Abstract

Mobile technologies create new opportunities to develop personalized human-computer interfaces that respond to everyday changes in behaviors. Such interventions are fueled by computational models that need information on behaviors in the real-world. Ideally, sensors embedded on mobile devices could measure these behaviors. But we currently need self-report to capture subjective experiences that sensors cannot measure directly (e.g., fatigue, pain, and productivity). Ecological momentary assessment (EMA) or experience sampling method (ESM) is one such approach that enables in-situ self-report data collection using smartphones. In EMA, users are prompted several times a day on their phones to answer sets of multiple-choice questions. The in-situ nature of EMA reduces recall bias compared to long-form traditional questionnaires. Additionally, the repeated nature of EMA helps capture variations in experiences unique to each user. But, this method of data collection poses a heavy burden on the end users, impacting both the quality and quantity of collected survey data. Aditya’s work is driven by a fundamental question: Given this trade-off between user burden and data quantity, how can we scale experience sampling in real-world settings?

In this talk, Aditya will present a novel (and first-of-its-kind) EMA approach called μEMA that uses micro-interactions on the Smartwatch to collect survey data in natural settings – both at high-frequency and at large-scale. The μEMA restricts EMA interruptions to single, cognitively simple questions that can be answered on a smartwatch with a single tap – a quick, glanceable microinteraction like checking time. Because of this microinteraction, μEMA permits substantially higher interruption than EMA without as much user burden. Aditya will present results from several pilot studies and an year long longitudinal evaluation of μEMA to show that this method: 1) yields higher response rates from end users, 2) poses less burden on users, and 3) produces accurate information on user’s momentary experiences. Finally, Aditya will discuss potential applications of μEMA method for data collection, user behavior modeling, and personalization.

About Aditya

Aditya is an interdisciplinary researcher interested in human-computer interaction and behavior science, with a focus on measuring and modeling user experiences. He is currently working as a Sr. Researcher at MongoDB focusing on developer experiences and growth. Previously he was a Research Scientist at Spotify (as a member of Human-AI Interaction Lab), where he developed and studied interactive recommender systems and computational approaches to help smaller podcasters and newer music content grow audience on the platform. He completed his PhD in Computer and Health Sciences from Northeastern University, Boston, MA, where his research led to the development of novel experience

sampling tools, crowdsourcing platforms, and open source tools to perform multi-level modeling and data annotation on high-frequency longitudinal mobile data. His research has been published in peer-reviewed competitive venues including ACM IMWUT (UbiComp), ACM CSCW, ACM IUI, ACM WebSci, ACM CHIPLAY, IEEE PerComm, NeurIPS Workshops, Behavior Research Methods, Journal of Medical Internet Research, and Translational Behavior Medicine and has won two best paper awards.

This Event will be held in person at 11 Leon Street, Boston, MA. Ryder Hall, Room 180 (first floor), and remotely.

Abstract: The pervasiveness of sensor-rich mobile, wearable, and IoT devices has enabled researchers to passively sense various user traits and characteristics, which in turn have the potential to detect and predict different mental- and behavioral-health outcomes. Upon detecting or anticipating a negative outcome, the same devices can be used to deliver in-the-moment interventions and support to help users. One important factor that determines the effectiveness of digital health interventions is delivering them at the right time: (1) when a person needs support, i.e., at or before the onset of a negative outcome, or a psychological or contextual state that might lead to that outcome (state-of-vulnerability); and (2) when a person is able and willing to receive, process, and use the support provided (state-of-receptivity).

In this talk, Varun will start with an overview of his research about when to deliver interventions by exploring and detecting both vulnerability and receptivity. A majority of this talk, however, will focus on the receptivity component. First, Varun will discuss a two-part project regarding methods to explore and detect receptivity to interventions aimed at improving physical activity and how it can guide the design, implementation, and delivery of future mHealth interventions. Next, Varun will discuss a project to understand how people interact with affective well-being interventions while driving in their daily life. Finally, Varun will discuss his long-term vision for building complete solutions that span the entire lifecycle of a digital health intervention (from sensing to intervention delivery) for various mental and behavioral health outcomes by answering “what,” “when,” and “how” to deliver interventions.

Bio: Dr. Varun Mishra is an assistant professor at Northeastern University, holding a joint appointment with the Khoury College of Computer Sciences and the Bouvé College of Health Sciences. Dr. Mishra’s research focuses on leveraging ubiquitous technologies like smartphones and wearables to enable effective digital health interventions for mental and behavioral health outcomes. His research is in the broad field of Ubiquitous Computing and lies at the intersection of mobile/wearable sensing, human-centered computing, data science, and behavioral science. Dr. Mishra’s work is highly interdisciplinary, and he regularly collaborates with clinicians, psychologists, engineers, and other computer scientists to design, build, and deploy the tools and systems needed for their collective research goals. Dr. Mishra’s work is supported by NIH/NIDA and has been published in top-tier venues in both computing and medicine, like UbiComp/IMWUT, ACM HEALTH, MobiCom, Annals of Behavioral Medicine, and JMIR.

This event will be held at Northeastern University in West Village H, Room 108 and online.

Abstract

We live a storied life — people’s stories at present and in the past guide our future actions. Although this narrative mode of knowing complements the pragmatic mode, personal health informatics systems typically only support the pragmatic mode of knowing. In this talk, I will present my research on personal health informatics that uses storytelling to support health behavior in marginalized communities. These studies examined how storytelling technologies can amplify social connections and knowledge within the family and neighbors, thus supporting health behavior as a collective community effort.

About Herman Saksono

Herman Saksono is an Assistant Professor at Northeastern University. Herman’s interdisciplinary research contributions include Personal Health Informatics, Human-Computer Interaction, and Digital Health Equity. His research investigates how digital tools can catalyze social interactions that encourage positive health behaviors, thus facilitating collective efforts toward health equity. He conducts the entire human-centered design process by designing, building, and evaluating innovative health technologies in collaboration with local community partners.

Location

This is a hybrid event, to be held at Northeastern University, 440 Huntington Ave, West Village H 110, Boston, MA and online using Zoom. The Zoom link will be provided to registered attendees ahead of the event.