Interactive system for similarity-based inspection and assessment of the well-being of mHealth users

Please always quote using this URN: urn:nbn:de:bvb:20-opus-252333
  • 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 ofRecent 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.show moreshow less

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Metadaten
Author: Subash Prakash, Vishnu Unnikrishnan, Rüdiger Pryss, Robin Kraft, Johannes Schobel, Ronny Hannemann, Berthold Langguth, Winfried Schlee, Myra Spiliopoulou
URN:urn:nbn:de:bvb:20-opus-252333
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie
Language:English
Parent Title (English):Entropy
ISSN:1099-4300
Year of Completion:2021
Volume:23
Issue:12
Article Number:1695
Source:Entropy (2021) 23:12, 1695. https://doi.org/10.3390/e23121695
DOI:https://doi.org/10.3390/e23121695
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:condition prediction; ecological momentary assessment; medical analytics; time series; visual analytics
Release Date:2023/05/26
Date of first Publication:2021/12/17
EU-Project number / Contract (GA) number:761307
OpenAIRE:OpenAIRE
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International