The rapidly growing market for health and wellness wearable devices presents unprecedented opportunities for data analysis in the pursuit of greater understanding of health and disease. Teams of scientists, researchers, and engineers worldwide apply artificial intelligence machine-learning and neural networking strategies to learn more about diseases and in some case improve diagnostics and treatment plans. For example, MIT’s DeepFaceLift study uses machine learning to predict pain level from video. Researchers at The Arnhold Institute for Global Health of the Icahn School of Medicine at Mount Sinai used machine learning technology to analyze diabetes study results.
Evidation Health and Tidepool launched the T1D Sleep Pilot, a study of the relationship between low blood sugar at night and daily behavior in people with Type I diabetes. Evidation Health is a health and behavior data company with established relationships with more than 100 health wearable data sources that feed data to its platform. Tidepool, an open source not-for-profit company that links data from diabetes devices, recently linked to Evidation’s platform. Other sources for Evidation data include Apple Health, Dexcom, and Fitbit. The T1D Sleep Pilot will capture data from patients who use continuous glucose monitors and insulin pumps; the researchers hope to find relationships between behavioral, sleep pattern, and heart rate data as they relate to nocturnal hypoglycemia.
Pooling data from multiple sources may raise some concerns about data security and value equality, but movement in the direction of open source data to benefit all concerned parties represents a giant step forward from the traditional insular protected studies. Tidepool will make all data from the Sleep Pilot available to patients, care teams, and to research and development groups working in the field. People with type 1 diabetes can learn more about joining the T1D Sleep Pilot at http://study.myachievement.com/t1dsleep.
