News | Samprit Banerjee Lab

Presentation at ENAR 2024

Dr. Younghoon Kim gave a presentation at the Eastern North American Region International Biometric Society Spring 2024 Meeting: ENAR – A Home for Every Biostatistician on March 11th in Baltimore, Maryland. His presentation was titled 'Detecting Emotionally Stressful Periods from Passive Sensing Data via Mobile Devices.'

Younghoon presenting at ENAR

Here is the abstract from his presentation: It is crucial for psychotherapy to prevent the negative effects of emotional stress in middle-aged and older adults with chronic pain and depression by detecting the early onset of stressful periods. This study proposes a data-driven emotional stress detection algorithm. The algorithm identifies participants' onset of emotionally stressful periods using passive sensing data related to physical activities, such as step counts and activity duration. The algorithm consists of three separate parts. Firstly, it estimates the time points of changes in the distributions of passive sensing data. Then, the algorithm validates whether the detected changes are statistically associated with changes in the response variable, constructed from self-reported stress levels and stress-related scores. Finally, the algorithm trains a classifier to predict whether the patient is in a stress period at future time points using statistical and physical features computed from each segmented passive sensing data. The algorithm is applied to the ALACRITY Phase 1 data, and its prediction performance and the effectiveness of each step are evaluated using various metrics.

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Presentation at AAGP 2024

Yuqing Qiu presented in the New Research Oral Presentation session on March 18th 2024 at the American Association for Geriatric Psychiatry (AAGP) Annual Meeting: Reimagining Geriatric Mental Health: Innovations to promote the well-being of patients and caregivers in Atlanta, Georgia. Her presentation was titled 'Gamification via mHealth to Improve Adherence to Psychotherapy and Clinical Outcomes in Depressed Older Adults.

Yuqing presenting at AAGP

Isabel Rollandi from WCM's Deparment of Geriatric Psychiatry also presented at this session, titled 'Suicidal Ideation and Treatment Response Among Depressed Elder Abuse Victims,' a collaboration with our team.

Presentation at UAB Biostatistics Seminar Series 2023

Dr. Samprit Banerjee gave a presenation at the University of Alabama at Birmingham School of Public Health as part of their Biostatistics Seminar Series on November 17th. His presentation was titled 'mHealth in Mental Health: What Smartphones Can Tell Us About Our Mental Health?'

Presentation at the 2023 Thomas R. Ten Have Symposium on Statistics in Mental Health

Dr. Wenna Xi presented on June 9th at the 10th Annual Thomas R. Ten Have Symposium on Statistics in Mental Health in Boston, Massachusetts hosted by the Mental Health Statistics Section of the American Statistical Association, McLean Hospital, and Harvard Medical School. Her presentation was titled 'Analysis of Big Data in Mental Health Research: Opportunities and Challenges.'

Wenna presenting at Tom Ten Have

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Presentation at CMStatistics 2023

Dr. Samprit Banerjee gave a presentation at the 16th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics) on December 16th in Berlin, Germany. His presentation was called 'Semi-supervised learning to predict adherence to psychotherapy with mHealth data.'

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Here is the abstract from his presentation: Smartphones provide an interactive interface that can passively measure various aspects of the users' behaviour from device sensors and actively collect self-ratings (e.g., mood, stress, etc.) obtained via daily ecological momentary assessment. Taken together with traditional clinical assessments, these measures have the potential to provide unique insight into the treatment trajectories of patients with major depressive disorder undergoing psychotherapeutic treatment. Specifically, patient adherence to psychotherapy sessions is a necessary first step to assess barriers to adherence and personalize future sessions in order to improve adherence and, therefore, efficacy. Such predictions have unique challenges due to the noisy nature (missing or under-reporting) of passive and active mHealth data. The nature of missing passive data is unique in the sense that the missed labels are not observed. These and other challenges of mHealth data analysis are introduced, and semi-supervised machine learning algorithms are proposed to address these challenges.