Abstract: Depression is a prevalent mental health disorder, and identifying effective treatment early remains a key challenge. Traditional assessments rely on clinical questionnaires, which are costly, infrequent, and prone to biases. With the rise of smartphones, new opportunities have emerged to monitor mental health using active self-reports and passive sensor data. To address the shortcomings of traditional assessments, we explore the use of smartphone-collected data—specifically, self-reported daily mood and anxiety ratings, medication survey and passive location-based sensory data—to predict depression treatment outcomes. In the first part, we evaluated 5-point Likert-scale mood and anxiety self-ratings as alternatives to clinical questionnaires. These self-reports significantly correlate with clinical scores (QIDS), achieving an F1 score of 0.65 using machine learning models like SVM, XGBoost, and Random Forest, compared to 0.71 using clinical questionnaires. In the second part, we investigate the use of passively collected location data for predicting treatment outcomes. To address the heterogeneity between Android and iOS data collection, we apply a domain adaptation technique (Correlation Alignment - CORAL) to align data from both platforms, significantly improving prediction performance. Our results show that combining location features with baseline clinical scores yields an F1 score of up to 0.67—comparable to periodic self-reported questionnaires—highlighting location data as a promising predictor of depression treatment outcomes.   In the third part, we examine whether smartphone data collected early in treatment, specifically during the initial 2–4 weeks, can reliably predict outcomes at the 12th week (the end of the treatment course). Early prediction of treatment outcomes is critical for enabling timely clinical decisions. This study integrates weekly medication surveys, daily mood and anxiety self-ratings, and location-based sensory data to predict depression treatment outcomes 8 to 10 weeks before treatment completion with help of deep learning models (GRU-D and BRITS). Our findings indicate that smartphone data collected in the early phases of treatment can achieve prediction accuracies comparable to weekly clinical questionnaires.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).

This therapy group provides a supportive and confidential space for Mandarin-speaking graduate students to explore challenges related to academic stress, cultural adjustment, relationships, and personal growth. Conducted in Mandarin, the group fosters connection and understanding among peers who share similar experiences. Provider: Michelle Chen, LPC

CCAM Seminar Series Speaker: Dr. Daniel Lobo, Associate Professor, Biological Sciences, University of Maryland Title: "Regulatory mechanisms of cell differentiation and body shape formation: from phenotypes to models" Abstract: Multicellular organisms develop tissues and body shapes through cell differentiation, proliferation, and migration. Understanding the regulatory mechanisms controlling spatial and dynamical patterns is a current challenge due to the complex feedback loops between molecular signals, mechanical forces, and the emergent cell types and tissue shapes they control. I will present our bioinformatics and systems biology approach, which integrates molecular assays, dynamic mathematical modeling, and de novo machine learning inference algorithms, to understand cell differentiation and body shape regulation. We demonstrated this methodology by understanding the whole-body regulation of planarian worm shapes, differentiation of human hematopoietic stem cells, and pattern formation of developmental synthetic biology systems. Location: Grossman Auditorium  Via Webex: https://uchc.webex.com/uchc/j.php?MTID=mf22ff6a646a108fce2728863c3525d53 (https://urldefense.com/v3/__https:/uchc.webex.com/uchc/j.php?MTID=mf22ff6a646a108fce2728863c3525d53__;!!Cn_UX_p3!jBUDSpwbSN_rW_NLEPwhm91Y0XBLQU5WXfOfDkC0s_Ji81eVoYhcZaxabbLEwHaaFOOePOvRPWWqqG_ykxo$) Meeting number (access code): 2863 037 1820 Meeting password: CCAMseminars Guest Host: Dr. Michael Blinov

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).