NHLBI Request for Information(RFI): Collaborative Translational Research Consortium to Develop T4 Translation of Evidence-based Interventions (NOT-HL-14-028)
Released July 2, 2014
Dr. Alex Blum formerly at OBSSR authors new study on the Impact of Socioeconomic Status Measures on Hospital Profiling in New York City
May 13, 2014
Change in Leadership of the NIH Office of Behavioral and Social Sciences Research
April 11, 2014
The Health Consequences of Smoking - 50 Years of Progress. A Report of the Surgeon General
July 20 - 25, 2014
2014 Training Institute for Dissemination and Implementation Research in Health
Babson Park, MA
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Ron Poropatich, MD
Executive Director of the Center for Military Medicine Research, Health Sciences
University of Pittsburgh
June 30, 2014 at 3pm - 4pm
Please send us your questions, comments and suggest on Twitter using hashtag #NIHmhealth. OBSSR handle on twitter is @ @NIHOBSSR
This presentation will cover a range of mHealth applications that supports the home health phase of rehabilitation for traumatic brain injury (TBI), spinal cord injury, amputee care and psychological stress. Topics include sleep assessment, cognitive training, vestibular and ocular training for TBI, and prosthetic mHealth tools for amputees. A comprehensive approach that focuses on individualized, self-management applications will be stressed.
To attend the webinar, please go to: https://webmeeting.nih.gov/mhealth-063014
Conference Number(s): 1-888-850-4523
Participant Code: 214908
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Robert Moss, Co-Founder
May 13, 2014 at 2pm
Please send us your questions, comments and suggest on Twitter using hashtag #mhealth. OBSSR handle on twitter is @ @NIHOBSSR
With an initial focus of improving patient/doctor communication about seizure activity and surrounding treatments, SeizureTracker.com has expanded into improving the quality and cost of collecting data in epilepsy related research as well. Launched in 2007, the Seizure Tracker system serves a global audience with a registered user base of over 18,000. Largely due to the expansion into a mobile platform, Seizure Tracker users record between 700 - 1000 seizure events daily. Mobile technology allows for real-time capture of event related chronic medical conditions and enables easy sharing of information into larger care systems. Seizure Tracker has seamlessly integrated the use of a mobile application to record the length of a seizure while videotaping the event as it happens. The events can then be synced with a larger web-based system. The website allows users to enter surrounding treatments including medications and diet therapies. SeizureTracker.com also allows for recording possible influences on seizure activity like hormonal fluctuations, missed medications, etc. While on the site, users can create reports which include graphical comparisons of the information entered into the areas of Seizure Tracker. These reports can then be easily emailed or printed and shared with the care team. The creation of Seizure Tracker has changed the way patients log seizures by providing a platform to record more accurate seizure data that is easily shared with their care providers. This presentation will highlight the use of technology to fill gaps in doctor/patient communication, how mobile technology can impact the quality of patient reported outcomes and how that data can impact clinical care along with research.
To attend the webinar, please go to: https://webmeeting.nih.gov/mhealth-moss/
Conference Number(s): 1-888-850-4523
Participant Code: 214908
Rebecca Schnall, RN PhDAssistant Professor of Nursing,
New York, NY
February 19, 2014
PowerPoint Slides with Audio (PPTX 84.4MB)
Presentation Slide (PDF 84.4MB)
Research on health information has primarily focused on the needs of adults or parents of children with chronic illnesses or consumers. There is limited research on the health information needs of adolescents and the use of technology for meeting those needs. This is particularly important as the use of mobile technology has made a huge impact on communication, access, and information/resource delivery to adolescents, who are the largest age group of users of this technology. We conducted a series of studies with urban minority adolescents to understand their health information needs, their use of mobile devices for accessing health information and their use of mobile technology for adherence to new health behaviors. The purpose of this presentation will be to discuss adolescents use and perceived usefulness of mobile technology for accessing health information resources and for adhering to improved health behaviors.
We are experiencing some technical difficulty with the webinar footage. We hope to get it resolved soon. In the meantime please find the slides below.
Deborah Estrin, Ph.D., Professor of Computer Science, Cornell NYC Tech
January 29, 2013
Presentation Slides (PDF 2.43MB)
The most significant health and wellness challenges increasingly involve multiple chronic conditions, from diabetes, hypertension, and asthma to depression, chronic-pain, sleep and neurological disorders. The promise of mobile health (mHealth) is that we can leverage the power and ubiquity of mobile and cloud technologies to monitor and understand symptoms, side effects and treatment outside the clinical setting, thereby closing the feedback loops of self-care, clinical-care, and personal-evidence-creation. However, to realize this promise, we must develop new data capture, processing and modeling techniques to convert the ‘digital exhaust’ emitted by mobile phone use into behavioral biomarkers. This calls for the sort of modular layered processing framework used in speech and vision in which low level state classifications of raw data (e.g., estimated activity states such as sitting, walking, driving from continuous accelerometer and location traces), are used to derive mid-level semantic features (e.g., total number of ambulatory minutes, number of hours spent out of house), that can then be mapped to particular behavioral biomarkers for specific diseases (e.g., chronic pain, GI dysfunction, MS, fatigue, depression, etc). The techniques needed to derive these markers will range from simple functions to machine learning classifiers, and will need to fuse diverse data types, but all will need to cope with noisy, erratic data sources.