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

Apps, Mobile, Wearables for Diabetes (382) Consumer & Patient Education and Shared-Decision Making (946) mHealth for Data Collection and Research (1285) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1838) Decision Support for Health Professionals (1674) Wearable Devices and Sensors (1240) Glucose Tracking and Self-Monitoring of Blood Glucose (210) Diabetes Self-Management (634) Datasets (20)

Published on in Vol 9 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62831, first published .
Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis

Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis

Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis

Authors of this article:

Takashi Miyakoshi1 Author Orcid Image ;   Yoichi M Ito2 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Tonacci A, Gorini F, Sansone F, Venturi F. Integrating Sensory Perception and Wearable Monitoring to Promote Healthy Aging: A New Frontier in Nutritional Personalization. Nutrients 2026;18(2):214 View
  2. Chou C, Soriano J, Lumbreras S. Turning sensors into predictors: the power of slope to anticipate hyper-and hypoglycemia. Biomedical Physics & Engineering Express 2026;12(3):037003 View

Conference Proceedings

  1. B S K, S N S, Patil S, Biradar S, D G, Prakashappa V. 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT). Diabetes Risk Prediction Using Stacked RF and SVM ML Models with SHAP and LIME Interpretability View