constrained within the traditional clinical paradigm. This study conceptualizes and classifies digital interventions for mental health (DIMH), delineates their core technologies, and discusses their strengths and limitations.Current Concepts: DIMH interventions predominantly utilize cognitive behavioral therapy frameworks, targeting conditions such as anxiety, depression, and psychological distress. Despite their potential, low user engagement remains a persistent challenge, necessitating personalized approaches tailored to individual needs. Platforms bridge users and therapists, facilitating hybrid online-offline care; however, service delivery may falter due to shortages in provider availability. Emerging social chatbots, while not specifically designed for treatment, show promising outcomes but are limited by insufficient consideration of social contexts and potential interactional errors. Digital phenotyping, which leverages behavioral data such as location tracking and smartphone usage patterns, enhances the personalization of interventions by embedding social context. Additionally, virtual reality (VR) offers effective solutions for exposure therapy and social interaction training, serving as a viable alternative to traditional therapy.Discussion and Conclusion: This review underscores the complementary nature of DIMH technologies and advocates for an integrated approach. The convergence of group-based interventions, chatbots, digital phenotyping, and VR technology presents opportunities for more effective and personalized mental health care. Such advancements could redefine mental health services, addressing unmet needs and fostering innovation.