2008 — 2012 |
Shah, Dhavan V. |
P50Activity Code Description: To support any part of the full range of research and development from very basic to clinical; may involve ancillary supportive activities such as protracted patient care necessary to the primary research or R&D effort. The spectrum of activities comprises a multidisciplinary attack on a specific disease entity or biomedical problem area. These grants differ from program project grants in that they are usually developed in response to an announcement of the programmatic needs of an Institute or Division and subsequently receive continuous attention from its staff. Centers may also serve as regional or national resources for special research purposes. |
Theory and Methods Core @ University of Wisconsin Madison
Analysis, Data; Architecture; Archives; Arts; Cancer Treatment; Clinical; Cognitive; Communication; Data Analyses; Data Banks; Data Bases; Databank, Electronic; Databanks; Database, Electronic; Databases; Engineering / Architecture; Ensure; Equation; Generalized Growth; Grant; Growth; Health Information System; Internet; Investigators; Knowledge; Linear Models; Malignant Neoplasm Therapy; Malignant Neoplasm Treatment; Measures; Methodology, Research; Methods; Methods and Techniques; Methods, Other; Metric; Modality; Modeling; Outcome; Participant; Process; Publications; QOL; Quality of life; R01 Mechanism; R01 Program; RPG; Reporting; Research; Research Design; Research Grants; Research Methodology; Research Methods; Research Personnel; Research Project Grants; Research Projects; Research Projects, R-Series; Researchers; Scientific Publication; Self Determination; Side; Study Type; Survey Instrument; Surveys; Techniques; Technology; Testing; Tissue Growth; Training; WWW; Work; anticancer therapy; cancer therapy; clinical data repository; clinical data warehouse; communication theory; data repository; design; designing; digital; improved; novel; ontogeny; psychologic; psychological; relational database; response; social; study design; theories; web; world wide web
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1 |
2019 — 2021 |
Curtin, John J. [⬀] Shah, Dhavan |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Contextualized Daily Prediction of Lapse Risk in Opioid Use Disorder by Digital Phenotyping @ University of Wisconsin-Madison
PROJECT SUMMARY Opioid use disorder is increasingly widespread, leading to devastating consequences and costs for patients and their families, friends, and communities. Available treatments for opioid and other substance use disorders (SUD) are not successful at sustaining sobriety. The vast majority of people with SUD relapse within a year. Critically, they often fail to detect dynamic, day-by-day changes in their risk for relapse and do not adequately employ skills they developed or take advantage of support available through continuing care. The broad goals of this project are to develop and deliver a highly contextualized, lapse risk prediction models for forecasting day-by-day probability of opioid and other drug use lapse among people pursuing drug abstinence. This lapse risk prediction model will be delivered within the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) mobile app, which has been established by RCT as a state-of-the-art mHealth system for providing continuing care services for alcohol and substance use disorders. To accomplish these broad goals, a diverse sample of 480 participants with opioid use disorder who are pursing abstinence will be recruited. These participants will be followed for 12 months of their recovery, with observations occurring as early as one week post-abstinence and as late as 18 months post-abstinence across participants in the sample. Well-established distal, static relapse risk signals (e.g., addiction severity, comorbid psychopathology) will be measured on intake. A range of more proximal, time-varying opioid (and other drug use) lapse risk signals will also be collected via participants? smartphones. These signals include self-report surveys every two months, daily ecological momentary assessments, daily video recovery ?check-ins?, voice phone call and text message logs, text message content, moment-by-moment location (via smartphone GPS and location services), physical activity (via smartphone sensors), and usage of the mobile A-CHESS Recovery Support app. The predictive power of these risk signals will be further increased by anchoring them within an inter-personal context of known people, locations, dates, and times that support or detract from participants? abstinence efforts. Machine learning methods will be used to train, validate, and test opioid (and other drug) lapse risk prediction models based on these contextualized static and dynamic risk signals. These lapse risk prediction models will provide participant specific, day-by-day probabilistic forecast of a lapse to opioid (or other drug) use among opioid abstinent individuals. These lapse risk prediction models will be formally added to the A-CHESS continuing care mobile app at the completion of the project for use in clinical care. These project goals position A-CHESS to make relapse prevention and recovery support, information, and risk monitoring available to patients continuously. Compared to conventional continuing care, A-CHESS will provide personalized care and be available and implemented during moments of greatest need. Integrated real-time risk prediction holds substantial promise to encourage sustained recovery through adaptive use of these continuing care services.
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0.915 |