Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform

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. Keywords Prospective assessment of neuropsychiatric symptoms • Retrospective assessment of neuropsychiatric symptoms • Self-help mobile applications • Tinnitus • Mobile data collection • Mobile crowdsensing B Rüdiger Pryss

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

“…Ecological Momentary Assessment (EMA) (Stone and Shiffman, 1994) denotes a range of research methods aiming to assess phenomena with ecological validity by allowing subjects and patients to repeatedly report in real time, in real-world settings, over time, and across contexts and therefore avoiding the bias of retrospective reports (Pryss et al, 2018a). Among numerous other aspects, EMA is characterized by several key features (Shiffman et al, 2008):…”

Smartphone Apps in the Context of Tinnitus: Systematic Review

“…For instance, the data collected from the TrackYourTinnitus app can be used to establish a connection between tinnitus and daily routine or activities [58]. Additionally, the same data can be analyzed to shape recruiting strategies for larger tinnitus-related studies [59], and to better understand tinnitus variability and tinnitus-stress associations [60]. Similarly, the similarities and differences in ecological momentary assessment data, collected from the TrackYourTinnitus app after a long time can also help physicians to understand the evolution of tinnitus patients [61].…”

Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform

“…However, the use of crowdsensing in the context of stress and related disorders is still rare. One example for a crowdsensing solution dealing with a chronic disorder that can be influenced by stress is the TrackY-ourTinnitus mHealth crowdsensing platform [14]- [17]. In turn, technical solutions that enable Ecological Momentary Assessments without using mobile crowdsensing technology have been already presented with valuable healthcare results [18], [19].…”

“…The result is then transferred to the database through the RESTful API if the mobile app is online; otherwise the result is locally stored until the device gets an online connection. A more detailed technical description of the presented features can be found in [8], [17].…”