Search Results for: smart

Donna Coffman Receives K Award from NIH

October 9, 2014:

Congratulations to Methodology Center Investigator Donna Coffman, who received a K01 Career Development Award from the National Institutes of Health Big dcoffData to Knowledge initiative! The award will fund 100% of Donna’s training and independent research for the next three years. Donna’s work will focus on the analysis of big data to promote healthy behavior related to physical activity, diet, stress management, and substance use.

K awards include a training component, and Donna will study methods from computer science and informatics in order to manage and analyze big data. She will study statistical methods for the integration and analysis of big data, including genomics data, ecological momentary assessments, and data from wearable devices.

 

“Physical inactivity, poor stress management, poor diet, and smoking are responsible for about 80 percent of coronary heart disease and cerebrovascular disease,” Donna said. “They are also partly responsible for other negative health outcomes including high lipids, high blood pressure, cancer, diabetes and obesity. By developing, extending, and applying big data methods to biobehavioral health data, we can help individuals develop and maintain healthy behavior regarding physical activity, diet, and stress management. This grant will allow me to develop the methods needed to make this possible. Thanks to smartphones and innovative experiments by biobehavioral researchers, we are living in an age where rich datasets are extremely commonplace. Amazon, Facebook, and Google all use new forms of big data to sell us things. As a society, we can use big data to improve the public health, once we build the right tools.”

 

Donna’s mentors for her K are Runze Li, Distinguished Professor of Statistics and a fellow Methodology Center principal investigator, Vasant Honavar, Professor and Edward Frymoyer Chair of Information Sciences and Technology at Penn State, and Joshua Smyth, Professor of Biobehavioral Health and Medicine at Penn State and Methodology Center affiliate.  The methods developed in this project will be used to develop adaptive, individualized health behavior interventions delivered in real-time, real-world contexts.

Penn State Faculty: Workshop on Methods for Constructing Adaptive Interventions

August 26, 2014:DAlirall

We are pleased to announce this semester’s Taste of Methodology workshop on the sequential, multiple assignment, randomized trial (SMART) for constructing adaptive interventions. The December 16 workshop will be presented by Methodology Center Investigator Daniel Almirall, research assistant professor at the University of Michigan’s Institute for Social Research.

Interventions that adapt at the right times can improve participant outcomes while decreasing the cost and burden of the intervention. SMART designs provide the data needed to construct high-quality adaptive interventions.

Taste of Methodology is a series of brief workshops for Penn State faculty that offers an overview of innovative methods along with lunch. This semester’s workshop will present the concepts and applications of SMART in order to give faculty an efficient way to assess SMART’s potential for their research.

 

A Taste of Methodology: Getting SMART About Design and Analysis Methods for Constructing Adaptive Interventions

PRESENTER: Methodology Center Investigator Daniel Almirall, research assistant professor at the University of Michigan’s Institute for Social Research

DATE: Tuesday, December 16, 2014, 10:30 am – 1:30 pm

LOCATION: Bennett Pierce Living Center, 110 Henderson Building

The workshop is intended for faculty who design behavioral interventions and would like to learn more about constructing adaptive interventions.

The three-hour workshop will include

  • an introduction to adaptive interventions;
  • a conceptual introduction to SMART, a novel experimental design method;
  • that allows scientists to operationalize adaptive clinical decision making;
  • planning of a hypothetical SMART;
  • resources to enable you or your trainees to learn more; and
  • lunch!

A Taste of Methodology is co-sponsored by the Social Science Research Institute (SSRI) and the Methodology Center, and is part of SSRI’s Innovative Methods Initiative. The workshop is FREE and open to all Ph.D.-level scientists at Penn State. Registration is required and places are limited. To register or if you have questions, email Tammy Knepp (TLKnepp@psu.edu).

Susan Murphy Named Distinguished University Professor

SMurphy 3August 19, 2014:

We are pleased to announce that the University of Michigan has named Susan A. Murphy distinguished university professor for her research, leadership, and service. Susan is a Methodology Center principal investigator, Herbert E. Robbins Distinguished University Professor of statistics, research professor at the Institute for Social Research, and professor of psychiatry.

Susan’s research focuses on development of innovative research approaches to improve the personalization of treatment. She developed the sequential, multiple assignment, randomized trial (SMART), which led to her being named a John D. and Catherine T. MacArthur Foundation fellow in 2013. SMART is an experimental design tool that allows scientists to build empirically based interventions that adapt according to patient characteristics and response to treatment. While development of SMART continues, Susan is also investigating the construction of just-in-time adaptive interventions (JITAIs), which use real-time data from mobile technologies to deliver personalized behavioral interventions exactly when interventions are needed.

Susan’s work has impacted the research of countless scientists and interventionists. SMARTs are in the field or have been completed that address a broad range of topics including cocaine abuse, depression, problem drinking, obesity, ADHD, and autism. Dozens of SMARTs have been funded by the National Institutes of Health, and multiple NIH program announcements request SMART designs specifically.

Susan is a fellow of the College of Problems of Drug Dependence, the MacArthur Foundation, the Center for Advanced Study in the Behavioral Sciences at Stanford University, the American Statistical Association, and the Institute of Mathematical Statistics. She is an elected member of the International Statistical Institute and from 2007-2009 served as co-editor of The Annals of Statistics.

Podcast: Physical Activity Research with David Conroy

June 2, 2014:Conroy_sm

In our latest podcast, host Aaron Wagner interviews David Conroy, professor of kinesiology and human development and family studies at Penn State, and investigator at The Methodology Center. The discussion focuses on David’s research on physical activity and sedentary behavior, how physical activity impacts our lives, and the technological opportunities and methodological challenges of this research. David’s multiple, fascinating projects with other Methodology Center investigators are also discussed.

Download Podcast 18

References

Physical activity guidelines for Americans

Conroy, D. E., Maher, J. P., Elavsky, S., Hyde, A. L., & Doerksen, S. E. (2013). Sedentary behavior as a daily process regulated by habits and intentions. Health Psychology. Advance online publication. doi: 10.1037/a0031629 PMC Journal- in process.

Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine. 46(6), 649-652.

Fitzsimmons, P. T., Maher, J. P., Doerksen, S. E., Elavsky, S. Rebar, A. L., & Conroy D. E. (2014) A daily process analysis of physical activity, sedentary behavior, and perceived cognitive abilities. Psychology of Sports and Exercise. 15(5), 498-504.

Podcast Timeline:
00:00 – Introduction
00:45 – Sedentary behavior, active behavior, and your health
06:51 – David’s background
08:25 – Staying healthy in a sedentary society
13:08 – ACT UP interventions to promote activity
19:25 – Promoting health with smartphones c
24:31 – Methodological issues: Intensive longitudinal data in this research

Using Dynamical Systems Models to Understand and Treat Behavior

April 17, 2014:kevin_4

Dynamical systems models were developed in engineering to describe complex systems using differential equations. Methodology Center Director Linda Collins and Daniel Rivera, professor of chemical engineering at Arizona State University, recently completed a National Institute on Drug Abuse (NIDA) Roadmap grant (R21 DA024266) in which they applied dynamical systems models to improve behavioral interventions. These models can be used to understand the psychological processes that contribute to the outcomes of behavioral treatments.

In a new publication, the authors applied dynamical systems models to better understand what contributes to relapse during the smoking cessation process. The article, “A dynamical systems approach to understanding self-regulation in smoking cessation behavior change,” appears in the May 2014 special issue of Nicotine and Tobacco Research. The research team included Daniel’s graduate student Kevin Timms, Daniel, Linda, and Megan Piper, assistant professor at the Center for Tobacco Research and Intervention at the University of Wisconsin – Madison.

In recent years, intensive measurements of behavioral data—like those gathered at frequent intervals in smartphone studies—have become common. This abundance of intensive longitudinal data has enabled the application of dynamical systems models in new areas. In smoking cessation research, accurately measuring self-regulation has been difficult. Self-regulation theory suggests that people smoke in order to correct some irregularity in their system; for example, someone may smoke because their mood or blood nicotine level is substantially lower than their normal, baseline level. In this article, the authors applied dynamical systems models to intensive longitudinal data to model smoking cessation as a self-regulation process. They found that their model explains most of the variance observed in craving and smoking levels after people quit. The results also indicated that pharmacological treatment and counseling during cessation lessen cravings and slow relapse into smoking.

The flexibility of the techniques and software used to estimate these models suggests that the same approach will be applicable to other behavioral research questions. In fact, the team’s work on dynamical systems for behavioral interventions has already extended beyond smoking research. Danielle Symons Downs, associate professor of kinesiology and obstetrics at Penn State, is the primary investigator on grant R01 HL119245 from the National Heart, Lung, and Blood Institute that will employ these methods to build a behavioral intervention to help pregnant women manage their gestational weight gain. Two graduate students working on the project, Kevin Timms of Arizona State University and Jessica Trail of Penn State, were awarded F-31 grants by NIDA to pursue research on these methods. Their work has led to publications in journals including Psychological Methods (Trail et al., 2013) and International Journal of Control (Timms et al., 2013) that contribute to the development and application of these methods.

Dynamical systems models hold great promise for intervention and prevention science. For more on the intersection of engineering methods and behavioral research, see Methodology Center work on the multiphase optimization strategy (MOST).This work on applying engineering methods to behavioral intervention research has caused both Daniel’s team and Linda’s team to learn new vocabulary and ways of conducting research. Listen to the podcast they recorded about their collaboration.

References

Timms, K. P., Rivera, D. E., Collins, L. M., & Piper, M. E. (2014). A dynamical systems approach to understanding self-regulation in smoking cessation behavior change. Nicotine and Tobacco Research, 16(Supplement 2): S159-S168, doi:10.1093/ntr/ntt149 PMC Journal-In Process

Timms, K. P., Rivera, D. E., Collins, L. M., & Piper, M. E. (2013). Continuous-time system identification of a smoking cessation intervention. International Journal of Control. Advance online publication. doi: 10.1080/00207179.2013.874080

Trail, J. B., Collins, L. M., Rivera, D. E., Li, R., Piper, M., & Baker, T. (2013).  Functional data analysis for the system identification of behavioral processes.  Psychological Methods. Advance online publication. doi: 10.1037/a0034035 PMC Journal-In Process

2014 Summer Institute on Adaptive Interventions

February 19, 2014:summer2014

We are pleased to announce that Drs. Daniel Almirall and Inbal “Billie” Nahum-Shani, Methodology Center researchers at the University of Michigan, will be teaching this year’s Summer Institute on Innovative Methods, “Experimental Design and Analysis Methods for Developing Adaptive Interventions: Getting SMART.”

Sponsored by Penn State’s Methodology Center and the National Institute on Drug Abuse, the 19th summer institute will introduce adaptive interventions; provide the background needed to plan a sequential, multiple assignment, randomized trial (SMART); and present the data analysis methods needed to construct adaptive interventions using SMART study data.

The institute will be held June 19-20, 2014, at the University of Michigan in Ann Arbor, Michigan.

Read more or apply

New Software: R Package for Building Adaptive Interventions

January 22, 2014:

We are pleased to announce the release of the qlaci (Q-learning with adaptive confidence intervals) R package. This is a tool for designing an adaptive intervention using data from a sequential, multiple assignment, randomized trial (SMART). Interventions that adapt at the right times can improve participant outcomes (e.g., intensifying for people who do not respond to the initial treatment) while decreasing the cost and burden of the intervention (e.g., stepping down treatment for participants who respond). SMART designs provide the data needed to construct high-quality adaptive interventions.

Qlaci requires R 2.15. or higher, which is available for free download. This package is designed to work on Windows, Mac, or Linux operating systems.

Read more about SMART

Download the software

Susan Murphy’s Mathematical Association of America (MAA) Carriage House Lecture on Adaptive Interventions

July 23, 2013:maa-murphy
Dr. Susan Murphy presented the talk, “Getting SMART About Adapting Interventions,” at the MAA Carriage House in Washington, DC on May 29.  Each year, the MAA invites one statistician to present a Carriage House Lecture. This year, Dr. Murphy discussed sequential, multiple assignment, randomized trials (SMARTs), the method she developed for building adaptive interventions.

 

Adaptive interventions allow clinicians to tailor the intensity or type of treatment based on a patient’s characteristics and response to treatment. In her lecture, Dr. Murphy described the rationale for adaptive interventions and the mechanisms through which a SMART design can help scientists build effective adaptive interventions. Follow the link below to watch the video of her lecture.

Read more

Watch the talk on YouTube