Project Adapt: Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: A Micro-randomized Trial

Project Adapt aims to promote adherence to dietary recommendations provided during lifestyle interventions focused on weight management. The research team has developed a smartphone-based “just-in-time” adaptive intervention that monitors risk for potential "lapses" from a program's dietary recommendations, uses machine learning to determine if risk for lapse is high, and provides in-the-moment suggestions to help a person prevent lapse when necessary. The finalized tool aims to personalize suggestions and prevent lapses during high-risk times by analyzing which intervention strategies are effective for different individuals.

The FUEL Program: Using Multimodal Real-Time Assessment to Phenotype Dietary Non-Adherence Behaviors that Contribute to Poor Outcomes in Behavioral Obesity Treatment

The FUEL Program uses digital health tools such as smartphones and wearable sensors to understand different types of lapses from the recommended diet during lifestyle interventions for weight management. Dietary lapses may involve different sets of behaviors, such as overeating or consuming foods outside of someone's planned diet. This project seeks to identify which types of lapse behaviors are associated with different outcomes—such as weight change and caloric intake—to determine which kinds of lapses could have the most clinical impact (and therefore warrant intervention). The results will allow for the development of targeted interventions for the most impactful behaviors instead of trying to prevent any and all lapses from an eating plan.    

The WATCH Study: Validating Sensor-based Approaches for Monitoring Eating Behavior and Energy Intake by Accounting for Real-World Factors that Impact Accuracy and Acceptability 

The WATCH Study aims to improve measurement of caloric intake by investigating the potential of smartwatches and smart rings to detect eating gestures (“bites” of food or “sips” of drinks) during mealtimes. The researchers are evaluating whether bites and sips measured via a smartwatch or smart ring can be used to estimate how many calories a person consumes during a meal. The study aims to create a reliable method for estimating calorie intake during meals, a valuable tool for researchers and clinicians to monitor dietary behaviors in various contexts.

PI: Stephanie Goldstein [supported by National Heart, Lung, and Blood Institute, National Institute of Diabetes, Digestive and Kidney Diseases]

Self-E

Summary: Self-E is an app that helps individuals run randomized experiments on themselves to better understand their own behaviors that affect their health, well-being, and productivity. 

Sochiatrist

Sochiatrist Social Data Extractor is an app that can help researchers understand how social messaging impacts mental health. The team is interested in understanding how private messaging (texts, direct messages, group chats) impacts a person’s emotional well-being and feeling of social support. The application can be used by researchers to collect anonymized messaging data for studies on mental health. 

PI: Jeff Huang [Self-E is funded by the National Science Foundation IIS-1656763 and an Advance-CTR Pilot Award. Sochiatrist is supported by NIH grants R21 HD088739-01, R01 MH108641-01A1, R01 MH110379-01A1, R01 MH105379-02S1, R01 HD095932-01A1, R01 MH124832-01, R01 HD104187-01, and Army Research Office grant 71881-NS-YIP.]

iENDURE: a combined web and text message-delivered intervention designed to support engagement in medication-based treatments for opioid use disorder. 

Designing an initial test of whether iENDURE improves clinical outcomes of medication-based treatments for opioid use disorder. The pilot randomized clinical trial has concluded, and data analysis is currently in progress. 

Project STAR (Supporting Transitions and Recovery) 

This study aims to assess the effectiveness of a mobile peer recovery support app for individuals receiving medication-based treatment for opioid use disorder while incarcerated. The research will investigate whether using the mobile app after release from jail enhances social connectedness and support and if this leads to improved treatment engagement and a reduced likelihood of relapse into substance use. Data collection for this study is currently in progress.

PI: Kirsten Langdon [supported by NIDA]

Exploring Patient Acceptance: A Qualitative Study of a Digital Pill System for Medication Adherence Monitoring

Dr. Rosen conducts qualitative health research. Her current work includes investigating the acceptability of a digital pill system.  Pills are labeled with an RFID tag that activates when swallowed and captures medication adherence. The data will allow clinicians to know when patients take their medications.

PI: Rochelle Rosen [supported by NIH NIDA R01DA047236]

Equitable Pulse Oximetry 

Pulse oximetry is a non-invasive technique to estimate arterial oxygen saturation. Pulse oximeters is based on relatively simple optics, comprising two or more wavelengths, photodetectors, and an algorithm that leverages empirically-derived values to estimate peripheral oxygen saturation. Unfortunately, several studies have found that the technology overestimates oxygen saturation levels for black and brown patients. The aim of this work is to develop a pulse oximeter that works equitably across all skin tones by leveraging advanced optical techniques and bioelectronics. 

PI: Kimani C. Toussaint, Jr. [supported by the Brown School of Engineering Hazeltine Award, the Brown University Office of the Vice President for Research, and the Brown Biomedical Innovations to Impact Award].