Track Your Tinnitus App

Uh? I thought constantly monitoring one's tinnitus was a bad idea. The Tinnitus Research Initiative has released a smartphone app that is supposed to do just that.

https://www.trackyourtinnitus.org/

The most useful part may be the statistics you must fill before downloading the app.

 

Has anyone used this app and have they found it useful - or not?

 

Differences between Android and iOS Users of the TrackYourTinnitus Mobile Crowdsensing mHealth Platform.

 

It's about TrackYourTinnitus app.

EXPLORING DIMENSIONALITY REDUCTION EFFECTS IN MIXED REALITY FOR ANALYZING TINNITUS PATIENT DATA

 

It seems it is a review of the TrackYourTinnitus app, but I have no access to the full article since I am not a student of the university of Ulm (Germany).

http://dbis.eprints.uni-ulm.de/1823/

 

It appears to be for research mostly. They do have a disclaimer stating that you should stop using the app if you think your tinnitus worsens as a result.

 

I keep a log every day... scale T, A & D at 1-10.

T Tinnitus
A Anxiety
D Depression
M Medication - what was taken (if any) that day
F Food consumed that day
W Weather

It works well for me. When days suck I look back at the better days and it always gives me hope. Spikes don't last forever.

Courage!

 

Some excerpts from chapter 13: Momentary assess of tinnitus - how smart mobile applications advance our understanding of tinnitus, from the book

Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics

https://books.google.fr/books?hl=fr...1VInWCUfqBZlwcYMWshfOcZr0#v=onepage&q&f=false

 

Comprehensive insights into the TrackYourTinnitus database

The ubiquity of smart mobile devices facilitates data collection in the healthcare domain. Two of the concepts, which can be applied in this context, are mobile crowdsensing (MCS) and ecological momentary assessment (EMA). TrackYourTinnitus (TYT) is an advanced mobile healthcare platform that combines both concepts enabling the monitoring and evaluation of the users’ individual variability of tinnitus symptoms. This paper describes the underlying data set and structure of the TYT mobile platform and highlights selected issues whose investigation provides advanced insights into the users of this mobile platform as well as their data.

 

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

Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.

Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data.

Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question.

Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used.

Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

Source: https://www.jmir.org/2020/6/e15547/