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Authorship Correction: A Community-Based Short Message Service Intervention to Improve Mothers’ Feeding Practices for Obesity Prevention: Quasi-Experimental Study

Authorship Correction: A Community-Based Short Message Service Intervention to Improve Mothers’ Feeding Practices for Obesity Prevention: Quasi-Experimental Study

Feeding Practices for Obesity Prevention: Quasi-Experimental Study” (JMIR Mhealth Uhealth 2019;7(6):e13828) wish to change the order of the authors on the publication so that Xu Qian is listed last.The previous order of authorship was as follows:Hong Jiang, Mu

Hong Jiang, Mu Li, Li Ming Wen, Louise Baur, Gengsheng He, Xiaoying Ma, Xu Qian

JMIR Mhealth Uhealth 2019;7(7):e15046

A Community-Based Short Message Service Intervention to Improve Mothers’ Feeding Practices for Obesity Prevention: Quasi-Experimental Study

A Community-Based Short Message Service Intervention to Improve Mothers’ Feeding Practices for Obesity Prevention: Quasi-Experimental Study

Children’s BMI at 12 and 24 months was calculated as weight in kg/(length in m)2, and BMI z-score and weight-for-length z-score were calculated using the lambda-mu-sigma method based on the WHO Child Growth Standards [19].

Hong Jiang, Mu Li, Li Ming Wen, Louise Baur, Gengsheng He, Xiaoying Ma, Xu Qian

JMIR Mhealth Uhealth 2019;7(6):e13828

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

interactions.Adoption of Electronic Health Records Among US HospitalsThe Health Information Technology for Economic and Clinical Health (HITECH) Act [11] was signed into law with the dual aims of accelerating EHR adoption and promoting their “meaningful use” (MU

Hadi Kharrazi, Claudia P Gonzalez, Kevin B Lowe, Timothy R Huerta, Eric W Ford

J Med Internet Res 2018;20(8):e10458

Next Generation Phenotyping Using the Unified Medical Language System

Next Generation Phenotyping Using the Unified Medical Language System

This already intricate landscape is further complicated by the disparity between billing and MU reporting.

Tomasz Adamusiak, Naoki Shimoyama, Mary Shimoyama

JMIR Med Inform 2014;2(1):e5