Anind K. Dey
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View article: Emotion-Adaptive LLM-Driven Clinical Decision Support: User Evaluation of the Empathic-CDSS Framework for Trust and Explainability (Preprint)
Emotion-Adaptive LLM-Driven Clinical Decision Support: User Evaluation of the Empathic-CDSS Framework for Trust and Explainability (Preprint) Open
BACKGROUND The increasing prevalence of cannabis use has motivated researchers to develop computational behavioral models that predict usage patterns and related health impacts in naturalistic environments. However, the opaque nature of m…
View article: Integrating Multi-Ancestry Polygenic Risk Scores and Wearable Data to Detect Depression and Gene-Environment Interactions in Youth
Integrating Multi-Ancestry Polygenic Risk Scores and Wearable Data to Detect Depression and Gene-Environment Interactions in Youth Open
Background Depression (DEP) has an estimated heritability of ∼37%. Polygenic risk scores (PRS), which aggregate common genetic variant effects, account for up to 8.4% variance in case versus control status. Understanding the relationships …
View article: A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios
A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios Open
Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformat…
View article: Towards Human-Centered Early Prediction Models for Academic Performance in Real-World Contexts
Towards Human-Centered Early Prediction Models for Academic Performance in Real-World Contexts Open
Supporting student success requires collaboration among multiple stakeholders. Researchers have explored machine learning models for academic performance prediction; yet key challenges remain in ensuring these models are interpretable, equ…
View article: Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study
Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study Open
Background Experiences of unfair treatment on college campuses are linked to adverse mental and physical health outcomes, highlighting the need for interventions. However, detecting such experiences relies mainly on self-reports. No prior …
View article: Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation
Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation Open
Background Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s environment may improve self-monitoring and clinical management for people with MS. Conventional symptom tracking methods rely on self-reports and clin…
View article: Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation (Preprint)
Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation (Preprint) Open
BACKGROUND Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s environment may improve self-monitoring and clinical management for people with MS. Conventional symptom tracking methods rely on self-reports and cli…
View article: Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments
Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments Open
Background Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s own environment may improve self-monitoring and clinical management for people with MS (pwMS). Objective We present a machine learning approach that en…
View article: Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study
Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study Open
Background Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana into…
View article: Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults
Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults Open
As an increasing number of states adopt more permissive cannabis regulations, the necessity of gaining a comprehensive understanding of cannabis's effects on young adults has grown exponentially, driven by its escalating prevalence of use.…
View article: Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention Open
Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explaina…
View article: Illuminating the Unseen: Investigating the Context-induced Harms in Behavioral Sensing
Illuminating the Unseen: Investigating the Context-induced Harms in Behavioral Sensing Open
Behavioral sensing technologies are rapidly evolving across a range of well-being applications. Despite its potential, concerns about the responsible use of such technology are escalating. In response, recent research within the sensing te…
View article: Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults
Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults Open
This study explores the possibility of facilitating algorithmic decision-making by combining interpretable artificial intelligence (XAI) techniques with sensor data, with the aim of providing researchers and clinicians with personalized an…
View article: Mental-LLM
Mental-LLM Open
Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. I…
View article: Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study
Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study Open
Background Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression. Objective This stu…
View article: A Framework for Designing Fair Ubiquitous Computing Systems
A Framework for Designing Fair Ubiquitous Computing Systems Open
Over the past few decades, ubiquitous sensors and systems have been an\nintegral part of humans' everyday life. They augment human capabilities and\nprovide personalized experiences across diverse contexts such as healthcare,\neducation, a…
View article: College Students’ Daily Mind Wandering is Related to Lower Social Well-Being
College Students’ Daily Mind Wandering is Related to Lower Social Well-Being Open
Objective. This study sought to examine how daily mind wandering is related to loneliness, felt connection to others, and school belonging among college students.Participants. Three samples (n = 209, n = 173, and n = 266) on two US campuse…
View article: Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study (Preprint)
Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study (Preprint) Open
BACKGROUND Acute marijuana intoxication can impair motor skills and cognitive functions such as attention and information processing. However, traditional tests, like blood, urine, and saliva, fail to accurately detect acute marijuana int…
View article: Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data Open
Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. I…
View article: Nightly sleep duration predicts grade point average in the first year of college
Nightly sleep duration predicts grade point average in the first year of college Open
Academic achievement in the first year of college is critical for setting students on a pathway toward long-term academic and life success, yet little is known about the factors that shape early college academic achievement. Given the impo…
View article: Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study
Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study Open
Background Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering ju…
View article: A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Controlled Trial
A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Controlled Trial Open
Background Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and prel…
View article: GLOBEM
GLOBEM Open
There is a growing body of research revealing that longitudinal passive sensing data from smartphones and wearable devices can capture daily behavior signals for human behavior modeling, such as depression detection. Most prior studies bui…
View article: GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization Open
Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fai…
View article: A chronology of SIGCHI conferences
A chronology of SIGCHI conferences Open
research-article Open AccessA chronology of SIGCHI conferences: 1983 to 2022 Authors: Neha Kumar Georgia Institute of Technology Georgia Institute of TechnologyView Profile , Julie A. Adams Oregon State University Oregon State UniversityVi…
View article: Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study (Preprint)
Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study (Preprint) Open
BACKGROUND Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering j…
View article: Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns
Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns Open
The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a considerat…
View article: A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Trial (Preprint)
A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Trial (Preprint) Open
BACKGROUND Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and pre…
View article: Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping
Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping Open
Background The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective We presented a machine learning approach leveraging passi…
View article: TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction
TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction Open
Smartphone overuse is related to a variety of issues such as lack of sleep and anxiety. We explore the application of Self-Affirmation Theory on smartphone overuse intervention in a just-in-time manner. We present TypeOut, a just-in-time i…