Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity

NCT ID: NCT01775826

Last Updated: 2019-08-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

225 participants

Study Classification

INTERVENTIONAL

Study Start Date

2013-03-31

Study Completion Date

2016-04-30

Brief Summary

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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.

Detailed Description

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Conditions

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Physical Activity Sedentary Lifestyle

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

BASIC_SCIENCE

Blinding Strategy

NONE

Study Groups

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All Purposes

All participants.

Group Type OTHER

Measurement

Intervention Type OTHER

Measured usual (day-to-day) behavior with body-worn sensors.

Interventions

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Measurement

Measured usual (day-to-day) behavior with body-worn sensors.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 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.


* 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.


* 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.
Minimum Eligible Age

6 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University of California, San Diego

OTHER

Sponsor Role lead

Responsible Party

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Marta Jankowska

Prinicipal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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UCSD

La Jolla, California, United States

Site Status

Countries

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United States

References

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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.

Reference Type BACKGROUND

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.

Reference Type BACKGROUND
PMID: 27089222 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 26673126 (View on PubMed)

Other Identifiers

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1R01CA164993-01A1

Identifier Type: NIH

Identifier Source: org_study_id

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