Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity

Physical Activity Clinical Trial

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Official title:

Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01775826
• Other study ID #: 1R01CA164993-01A1
• Status: Completed
• Phase: N/A
• Start date: March 2013
• Est. completion date: April 2016

Study information

• Verified date: August 2019
• Source: University of California, San Diego
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 225
• Est. completion date: April 2016
• Est. primary completion date: April 2016
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 6 Years to 85 Years

Eligibility

Inclusion Criteria:

Inclusion Criteria for participants 6-17 yr olds:

• provide written parental consent to complete study protocols;

• provide verbal assent to complete study protocols;

• willingness to complete 2 visits to UCSD offices;

• willingness to wear multiple sensor devices on 7 days for 12 hours per day;

• willingness to wear wrist accelerometer on 7 days for 24 hours per day;

• willingness to have their height and weight measured;

• be able to walk unassisted

• able to read and understand study materials in English.

Inclusion Criteria for participants 18-64 yr old:

• provide written consent to complete study protocols;

• willingness to complete 2 visits to UCSD offices;

• willingness to wear multiple sensor devices on 7 days for 12 hours per day;

• willingness to wear wrist accelerometer on 7 days for 24 hours per day;

• complete a survey assessing their demographic characteristics;

• willingness to have their height and weight measured;

• be physically and cognitively able to walk unassisted,

• able to read and understand study materials in English.

Inclusion Criteria for participants 65-85 yr olds:

• provide written consent to complete study protocols;

• correctly answer verbal questions about their comprehension of the informed consent;

• willingness to complete 2 visits to UCSD offices;

• willingness to wear multiple sensor devices on 7 days for 12 hours per day;

• willingness to wear wrist accelerometer on 7 days for 24 hours per day;

• complete a survey assessing their demographic;

• willingness to have their height and weight measured;

• be physically and cognitively able to walk without the assistance of another person (walking aids are permitted)

• able to read and understand study materials in English.

Exclusion Criteria:

• unable to ambulate;

• attends a workplace or school on monitoring days that prohibits static images being taken by a SenseCam worn around the neck of the participant;

• pregnancy in second or third trimester.

Related Conditions & MeSH terms

• Physical Activity
• Sedentary Lifestyle

Intervention

Other:
• Measurement
Measured usual (day-to-day) behavior with body-worn sensors.

Locations

Country	Name	City	State
United States	UCSD	La Jolla	California

Sponsors (1)

Lead Sponsor	Collaborator
University of California, San Diego

Country where clinical trial is conducted

United States, 

References & Publications (3)

Ellis K, Kerr J, Godbole S, Staudenmayer J, Lanckriet G. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification. Med Sci Sports Exerc. 2016 May;48(5):933-40. doi: 10.1249/MSS.0000000000000840. — View Citation

Jacqueline Kerr, Ruth E. Patterson, Katherine Ellis, Suneeta Godbole, Eileen Johnson, Gert Lanckriet, John Staudenmayer. Objective Assessment of Walking and Sitting: New behavior classifiers for Public Health. MSSE 2015, in press.

Moghimi, Mohammad**; Kerr, Jacqueline; Johnson, Eileen; Godbole, Suneeta; Belongie, Serge Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences MultiMedia Modeling 2015 357-368.

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	physical activity behavior classification using study sensors (accelerometers, Sensecam and GPS)	Using an annotated data set of SenseCam images in three free-living population subgroups, we will compare sensitivity, specificity and percent agreement between behavioral classifiers derived from: (a) single axis vs. multi axis accelerometers; (b) aggregated movement counts vs. raw acceleration data; (c) hip vs. wrist mounted accelerometers.; Determine (a) the extent to which adding GPS data improves discrimination accuracy over accelerometer only behavior classification (i.e., best classifier resulting from Aim 1); and (b) the extent to which adding GIS data improves discrimination accuracy over accelerometer and GPS behavior classification alone (i.e., best classifier resulting from Aim 2a).	Baseline