NCT01775826

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
225

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started Mar 2013

Typical duration for not_applicable

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 17, 2013

Completed
8 days until next milestone

First Posted

Study publicly available on registry

January 25, 2013

Completed
1 month until next milestone

Study Start

First participant enrolled

March 1, 2013

Completed
3.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 1, 2016

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 1, 2016

Completed
Last Updated

August 20, 2019

Status Verified

August 1, 2019

Enrollment Period

3.1 years

First QC Date

January 17, 2013

Last Update Submit

August 15, 2019

Conditions

Physical ActivitySedentary Lifestyle

Keywords

physical activitysedentary behavioraccelerometerGPSGISmachine learning

Outcome Measures

Primary Outcomes (1)

  • 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

Study Arms (1)

All Purposes

OTHER

All participants.

Other: Measurement

Interventions

MeasurementOTHER

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

All Purposes

Eligibility Criteria

Age6 Years - 85 Years
Sexall
Healthy VolunteersYes
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • 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,
  • +10 more criteria

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

UCSD

La Jolla, California, 92093, United States

Location

Related Publications (3)

  • 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.

    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.

    PMID: 27089222BACKGROUND

  • 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.

    PMID: 26673126BACKGROUND

MeSH Terms

Conditions

Motor ActivitySedentary Behavior

Interventions

Weights and Measures

Condition Hierarchy (Ancestors)

Behavior

Intervention Hierarchy (Ancestors)

Investigative Techniques

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
BASIC SCIENCE
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Prinicipal Investigator

Study Record Dates

First Submitted

January 17, 2013

First Posted

January 25, 2013

Study Start

March 1, 2013

Primary Completion

April 1, 2016

Study Completion

April 1, 2016

Last Updated

August 20, 2019

Record last verified: 2019-08

Locations

US(1)