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Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

Other app features include: an audio warning when a g-force event is triggered, a capability of uploading JSON formatted files to a centralized database, and a fully integrated navigation solution.This navigation feature may reduce the driver’s desire to switch

Raisa Z Freidlin, Amisha D Dave, Benjamin G Espey, Sean T Stanley, Marcial A Garmendia, Randall Pursley, Johnathon P Ehsani, Bruce G Simons-Morton, Thomas J Pohida

JMIR Mhealth Uhealth 2018;6(4):e69

CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions

CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions

While JMIR was the journal which had most trials published, the distribution has a very long tail, with relevant articles scattered in a wide range of other journals (see Table 1).

Gunther Eysenbach, CONSORT-EHEALTH Group

J Med Internet Res 2011;13(4):e126

Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change

Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change

Native apps run locally on a smartphone’s operating system in a way that is analogous to programs running on a desktop computer.

Lana Hebden, Amelia Cook, Hidde P. van der Ploeg, Margaret Allman-Farinelli

JMIR Res Protoc 2012;1(2):e9

Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach

Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach

with the goal of providing a set of stand-alone tools that can be easily combined in a processing pipeline.

João Figueira Silva, João Rafael Almeida, Sérgio Matos

JMIR Med Inform 2020;8(12):e22898

Metadata Correction: Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: Prospective Intervention

Metadata Correction: Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: Prospective Intervention

In the paper by David Thompson et al, “Direct Adherence Measurement Using an Ingestible Sensor Compared With Self-Reporting in High-Risk Cardiovascular Disease Patients Who Knew They Were Being Measured: A Prospective Intervention” (JMIR Mhealth Uhealth 2017

David Thompson, Teresa Mackay, Maria Matthews, Judith Edwards, Nicholas S Peters, Susan B Connolly

JMIR Mhealth Uhealth 2018;6(4):e13

Evaluating the Accuracy of Google Translate for Diabetes Education Material

Evaluating the Accuracy of Google Translate for Diabetes Education Material

MethodsMaterials to be TranslatedWe chose a freely accessible diabetes patient education pamphlet as a heuristic example for evaluating the accuracy of machine translation devices.

Xuewei Chen, Sandra Acosta, Adam Etheridge Barry

JMIR Diabetes 2016;1(1):e3