| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | September 15, 2017 |
| Latest Amendment Date: | September 1, 2022 |
| Award Number: | 1722792 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2017 |
| End Date: | September 30, 2023 (Estimated) |
| Total Intended Award Amount: | $640,195.00 |
| Total Awarded Amount to Date: | $640,195.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
140 Governors Drive Amherst MA US 01003-9264 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Information Technology Researc, Smart and Connected Health |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
According to the Centers for Disease Control, tobacco use remains the leading preventable cause of death in the US, causing approximately 480,000 deaths each year, and incurring over $150 billion in healthcare costs. As a result, scalable, low cost, and effective smoking cessation interventions are clearly needed. This research aims to develop and validate new messaging-based smoking cessation support intervention systems that will leverage recent advances in smart wearable technologies to significantly enhance efficacy. The system will integrate wearable sensors that continuously estimate an individual's level of stress and craving as well as the occurrence of smoking. This information will be used to enhance the context awareness of the intervention system, allowing it to continuously adapt both the content and delivery timing of intervention components for each individual. By developing scalable messaging-based smoking cessation support interventions with improved personal relevance, this research has the potential to lead to direct benefits to society by more effectively helping individuals to quit smoking.
To accomplish the goal of providing effective, personalized smoking cessation interventions, this research will develop and evaluate the models, algorithms, and wearable-phone-cloud computational infrastructures required to support the context inferences, personalization, and delivery timing optimizations required. Starting from the team's extensive prior work, this research will contribute to (1) advances in mobile health sensing and context inference with low-cost, low-power sensors; (2) advances in real-time, stream-based active learning for personalizing context inference models; (3) advances in contextualized recommender systems to personalize message selection based on inferred contexts; and (4) advances in robust, real-time wearable-phone-cloud data analytics systems. This work will also make substantial contributions to enhancing research infrastructure through open source software releases that can be leveraged by the research community to yield benefits in other high-profile health areas including heart disease, obesity, and addiction.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The last decade of research and technology development has seen significant advances in mobile and wearable technologies including smart watches and other wearable health monitors. A key open question in this area is how to use data from these devices to support a range of health behavior changes from increasing physical activity to reducing over-eating and decreasing the use of tobacco products. Within the field of behavioral science, adaptive interventions are gaining traction as an approach to supporting positive behavior change. Adaptive interventions aim to use data from mobile and wearable devices to tailor the help provided to an individual in different situations (or contexts). However, the usefulness of the adaptive intervention approach depends heavily on how accurately contextual factors can be detected and predicted from mobile and wearable device data.
In this project, we studied the detection of stress and sleep from mobile device data as two important contexts for adaptive interventions. In particular, we focused on how to personalize predictive detection algorithms to improve their accuracy while minimizing the amount of input needed from users. We also studied how properties including the uncertainty of context detection algorithms impact the ability to learn high-quality adaptive intervention policies from data. The methodological research conducted under this award resulted in multiple new algorithms and findings that were disseminated to the research community through research papers, presentations, and open source software releases.
To support this work, we developed substantial new software infrastructure for conducting research studies leveraging streaming data from smartphones and mobile devices. We used the developed research infrastructure to run two primary studies. The first study collected data to support research on personalization of stress detection. The second study deployed a novel personalized and adaptive smoking cessation support intervention based on motivational messaging. This study also included the collection of stress data as an important contextual factor. Further, we released an open source version of this research infrastructure toolkit that is freely available to other researchers to support future studies.
Finally, this project contributed to broadening participation in computing and mobile health research through significant training and outreach activities. Despite interruptions to these activities due to the COVID-19 pandemic, the outreach and training activities conducted under this award reached a total of approximately 140 individuals. Specific efforts included leading a week-long summer workshop under the Girls Inc. and UMass Amherst Eureka! summer program for high school girls, participating on the faculty of the NIH-sponsored Mobile Health Training Institute, and lecturing in the University of Texas at Austin T32 Precision Health Training Program.
Last Modified: 01/29/2024
Modified by: Benjamin M Marlin
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