The intersections of mental health and computing education is under-examined. In this systematic literature review, we evaluate the state-of-the-art of research in mental health and well-being interventions, assessments, and concerns like anxiety and depression in computer science and computing education. The studies evaluated occurred across the computing education pipeline from introductory to PhD courses and found some commonalities contributing to high reporting of anxiety and depression in those studied. In addition, interventions that were designed to address mental health topics often revolved around self-guidance. Based on our review of the literature, we recommend increasing sample sizes and focusing on the design and development of tools and interventions specifically designed for computing professionals and students.
This research paper studies about the role of social media use and increase the risk factor of mental health during covid 19 or lockdown. Although few studies have been conducted on the role about the effect of social media use on mental health during lockdown and impact on human reactive nature during lockdown. As a rapidly spreading pandemic, a biomedical disease has serious physical and tremendous mental health implications. An occupational community of internal migrant workers is one of the most vulnerable, but neglected, and is likely to develop psychological ill-effects due to COVID-19's…
E-mental health is an area within e-health in which the key role of IS/IT has not been well established. Both clinicians and scholars are uncertain as to the role of IS/IT and its potential benefits. This research review is introduced to assist in understanding the enabling role in e-mental health and it focused on one area of mental health, Attention Deficit Hyperactivity Disorder (ADHD) in university students. ADHD is estimated to affect approximately 6% of university students by negatively impacting students' academic performance and social life; hence, fewer of these students complete thei…
This paper seeks to present the findings of a focus group and questionnaire in assessing how aware mental health professionals, who have experience with people with Borderline Personality Disorder (BPD), are in the extent of ICT based support for people with BPD. The methods used were both qualitative and quantitative and used descriptive data. Content analysis was used to explore specific themes and results were cross-examined between the two methods. The work should be viewed as an exploratory study into the viability and likely acceptance of a virtual support community specifically designed…
Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders filling the much-needed patient-provider accessibility gap. However, it also raises significant concerns with sensitive information leakage.The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermini…
Using the metrics of the World Health Organisation, the Global Burden of Disease Study has found that mental health difficulties are currently the leading cause of disability in developed countries [1]. Projections also indicate that the global burden of mental health difficulties will continue to rise in the coming decades. The human and economic costs of this trend will be substantial. In this paper we discuss how effectively designed interactive systems, developed through collaborative, interdisciplinary efforts, can play a significant role in helping to address this challenge. Our discussi…
University of Florida（佛罗里达大学）；TAO Connect, Inc.（TAO Connect公司）
As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online. Most recent research has focused on mining social media for public user opinion about external entities such as product reviews or sentiment towards political news. However, less attention has been paid to analyzing users' internalized thoughts and emotions from a mental health perspective. In this paper, we quantify the semantic difference between public Tweets and private mental health journals used in online cognitive behavioral therapy. We…
Many people struggling with mental health issues are unable to access adequate care due to high costs and a shortage of mental health professionals, leading to a global mental health crisis. Online mental health communities can help mitigate this crisis by offering a scalable, easily accessible alternative to in-person sessions with therapists or support groups. However, people seeking emotional or psychological support online may be especially vulnerable to the kinds of antisocial behavior that sometimes occur in online discussions. Moderation can improve online discourse quality, but we lack…
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions
Sharif University of Technology（谢里夫理工大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University at Buffalo–SUNY（纽约州立大学布法罗分校）；North Carolina State University（北卡罗来纳州立大学）
This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the practitioner's toolbox. The goal of this chapter is to provide a brief overview of the field, discuss the recent use of robotics technology in mental health care practice, explore some of the design issues and ethical issues of using robots in this space, and finally to explore the potential of emerging technology.
Interpersonal relationships are necessary for successful daily functioning and wellbeing. Numerous studies have demonstrated the importance of social connectivity for mental health, both through direct peer-to-peer influence and by the location of individuals within their social network. Passive monitoring using smartphones provides an advanced tool to map social networks based on the proximity between individuals. This study investigates the feasibility of using a smartphone app to measure and assess the relationship between social network metrics and mental health. The app collected Bluetoot…
While non-suicidal self-injury (NSSI) is not a new phenomenon, there is still a limited yet little is still known about understanding of the behavior, the intent behind the behavior and what the individuals themselves say about their behavior. This study collected pro-NSSI public blog posts from Reddit on pro-NSSI and analyzed the content linguistically using LIWC software, in order to examine the use of NSSI specific words, linguistic properties and the psychological linguistic properties. were examined. The results inform current counseling practices by dispelling myths and providing insight…
The Cost-Benefit of Interdisciplinarity in AI for Mental Health
Center for Social Data Science, Faculty of Social Sciences, University of Copenhagen（哥本哈根大学社会科学学院社会数据科学中心）；Dept. of Applied Mathematics and Computer Science, Technical University of Denmark（丹麦技术大学应用数学与计算机科学系）；Institute for Ethics in Artificial Intelligence, School of Social Sciences and Technology, Technical University of Munich（慕尼黑技术大学社会科学与技术学院人工智能伦理研究所）；Dept. of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）；Child and Adolescent Mental Health Center, Copenhagen University Hospital（哥本哈根大学医院青少年与儿童心理健康中心）
Purpose. We present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on language expressions used in social media. This approach permits anticipating high prevalence periods in short- to medium-term time horizons. Design. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, we build descriptive timelines for decision makers to monitor the evolution of markers, and their correlation with crisis events. Second, we model the timelines as time ser…
A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots
University of Illinois Urbana-Champaign, Urbana, United States（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis, Indianapolis, United States（印第安纳大学印第安纳波利斯分校）
University of Warwick（华威大学）；The Alan Turing Institute（艾伦·图灵研究所）；Durham University（杜伦大学）
Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies. Such approaches have the potential to revolutionise mental health assessment, if their development and evaluation follows a real world deployment setting. In this work we take a closer look at state-of-the-art approaches, using different mental health datasets and indicators, different feature sources and multiple simulations, in order to assess their ability to generalise. We demonstrate that under a pragm…
Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
IDEAS Research Institute（IDEAS研究 institute）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；AMU Center for Artificial Intelligence（AMU人工智能中心）；Poznań University of Medical Sciences（波兹南医学科学大学）；Maria Curie-Skłodowska University（玛丽·居里-斯克洛多夫斯卡大学）
Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis
University of Minnesota（明尼苏达大学）；Northwestern University（西北大学）；Dartmouth College（达特茅斯学院）；Microsoft（微软）；Mental Health America（心理健康美国）；Microsoft Research（微软研究院）
A Conditional Companion: Lived Experiences of People with Mental Health Disorders Using LLMs
Center for Advanced Internet Studies Bochum Germany；Center for Advanced Internet Studies \& University of Wuppertal Bochum Germany；Center for Advanced Internet Studies；Center for Advanced Internet Studies \& University of Wuppertal
Mental health conditions cause a great deal of distress or impairment; depression alone will affect 11% of the world's population. The application of Artificial Intelligence (AI) and big-data technologies to mental health has great potential for personalizing treatment selection, prognosticating, monitoring for relapse, detecting and helping to prevent mental health conditions before they reach clinical-level symptomatology, and even delivering some treatments. However, unlike similar applications in other fields of medicine, there are several unique challenges in mental health applications wh…
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Department of Electrical Engineering and Computer Science, Indian Institute of Science Education and Research Bhopal（电子工程与计算机科学系，比哈尔印度科学教育与研究中心）
University of California, Irvine（加州大学尔湾分校）；University of Turku（图尔库大学）
Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits. Recent advances in wearable and mobile technology have generated increased interest in digital mental healthcare that enables individuals to address episodic mental health symptoms. However, these efforts are typically reactive and symptom-focused and do not provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds…
While the applications and demands of Machine learning (ML) systems in mental health are growing, there is little discussion nor consensus regarding a uniquely challenging aspect: building security methods and requirements into these ML systems, and keep the ML system usable for end-users. This question of usable security is very important, because the lack of consideration in either security or usability would hinder large-scale user adoption and active usage of ML systems in mental health applications. In this short paper, we introduce a framework of four pillars, and a set of desired proper…
Université du Québec à Montréal（魁北克大学蒙特利尔分校）；Montreal AI Ethics Institute（蒙特利尔人工智能伦理研究所）；Microsoft（微软）
Introduction: To improve current public health strategies in suicide prevention and mental health, governments, researchers and private companies increasingly use information and communication technologies, and more specifically Artificial Intelligence and Big Data. These technologies are promising but raise ethical challenges rarely covered by current legal systems. It is essential to better identify, and prevent potential ethical risks. Objectives: The Canada Protocol - MHSP is a tool to guide and support professionals, users, and researchers using AI in mental health and suicide prevention.…
Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
Department of Economics University of Southern California（经济学系 美国南加州大学）；Electrical and Computer Engineering Purdue University（电气与计算机工程系 帕克森大学）；Information Sciences Institute University of Southern California（信息科学研究所 美国南加州大学）
