amsys 18(17): e2

Research Article

Secure Mobile Automation of Ecological Momentary Assessments (EMA) For Structured Querying

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  • @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
Nikhil Yadav1,*, Mehrdad Aliasgari2, Christopher Azzara1, Fazel Keshtkar1
  • 1: Division of Computer Science, Mathematics and Science, St. John’s University, Queens, New York
  • 2: Department of Computer Engineering and Computer Science,California State University, Long Beach, California
*Contact email: yadavn@stjohns.edu

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 e ect 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.