Development and evaluation of a 3D imaging system for child anthropometry Open Access

Conkle, Joel (Fall 2017)

Permanent URL: https://etd.library.emory.edu/concern/etds/3r074t92z?locale=en
Published

Abstract

 The usefulness of anthropometry is undermined by poor measurement quality, which has led to calls from the global nutrition community for new technology to improve the quality of child anthropometry. In response, a full-body 3D imaging system, AutoAnthro, was designed to measure child stature, arm circumference (MUAC) and head circumference (HC). The research that makes up this dissertation came from the Body Imaging for Nutritional Assessment Study (BINA), which was a large-scale validation study of AutoAnthro.

 

BINA collected manual and 3D scan-derived measurements from 474 children under five years of age in Atlanta, USA. We first analyzed manual measurement quality to confirm that we collected gold standard anthropometry. We then evaluated the reliability and accuracy of 3D scan-derived measurements against manual measurements, and included an assessment of how similar the two methods were in classifying nutritional status. Finally, we evaluated the efficiency, invasiveness, and user experience of 3D imaging by conducting a time-motion study on a subsample of BINA participants, and by interviewing BINA anthropometrists. To place our research into context we carried out literature reviews on anthropometric data quality and the use of 3D imaging for anthropometry.

 

After finding excellent quality of manual measurements we concluded that BINA could provide a meaningful evaluation of 3D imaging for child anthropometry. In comparing the two methods we found that measurement reliability of repeated scans was excellent, and similar to manual measurement reliability for stature, HC and MUAC. We found systematic bias when analyzing accuracy — 3D imaging overestimated stature and HC and underestimated MUAC. After adjusting scan-derived measurements to remove systematic bias, 3D imaging and manual measurement yielded similar mean z-scores, z-score standard deviations (SD), and prevalence. Sensitivity and specificity of adjusted, scan-derived measurements was good to excellent for all measures. Qualitative data showed anthropometrists considered the use of AutoAnthro an easy, ‘streamlined experience’ when measuring cooperative children, but scanning uncooperative children was difficult. We found that scanning took less time and was less stressful for children than manual measurement.

Technology could be the most efficient driver of anthropometric data quality improvement. We do not yet know if AutoAnthro will lead to improved quality of child measurements, but BINA showed that a 3D imaging system produced reliable measurements of children under five years of age, which suggests that 3D imaging can be an appropriate anthropometric tool for infants and young children. Further research and development is needed, particularly to determine if AutoAnthro improves quality and to address our findings of systematic inaccuracy and anthropometrists’ lack of confidence in scanning uncooperative children. The potential value of 3D imaging for anthropometry is not limited to quality improvement; adoption of the technology could result in collection of hundreds of measurements during regular nutritional assessment, and lead to the discovery of new indicators that make anthropometry a better predictor of outcomes of interest.

Table of Contents

 Acknowledgements       vi

List of Figures    x

List of Tables      xi

Research in Technology Context               xii

Chapter 1 . Introduction                1

1.1 Calls for Improved Anthropometric Data Quality         2

Chapter 2 . Background 7

2.1. 3D Imaging for Anthropometry – The Past (1800s and 1900s)               7

2.2. 3D Imaging for Anthropometry – 2000-Present          9

Chapter 3 . Improving the Quality of Child Anthropometry: Manual Anthropometry in the 3D Body Imaging for Nutritional Assessment Study (BINA)     20

3.1. Introduction              22

3.2. Materials and Methods        23

3.3. Results from Quality Tests   30

3.4. Discussion   32

3.5. Conclusions                37

3.6. Figures and Tables  39

References (Chapter 3) 42

Chapter 4 . Accuracy and Reliability of a Low-Cost, Handheld 3D Imaging System for Child Anthropometry             46

4.1. Introduction              48

4.2. Materials and Methods        49

4.3. Results         54

4.4. Discussion   59

4.4. Figures and Tables  63

Chapter 5 . Supplementary Materials for Accuracy and Reliability of a Low-Cost, Handheld 3D Imaging System for Child Anthropometry     70

5.1. Supplementary Methods     70

5.2 Supplementary Discussion    74

5.3 Supplementary Figures and Tables   82

Chapter 6 . A collaborative, mixed-methods evaluation of a low-cost, handheld 3D imaging system for child anthropometry 94

6.1. Introduction              96

6.2. Materials and Methods        97

6.3 Results          102

6.4 Discussion    107

6.5 Conclusions 114

6.6 Figures and Tables    115

References (Chapter 6) 122

Chapter 7 . Discussion    126

7.1. Summary of Findings             126

7.2. AutoAnthro anthropometry quality in relation to other 3D imaging systems 127

7.3. Study Strengths and Limitations        130

7.4. AutoAnthro Research and Development Needs        132

7.5. 3D Imaging for Anthropometry – The Future              144

7.6. Conclusions                155

7.7 Supplementary Tables and Figures   156

References (Chapter 7) 158

 

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