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Grant Details

Grant Number: 5R21CA194492-02 Interpret this number
Primary Investigator: Carlson, Jordan
Organization: Children'S Mercy Hosp (Kansas City, Mo)
Project Title: Automated Ecological Video Identification of Physical Activity (E-Vip) Software
Fiscal Year: 2017


 DESCRIPTION (provided by applicant): This proposal addresses multiple priorities of PAR-12-197 "Improving Diet and Physical Activity (PA) Assessment" by advancing assessment of population PA in common settings. Physical inactivity is responsible for ≈200,000 deaths in the US and 5 million deaths worldwide annually. About 10% of breast and 10% of colon cancers are attributable to insufficient PA, and inactivity causes 9% of total premature mortality. Parks, schools and youth sports are important contributors of PA that are relevant to a majority of the population. Supporting PA in these settings would contribute to improvements in population levels of PA and disease prevention and control. We will develop a novel video analysis software system, named E-VIP (Ecological Video Identification of PA), which will estimate the number of people and aggregated volume of PA from video recordings of PA- relevant settings. E-VIP will provide automated and continuous (rather than momentary) assessment, which will allow ongoing feedback to inform adaptive interventions and decision making. E-VIP is based on computer science methods used to count crowds and identify behaviors. We will train the E-VIP machine learning algorithm on 32,400 seconds of video from 2 parks, 2 schools, and 2 youth sports settings. Participants will engage in a variety of activities while wearing accelerometers to capture PA intensity in Metabolic Equivalents (METs). The testing phase involves capturing 900, 5-minute videos of free-living behavior across 4 parks, 4 schools, and 4 youth sports settings, with half of the settings/zones being the same as from the training phase. A range of activities and density of people will be captured in both the training and testing phase to maximize coverage of more-difficult high-density situations. Systematic direct observation will be conducted on each time sample to provide the criterion measure for testing validity. The metrics that will be tested include the average number of people, average proportion of people in each activity category (sedentary, light, moderate, vigorous, very vigorous), and sum PA MET-minutes in the setting over the 5-minute (or any given) time period. A gender and age group classifier will be explored. PE classes taught by PE specialists vs classroom teachers will be compared to test construct validity of E-VIP. Bland Altman methods and intraclass correlation coefficients will be used to assess agreement. Potential sources of error such as occlusions (e.g., trees, shadows, other people) will be assessed using moderator analyses. By automating ecological PA assessment, E-VIP will be feasible for widespread use. E-VIP's capability of continuous assessment will improve precision by collecting higher resolution data than collected by existing direct observation tools. When embedded in specific settings through commonly-used security cameras or webcams, or by purposefully placing video recorders, E-VIP will be capable of ongoing assessment which will inform public health surveillance, intervention, and evidence-based decision making (e.g., optimizing intervention strategies, monitoring school Physical Education mandates, providing needs assessment prior to and evaluation after environmental modifications).


Automated High-Frequency Observations of Physical Activity Using Computer Vision.
Authors: Carlson J.A. , Liu B.O. , Sallis J.F. , Hipp J.A. , Staggs V.S. , Kerr J. , Papa A. , Dean K. , Vasconcelos N.M. .
Source: Medicine and science in sports and exercise, 2020 Sep; 52(9), p. 2029-2036.
PMID: 32175976
Related Citations

Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.
Authors: Carlson J.A. , Hipp J.A. , Kerr J. , Horowitz T.S. , Berrigan D. .
Source: Journal for the measurement of physical behaviour, 2018 Sep; 1(3), p. 143-154.
PMID: 31263802
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Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.
Authors: Carlson J.A. , Liu B. , Sallis J.F. , Kerr J. , Hipp J.A. , Staggs V.S. , Papa A. , Dean K. , Vasconcelos N.M. .
Source: International journal of environmental research and public health, 2017-12-01; 14(12), .
EPub date: 2017-12-01.
PMID: 29194358
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