Illustration of measurement error models for reducing biases in nutrition and obesity research using 2D body composition data

Principal Investigator(s):  View help for Principal Investigator(s) Olivia Affuso, The University of Alabama at Birmingham

Version:  View help for Version V1

Name File Type Size Last Modified
Photobody-Study-Data-for-ICPSR.csv text/csv 32.6 KB 10/26/2018 02:00:PM
Photobody-Study-RCode-for-ICPSR-Final.R text/x-r-syntax 61.3 KB 10/26/2018 02:00:PM

Project Citation: 

Affuso, Olivia. Illustration of measurement error models for reducing biases in nutrition and obesity research using 2D body composition data. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2018-10-26. https://doi.org/10.3886/E106966V1

Project Description

Summary:  View help for Summary The files required to reproduce the results of our manuscript entitled, “Illustration of measurement error models for reducing biases in nutrition and obesity research using 2D body composition data” published in Obesity are provided. The data and data dictionary for the Photobody Study and codes corresponding to this manuscript are in this folder. 
Funding Sources:  View help for Funding Sources United States Department of Health and Human Services. National Institutes of Health. National Heart Lung and Blood Institute (R01HL107916); United States Department of Health and Human Services. National Institutes of Health. National Institute of Diabetes and Digestive and Kidney Diseases (P30DK056336); United States Department of Health and Human Services. National Institutes of Health. National Heart Lung and Blood Institute (T32HL072757); United States Department of Health and Human Services. National Institutes of Health. National Institute of Diabetes and Digestive and Kidney Diseases (T32DK062710); United States Department of Health and Human Services. National Institutes of Health. National Center for Advancing Translational Sciences (KL2TR001419); United States Department of Health and Human Services. National Institutes of Health. National Cancer Institute (U01-CA057030-29S2)

Scope of Project

Subject Terms:  View help for Subject Terms measurement error; regression calibration; multiple imputation; simulation extrapola; body composit; body fat; diabetes; physical activity
Geographic Coverage:  View help for Geographic Coverage Alabama
Time Period(s):  View help for Time Period(s) 11/1/2012 – 9/30/2015
Collection Date(s):  View help for Collection Date(s) 11/1/2012 – 9/30/2015
Universe:  View help for Universe The inclusion criteria were: adults aged 19 to 80 years, living in Birmingham, Alabama.  The exclusion criteria were: 1) weight greater than 450 lbs (weight limit of DXA equipment); 2) presence of health conditions that would prevent participants from lying down for DXA scans or standing for taking photographs or that may alter body composition (e.g., cancer, cachexia, or rheumatoid arthritis); 3) missing body parts (except a finger or toe); and 4) pregnant individuals.
Data Type(s):  View help for Data Type(s) clinical data
Collection Notes:  View help for Collection Notes Specific details about the recruitment and study design can be found in the Methods section in our publication or one of the original papers:

1.      Pradhan, L., et al. Feature extraction from 2D images for body composition analysis. in Multimedia (ISM), 2015 IEEE International Symposium on. 2015. IEEE.

2.      Capers, P.L., et al., Visual Representation of Body Shape in African-American and European American Women: Clinical Considerations. Clinical medicine insights. Women's health, 2016. 9(Suppl 1): p. 63.

3.       Affuso, O., et al., A method for measuring human body composition using digital images. PloSOne (in press, 2018). 

Methodology

Response Rate:  View help for Response Rate N/A
Sampling:  View help for Sampling N/A
Data Source:  View help for Data Source Individuals
Collection Mode(s):  View help for Collection Mode(s) other
Scales:  View help for Scales N/A
Weights:  View help for Weights N/A
Unit(s) of Observation:  View help for Unit(s) of Observation Individuals
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V1 [2018-10-26]

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