People may appreciate online apps that provide advice on health and fitness, but they seem to draw the line when those apps use their social media networks for data, according to researchers.
In a study, users showed a strong preference for fitness recommendations that were personalized for them based on their self-reported preferences. They also liked systems that allowed users to choose among different recommendation approaches, which made them feel more in control.
“As big data gives people new opportunities to personalize their health and fitness routines, it also calls into question how this data is collected,” said S. Shyam Sundar, James P. Jimirro Professor of Media Effects in the Donald P. Bellisario College of Communications and co-director of the Media Effects Research Laboratory at Penn State.
According to Sundar, people are using recommendation systems to help make more decisions, such as choosing entertainment activities and weighing vacation options. Health and exercise are natural areas of applications; however, the sensitivity of health data could make people wary of such systems.
The researchers presented their findings today (April 24) at the ACM CHI Conference on Human Factors in Computing Systems, and reported them in its proceedings,
“We are moving toward an era where fitness plans, diet regimens, exercise routines and other forms of preventive health management can be tailored to our specific individual needs and situations,” added Sundar, who is also an affiliate of Penn State’s Institute for Computational and Data Sciences. “It’s the technology and the availability of big data that make this possible, but it also raises questions about the information it uses for tailoring. Does it tailor based on your own preferences, for example, or does it tailor them based on your demographics? Or is it based on other people who have used