Jet-Lag Sleep App is a Viable Way to Collect Big Data
- by 7wData
Can scientists trust a mobile app as a reliable vehicle for collecting health data? A study published today in Science Advances suggests the answer is yes, it’s possible, at least for sleep studies.
Researchers have for years been eyeing the trove of health data sets that could be collected via mobile apps. Cheap to build and easy to distribute, apps can make recruitment of massive, global study populations possible on a grad student–like budget.
But the reliability of that kind of data is still largely unproven, and presents a risk for scientists. Unlike laboratory-based research or phone surveys, there’s no study coordinator to keep people honest and on track, and no voice awaiting a response at the other end of the line.
But researchers at the University of Michigan in Ann Arbor say that global sleep data collected from their custom app yielded reliable results. “It validates mobile apps as a data collection method,” says Olivia Walch, a graduate student at the University of Michigan and lead author of the report.
Walch and her team designed an app called Entrain to help travelers adjust to new time zones. Users input information about their sleep habits, home time zone, and where they would be traveling, and the app provided advice on minimizing jet lag.
The travel advice was the carrot that motivated people to use the app, but was actually beside the point. What the scientists actually cared about was the chance to get information about baseline sleep habits and home time zones. App users could opt to share that information with the research team; eight percent of them did. That gave the Michigan team sleep data from more than 8,000 respondents around the world “at essentially no cost,” the researchers said in their report.
To validate the data, the Michigan team compared it with our knowledge of circadian rhythms and sleep data collected using more traditional methods, such as sleep labs and questionnaires. The researchers used mathematical models to simulate bed times and wake times in various time zones—what people’s sleep habits would be if they were governed by sunsets and sunrises, rather than social influences.
To their relief, their data lined up with known trends in sleep habits amassed from more traditional studies. “There were many sleepless nights” waiting for those results, says Walch. “To see it line up was really gratifying.
[Social9_Share class=”s9-widget-wrapper”]
Upcoming Events
Strategies for simplifying complex Salesforce data migrations – Free Webinar
27 March 2024
5 PM CET – 6 PM CET
Read MoreYou Might Be Interested In
Create Real Value with Augmented (not Artificial) Intelligence
23 Oct, 2017Ransomware is one of the fastest growing types of malware, and new breeds that escalate quickly ar As long as …
The Different Types of Cloud Computing and How They Differ
16 Jan, 2021Although the term “cloud” often gives cloud computing a somewhat mystical connotation, in reality, it isn’t all that different from …
10 predictions for the Internet of Things and big data in 2017
2 Dec, 2016The Internet of Things and big data technologies have progressed enormously in 2016 – and 2017 is set to be …
Recent Jobs
Do You Want to Share Your Story?
Bring your insights on Data, Visualization, Innovation or Business Agility to our community. Let them learn from your experience.