5th International ICST Conference on Pervasive Computing Technologies for Healthcare

Research Article

Mobile Application and Wearable Sensors for Use in Cognitive Behavioral Therapy for Drug Addiction and PTSD

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2011.246105,
        author={Richard Fletcher and Sharon Tam and Olufemi Omojola and Richard Redemske and Szymon Fedor and Joseph Mugisha},
        title={Mobile Application and Wearable Sensors for Use in Cognitive Behavioral Therapy for Drug Addiction and PTSD},
        proceedings={5th International ICST Conference on Pervasive Computing Technologies for Healthcare},
        publisher={IEEE},
        proceedings_a={PERVASIVEHEALTH},
        year={2012},
        month={4},
        keywords={mobile health interventions wearable sensors phone Android EMA PTSD mental health CBT drug addiction therapy},
        doi={10.4108/icst.pervasivehealth.2011.246105}
    }
    
  • Richard Fletcher
    Sharon Tam
    Olufemi Omojola
    Richard Redemske
    Szymon Fedor
    Joseph Mugisha
    Year: 2012
    Mobile Application and Wearable Sensors for Use in Cognitive Behavioral Therapy for Drug Addiction and PTSD
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2011.246105
Richard Fletcher1,*, Sharon Tam1, Olufemi Omojola1, Richard Redemske1, Szymon Fedor1, Joseph Mugisha1
  • 1: MIT
*Contact email: fletcher@media.mit.edu

Abstract

We present a mobile system for cognitive behavioral therapy (CBT) developed for an ongoing study for patients with drug-addiction and post-traumatic stress disorder (PTSD). The mobile platform consists of two parts: a wearable sensor system for collecting algorithm training data in the lab, and a mobile phone application used to deliver therapeutic interventions as triggered by real-time sensor data. Ecological momentary assessments (EMA) are also used as a means of collecting subjective data and validating the sensor classification algorithm. We provide a brief description of the wearable sensors, mobile phone software and network architecture used in the study.