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

Wearable Devices and Sensors (1133) mHealth for Wellness, Behavior Change and Prevention (3315) Ecological Momentary Assessment (EMA) (405) mHealth for Data Collection and Research (1196) Innovations and Technology for Healthy Eating Education (487) Evaluation and Research Methodology for mHealth (242) Instruments and Questionnaires for Nutrition and Food Intake (211)

Published on in Vol 10, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30211, first published .
Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study

Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study

Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study

Authors of this article:

Brooke Marie Bell1, 2 Author Orcid Image ;   Ridwan Alam3, 4 Author Orcid Image ;   Abu Sayeed Mondol5 Author Orcid Image ;   Meiyi Ma5, 6 Author Orcid Image ;   Ifat Afrin Emi5 Author Orcid Image ;   Sarah Masud Preum5, 7 Author Orcid Image ;   Kayla de la Haye2 Author Orcid Image ;   John A Stankovic5 Author Orcid Image ;   John Lach4, 8 Author Orcid Image ;   Donna Spruijt-Metz2, 9, 10 Author Orcid Image
Article Authors Cited by (8) Tweetations (9) Metrics

Journals

  1. Wang L, Chan V, Allman-Farinelli M, Davies A, Wellard-Cole L, Rangan A. Wearable Cameras Reveal Large Intra-Individual Variability in Timing of Eating among Young Adults. Nutrients 2022;14(20):4349 View
  2. Chan V, Davies A, Wellard-Cole L, Allman-Farinelli M. The energy density of meals and snacks consumed by young Australian adults (18–30 years old) are influenced by preparation location but not screen use nor social interactions: findings from the MYMeals wearable camera study. Journal of Nutritional Science 2022;11 View
  3. O'Connor S, O’Connor L, Higgins K, Bell B, Krueger E, Rawal R, Hartmuller R, Reedy J, Shams-White M. Conceptualization and Assessment of 24-H Timing of Eating and Energy Intake: A Methodological Systematic Review of the Chronic Disease Literature. Advances in Nutrition 2024;15(3):100178 View
  4. Phalle A, Gokhale D. Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications. Frontiers in Nutrition 2025;12 View
  5. Drexl K, Ralisa V, Rosselet-Amoussou J, Wen C, Urben S, Plessen K, Glaus J. Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update. Journal of Medical Internet Research 2025;27:e65710 View
  6. Sun Y, Jaiswal A, Slade C, Phillips K, Benzo R, Washington P. Associations Between Social Determinants of Health and Adherence in Mobile-Based Ecological Momentary Assessment: Scoping Review. Journal of Medical Internet Research 2025;27:e69831 View
  7. Heron K, Shipley J, Panza E, Sandoval C, Turner M, Hawn S, Braitman A. Are affective states associated with disordered eating behaviors in the daily lives of sexual minority and heterosexual young women?: an ecological momentary assessment study. Annals of Behavioral Medicine 2025;59(1) View
  8. Avdeeva M, Gogoleva M, Rizakhanova O, Shatalova M, Nosacheva V, Sandgarten L, Signaevskaya A, Chestnyi I. Modern approaches to formation people eating behavior using digital technologies and artificial intelligence. Russian Journal of Preventive Medicine 2025;28(10):33 View