NCT01775826

Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 225 (actual)
• Sponsor: University of California, San Diego · Academic / Other
• Sex: All
• Age: 6 Years – 85 Years
• Healthy volunteers: Accepted

Summary

The majority of the US population spends most of the day sitting and the we have new scientific evidence that this can contribute to poor health regardless of how much physical activity a person does. However, we do not measure sitting time very accurately and when we ask people to tell us how much they do, their answers are unreliable. Our study will use small sensors to objectively measure when people sit or do physical activity, and we will use sophisticated computational techniques to summarize these movement patterns.

Conditions

• Physical Activity
• Sedentary Lifestyle

Interventions

Type	Name	Description
OTHER	Measurement	Measured usual (day-to-day) behavior with body-worn sensors.

Timeline

• Start date: 2013-03-01
• Primary completion: 2016-04-01
• Completion: 2016-04-01
• First posted: 2013-01-25
• Last updated: 2019-08-20

Locations

1 site across 1 country: United States