This paper is in the following e-collection/theme issue:

mHealth for Wellness, Behavior Change and Prevention (2871) JMIR Theme Issue: COVID-19 Special Issue (2452) Big Data (240) Obesity and Nutrition as Public Health Problem (304) mHealth for Data Collection and Research (955)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26290, first published .
Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Exploring Associations Between Children’s Obesogenic Behaviors and the Local Environment Using Big Data: Development and Evaluation of the Obesity Prevention Dashboard

Authors of this article:

Dimitris Filos1 Author Orcid Image ;   Irini Lekka1 Author Orcid Image ;   Vasileios Kilintzis1 Author Orcid Image ;   Leandros Stefanopoulos1 Author Orcid Image ;   Youla Karavidopoulou1 Author Orcid Image ;   Christos Maramis1 Author Orcid Image ;   Christos Diou2, 3 Author Orcid Image ;   Ioannis Sarafis3 Author Orcid Image ;   Vasileios Papapanagiotou3 Author Orcid Image ;   Leonidas Alagialoglou3 Author Orcid Image ;   Ioannis Ioakeimidis4 Author Orcid Image ;   Maria Hassapidou5 Author Orcid Image ;   Evangelia Charmandari6 Author Orcid Image ;   Rachel Heimeier7 Author Orcid Image ;   Grace O'Malley8 Author Orcid Image ;   Shane O’Donnell9 Author Orcid Image ;   Gerardine Doyle10 Author Orcid Image ;   Anastasios Delopoulos3 Author Orcid Image ;   Nicos Maglaveras1 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

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  2. Li C, Jordan A, Song J, Ge Y, Park A. A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study. Journal of Medical Internet Research 2022;24(12):e39340 View
  3. Tufford A, Diou C, Lucassen D, Ioakimidis I, O'Malley G, Alagialoglou L, Charmandari E, Doyle G, Filis K, Kassari P, Kechadi T, Kilintzis V, Kok E, Lekka I, Maglaveras N, Pagkalos I, Papapanagiotou V, Sarafis I, Shahid A, van ’t Veer P, Delopoulos A, Mars M. Toward Systems Models for Obesity Prevention: A Big Role for Big Data. Current Developments in Nutrition 2022;6(9):nzac123 View
  4. Tufford A, Brennan L, van Trijp H, D'Auria S, Feskens E, Finglas P, Kok F, Kolesárová A, Poppe K, Zimmermann K, van ’t Veer P. A scientific transition to support the 21st century dietary transition. Trends in Food Science & Technology 2023;131:139 View
  5. Diou C, Kyritsis K, Papapanagiotou V, Sarafis I. Intake monitoring in free-living conditions: Overview and lessons we have learned. Appetite 2022;176:106096 View
  6. Ising A, Waller A, Frerichs L. Evaluation of an Emergency Department Visit Data Mental Health Dashboard. Journal of Public Health Management and Practice 2023;29(3):369 View
  7. Fraisl D, See L, Estevez D, Tomaska N, MacFeely S. Citizen science for monitoring the health and well-being related Sustainable Development Goals and the World Health Organization’s Triple Billion Targets. Frontiers in Public Health 2023;11 View
  8. Vasquez H, Pianarosa E, Sirbu R, Diemert L, Cunningham H, Harish V, Donmez B, Rosella L. Human factors methods in the design of digital decision support systems for population health: a scoping review. BMC Public Health 2024;24(1) View