Published on in Vol 22, No 7 (2020): July
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/17031, first published
.
Journals
- Kim G, Lee J, Lee S. A Technology-Mediated Interventional Approach to the Prevention of Metabolic Syndrome: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health 2021;18(2):512 View
- Cilli E, Ranieri J, Guerra F, Ferri C, Di Giacomo D. Naturalizing digital and quality of life in chronic diseases: Systematic review to research perspective into technological advancing and personalized medicine. DIGITAL HEALTH 2022;8:205520762211448 View
- Edney S, Chua X, Müller A, Kui K, Müller‐Riemenschneider F. mHealth interventions targeting movement behaviors in Asia: A scoping review. Obesity Reviews 2022;23(4) View
- Ju H, Kang E, Kim Y, Ko H, Cho B. The Effectiveness of a Mobile Health Care App and Human Coaching Program in Primary Care Clinics: Pilot Multicenter Real-World Study. JMIR mHealth and uHealth 2022;10(5):e34531 View
- Kim H, Lee K, Lee J, Youk H, Lee H. The Effect of a Mobile and Wearable Device Intervention on Increased Physical Activity to Prevent Metabolic Syndrome: Observational Study. JMIR mHealth and uHealth 2022;10(2):e34059 View
- Thomas J, Patel A, Das Sivadasan S, Sreevallabhan S, Illathu Madhavamenon K, Mohanan R. Clove bud (Syzygium aromaticum L.) polyphenol helps to mitigate metabolic syndrome by establishing intracellular redox homeostasis and glucose metabolism: A randomized, double-blinded, active-controlled comparative study. Journal of Functional Foods 2022;98:105273 View
- Gonzalez-Fimbres R. Diferencias de género en uso de aplicaciones móviles de ejercicio en alumnos de Entrenamiento Deportivo. Revista de Ciencias del Ejercicio FOD 2022;17(1) View
- Wang W, Cheng J, Song W, Shen Y. The Effectiveness of Wearable Devices as Physical Activity Interventions for Preventing and Treating Obesity in Children and Adolescents: Systematic Review and Meta-analysis. JMIR mHealth and uHealth 2022;10(4):e32435 View
- Ha J, Park J, Lee S, Lee J, Choi J, Kim J, Cho S, Jeon G. Predicting Habitual Use of Wearable Health Devices Among Middle-aged Individuals With Metabolic Syndrome Risk Factors in South Korea: Cross-sectional Study. JMIR Formative Research 2023;7:e42087 View
- Lee J, Lee S. Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study. JMIR Formative Research 2023;7:e42756 View
- Anisha S, Sen A, Bain C. Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review. Journal of Medical Internet Research 2024;26:e56114 View
- Dominguez Miranda S, Rodriguez Aguilar R. Machine learning models in health prevention and promotion and labor productivity: A co-word analysis. Iberoamerican Journal of Science Measurement and Communication 2024;4(1):1 View
Books/Policy Documents
- Domínguez-Miranda S, Rodríguez-Aguilar R. Computer Science and Engineering in Health Services. View