Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview

Journals

1. Engelhard M, Oliver J, McClernon F. Digital envirotyping: quantifying environmental determinants of health and behavior. npj Digital Medicine 2020;3(1) View
2. Sparling T, White H, Kadiyala S. PROTOCOL: Evidence and gap map protocol: Understanding pathways between agriculture and nutrition: An evidence and gap map of tools, metrics and methods developed and applied in the last 10 years. Campbell Systematic Reviews 2019;15(3) View
3. Downing K, Janssen X, Reilly J. Feasibility of wearable cameras to assess screen time and time spent restrained in children aged 3 to 5 years: a study protocol. BMJ Open 2019;9(5):e028265 View
4. Smith C, Galland B, de Bruin W, Taylor R. Feasibility of Automated Cameras to Measure Screen Use in Adolescents. American Journal of Preventive Medicine 2019;57(3):417 View
5. del Pozo-Cruz B, Perales F, Parker P, Lonsdale C, Noetel M, Hesketh K, Sanders T. Joint physical-activity/screen-time trajectories during early childhood: socio-demographic predictors and consequences on health-related quality-of-life and socio-emotional outcomes. International Journal of Behavioral Nutrition and Physical Activity 2019;16(1) View
6. Chen H, Feng Y. Moving Forward to the Future: A Review of Microbial Food Safety Education in China. Foodborne Pathogens and Disease 2021;18(8):547 View
7. Barrett T, Feng Y. Effect of Observational Evaluation of Food Safety Curricula on High School Students' Behavior Change. Journal of Food Protection 2020;83(11):1947 View
8. Kininmonth A, Smith A, Llewellyn C, Dye L, Lawton C, Fildes A. The relationship between the home environment and child adiposity: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 2021;18(1) View
9. Doulah A, Ghosh T, Hossain D, Imtiaz M, Sazonov E. “Automatic Ingestion Monitor Version 2” – A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images. IEEE Journal of Biomedical and Health Informatics 2021;25(2):568 View
10. Ghattas H, Jamaluddine Z, Semaan A, El-Helou N, Safadi G, Elghossain T, Akl C, Elbassuoni S, Chalak A, El Ati J, Saleem S. School and community drivers of child diets in two Arab cities: The SCALE protocol and innovative tools to assess children’s food environments. PLOS ONE 2022;17(7):e0264963 View
11. Kininmonth A, Schrempft S, Smith A, Dye L, Lawton C, Fisher A, Llewellyn C, Fildes A. The Home Environment Interview and associations with energy balance behaviours and body weight in school-aged children – a feasibility, reliability, and validity study. International Journal of Behavioral Nutrition and Physical Activity 2021;18(1) View
12. Bulungu A, Palla L, Nambooze J, Priebe J, Forsythe L, Katic P, Varley G, Galinda B, Sarah N, Wellard K, Ferguson E. Automated wearable cameras for improving recall of diet and time use in Uganda: a cross-sectional feasibility study. Nutrition Journal 2023;22(1) View
13. Pan Z, Forjan D, Marden T, Padia J, Ghosh T, Hossain D, Thomas J, McCrory M, Sazonov E, Higgins J. Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices. Frontiers in Nutrition 2022;9 View
14. Kininmonth A, Schrempft S, Smith A, Dye L, Lawton C, Fisher A, Llewellyn C, Fildes A. Associations between the home environment and childhood weight change: a cross-lagged panel analysis. International Journal of Obesity 2022;46(9):1678 View
15. Liu C, Wang W, Khan G. Digital Transformation in Measuring Social Determinants of Health. Journal of Organizational and End User Computing 2023;35(3):1 View
16. Su N, Zhang Z, Chen J, Li W, Long Y. Assessing personal screen exposure with ever-changing contexts using wearable cameras and computer vision. Building and Environment 2024;261:111720 View