Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback [original]

Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback

R¨

udiger Pryss1, Winfried Schlee2, Berthold Langguth2, Manfred Reichert1

1Institute of Databases and Information Systems, Ulm University, Germany

2Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Germany

1{ruediger.pryss, manfred.reichert}@uni-ulm.de

[email protected], berthold.lang[email protected]

Abstract—Assessment of chronic disorders requires new

ways of data collection compared to the traditional pen &

paper based approaches. For example, tinnitus, the phantom

sensation of sound, is a highly prevalent disorder that is difficult

to treat; i.e., available treatments are only effective for patient

subgroups. In most individuals with tinnitus, loudness and

annoyance of tinnitus varies over time. Currently, established

assessment methods of tinnitus neither systematically assess

this moment-to-moment variability nor environmental factors

having an effect on tinnitus loudness and distress. However,

information of individual fluctuations and the effect of envi-

ronmental factors on the tinnitus might represent important

information for tinnitus subtyping and for individualized treat-

ment. In this context, a promising approach for collecting

ecological valid longitudinal datasets at rather low costs is

mobile crowdsensing. In the TrackYourTinnitus project, we

developed an advanced mobile crowdsensing platform to reveal

more detailed information about the course of tinnitus over

time. In this paper, the patient mobile feedback service as

a particular component of the platform is presented. It was

developed to provide patients with aggregated information

about the variation of their tinnitus over time. This mobile

feedback service shall help a patient to demystify the tinnitus

and to get better control of it, which should facilitate coping

with this chronic health condition. As the basic principles

and design of this mobile services are also applicable to

other chronic disorders, promising perspectives for disorder

management and clinical research arise.

Keywords-Mobile Crowdsensing, Mobile Healthcare Appli-

cation, Patient Feedback, Mobile Healthcare Service, Mobile

Service.

I. INTRODUCTION

Healthcare craves for new ways of collecting large and

ecological valid longitudinal data. This applies to the as-

sessment of tinnitus as well. Tinnitus is a highly prevalent

disorder, for which currently no sufficient therapy exist [1].

Furthermore, Tinnitus is a purely subjective sensation that

can only be assessed by the report of the individual patient.

The pathophysiology of tinnitus is incompletely understood

and clinical trials frequently reveal contradictory results.

Presumably, these non-conclusive results can be explained

by the fact that tinnitus is not a homogeneous clinical entity.

Instead, there exist many forms of tinnitus, varying in their

clinical characteristics as well as in the response to specific

therapeutic interventions [2], [3]. Additional complexity is

introduced by the fact that the perception of tinnitus loudness

and distress is not constant in most cases, but varies over

time depending on the context (e.g., environmental sound

level or stress) [4].

Currently, tinnitus is assessed based on questionnaires,

visual analogue scales or psychoacoustic measurements.

However, these assessment methods, which are used both

in clinical practice and research, do not capture the within-

day and between-day variability of tinnitus loudness and

distress over time. Moreover, contextual and environmental

influence on Tinnitus loudness questions the current routine,

where assessments are performed in most cases in clinics

or at home, but practically never during work or any other

activity of daily life. In order to mitigate these shortcomings,

new ways of collecting ecological valid longitudinal datasets

at rather low costs from patients during their daily life

are required. For this purpose, we developed the mobile

crowdsensing platform TrackYourTinnitus (TYT). The latter

tracks individual tinnitus perception using smart mobile

devices of users. The tracking procedure comprises a specific

questionnaire we developed to assess tinnitus perception

and tinnitus-related parameters during the daily routine of

a user. Additionally, the smart mobile device of a user

records the environmental sound level, while the user fills

in the assessment questionnaire. Results are transferred to

the TYT backend that, in turn, offers features enabling

researchers to evaluate gathered patient data. Note that in

the context of personalized healthcare, mobile crowdsensing

offers completely new perspectives [4]–[7] on the daily

routine of patients.

The analysis of the first data assessed with TYT [4], [8],

[9] has confirmed the hypotheses of (1) a relevant variability

of tinnitus loudness and annoyance for the majority of pa-

tients and (2) an interaction with exogenous and endogenous

factors. These findings have high relevance for individual

patients: The TYT may detect specific relationships between

influencing factors and tinnitus annoyance, which have not

been identified by patients in conventional studies before.

For example, tinnitus annoyance may depend on the stress

level the patient had the day before. Information of the

patients about such relationships may (1) help gaining more

control about a symptom that seemed to be completely un-

controllable, (2) provide guidance for behavior and thus help

to better cope with tinnitus and perceive the tinnitus as less

stressful. Moreover, smart feedback on tinnitus variability

and influencing factors is expected to motivate users to use

the mobile TYT services.

Such results highlight the potential of Ecological Mo-

mentary Assessment (EMA; also known as: ambulatory

assessment & experience sampling), which is provided by

TYT, to support clinicians in assessing neuropsychiatric

symptoms accurately and in making valid diagnoses. In

EMA, the variable in question (e.g., symptoms) is assessed

repeatedly in daily life [10]. Instead of retrospectively asking

the individuals (in an interview or questionnaire) how strong

they experienced a symptom in a given past time interval,

the individuals are asked how they currently experience the

symptom; this is done at several time points within the given

time interval.

This paper presents the mobile feedback service of the

TYT platform. We provide detailed backgrounds, present

technical issues, and discuss the perspective of patients on

the feedback service. In this context, the developed feedback

service is expected to increase general user motivation. The

remainder of this paper is organized as follows: Section II

introduces the TYT platform and its main features. In Section

III, the mobile service for patient feedback is presented.

Finally, Section IV discusses related work and Section V

concludes the paper with a summary and outlook.

II. THE TRACKYOURTINNITUS PLATFORM

The TYT mobile crowdsensing platform aims at measuring

fluctuations of tinnitus perception and tinnitus distress under

real life conditions during the patient’s day. In particular,

mobile crowdsensing services shall enable researchers to

gather data from huge numbers of users. Note that this

allows tracking the moment-to-moment fluctuation of the

tinnitus. Furthermore, tracked data may be related to ev-

eryday behavior as well as the daily routine of patients

to systematically identify relationships between individual

routines and tinnitus fluctuations. Moreover, the TYT mobile

crowdsensing platform can be further developed to assess the

effects of specific standardized therapeutic interventions.

We developed the TYT mobile crowdsensing platform as a

multidisciplinary research team consisting of psychologists,

physicians, and computer scientists. The platform comprises

a website for user registration, two mobile applications (for

iOS and Android), and a MySQL database as a central

repository for the data collected [6], [11], which can be

made available to the clinicians and researchers. The website

also provides two important other features: (1) users can

visualize their recorded tinnitus data and (2) users can

provide information about their current tinnitus treatment.

In order to be able to track the daily tinnitus perception, the

following procedure must be accomplished by a user.

First, users have to create an TYT account using our

website.

Second, after registering, users have to fill in three regis-

tration questionnaires. First, users have to fill in the “Mini-

TQ-12” questionnaire, which measures tinnitus-related psy-

chological problems. Second, users have to fill in the “Tin-

nitus Sample Case History Questionnaire (TSCHQ)”. The

TSCHQ questionnaire determines the current tinnitus status

of the user as well as his tinnitus history. Finally, users

have to fill in the “Worst Symptom” questionnaire. This

questionnaire asks the user about his current worst symptom

caused by tinnitus. While the first two questionnaires consti-

tute already used instruments, the third one have been newly

developed by the authors. Altogether, users have to complete

58 questions with respect to the three questionnaires. The

completion of these three questionnaires is a prerequisite to

be able to use the TYT website features as well as the mobile

applications.

Third, after the registration questionnaires have been

completed, a user can use the mobile applications to track

the daily tinnitus perception. Therefore, the user has to log

in to the Android or iOS mobile application. Then, he is

asked to fill in the assessment questionnaire developed by

us. The questionnaire comprises 8 questions (cf. Table I) and

rates the tinnitus perception of the user when being asked

(e.g., current tinnitus loudness).

Fourth, the assessment questionnaire, in turn, is provided

in two ways: (1) the mobile application automatically applies

the questionnaire to the user or (2) the user makes the

conscious decision to fill in the questionnaire. The first

way is our desired procedure and realized as follows: The

assessment questionnaire is randomly presented to the user

up to 12 times per day. Therefore, we realized a notification

feature for Android and iOS as well as a notification

algorithm [6]. This procedure of applying the assessment

questionnaire ensures that (1) users cannot foresee the time

of being asked and that (2) users are asked in various daily

situations. Such a randomized approach was realized in order

to improve the ecological validity of the method applied.

Fifth, while filling in the assessment questionnaire, the

smart mobile device of a user records the environmental

sound level. Currently, the sound level measurements are

evaluated in more detail. One question, among others, that

arises is based on the fact whether measurements of the iOS

platform and the Android platform are comparable.

Sixth, finally, results gathered with the assessment ques-

tionnaire and sound recording are transferred to the TYT

database. The latter, in turn, offers features enabling re-

searchers to evaluate gathered patient data. This feature has

been used for the results presented in this paper.

III. PATIENT FEEDBACK

Experiments we had conducted with the TYT platform

and its mobile services revealed that proper feedback on

the collected data is essential for users in order to increase

their motivation for regularly using the mobile app. Note that

Question Scale Measurement

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 Concentration

Do you feel irritable right now? BS Irritability

BS=Binary Scale, VAS=Visual Analogue Scale

Table I: TrackYourTinnitus Assessment Questions

proper feedback constitutes a salient incentive for patient

engagement in the context of mobile healthcare services

in general [12]. Regarding tinnitus, for example, a well-

designed feedback function should provide the patients

with information that allows them to better understand the

dependencies between tinnitus loudness and annoyance on

environmental factors. This information shall help them to

demystify tinnitus, to obtain an improved control, and to

better cope with their tinnitus. Our results confirm that about

40 percent of the tinnitus variance can be explained with the

variance of exogenous and endogenous factors. If individual

users have understood this relationship, they can get better

control over their tinnitus. Motivated by this data gathered

with TYT, we developed a sophisticated mobile feedback

service. The latter was integrated with both the TYT backend

and the Android mobile application.

In general, different approaches for providing mobile

feedback can be distinguished. First of all, feedback could

be provided by medical experts based on the information

gathered with the smart mobile device. Alternatively, feed-

back can be automatically generated by smart services and

information systems respectively. Furthermore, the way how

feedback is provided to users is essential. TYT comprises a

mobile feedback service that automatically generates user

feedback and additionally provides the option for transfer-

ring selected information to the treating physician, who can

then give feedback. Whether feedback is based on real time

data only or also considers historical data constitutes another

differentiation. The TYT service considers historical data

gathered with the assessment questionnaire. Based on this

data, individual feedback is calculated automatically.

Finally, we learned that the ability to configure parameters

relevant for feedback calculation is highly welcome by users.

TYT allows them to specify a time window that shall be

applied to the personal data gathered with the assessment

questionnaire. If a user specifies the respective parameter,

feedback calculation will be limited to the specified time

window. Therefore, the parameter allows patients to check

whether the received feedback has evolved over time.

This section presents the TYT mobile feedback service

along three perspectives. First, we sketch the overall feed-

back procedure and present factors relevant in this context.

Second, we discuss the user perspective on the feedback

service. Finally, we present technical issues related to the

developed feedback algorithms.

A. Overall Feedback Procedure

Fig. 1 gives an overview on the mobile feedback service.

Its general idea is to categorize patients based on the

collected data and to provide specific feedback depending on

the category the patient is assigned to. Accordingly, patients

are categorized based on their questionnaire data. The cat-

egorization of individuals will be calculated automatically.

For example, if in a given individual the tinnitus loudness

correlates with stress levels, the person will be automatically

assigned to category stress.

Altogether, we identified four categories (cf. Fig. 1 5

).

The four patient categories are derived based on analyses of

the data collected by all TYT users (cf. Fig. 1 2

). Thereby,

we focused on the correlation of subjective loudness (cf.

Table I, Question 2) with other measurements (cf. Table I,

Questions 3-8). If we had observed a particular correlation

for a considerable subset of the patients, we derived a

corresponding feedback category. This analysis revealed that

a correlation with Question 1 is not relevant. Note that

Question 1 solely considers the current tinnitus situation.

That means, patients may not perceive the tinnitus right now,

but perceive it in general. The remaining correlations for

strain (cf. Table I, Question 3) and irritability (cf. Table

I, Question 8) require further considerations before taking

them into account.

Assigning patients to one of the four categories constitutes

the first part of the feedback procedure (cf. Fig. 1 5

).

Furthermore, each category is coupled with specific interpre-

tations. These interpretations, in turn, are created by medical

experts using the TYT backend and include, for example,

general recommendations (cf. Fig. 1 8

). If a relationship

between perceived stress and perceived tinnitus is detected,

for example, the feedback about it further includes the

information that there exist specific approaches for stress

reduction (cf. Fig. 1 6

). These interpretations, in turn, are

assigned to one or more of the categories, again with

the help of the TYT backend (cf. Fig. 1 6

). Furthermore,

interpretations are associated with detailed explanations that

will be created by the medical experts as well. Patients

may rate these explanations (cf. Fig. 1 7

) to inform the

medical experts whether they have benefited from it. The

interpretations together with the explanations constitute the

second part of the feedback. Moreover, the two discussed

parts form the entire feedback for an individual patient (cf.

Fig. 1 4

). Technically, the feedback will be provided by the

TYT mobile feedback service.

There are two additional aspects of the TYT mobile feed-

back service. First, we developed a metrics called degree

of reliability (dor) (cf. Fig. 1 9

). The latter is calculated for

Backend with

Sensor

Framework

TrackYourTinnitus Individual Patient

Feedback

REST-Calls

Medical and Psychological

Experts

Using Backend

TyT Users

manually provided feedback data basis automatically determined

Degree of

Reliability (DoR)

Algorithm

Development

Categorization

Interpretations

and Explanations

Individual Patient Feedback

DoR

Calculation

4 Categories

Stress

Emotions

Arousal

Concentration

(1) Interpretations with (2)

Explanations

4 Categories Interpretations

with Explanations

Individual Patient

TrackYourTinnitus Crowd

Medical and Psychological

Experts Feedback

1 2 3 45 6

Figure 1: Patient Feedback Overview

each feedback category, indicating whether the amount of

collected patient data is sufficient. To evaluate sufficiency for

an individual patient, we considered collected data of all TYT

users. Thereby, different perspectives were considered. For

example, we calculated dor for category stress and related

it to the amount of user notifications (cf. Fig. 2). Based on

this, we developed a scale for dor as depicted in Fig. 4 3

.

Note that dor ≥0.6must hold in order to start calculating

the correlation for a category. For example, if the calculated

dor for category stress is less than 0.6, the calculation

needed for deciding whether the patient belongs to this

category will not be started. If there are not enough data for

categorizing a user, the TYT mobile feedback service returns

this information to TYT users. Note that each category is

considered independently with respect to the needed amount

of data. This allows patients to obtain direct and valuable

feedback on their collected data. Either they unveil that not

enough data was collected or that feedback evolves over

time.

Second, the correlation of a category is based on the

Pearson product-moment correlation coefficient (PCC) [13]

(cf. Fig. 110

). Recall that the degree of reliability (dor)

is coupled with the correlation calculation such that PCC

is only calculated if dor ≥0.6holds. To tie correlation

calculation with the degree of reliability revealed two advan-

tages: First, patients may compare their assignments among

the four categories. For example, if a patient belongs to

category stress, but not to concentration, more data needs

to be collected with respect to concentration (cf. Table I

Question 7). Second, the dor scale is based on all patients.

Therefore, individuals will benefit from collected data of all

TYT users.

B. Patient Perspective

Another fundamental perspective on the TYT mobile

feedback service is the one of the patient. Fig. 3 presents

examples of feedback screens regarding the smart mobile

device of a patient. Additionally, the interactions between

the screens are shown in Fig. 3. Patients use the feedback

as follows: First, they click on My Feedback (cf. Fig. 3 1

).

Second, after clicking on My Tinnitus, they configure the

period of time for feedback calculation (cf. Fig. 3 2

). Note

that Fig. 3 2

solely illustrates the specification of the start

date for this calculation. Another screen is used for the

end date. Third, the screen showing the categorization is

presented to the mobile user (cf. Fig. 3 3

). Note that this

screen is only presented if at least one degree of reliability

of {mood, arousal, stress, concentration} ≥ 0.6holds (cf.

Figs. 3 3

, 4 2

). In Fig. 3, for example, the result for

category arousal indicates no correlation for the tinnitus of

the respective patient, as the calculated degree of reliability

is less than 0.6.Fourth, a patient may expand a category

in order to get all interpretations assigned to this category

(cf. Fig. 3 4

). If patients click on interpretations, the screen

presented in Fig. 3 5

is displayed. Finally, patients may

return feedback to the selected interpretation (cf. Fig. 3 8

).

Degree of Reliability of Category Stress

# User Notifications

Figure 2: Degree of Reliability of Category Stress and User

Notifications

C. Technical Perspective

We developed two algorithms for calculating proper user

feedback. One of them determines the degree of reliability

(dor), the other algorithm assigns patients to one of the four

categories presented; e.g., if the tinnitus loudness correlates

with stress of the respective user, the user will be assigned

to the category stress. Note that both algorithms operate

on the patient data gathered with the mobile assessment

questionnaire. This data, in turn, is captured by entity

standardanswers (cf. Table II).

As shown in Table II, entity standardanswers comprises

17 attributes. In the context of the two algorithms, several

of these attributes are considered: First, attributes question2

and question4-question7are used to calculate the degree

of reliability as well as the assignment of users to categories.

Second, attribute user id represents the particular user for

whom the feedback shall be calculated. Third, attribute

created at represents the date the assessment questionnaire

was stored in the TYT database. Finally, the SQL command

depicted at the bottom of Table II is used for calculating the

patient feedback.

Prior to the feedback calculation (cf. Algorithm 2), the

degree of reliability is determined by Algorithm 1. The

decision which degree of reliability will be calculated by

this algorithm is based on input parameter correlation.

For example, if correlation has value ’Stress’, the degree

of reliability for category stress is calculated. Furthermore,

input parameter standardanswers is used. Note that pa-

rameters loudness and tocorrelatewith are important as

they constitute the two dimensions the degree of reliabil-

ity is calculated for. Following this, tocorrelatewith may

have values mood, arousal, stress, concentration. Based on

these considerations, Algorithm 1 calculates the degree of

reliability as follows (cf. Algorithm 1, Lines 17-29):

1) Sort array loudness in ascending order.

Name Type Explanation

PK id int −

FK user id int −

For detailed question explanations see Table I

question1 tinyint −

Next line represents 6 database rows

question2-7 float Rows for Question 2 -

Question 7

question8 tinyint −

soundlevel float −

save date datetime Local Storage Time on Mobile Device

notification date datetime Local Notification Time on Mobile Device

autosaved tinyint User Forgets Pressing Save Button

→Try Automatic Save

notification fixed tinyint Indicates Notification Schema

→Random (0) or Fixed (1)

created at datetime Storage Time on Remote Database

user agent text Device Attributes

SQL-Command for Date Input (startdate,enddate) of User $feedbackuser

SQLF=”SELECT question2, question4, question5, question6, question7 FROM ‘standardanswers‘ WHERE

user id = $feedbackuser and (created at BETWEEN $startdate AND $enddate) ORDER BY question2 ASC”

−= no explanation needed due to name of row

Table II: Entity standardanswers

2) Sort array tocorrelatewith according to the ordering

of loudness. To identify corresponding entries, the

primary key of entity standardanwers is used.

3) Split array loudness into two subarrays of equal

length. The first subarray comprises all loudness el-

ements of standardanswers with even index num-

ber; the second one, the elements with uneven index

number.

4) Split array tocorrelatewith into two arrays of equal

length.

5) Note that Steps 1 to 4 became necessary to ensure

that the variance among the subarrays is equal from a

statistical point of view.

6) Calculate Pearson product-moment correlation coef-

ficients (PCC) [13], [14]1: The first PCC is calcu-

lated based on the first subarray of loudness and

the corresponding subarray of tocorrelatewith. The

second PCC, in turn, is calculated based on the second

subarray of loudness and the corresponding subarray

tocorrelatewith.

7) Normalize the results by adding 1 to both PCCs; i.e.,

ensure that the result of the next calculation will be

between 0 and 1.

8) Evaluate which PCC has a higher value and divide the

lower PCC by the higher PCC to ensure normalization.

Note that the result of the division takes two PCCs into

account and hence establishes the degree of reliability

between arrays loudness and tocorrelatewith.

9) Store division result to variable dor ∈[0,1].

The scale to evaluate degree of reliability values is shown

in Fig. 4 3

. Values below 0.6indicate that not enough

data for the respective category exist. Accordingly, the

respective patient gets the feedback that not enough data

has been gathered so far (cf. Fig. 3, Category arousal). In

turn, values above 0.6indicate that enough data has been

1PCC represents a common way to calculate a correlation of two sets.

Feedback Feature Individual Period for

Feedback Calculation Pearson Correlations Interpretations Interpretation with

Explanation

/interaction:

expand

/interaction:

click

/interaction:

select

/interaction:

click

/interaction:

click

/variables:

$startdate

and $enddate

/variable:

$feedbackuser

Categorization Tips with Interpretations

and Returning Feedback

Phases Prior to Feedback Calculation

1 2 3 4 5

Degree of

Reliability

Figure 3: Patient Feedback Interaction

gathered. Altogether, first results indicate that the developed

degree of reliability is appropriate for feedback calculation.

Fig. 4 summarizes the basic steps for degree of reliability

calculation. Algorithm 2, in turn, utilizes Algorithm 1 and

assigns users to the four presented categories:

1) Load all relevant data from the database using the SQL

command SQLF (cf. Table II).

2) Calculate the four required degrees of reliability (dors)

with Algorithm 2 (cf. Lines 4 to 7).

3) If at least one dor is above 0.6, start feedback calcu-

ation (cf. Fig. 4 2

).

4) Calculate the PCCs for those categories with a dor

above 0.6. Otherwise set the respective PCC to −1;

i.e., return feedback to patients that not enough data

has been gathered (cf. category arousal in Fig. 3).

Note that the evaluation of all patient data revealed that a

least 15 assessment questionnaires need to be completed to

be able to provide valuable feedback (cf. Algorithm 2, Line

3).

IV. RELATED WORK

In general, mobile crowdsensing is an emerging research

topic in various application domains [15], [16]. Interestingly,

in the medical domain, mobile crowdsensing applications

have been less proposed so far. One reason that the medical

domain is less considered might be related to legal and data

privacy issues [17]. However, using mobile crowdsensing

in the medical context is promising [18], since mobile

crowdsensing has unique features to gather valuable data

[19]. In particular, mobile crowdsensing may gather context-

aware [20] as well as daily life data [21] more effectively.

Altogether, mobile crowdsensing is an emerging topic. The

utilization of its possibilities, in turn, is still at the beginning.

Mobile systems offer also opportunities to measure behav-

ioral or physiological data in daily life [22]. In this context,

EMA approaches are considered to offer unprecedented

Algorithm 1: Algorithm for Degree of Reliability Calculation

Data:

$correlation: element of {mood,arousal,stress,concentration}

$standardanswers: array of two dimensions (1) loudness and (2) $correlation

Result:

dor: calculated dor for $correlation

1begin

2loudness ←− standardanswers[0];

3dimension ←− 0;

4if $correlation =mood then

5dimension ←− 1;

6end

7else if $correlation =arousal then

8dimension ←− 2;

9end

10 else if $correlation =stress then

11 dimension ←− 3;

12 end

13 else if $correlation =concentration then

14 dimension ←− 4;

15 end

16 tocorrelatewith ←− standardanswers[dimension];

/*Split arrays loudness and tocorrelatewith into equal arrays

comprising all elements with odd or uneven array indexes. Prior to

splitting these arrays, two additional steps are required. First,

array loudness is sorted in ascending order. Second, array

tocorrelatewith is sorted corresponding to array loudness using

the primary key of entity standardanswers.*/

17 split array loudness into two subarrays loudness sa1, loudness ss2of equal

length; /*sa=subarray */

18 split array tocorrelatewith into two subarrays

tocorrelatewith sa1, tocorrelatewith sa2of equal length;

/*Calculate Pearson product-moment correlation coefficients (PCC).

Compare [13], [14] for required PCC formula. */

19 corrV alue1←−

calculateP CC(loudness sa1, tocorrelatewith sa1);

20 corrV alue2←−

calculateP CC(loudness sa2, tocorrelatewith sa2);

/*Add 1 to corrV alue1and corrV alue2. Hence, final dor is between

0 and 1 */

21 corrV alue1++;

22 corrV alue2++;

23 dor ←− 0;

24 if corrV alue1>=corrV alue2then

25 dor ←− corrV alue2/corrV alue1;

26 end

27 else

28 dor ←− corrV alue1/corrV alue2;

29 end

30 end

Algorithm 2: Algorithm for Feedback Calculation

Data:

$feedbackuser: ID of patient for feedback calculation; SQLF : SQL-command (cf. Table II)

startdate, enddate: start and end date for feedback calculation provided by user

Result:

corrV alues: array of correlation values (mood, arousal, stress, concentration)

1begin

/*use $feedbackuser, $startdate, $enddate in SQLF */

2standardanswers ←− SQLF ;/*multidimensional array */

/*as a necessary prerequisite for calculating feedback, at least 15

questionnaires must be processed by a patient */

3if sizeOf(standardanswers)>14 then

4dor mood ←− call Algorithm 1 with $standardanswers and

$correlation =mood;

5dor arousal ←− call Algorithm 1 with $standardanswers and

$correlation =arousal;

6dor stress ←− call Algorithm 1 with $standardanswers and

$correlation =stress;

7dor concentration ←− call Algorithm 1 with $standardanswers and

$correlation =concentration;

8if dor mood >= 0.6or dor arousal >= 0.6or dor stress >=

0.6or dor concentration >= 0.6then

9loudness ←− standardanswers[0];

10 mood ←− standardanswers[1];

11 stress ←− standardanswers[2];

12 arousal ←− standardanswers[3];

13 concentration ←− standardanswers[4];

/*Calculate Pearson product-moment correlation coefficients

(PCC). Compare [13], [14] for required PCC formula. */

14 if dor mood >= 0.6then

15 corrV alues[0] ←− calculateP CC(loudness, mood);

16 else

17 corrV alues[0] ←− −1;

18 end

19 end

20 if dor stress >= 0.6then

21 corrV alues[1] ←− calculateP CC(loudness, stress);

22 else

23 corrV alues[1] ←− −1;

24 end

25 end

26 if dor arousal >= 0.6then

27 corrV alues[2] ←−

calculateP CC(loudness, arousal);

28 else

29 corrV alues[2] ←− −1;

30 end

31 end

32 if dor concentration >= 0.6then

33 corrV alues[3] ←−

calculateP CC(loudness, concentration);

34 else

35 corrV alues[3] ←− −1;

36 end

37 end

38 end

39 end

40 end

opportunities to study neuropsychiatric symptoms under eco-

logically valid conditions [23]. Besides TrackYourTinnitus,

two further studies, namely [24] as well as [25], presented

EMA approaches to track tinnitus in daily life.

Moreover, there exist approaches enabling immediate mo-

bile feedback based on personally gathered data. In general,

data sensing with smart mobile devices offers new ways to

support mobile users in various scenarios [5]. In the con-

text of personalized healthcare, for example, many patients

crave for immediate feedback. Furthermore, patients expect

feedback directly provided to their smart mobile device [12],

[26]. In this context, [27] presents a mobile application that

provides patients with valuable information for their daily

insulin dosages. Based on previous dosages, in combination

with context data, the application indicates whether the

current situation is similar to previously recorded situations.

A broader perspective for combining personal data gathered

with mobile devices with context information is presented

in [28]. The latter describes feedback as a crucial incentive

to increase patient motivation. Finally, [29] presents mobile

Question 2

Loudness

Question 4

Mood

Question 5

Arousal

Question 7

Concentration

Question 6

Stress

dor1=

Algorithm 2($correlation= mood )

If dor1 >= 0.6 or dor2 >= 0.6 or dor3 >= 0.6 or

dor4 >= 0.6 calculate feedback

3dor scale with explanation and indicating colours

overall dor threshold to start feedback calculation

1dor calculations

dor < 0.6

dor >= 0.6 and dor < 0.8

dor > = 0.8

not reliable

reliable

strongly reliable

grey

yellow

green

dor2=

Algorithm 2($correlation= arousal )

dor3=

Algorithm 2($correlation= stress )

dor4=

Algorithm 2($correlation= concentration )

Figure 4: Degree of Reliability Calculation

applications that systematically measure vital signs enabling

immediate feedback to users. Overall, the use of longitudinal

patient data, gathered with a mobile crowdsensing service,

for providing immediate feedback has been less considered

by other approaches so far. To conclude, in various life

domains, the feasibility of mobile crowdsensing has proven

its usefulness. The medical field, albeit a highly promising

application for mobile crowdsensing approaches, has been

neglected so far.

V. SUMMARY AND OUTLOOK

Using mobile crowdsensing offers promising perspectives

for tinnitus assessment, therapy and research as well as for

the medical field in general. With TYT, we obtained results

that allow for totally new insights regarding tinnitus variabil-

ity. The results further provide the basis for developing novel

mobile crowdsensing services that foster tinnitus assessment,

therapy, and research. In this context, we presented the

patient feedback service we developed. In particular, we

described a method to identify patient subgroups. Note that

required data for such identification could not have been

gathered without using mobile crowdsensing services.

The feedback service was integrated with the TYT back-

end as well as the Android mobile application. In future

work, we will integrate the feedback service with the iOS

mobile application as well. Furthermore, we will enhance

the feedback service. We are working on techniques that

allow medical experts to flexibly create feedback rules on

their own. However, the feedback services already indicate

that users are actually motivated to use this novel service.

Notably, still more incentives and features are required to

increase user motivation and hence to gather more valuable

data on the tinnitus disease. In order to provide even more

valuable feedback to users, medical experts as well as

researchers are working on novel algorithms to automatically

evaluate patient data. Altogether, over the next few years,

mobile crowdsensing services will become increasingly im-

portant for collecting large and ecological valid longitudinal

datasets in the context of clinical research.

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