Pryss, Rüdiger and Probst, Thomas and Schlee, Winfried and Schobel, Johannes and Langguth, Berthold and Neff, Patrick and Spiliopoulou, Myra and Reichert, Manfred (2019) Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform. International Journal of Data Science and Analytics, 8 . pp. 327-338. ISSN 2364-4168
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Abstract
Many symptoms of neuropsychiatric disorders, such as tinnitus, are subjective and vary over time. Usually, in interviews or self-report questionnaires, patients are asked to retrospectively report symptoms as well as their severity, duration and influencing factors. However, only little is known to what degree such retrospective reports reflect the actual experiences made in daily life. Mobile technologies can remedy this deficiency. In particular, mobile self-help services allow patients to prospectively record symptoms and their severity at the time (or shortly after) they occur in daily life. In this study, we present results we obtained with the mobile crowdsensing platform TrackYourTinnitus. In particular, we show that there is a discrepancy between prospective and retrospective assessments. To be more precise, we show that the prospective variation of tinnitus loudness does not differ between the users who retrospectively rate tinnitus loudness as “varying” and the ones who retrospectively rate it as “non-varying.” As another result, the subjectively reported stress-level was positively correlated with tinnitus (loudness and distress) in the prospective assessments, even for users who retrospectively rated that stress reduces their tinnitus or has no effect on it. The results indicate that mobile technologies, like the TrackYourTinnitus crowdsensing platform, go beyond the role of an assistive service for patients by contributing to more detailed information about symptom variability over time and, hence, to more elaborated diagnostics and treatments.
Item Type: | Article |
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Subjects: | DBIS Research > Publications |
Divisions: | Faculty of Engineering, Electronics and Computer Science > Institute of Databases and Informations Systems > DBIS Research and Teaching > DBIS Research > Publications |
Depositing User: | Ruediger Pryss |
Date Deposited: | 23 Jul 2018 14:19 |
Last Modified: | 18 May 2021 09:25 |
URI: | http://dbis.eprints.uni-ulm.de/id/eprint/1654 |