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mHealth Blog

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The mHealth Monitor blog is an open, collaborative space for experts and practitioners in the field of mHealth to share ideas. The foundational posts will feature thinking that originated at a workshop in December 2015, when the National Cancer Institute (NCI) convened a group of 20 thought leaders in the field of mHealth, smoking cessation, and evaluation. The primary objective of this meeting was to find ways to explore key scientific methods that allow for evaluation of smart design efforts to help people quit smoking, while keeping pace with technology. While the first set of posts will focus on evaluation, our goal is to amplify the conversation within the larger mHealth community, with the aim of improving how mHealth interventions are designed, delivered, and evaluated to effectively carry out health behavior change.

If you are reading this, you are probably interested in these topics and that likely means you have something relevant to share. We invite you to add your comments and consider posting on a topic that is of interest to you. We hope this will be a lively space for people to toss out ideas, share opinions, respectfully debate one another, and collectively move the thinking forward on how to build well designed, effective mHealth interventions.

Posted on: Wed, 04/26/2017 - 13:02
“Am I reaching the right people?” is a question that anyone who has ever been a part of a public health campaign has asked themselves at one time or another. It is also a question that is extremely hard to answer. 
 
One of the greatest challenges that communicators face is understanding and reaching their intended target audiences. This can be particularly challenging for digitally-delivered programs and interventions available to anyone with an...
Posted on: Fri, 01/27/2017 - 14:23

Most people who do qualitative research, which analyzes non-numerical information, such as interviews, open-ended questionnaires, and observations, know that it includes a lot of coding. Coding is a standardized process of classifying qualitative data (i.e., non-numerical data) by using one unified model, or “coding scheme,” to analyze numerous sets of data. Coding aims to reduce subjective opinion and analysis of qualitative data, and instead ensure a more objective analytical process with...