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Sensors 2017, 17(1), 127; doi:10.3390/s17010127

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

1
Graduate Program in Electrical Engineering, Federal University of Maranhão, 65080-805 São Luís, Brazil
2
Centre for Information Systems and Computer Graphics, Institute for Systems Engineering and Computers, Technology and Science, 4200-465 Porto, Portugal
3
Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal
4
Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
5
Department of Informatics, Pontifícia Universidade Católica do Rio de Janeiro, 22453-900 Rio de Janeiro, Brazil
This paper is an extended version of our paper published in Ariel S. Teles, Artur Rocha, Francisco J. Silva, João Correia Lopes, Donal O’Sullivan, Pepijn Van de Ven and Markus Endler. Towards Situation-Aware Mobile Applications in Mental Health. In Proceedings of the IEEE 29th International Symposium on Computer-Based Medical Systems, Dublin, Ireland, 20 June 2016; Belfast, Ireland, 21–23 June 2016.
*
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 1 November 2016 / Revised: 12 December 2016 / Accepted: 3 January 2017 / Published: 10 January 2017
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract

Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient’s daily routine (e.g., “studying”, “at work”, “working out”). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations. View Full-Text
Keywords: mobile mental health; situation awareness; ecological momentary assessment; mental disorder treatment; fuzzy logic mobile mental health; situation awareness; ecological momentary assessment; mental disorder treatment; fuzzy logic
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Soares Teles, A.; Rocha, A.; José da Silva e Silva, F.; Correia Lopes, J.; O’Sullivan, D.; Van de Ven, P.; Endler, M. Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness. Sensors 2017, 17, 127.

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