Mobilize Center faculty member Jure Leskovec and colleagues published a paper in Nature this week where they analyzed physical activity data from over 700,000 users of a smartphone fitness app. Their analysis shows that the disparity of physical activity distribution within a country, what they refer to as “activity inequality,” is a better predictor of obesity levels within a country than average activity levels. In other words, the greater the activity inequality or activity disparity is in a country, the higher the prevalence of obesity. They also identify associations between a city’s walkability and activity inequality. Visit http://activityinequality.stanford.edu/ to access findings from the study, including data and code. Read more.
Mobilize Center researchers have just launched a study to learn the best ways to reduce sedentary time and motivate physical activity using activity data from the Pebble smartwatch. Anyone who owns a Pebble smartwatch can participate in the study. Learn more
The Mobilize Center has organized a symposium on “Data Science in Biomechanics” at the 26th Congress of the International Society of Biomechanics (ISB) taking place July 23-27, 2017 in Brisbane, Australia. The symposium will highlight current applications of data science methods in biomechanics research, foster discussions of the challenges and opportunities with biomechanics data, and promote the sharing of data and knowledge within the community. Register to join us.
The two-day mHealth: Moving Toward Impact symposium brought together device and app developers, clinicians, and researchers [link the list of participants to “device and app…researchers”] for thoughtful discussions on how to increase the use of wearable devices for clinical use. Recordings of the public presentations and discussion panel, which discussed the successes and challenges of wearable technologies within clinical and research environments, are available here.
Do you want to automatically identify biomarkers reported within the scientific literature that are related to a particular disease?
Do you have a large collection of text-based documents (e.g., articles, webpages, reports, catalogs) from which you want to create a database of experimentally derived parameters, like P53 concentration levels or tissue stiffness?
Do you want to analyze clinical notes to extract patient-reported functional capabilities related to a given treatment?
In our two-day workshop for “Rapid Biomedical Knowledge Base Construction from Unstructured Data,” you will learn how to use a tool called Snorkel to perform these types of tasks. Snorkel automatically extracts information from unstructured data sources, such as the scientific literature and clinical notes, without using large, labeled training datasets, which are often lacking in biomedicine. The workshop is being held at Stanford University on July 19-20, 2017. Learn more
mHealth Connect aims to improve the use of physical activity wearables and apps for clinical purposes by bringing together device and app developers with a diverse group of clinicians and researchers.
This year we invite the general public to join us on the first day of the event, Tuesday, April 18, 2017, at Stanford University. Connect with device and app developers, clinicians, and researchers who are generating new insights with these devices. Hear how organizations are currently utilizing consumer wearable devices to change the way they operate, the challenges they face, and their vision of the future. Speakers include:
- David Shaywitz – Chief Medical Officer, DNAnexus
Co-Founder and West Coast Innovation Lead,
Center for Assessment Technology and Continuous Health (CATCH), Massachusetts General Hospital
- Michael Snyder – Professor of Genetics, Stanford University
- Spyros Papapetropoulous – Vice President, Global Development Head, Neurodegenerative Diseases and Movement Disorders,
Teva Pharmaceutical Industries
- Laura Wilt – Senior Vice President, Chief Information Officer, Ochsner Health System
- Cedric Hutchings – Vice President, Digital Health, Nokia Technologies
Co-founder and past CEO, Withings
Click here for more information about the event.
Mobilize Center graduate student Tim Althoff, Computer Science graduate student Pranav Jindal, and Mobilize Center faculty Jure Leskovec published a research paper in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM 2017), where they investigated the effect of social networks on users’ online and offline behaviors, including user engagement, retention, and physical activity levels.
The results are based on an analysis of data from 6 million people from over 100 countries collected over a 5-year period using the physical activity tracking application Argus. Althoff and colleagues found that participating in the social network increases both in-application activity as well as physical activity levels. They were also able to take advantage of a natural experiment within the dataset to distinguish between correlation and causation.
Mobilize Center Postdoctoral Fellow Lukasz Kidzinski has organized a challenge to make a musculoskeletal model walk as far as possible in a virtual environment in 5 seconds using a reinforcement learning approach. The winner will be invited to the 2nd Applied Machine Learning Days at EPFL in Switzerland on January 2018, with travel and accommodations paid for (see program for 2017 event here).
The challenge focuses on programming a model of the brain’s motor control unit, which controls movement, within the OpenSim simulation environment. Understanding how the brain functions in normal conditions and for neurological disorders, such as cerebral palsy, multiple sclerosis or stroke, is key to improving treatments for these disorders.
Read more about the evaluation, rules and resources.
Mobilize Center postdoc Jessilyn Dunn and colleagues published a research paper in PLOS Biology where they recorded over 250,000 daily physiological measurements from 43 participants using multiple wearable devices to investigate the role of these devices to diagnose and analyze disease. Combining the information from these devices and from medical measurements, such as heart rate and oxygen levels, researchers found these devices could be used to identify abnormal physiological signals, in this case arising from the onset of Lyme disease. The signals also showed differences between being insulin-sensitive versus insulin-resistant, raising the possibility of using these devices to help detect the risk for type 2 diabetes.
Read a summary of this work in the Stanford Medicine News