Does Tinnitus Depend on Time-of-Day? An Ecological Momentary Assessment Study with the “TrackYourTinnitus” Application

Abstract

Only few previous studies used ecological momentary assessments to explore the time-of-day-dependence of tinnitus. The present study used data from the mobile application “TrackYourTinnitus” to explore whether tinnitus loudness and tinnitus distress fluctuate within a 24-h interval. Multilevel models were performed to account for the nested structure of assessments (level 1: 17,209 daily life assessments) nested within days (level 2: 3,570 days with at least three completed assessments), and days nested within participants (level 3: 350 participants). Results revealed a time-of-day-dependence of tinnitus. In particular, tinnitus was perceived as louder and more distressing during the night and early morning hours (from 12 a.m. to 8 a.m.) than during the upcoming day. Since previous studies suggested that stress (and stress-associated hormones) show a circadian rhythm and this might influence the time-of-day-dependence of tinnitus, we evaluated whether the described results change when statistically controlling for subjectively reported stress-levels. Correcting for subjective stress-levels, however, did not change the result that tinnitus (loudness and distress) was most severe at night and early morning. These results show that time-of-day contributes to the level of both tinnitus loudness and tinnitus distress. Possible implications of our results for the clinical management of tinnitus are that tailoring the timing of therapeutic interventions to the circadian rhythm of individual patients (chronotherapy) might be promising.

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

“…This provokes the question of whether users with few recordings belong to the same population as users with many recordings. In [8], Probst et al considered both users with few days of recordings and users with many days of recordings for their Multi-Level Analysis (median number of days: 11, with range from 1 to 415 days), but demanded at least 3 EMA per day, each of them containing answers for the three EMA items under study [8]. In this work, we do not attempt to win insights that pertain to a specific group of users, but rather to assess whether the EMA of users with few recordings can be predicted by models learned on users with many recordings.…”

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

“…In particular, they allow complementing the retrospective reports of the patients with prospective assessments of symptom variation over time. In this article, we describe how the mobile crowdsensing platform TrackYourTinnitus [31,32,[36][37][38]42], which we developed during the last years, contributes to prospectively monitor symptom variability over time for individuals with tinnitus. Tinnitus can be described as the phantom perception of sound.…”

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

“…Nevertheless, we are far from using the results of this work in clinical practice. On the other hand, together with already-revealed medical insights on TYT [ 6 , 37 - 40 ], the results of this work show that new opportunities are possible in the broader EMA and mobile crowdsensing contexts. In particular, EMA data that were gathered by mobile devices, as well as the crowdsensing paradigm, seem to be promising targets for the application of machine learning algorithms.…”