Last year, The University of San Diego’s California Institute for Telecommunications and Information Technology's (Calit2) data sharing initiative, called Health Data Exploration (HDE), awarded a total of $200,000 to five projects that aim to use aggregated personal health data to advance research. Now, the five HDE projects have released results from their first year.

Calit2, which received the support of the Robert Wood Johnson Foundation for this initiative, first launched the HDE project in May 2013. The project was led by Kevin Patrick and Jerry Sheehan of Calit2.

“These projects demonstrate the enormous potential inherent in the new digital health ecosystem,” Patrick said in a statement. “Ranging from context-aware technologies that can prompt improved behaviors to methods by which privacy can be protected while using wearable technologies, our network of researchers and companies are demonstrating important methodological advances in the area of personal health data research.”

Here are updates from the five HDE projects:

Arizona State University Assistant Professor Eric B. Hekler received $25,528 last year to study how the use of smartwatches and home-based sensors could provide people with context-appropriate support to stay physically active. After starting the project, Hekler and his colleague Sayali Phatak partnered with Aaron Coleman from the Small Steps Labs. Together the team developed an open source, iOS-based tool that uses indoor proximity sensors to track health and provide interventions when necessary.

Another researcher, PatientsLikeMe Research Director Emil Chiauzzi, received $37,700 to study the impact of merging behavior change programs with the use of wearables for patients with MS. Following a study that used wearable devices to manage MS, which was conducted with a major pharmaceutical company, PatientsLikeMe researchers found that 25 percent of study participants continued to use their devices after the study conclude. This led the team to question what patients were doing with the devices and how patients could use their devices to regulate their own behavior.

For the HDE project, researchers conducted a 30-day study with Arizona State University to interview participants one-on-one and use that data to build a behavior change program that helps participants track their mood and activity by setting daily goals. Results from the study showed that patients found value and benefited from the program.

Center for Democracy and Technology Deputy Director Michelle De Mooy, who received $50,000 last year, partnered with Shelten Yuen, director of research and development at Fitbit, to study how companies that collect large amounts of personal health data should conduct internal research. “We've spent that last several months mapping internal research practices at Fitbit and applying relevant privacy and ethics frameworks to research scenarios,” she said in January. “It's clear that some standards for institutional review board-regulated entities, such as those that address harm and risk reduction and that prescribe ethical considerations, make sense for companies like Fitbit.” The report is due out in July.

New York University Assistant Professor Rumi Chunara received $50,000 last year to develop a platform that aggregates Runkeeper data and uses it to study the relationship between the environment and how types and amounts of exercise vary over time. Researchers created a program with Open Humans that allows Runkeeper users to send their data to the study securely. The study is still ongoing, and researchers aim to eventually collect data from 300 activities in one location.

Finally, University of Washington Associate Professor Julie Kientz, who received $36,772, is working on a project that uses passive sensing of circadian rhythms to develop individualized models of cognitive performance. She partnered with Cornell University Associate Professor Tanzeem Choudhury to conduct a three-week study that analyzed people’s alertness throughout the day. The researchers also collected passive and self-reported data, including body clock type, sleep data, and intake of stimulants, like caffeine. The study showed how apps related to productivity and entertainment correlate with adequate and inadequate sleep.