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Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users

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Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany
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Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, Germany
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Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
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Institute DigiHealth, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany
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WSAudiology, Sivantos GmbH, 91058 Erlangen, Germany
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Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
*
Authors to whom correspondence should be addressed.
Academic Editor: Carlos M. Travieso-González
Entropy 2021, 23(12), 1695; https://doi.org/10.3390/e23121695
Received: 26 October 2021 / Revised: 13 December 2021 / Accepted: 13 December 2021 / Published: 17 December 2021
(This article belongs to the Special Issue Challenges of Health Data Analytics)
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported. View Full-Text
Keywords: medical analytics; condition prediction; ecological momentary assessment; visual analytics; time series medical analytics; condition prediction; ecological momentary assessment; visual analytics; time series
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MDPI and ACS Style

Prakash, S.; Unnikrishnan, V.; Pryss, R.; Kraft, R.; Schobel, J.; Hannemann, R.; Langguth, B.; Schlee, W.; Spiliopoulou, M. Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy 2021, 23, 1695. https://doi.org/10.3390/e23121695

AMA Style

Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Langguth B, Schlee W, Spiliopoulou M. Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy. 2021; 23(12):1695. https://doi.org/10.3390/e23121695

Chicago/Turabian Style

Prakash, Subash, Vishnu Unnikrishnan, Rüdiger Pryss, Robin Kraft, Johannes Schobel, Ronny Hannemann, Berthold Langguth, Winfried Schlee, and Myra Spiliopoulou. 2021. "Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users" Entropy 23, no. 12: 1695. https://doi.org/10.3390/e23121695

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