Research Article
Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying
@ARTICLE{10.4108/eai.23-3-2018.154373, author={Nikhil Yadav and Mehrdad Aliasgari and Christopher Azzara and Fazel Keshtkar}, title={Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying}, journal={EAI Endorsed Transactions on Ambient Systems}, volume={5}, number={17}, publisher={EAI}, journal_a={AMSYS}, year={2018}, month={3}, keywords={Mobile health, data collection, health surveys}, doi={10.4108/eai.23-3-2018.154373} }
- Nikhil Yadav
Mehrdad Aliasgari
Christopher Azzara
Fazel Keshtkar
Year: 2018
Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying
AMSYS
EAI
DOI: 10.4108/eai.23-3-2018.154373
Abstract
The ubiquitous nature of mobile devices like smartphones and tablets make them ideal platforms for engaging users in Ecological Momentary Assessments (EMA). In EMA, participants are repeatedly assessed frequently (daily or multiple times per day) through a set of questionnaires. Fluctuations in psychological states, such as cognition and eect can be recorded in real time using mobile devices. EMA results can further be coupled with other physiological sensor data procured through wearables and smartphones, to validate and correlate patient experiences and responses to certain treatments and medications. This can be useful for health care organizations which are interested in the impact of their treatment techniques on patient populations. In this paper, we present an EMA platform developed using Android mobile devices. The collected results are shown and techniques used to query the data are demonstrated. The platform is flexible and can scale up to perform data mining algorithms for sentiment analysis based on the stimulus to a medication or treatment over a prolonged period of time.
Copyright © 2018 Nikhil Yadav et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.