Clinical trial NCT01775826: Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity
Official title: Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Physical Activity
Sedentary Lifestyle
Keywords: physical activity
sedentary behavior
accelerometer
GPS
GIS
machine learning
Lead sponsor: University of California, San Diego
Sponsor class: OTHER
First posted: 2013-01-25
Enrollment: 225
Sex: ALL
Minimum age (years): 6
Maximum age (years): 85
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: BASIC_SCIENCE

Brief 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.


Eligibility 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 18-64 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.

Primary outcomes:
measure: physical activity behavior classification using study sensors (accelerometers, Sensecam and GPS)
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: La Jolla
country: United States
facility: UCSD
state: California
Study officials:
References:
citation: 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.
citation: Kerr J, Patterson RE, Ellis K, Godbole S, Johnson E, Lanckriet G, Staudenmayer J. Objective Assessment of Physical Activity: Classifiers for Public Health. Med Sci Sports Exerc. 2016 May;48(5):951-7. doi: 10.1249/MSS.0000000000000841.
citation: 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.