Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity (iWatch)

This study has been completed.

Sponsor:

ClinicalTrials.gov Identifier:

NCT01775826

First Posted: January 25, 2013

Last Update Posted: May 13, 2016

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.

Information provided by (Responsible Party):

Jacqueline Kerr, University of California, San Diego

Purpose

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.

Condition
Physical Activity Sedentary Lifestyle


Study Type:	Observational
Study Design:	Observational Model: Cohort Time Perspective: Cross-Sectional
Official Title:	Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity

Resource links provided by NLM:

MedlinePlus related topics: Exercise and Physical Fitness Health Checkup Health Risks of an Inactive Lifestyle

U.S. FDA Resources

Further study details as provided by Jacqueline Kerr, University of California, San Diego:

Primary Outcome Measures:

physical activity behavior classification using study sensors (accelerometers, Sensecam and GPS) [ Time Frame: Baseline ]
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).


Enrollment:	225
Study Start Date:	March 2013
Study Completion Date:	April 2016
Primary Completion Date:	April 2016 (Final data collection date for primary outcome measure)

Groups/Cohorts
Child and adolescent 6-17 years of age
Adult 18-55 years of age
Older adults 65-85 years of age

Eligibility

Information from the National Library of Medicine

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Ages Eligible for Study:	6 Years to 85 Years (Child, Adult, Senior)
Sexes Eligible for Study:	All
Accepts Healthy Volunteers:	Yes
Sampling Method:	Non-Probability Sample

Study Population

We will recruit 210 volunteer participants over a 2-yr period. Fifty percent of the sample will be women, and we will quota sample three specific age groups: 6-10 year olds (n = 70); 16-55 year olds (n=70); and 65-85 year olds (n= 70).

Criteria

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 18-55 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.

Contacts and Locations

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01775826

Locations


United States, California
UCSD
La Jolla, California, United States, 92093

Sponsors and Collaborators

University of California, San Diego

More Information

Publications:

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.

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.

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.


Responsible Party:	Jacqueline Kerr, Assistant Professor, University of California, San Diego
ClinicalTrials.gov Identifier:	NCT01775826 History of Changes
Other Study ID Numbers:	1R01CA164993-01A1 ( U.S. NIH Grant/Contract )
First Submitted:	January 17, 2013
First Posted:	January 25, 2013
Last Update Posted:	May 13, 2016
Last Verified:	May 2016

Keywords provided by Jacqueline Kerr, University of California, San Diego:


physical activity sedentary behavior accelerometer	GPS GIS machine learning

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