[R01] PASSPORT: PASsive Sensing to Prevent stress-related Overeating in Real-Time
Ente: National Heart Lung and Blood Institute
Scadenza: 2030-06-30
Importo max: 662.787 EUR
Paese: US
Descrizione
Project Summary/Abstract
Stressful events, overeating, and obesity have been associated with cardiovascular disease (CVD).
Characterizing the relationship between these factors may inform targeted and timely interventions to prevent
overeating episodes. To date most research relies on self-reports to identify stress and overeating episodes.
Subjective self-reports are often retrospective and do not capture continuous physiological patterns to enable
automated predictors of overeating. There is a need for objective approaches that continuously measure stress
and overeating in real-time to further advance understanding of stress patterns that contribute to overeating.
Wearable devices have become a powerful source for collecting health-related data using embedded sensors.
Significant advances in technology have been made, including use of machine learning algorithms that process
wearable data in real time. However, it is unclear how these real time indicators advance our understanding of
the relationship between stress, overeating, and obesity status. Moreover, most wearable devices collect
sensor data in real time and process the data offline post hoc. We have developed a Band-Aid-like flexible
wearable that can collect electrocardiography, photoplethysmography, and skin temperature data and an
infrared (IR)-enabled camera that can collect both IR and color images. Building upon our preliminary
research, the device can detect 1) stress using the Band-Aid-like device’s data and 2) eating using the IR-
enabled camera’s data among a general population in free-living conditions. Data from these devices will then
be used to build reliable and resilient (i.e., adaptive to the individual over time) machine-learned models that
run in real time. Deploying machine learning algorithms with these novel features will likely improve stand-
alone devices that can detect stress or overeating in real time, making them a viable option for timely
intervention compared to existing wearable devices that simply collect sensor data for offline analysis.
The aims of this study are to refine and deploy machine learning algorithms to detect stress (on a Band-Aid-
like device) and overeating (on an IR-enabled camera) in real time. We will first assess the robustness of the
stress detection model and refine the machine learning algorithms in a controlled setting where we can induce
stress. Next, we will test the performance of these algorithms in real-world settings. We will then use this
information to identify patterns of stress that can predict overeating. As an exploratory aim, we will determine if
dynamically changing and personalizing the models to each individual improves model performance. This
project has far reaching implications as it will further understanding of patterns in stress and overeating, and
their relationship, to help predict overeating - thus providing fundamental knowledge about how we can deliver
timely behavioral interventions to reduce CVD risk.
Istituzione: NORTHWESTERN UNIVERSITY
PI: Nabil Alshurafa, Angela Fidler Pfammatter
Progetto: 5R01HL173291-02
Settori: National Heart Lung and Blood Institute
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