Personalized Research for Monitoring Pain Treatment (PREEMPT)
Welcome to the PREEMPT study
PREEMPT seeks to develop a smartphone app called Trialist that allows chronic pain patients and clinicians to run personalized experiments (n-of-1 trials), comparing two different pain treatments. The goal is to help patients engage actively and collaboratively with their clinicians to identify the best pain treatment for them.
PREEMPT was set up through a collaboration between researchers at UC Davis, Veteran’s Administration-Northern California Health Care System (VANCHCS), UC San Francisco, Brown University, and Cornell Tech as an effort to develop better medicine management for chronic pain patients.
Clinicians often begin treating chronic pain with acetaminophen or non-steroidal anti-inflammatory drugs, and prescribing opioids when pain is severe or unresponsive. These approaches are usually employed in a trial-and-error fashion. Trial and error can work but may lead to poor therapeutic decisions in the long run. To improve treatment precision, n-of-1 trials can be conducted. N-of-1 trials are single-subject crossover experiments where a patient completes repeated treatments comparing two treatment regiments.
PREEMPT partnered with Open mHealth to develop the technology to make n-of-1 trials easier to complete. PREEMPT is a National Institute of Nursing Research funded study (RO1 NR013938).
The following video provides a brief overview about the PREEMPT study
Our app, Trialist, which was built by Open mHealth allows chronic pain patients and their health care providers to run personalized experiments (n-of-1 trials) comparing two different pain treatments with the goal to identify effective treatments more quickly.
Once downloaded, the patient works with their clinician to design a trial. The clinician and patient choose the pain treatments they want to compare and how long their trial will last. Using the Trialist, the patient answers questions to track levels of pain and side effects of treatment, such as fatigue and drowsiness on a daily basis. After the trial has ended the data collected are analyzed and processed to produce a statistical report, including graphs on the outcomes of each pain treatment. The patient and clinician review the graphs to make treatment decisions during a regular clinic visit.
In the PREEMPT study, around 250 patients will be enrolled. Half the patients will use Trialist app and half will continue with their regular care (i.e., will not use the app). These two groups of patients will be compared to see if the Trialist app is successful in improving long term pain outcomes.
The following videos allow patient and clinicians in the PREEMPT study to review the steps involved in 1) setting up a trial; 2) using the Trialist app; and 3) reviewing trial results. Anyone can view the videos if they would like to find out more information about PREEMPT.
Setting up a Trial
A trial is set up during a regularly scheduled primary care appointment. Clinicians work with their patient to set up a trial.
The Trialist App
Once a trial is set up, patients will use the Trialist app to track levels of pain and side effects of treatment.
Reviewing Trial Results
After the patients trial is complete, the clinician and patient review the trial results during a regularly scheduled primary care appointment.
Richard L. Kravitz, M.D., M.S.P.H
Dr. Kravitz is a professor and attending physician in the UC Davis Health System. As principal investigator on the PREEMPT study, Dr. Kravitz oversees the daily operations of the study and is involved in clinician recruitment.
Barth Wilsey, M.D.
Dr. Wilsey is a Pain Medicine specialist and holds appointments at the UC Davis Medical Center and at the VA Northern California Health Care System.
Maria Marois, Ph.D.
Dr. Marois began her career at UC Davis in 1992 as a health educator for the Center for Aging and Health. As the project manager on the PREEMPT study, Maria is responsible for the day-to-day running of study, including recruitment and protocol development.
Rima Cabrera, M.S.W.
As a part of the PREEMPT Study, Rima is primarily responsible for patient recruitment, and for the day-to-day implementation of study. Before joining the PREEMPT Study, Rima previously worked for UC Davis with the Departments of Family Medicine and Pediatrics.
Navjot Dhammi, B.S.
As a part of the PREEMPT Study, Navjot is primarily responsible for patient recruitment, and for the day-to-day implementation of study. Prior to her work with PREEMPT, she was an undergraduate student at the University of California, Davis
Open mHealth is a non-profit start-up unlocking the potential for data-driven, integrated, personalized healthcare through and open architecture and intelligent software. Open mHealth built the Trialist app and provided technical support throughout the project.
Contact: David Haddad
Ohmage is an open-source participatory technology platform developed by Open mHealth, and was used as the foundation for Trialist. Ohmage supports expressive project authoring, including mobile phone-based data capture through inquiry-based surveys, automated data capture, temporally and/or spatially triggered reminders, data visualization and real-time feedback.
Contact: Josh Selsky
Marc Schwart holds a B.F.A. from Rhode Island School of Design and a M.S. from MIT. Marc continued his education by accepting a Fulbright Scholarship to study uses of mobile telephony in Japan. Since then, Marc has worked on a series of diverse software projects for varying clients and communities. His body of work ranges from operating systems to surgical simulators to mobile software for conducting scientific and medical clinical trials.
Contact: Marc Schwartz
Dr. Ida Sim, M.D., Ph.D. General Internal Medicine, UC San Francisco
Dr. Barth Wilsey, M.D. Pain Medicine, Anesthesiology
Dr. Deborah Ward, R.N., Ph.D. Betty Irene Moore School of Nursing at UC Davis
Dr. Chris Schmid, Ph.D. Professor of Biostatistics, Brown University
- Barr, C., Marois, M., Sim, I., ... Kravitz, R. L. (2015). The PREEMPT study - evaluating smartphone assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial. Trials, 16:67. Doi: http://dx.doi.org/10.1186/s13063-015-0590-8
Other Relevant Publications
Kravitz, R. L., & Duan, N. (Ed.), & the DEciDE Methods Center N-of-1 Guidance Panel (2014). Design and implementation of N-of-1 Trials: A user's guide. Rockville, MD: Agency for Healthcare Research and Quality. AHRQ Publication No. 13(14)-EHC122-EF. http://www.effectivehealthcare.ahrq.gov/N-1-Trials.cfm
Duan, N., Kravitz, R. L., & Schmid, C. H. (2013). Single-patient (n-of-1) trials: a pragmatic clinicial decision methodology for patient-centered comparative effectivess research. Journal of Clinical Epidemiology, 66, S21-S28. Doi: http://dx.doi.org/10.1016/j.jclinepi.2013.04.006
Gabler, N. B., Duan, N., Vohra, S., & Kravitz, R. L. (2011). N-of-1 trials in the medical literature: a systematic review. Medical Care, 49, 761-768. Doi: http://dx.doi.org/10.1097/MLR.0b013e318215d90d
Chen, C., Haddad, D., Selsky, J., Hoffman, J. E., Kravitz, R. L., Estrin, D. E., Sim, I. (2012). Making sense of mobile health data: An open architecture to improve individual- and population-level health. Journal of Medical Internet Research, 14(4), e112. Doi: http://dx.doi.org/10.2196/jmir.2152
Estrin, D., & Sim, I. (2010). Open mHealth architecture: an engine for health care innovation. Science, 330, 759-760. Doi: http://dx.doi.org/10.1126/science.1196187
If you are a clinician in the UC Davis Primary Care Network or a clinician from the VANCHCS and are interested in learning more about the PREEMPT study, please contact our research team. We would be happy to schedule a visit to your primary care site and discuss in person how the PREEMPT study may be beneficial to your patients and your clinical practice.
Phone: Toll-free 1-844-PREEMPT (773-3678)
The above link allows PREEMPT clinicians to create a trial and review trial results
Title: Small Data and N-of-1 Trials: Developing Personalized Biostatistics for Personalized Medicine and Individualized Health Care Delivery
As our understanding of hereditary and environmental contributors to disease expands, it is expected that more customized “personalized medicine” approaches will supplant traditional "one size fits all" diagnostic and therapeutic strategies. To achieve its promise, personalized medicine requires a partner in personalized biostatistics that accommodates individual information needs and preferences. While big data analytics have been deployed extensively in recent years for applications in personalized medicine, small data studies, powered by personalized biostatistics, may further advance the methodological underpinnings of personalized medicine.
"Small Data is the myriad of data traces we each generate every day. Unfortunately, that data is often unavailable to us in a form that we can make sense of or act upon. Imagine a special kind of app running in the cloud that privately and securely turns your small data into big insights." (Deborah Estrin, http://smalldata.io/) The primary goal in this emerging paradigm is to serve the needs of individual consumers using each individual's own data.
The realization of small data studies requires the development and deployment of personalized biostatistics, methodological tools such as user-friendly apps on mobile devices that can be used by individual patients and their providers to serve their personal health-related needs. Such apps can be used to collect their own health-related data, to analyze such data to inform their own individual clinical and lifestyle decisions, to identify their personal risk factors and triggers for illness, etc. Personalized medicine aims to customize the delivery of health care services to accommodate each individual patient's needs and preferences for diagnostic and therapeutic services. Likewise, personalized biostatistics aims to customize the delivery of biostatistical services to accommodate each individual consumer's information needs and preferences. For example, in the PREEMPT Study, an NIH-funded study led by Richard Kravitz to compare N-of-1 trial (single-patient repeated crossover trial) versus usual care among patients with chronic pain, each patient and provider choose how the N-of-1 trial is designed and implemented, using a flexible app provided by the study. Similar customization can also be made for the analytic procedure and presentation of the findings, say, how weights are placed on each consumer's own data versus borrowing-from-strength, or whether comparative effectiveness is presented as text or graphs and as estimates, confidence intervals or probabilities of relative benefit.
In this session, we discuss the design and analysis of small data studies, powered with personalized biostatistics, utilizing experimental methods such as N-of-1 trials, and non-experimental methods such as N-of-1 case-crossover designs, with applications to personalized treatment choices and lifestyle decisions, and identification of personal risk factors and illness triggers. Such applications of small data and personalized biostatistics have become feasible in recent years with advances in personal communication and information technologies. It is timely for the biostatistics community to begin to serve the needs of consumers who are enpowered by smart mobile devices and want to take an active role in enhancing their own health, as demonstrated in the extensive practice of self-tracking and self-experimentation among members of QuantifiedSelf.com.
This is an emerging area in which biostatisticians can help transform health care delivery by supplementing the traditional "top down" organization of knowledge production and deployment with a relatively new "bottom up" paradigm, in which biostatistical methods are applied directly in day-to-day clinical care and lifestyle decisions for individual consumers. This new paradigm may in turn expand the constituency for biostatistics, empowering and engaging the lay population to participate actively and directly in the practice of creating and harvesting small data using personalized biostatistics tools and apps.
The session will facilitate a dynamic and timely inter-disciplinary exchange on the state-of-the-art and future directions of small data and personalized biostatistics.
List of Invited Panelists:
Dr. Deborah Estrin is Associate Dean, Tishman Professor of Computer Science, and Founder of the Health Tech Hub at Cornell Tech. E-Mail: firstname.lastname@example.org. Dr. Estrin is the founding director of the Small Data Lab, http://smalldata.io, which has been pioneering the development of consumer-facing applications that capture and make sense of health-relevant digital traces, to inform both personal and clinical decision-making. She is the co-founder of Open mHealth and sits on advisory boards for several startups and scientific research centers.
Dr. Mark T. Drangsholt is Professor and Chair, Oral Medicine, and Professor, Oral Health Sciences, at University of Washington. E-Mail: email@example.com. Dr. Drangsholt holds a PhD in epidemiology and actively treats chronic pain patients using practical n-of-1 trials in multi-disciplinary pain centers. He also has collaborated with QuantifiedSelf.com, a consumer organization that facilitates self-tracking and self-experimentation (including N-of-1 trials) for consumers who want to better understand their own health conditions and seek informed personalized clinical and lifestyle decisions. He has presented multiple single subject design studies on himself, and shown examples of other non-experimental N-of-1 designs such as case-crossover that can identify the precipitants of disorders within a single individual.
Dr. Christopher Schmid is Professor of Biostatistics and Co-Director, Center for Evidence Synthesis in Health at Brown University. E-Mail: firstname.lastname@example.org. Dr. Schmid has worked extensively on N-of-1 trials, particularly on developing methods for the meta-analysis of collections of N-of-1 trials, and is currently leading the biostatistics core of three different ongoing N-of-1 studies including the PREEMPT Study, an innovative study that (1) developed a smartphone app called Trialist that allows chronic pain patients and clinicians to run individually customized N-of-1 trials, followed by (2) a randomized trial that compares patients randomized to receive N-of-1 trials facilitated with Trialist, versus patients randomized to receive usual care.
Dr. Ying Kuen (Ken) Cheung is Professor of Biostatistics at Columbia University Medical Center. E-Mail: email@example.com. Dr. Cheung has worked extensively in adaptive trials and N-of-1 trials, and is currently working on the study "Engaging Stakeholders in Building Patient-Centered, N-of-1 Randomized and Other Controlled Trial Methods", initiated by Vice Dean Karina Davidson at Columbia University Medical Center, with a mission to transform medical practice and service into a scientifically-informed, personalized therapeutics enterprise.
Dr. Richard Kravitz is Professor of Internal Medicine and Director of UC Center Sacramento at UC Davis. E-Mail: firstname.lastname@example.org. Dr. Kravitz is a general internist with research interests in physician and patient behavior, primary care, and individualization of treatment. He was Principal Investigator of the PREEMPT Study, a large randomized trial of mobile health-assisted n-of-trials in the treatment of chronic musculoskeletal pain. With Dr. Duan, he also co-edited the Agency for Health Care Quality and Research monograph entitled, Design and Implementation of N-of-1 Trials: A User’s Guide (2014).
Dr. Xiao-Li Meng is Dean of the Graduate School of Arts and Sciences and Whipple V. N. Jones Professor of Statistics at Harvard University. E-Mail: email@example.com. Among his many accomplishments, Dr. Meng worked extensively in recent years on individualized inference for individualized treatments, mainly from a big data approach. His insight will help put into perspective the potential integration and synergy between small data and big data approaches.