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

International Journal of Data Science and Analytics (2019) 8:327–338

https://doi.org/10.1007/s41060-018-0111-4

REGULAR PAPER

Prospective crowdsensing versus retrospective ratings of tinnitus

variability and tinnitus–stress associations based on the

TrackYourTinnitus mobile platform

Rüdiger Pryss1·Thomas Probst2·Winfried Schlee3·Johannes Schobel1·Berthold Langguth3·

Patrick Neﬀ4·Myra Spiliopoulou5·Manfred Reichert1

Received: 28 September 2017 / Accepted: 24 February 2018 / Published online: 12 March 2018

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

BRüdiger Pryss

ruediger[email protected]

Thomas Probst

Winfried Schlee

Johannes Schobel

Berthold Langguth

Patrick Neff

patrick.nef[email protected]

Myra Spiliopoulou

[email protected]urg.de

Manfred Reichert

[email protected]

1 Introduction

The assessment of neuropsychiatric symptoms is essential in

psychology, medicine and neuroscience. For many neuropsy-

1Ulm University, James-Franck-Ring, 89081 Ulm, Germany

2Department for Psychotherapy and Biopsychosocial Health,

Danube University Krems, Dr.-Karl-Dorrek-Straße 30, 3500

Krems, Austria

3Clinic and Policlinic for Psychiatry and Psychotherapy,

University of Regensburg, Universitätsstraße 84, 93053

Regensburg, Germany

4Neuroplasticity and Learning in the Healthy Aging Brain,

University of Zurich, Andreasstrasse 15, 8050 Zurich,

Switzerland

5Department of Technical and Business Information Systems,

Otto-von-Guericke-University Magdeburg, Universitätsplatz

2, 39106 Magdeburg, Germany

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328 International Journal of Data Science and Analytics (2019) 8:327–338

chiatric disorders, the severity and duration of symptoms

constitute essential criteria for diagnosis. For example, a

major depressive episode can only be diagnosed if the

patients have suffered from depressive symptoms for at least

2 weeks. Moreover, factors making the symptoms more or

less strong (i.e., correlates of the symptoms) need to be

identified for case conceptualization and treatment planning.

Hence, psychologists, physicians and researchers need to

reliably assess not only symptoms and their severity, but also

their fluctuations over time. However, most indicators of the

symptoms as well as their severity, duration and correlates

are subjective. In current practice, usually, their assessment

is based on retrospective reports of the patients. In turn, this

raises the question to what degree patients are able to remem-

ber the severity, duration and correlates of the symptoms they

have actually experienced.

Mobile technologies can effectively contribute to shed

light on this question. In particular, they allow complement-

ing the retrospective reports of the patients with prospective

assessments of symptom variation over time. In this arti-

cle, we describe how the mobile crowdsensing platform

TrackYourTinnitus [31,32,36–38,42], which we developed

during the last years, contributes to prospectively monitor

symptom variability over time for individuals with tinni-

tus. Tinnitus can be described as the phantom perception

of sound. Depending on its definition and duration as well

as on the patient age and birth cohort, between 5.1 and

42.7% of the population worldwide experience tinnitus at

least once during their lifetime [26]. On the one hand, tinni-

tus varies among patients (i.e., inter-individual variability);

on the other hand, it may vary for a particular patient (i.e.,

intra-individual variability) as well. Moreover, the diagnosis

and treatment of tinnitus and potential comorbidities require

assessments of several symptoms like loudness and varia-

tion of the perceived sound(s), stress-level, depressive and

anxiety symptoms as well as concentration. In this work,

we aim to compare findings from prospective and retrospec-

tive assessments of tinnitus symptoms (loudness and distress)

as well as the potential influencing factor/correlate “stress-

level,” which is often reported in the context of tinnitus

[24].

The limited (ecological) validity of retrospective self-

reports has been shown in several studies on other neu-

ropsychiatric disorders. For example, [1] assessed physical

activities of patients with eating disorders by retrospec-

tive self-reports as well as prospective assessments with an

accelerometer. Patients reported significantly less physical

activity retrospectively compared to the prospective mea-

surements with the accelerometer. In turn, [23] investigated

retrospectively as well as prospectively assessed anxiety and

related cognition in patients with agoraphobia. While anx-

iety did not differ between retrospective and prospective

assessments, cognition did.

Suchresultshighlighttheimportanceofecological momen-

tary assessment (EMA; also known as ambulatory assess-

ment & experience sampling) to support clinicians in assess-

ing neuropsychiatric symptoms as well as their correlates

accurately in real time. In EMA, the variable in question

(e.g., symptoms) is assessed repeatedly in daily life [46].

Instead of retrospectively asking the individuals, through an

interview or questionnaire, how strongly they experienced a

symptom in a given past time interval, the individuals are

asked how they currently experience it as well as its sever-

ity and potential correlates. In turn, this is accomplished at

several time points within the given time interval.

In the aforementioned studies, prospective and retrospec-

tive assessments were juxtaposed manually. To effectively

exploit the prospective assessments in a clinical setting, how-

ever, an integrated solution is needed, i.e., the EMA of a

patient should be transferred automatically to a database and

be made available to the responsible clinician(s) [34,43],

given the consent of the patient. Note that it has been already

reported for a long time that electronic systems are appreci-

ated by study participants [17], increase data accuracy [29],

lead to more complete datasets [22] and reduce costs [30]

compared to traditional paper-based methods. However, the

exploitation of the prospective assessments next to the ret-

rospective reports has not been investigated in the area of

tinnitus yet.

This paper presents the TrackYourTinnitus (TYT) mobile

crowdsensing platform [37,38] for the juxtaposition of retro-

spective and prospective assessments, with a focus on tinnitus

loudness, tinnitus distress and psychological stress. We elab-

orate how the prospective data are collected and how they

should be maintained for further usage. The paper provides

a significant extension of the work we presented in [35]. In

particular, [35] did not include the analysis of tinnitus dis-

tress and psychological stress. As the latter is associated with

several disorders (in general [3]; for tinnitus [24]), this anal-

ysis constitutes another fundamental comparison between

real-time assessments and retrospective reports. We provide

detailed backgrounds on the gathered and evaluated data as

well as the results for psychological stress. This additional

analysis reconfirms that mobile crowdsensing services will

become increasingly important for collecting large and eco-

logically valid longitudinal datasets in the context of clinical

research.

The remainder of the paper is organized as follows.

Related work is discussed in Sect. 2. In Sect. 3, we describe

the TYT mobile crowdsensing platform and explain the

workflow we implemented for collecting and maintaining

prospective assessments. Section 4presents the data as well

as the statistics used for juxtaposing the prospective with

the retrospective assessments. In Sect. 5, we present the

results of the statistical analyses, which suggest that integrat-

ing prospective assessments into the diagnostic–therapeutic

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International Journal of Data Science and Analytics (2019) 8:327–338 329

processis crucial for optimizing diagnostics and, hence, treat-

ments. The paper concludes with a summary and outlook in

Sect. 6.

2 Related work

Mobile crowdsensing is an emerging research topic in various

application domains [19,20,39,45]. In the medical domain,

however, this research direction has been neglected so far.

The fact that the medical domain is less considered by con-

temporary crowdsensing approaches might be explainable

by legal and data privacy issues [2]. Nevertheless, mobile

crowdsensing offers promising perspectives for the medical

domain [8], as it exhibits unique features for gathering valu-

able patient data in the large scale [5]. In particular, mobile

crowdsensing allows for the effective, context-aware gather-

ing [21] of daily life patient data [33], which, in turn, will

shift clinical research to a new level.

Besides TrackYourTinnitus (TYT), other studies have

applied EMA approaches to track tinnitus in daily life

[9,11,27,48]. Yet, their focus was not to compare retrospec-

tive ratings and prospectively crowdsensed data. [48] and

[11] were pilot studies that showed for example that tinni-

tus tracking is feasible without negative consequences for

the participants and that tinnitus varies within and between

participants. [9] investigated fluctuations of tinnitus as well

as associations between tinnitus and stress. [27] tracked tin-

nitus and other symptoms (e.g., dizziness) in patients with

Meniere’s syndrome. Contrary to these approaches, TYT is

an open-source application, available for download in the

iOS app store or Google play store, so that there is a larger

and more representative sample compared to the participants

of the cited studies.

Beyond tinnitus, EMA approaches capturing many other

aspects such as pain [14] or feelings [15] in daily life were

scientifically evaluated. In addition, EMA approaches were

studied in the areas of mood disorders and mood dysregula-

tion [40,47] as well as in the context of substance use [44]

and eating disorders [7]. In psychotherapy research, EMA

has been used to predict patient progress [13]. Although most

neuropsychiatric symptoms are subjective experiences and,

thus, most EMA approaches use self-reports to capture these

symptoms, some neuropsychiatric symptoms are behavioral

(e.g., avoidance in anxiety disorders) or physiological (e.g.,

increase of heart rate in anxiety disorders). Note that mobile

systems offer opportunities to measure behavioral or physi-

ological data in daily life [6].

Altogether, EMA approaches provide unprecedented

opportunities to study neuropsychiatric symptoms under eco-

logically valid conditions [28], even though the utilization of

its possibilities is still in its infancy, especially in the medical

domain.

3 The TrackYourTinnitus platform

TrackYourTinnitus (TYT) is a mobile crowdsensing platform

that comprises a Web site for user registration, two mobile

applications (for iOS and Android) and a relational database

(MySQL) as the central repository storing the collected data

[37]. In particular, the anonymized or pseudonymized TYT

data from the repository are made available to clinicians as

well as researchers. The Web site further provides two funda-

mental features: First, users can visualize recorded tinnitus

data; second, they can report on their current tinnitus treat-

ment. In general, TYT was developed to track the individual

tinnitus perception of users. In this context, the procedure

depicted in Fig. 1is applied to the TYT users.1

Following this procedure, TYT pursues three goals. First,

data shall be collected on a daily basis (cf. Fig. 1,3

). How-

ever, a crowd user shall not foresee the times he or she is

asked to sense data (cf. Fig. 1,2

). This is ensured by asking

the crowd users in various daily life situations. Second, the

collected data shall enable new kinds of data analytics like

juxtaposing real-time assessments and retrospective reports

(cf. Fig. 1,1

). Third, gathered data shall be used to provide

feedback to the mobile crowd users.

To enable the use of TYT as well as to provide data being

appropriate for the data analysis applied in the context of this

paper, the following procedure has to be accomplished by the

users (cf. Fig. 1).

First, users have to create a TYT account, by using either the

TYT Web site or the TYT mobile applications.

Second, users have to fill in three registration questionnaires

(cf. Fig. 5). First of all, they have to fill in the “Mini-TQ-12”

questionnaire (cf. Fig. 5, Mini-TQ-12 [12]), which measures

tinnitus-related psychological problems. Second, they have

to fill in the “Tinnitus Sample Case History Questionnaire”

(TSCHQ) (cf. Fig. 5,TSCHQ[18]), in which details about

the current tinnitus status, relevant co-morbidities and the

tinnitus history are assessed. Note that TSCHQ comprises

two questions being crucial for the results of this paper. To

be more precise, the 11th item of TSCHQ asks the user

retrospectively whether or not the tinnitus loudness varies

from day to day. The 26th item of TSCHQ, in turn, requests

from users to rate retrospectively whether stress is associ-

ated with their tinnitus. Third, users have to fill in the “Worst

Symptom” questionnaire (cf. Fig. 5, Worst Symptom ques-

tionnaire), which asks the users about the worst symptom

currently caused by their tinnitus. While the first two ques-

tionnaires constitute already used instruments, the third one

was newly developed in the context of the presented research.

Altogether, the completion of the three questionnaires with

1More detailed information about the procedure can be found at https://

www.trackyourtinnitus.org/process.pdf

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330 International Journal of Data Science and Analytics (2019) 8:327–338

Fig. 1 Mobile crowdsensing collection procedure

Fig. 2 Impression of assessment questionnaire in iOS

their 58 questions in total is a fundamental prerequisite for

users who want to access the TYT Web site features as well

as the TYT mobile applications (Fig. 2).

Third, after registering and completing the required ques-

tionnaires, users may exploit the mobile applications to

track their tinnitus and potential correlates during daily life.

For this purpose, a user needs to log in to the Android

or iOS mobile application. Then, he/she is asked to fill

in the assessment questionnaire developed for TYT (cf.

Fig. 5, assessment questionnaire). This repeatedly admin-

istered assessment questionnaire comprises 8 items (cf.

Table 1) including questions on the current tinnitus loudness,

tinnitus distress and subjective stress-level. Figure 3gives an

impression of how the questionnaire looks like in iOS.

Fourth, the assessment questionnaire is provided in two

ways. Either the mobile application automatically displays

the questionnaire to the user or the user himself makes the

conscious decision to fill out the questionnaire (cf. Fig. 5,

conscious decision). The first procedure is the preferred one

and is realized as follows: The assessment questionnaire is

randomly presented to the user up to 12 times per day. Fig-

ure 3gives an impression of the notification settings in iOS.

Table 1 TrackYourTinnitus

assessment questions Question Scale M

Did you perceive the tinnitus right now? BS Perception

How loud is the tinnitus right now? VAS Loudness

How stressful is the tinnitus right now? VAS Strain

How is your mood right now? VAS Mood

How is your arousal right now? VAS Arousal

Do you feel stressed right now? VAS Stress

How much did you concentrate on the

things you are doing right now?

VAS Con.

Do you feel irritable right now? BS Irritability

BS binary scale, VAS visual analogue scale, Mmeasurement of, Con. concentration

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International Journal of Data Science and Analytics (2019) 8:327–338 331

Fig. 3 Impression of notification settings in iOS

For the application of the assessment questionnaire, noti-

fication features for both Android and iOS as well as a

notification algorithm were realized [37]. We only present

the algorithm running on iOS (cf. Algorithm 1) and the cal-

culated notifications for a single day. In practice, notifications

are calculated in advance. The algorithm, in turn, works as

follows:

1. It partitions the time window a user has specified with

respect to a particular day into ntime intervals of equal

length. ncorresponds to the number of notifications the

user has chosen.

2. The algorithm then calculates exactly one notification for

each interval. Thereby, it ensures that for each notification

the points in time are randomly calculated.

3. Finally, it is ensured that there is an interval of at least

15 min between two notifications.

On the one hand, the procedure ensures that users cannot

foresee the time when being asked; on the other hand, it

ensures that they are sensed in various daily situations. Note

that this randomized approach was realized to improve the

ecological validity of the applied method. The approach to

randomly apply the assessment questionnaire is illustrated in

2arc4random_uniform(upper_bound): iOS internal function to return

a uniformly distributed random number less than upper_bound.

Algorithm 1: iOS algorithm for daily notifications of a user

Data:

timeInterval: time interval a user has specified for a day

numberOf NotificationsPerDay: notifications specified for a day

Result:

scheduleLocalNotification: calculated random notifications for a day

1begin

2lengthOfIntervall = timeInterval/numberOfNotificationsPerDay;

3lastNotification = 900; /* the 15 minutes */

4foreach n∈numberOf Notif icationsPerDay do

5secondsSinceStartOfInterval =

arc4random_uniform2(lengthOfIntervall);

6absoluteInterval=

7secondsSinceStartOfInterval+(n*lengthOfIntervall);

/* check the 15 minutes */

8if absoluteInterval −lastNoti f ication <900 then

9absoluteInterval = 2*absoluteInterval - lastNotification;

10 end

11 lastNotification = absoluteInterval;

/* check if notification is in

absoluteInterval */

12 if absoluteInterval <timeInterval then

/* notification found */

13 scheduleLocalNotification =

scheduleLocalNotification ∪absoluteInterval;

14 end

15 end

16 end

Fig. 4 Possible user action after a notification

Fig. 4. It works on both mobile operating systems in exactly

the same ways.

After a notification appears, the user may click on it. In the

latter case, the TYT mobile app is started (if not already run-

ning) and the assessment questionnaire is directly displayed

to the user. Then, he or she can fill out the questionnaire and

finally save the entered data. After saving the questionnaire

data, the mobile app is terminated 3 s later. Within these 3 s

the result is transferred to the TYT backend (if the mobile

app is online; otherwise, the result is locally stored until the

device gets an online connection).

The procedure to automatically terminate the app shall

speed up the process to fill out questionnaires after being

notified. Note that the user feedback we have received so far

supports this technical procedure. If the user does not save

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