Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users
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 visua...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/12/1695 |
| _version_ | 1797504926876696576 |
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| author | Subash Prakash Vishnu Unnikrishnan Rüdiger Pryss Robin Kraft Johannes Schobel Ronny Hannemann Berthold Langguth Winfried Schlee Myra Spiliopoulou |
| author_facet | Subash Prakash Vishnu Unnikrishnan Rüdiger Pryss Robin Kraft Johannes Schobel Ronny Hannemann Berthold Langguth Winfried Schlee Myra Spiliopoulou |
| author_sort | Subash Prakash |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:11:21Z |
| format | Article |
| id | doaj.art-a20b34bfd91c4b9299f6d387e1f10e19 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T04:11:21Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a20b34bfd91c4b9299f6d387e1f10e192023-11-23T08:11:48ZengMDPI AGEntropy1099-43002021-12-012312169510.3390/e23121695Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth UsersSubash Prakash0Vishnu Unnikrishnan1Rüdiger Pryss2Robin Kraft3Johannes Schobel4Ronny Hannemann5Berthold Langguth6Winfried Schlee7Myra Spiliopoulou8Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, GermanyKnowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, GermanyInstitute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, GermanyInstitute of Databases and Information Systems, Ulm University, 89081 Ulm, GermanyInstitute DigiHealth, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, GermanyWSAudiology, Sivantos GmbH, 91058 Erlangen, GermanyDepartment of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, GermanyDepartment of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, GermanyKnowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, GermanyRecent 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.https://www.mdpi.com/1099-4300/23/12/1695medical analyticscondition predictionecological momentary assessmentvisual analyticstime series |
| spellingShingle | Subash Prakash Vishnu Unnikrishnan Rüdiger Pryss Robin Kraft Johannes Schobel Ronny Hannemann Berthold Langguth Winfried Schlee Myra Spiliopoulou Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users Entropy medical analytics condition prediction ecological momentary assessment visual analytics time series |
| title | Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users |
| title_full | Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users |
| title_fullStr | Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users |
| title_full_unstemmed | Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users |
| title_short | Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users |
| title_sort | interactive system for similarity based inspection and assessment of the well being of mhealth users |
| topic | medical analytics condition prediction ecological momentary assessment visual analytics time series |
| url | https://www.mdpi.com/1099-4300/23/12/1695 |
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