Prior research on young adults' mental health help-seeking mostly focuses on one particular resource such as a mobile app or digital platform, paying less attention to their lived experiences interacting with the ecosystem of resources. We conducted in-depth interviews with 18 participants about their help-seeking and non-help-seeking experiences. Guided by Social Ecological Theory, we proposed a Socio-technical Ecosystem Framework for mental health care, consisting of four levels of resources, including technological-, interpersonal-, community-, and societal level resources. Using this framework, we identified two types of support systems for help-seeking, single-resource support system and multi-resource support system. These resources support young adults' help-seeking via three mechanisms, \textit{care-giving}, \textit{care-mediating}, and \textit{care-outreaching}, forming various pathways to care. We then pointed out the barriers to resource use at each level and the general challenges in finding a support system. Our findings contributed to a conceptual framework to categorize mental health care. It also serves as a practical framework to identify challenges in the pathways to care and discover design implications.
Co-Designing AI For Mental Health Support With Young Adults of Color (YOC): Needs, Expectations, and Implications for AI Literacy
University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）；University of Cincinnati（辛辛那提大学）；University of Washington（华盛顿大学）；Center for Democracy & Technology（民主与技术中心）
Predicting suicidal behavior among Indian adults using childhood trauma, mental health questionnaires and machine learning cascade ensembles
Indian Institute of Technology Mandi（印度理工学院曼迪分校）；Wellness Space LLP（Wellness Space 有限责任合伙企业）；Post Graduate Institute of Medical Education and Research（医学教育与研究生院）
A critical component of user studies is gaining access to a representative sample of the population researches intend to investigate. Nevertheless, the vast majority of human-computer interaction (HCI)studies, including augmented reality (AR) studies, rely on convenience sampling. The outcomes of these studies are often based on results obtained from university students aged between 19 and 26 years. In order to investigate how the results from one of our studies are affected by convenience sampling, we replicated the AR-supported language learning study called VocabulARy with 24 teenagers, age…
"It feels like hard work trying to talk to it": Understanding Older Adults' Experiences of Encountering and Repairing Conversational Breakdowns with AI Systems
Effective communication and strong therapeutic relationships are critical to successful mental health interventions. For example, in 1957 Carl Rogers, a pioneer of person-centred therapy, proposed that an empowering relationship could, in and of itself, create the necessary and sufficient conditions for positive therapeutic outcomes [1]. Whilst modern psychological theories no longer favour an exclusive focus on relationships, positive relationships and the dynamics of client-therapist communication remain cornerstones of mental health intervention theories. A more recent meta-review concluded…
"I Don't Want My Mental Health App To Give Me Mental Health Barriers": Unpacking The Need For Digital Mental Health Tracking Services With And For The Blind Community
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
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）
Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Maryland, Baltimore（马里兰大学巴尔的摩分校）
Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal process…
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）
A Comparative Analysis of Peer Support in Forum-based and Chat-based Mental Health Communities: Technical-Structural-Functional Model of Social Support
Extended Reality for Mental Health Evaluation -A Scoping Review
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机科学学院）；School of Information Technology and Electrical Engineering, University of Queensland（昆士兰大学信息技术与电气工程学院）；Department of Psychiatry, Dalhousie University（达尔豪斯大学精神病学系）
A diverse representation of different demographic groups in AI training data sets is important in ensuring that the models will work for a large range of users. To this end, recent efforts in AI fairness and inclusion have advocated for creating AI data sets that are well-balanced across race, gender, socioeconomic status, and disability status. In this paper, we contribute to this line of work by focusing on the representation of age by asking whether older adults are represented proportionally to the population at large in AI data sets. We examine publicly-available information about 92 face…
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…
