Supporting Remote Therapeutic Interventions with Mobile Processes [original]

Supporting Remote Therapeutic Interventions with Mobile Processes

Marc Schickler, R¨

udiger Pryss, Johannes Schobel, Manfred Reichert

Institute of Databases and Information Systems, Ulm University, Germany

{marc.schickler, ruediger.pryss, johannes.schobel, manfred.reichert}@uni-ulm.de

Abstract—Many studies have revealed that homework (e.g.,

relaxation exercises) are crucial for remote therapeutic inter-

ventions. In this context, to monitor whether patients actually

perform their homework and to check whether they perform

it in the right way constitute complex tasks. So far, therapeutic

interventions have not been properly supported by IT systems

and, hence, the opportunities provided by mobile assistance

have been neglected. For example, a smart mobile device may

notify a patient about an assigned homework or motivate him

to accomplish it in time. Moreover, the patient might be further

assisted through a video providing detailed instructions. In

turn, the smart mobile device could inform the therapist of

the homework outcome. In practice, a proper support of the

various types of homework is challenging, even when using

modern IT systems. To remedy this drawback, we propose an

approach integrating mobile services with process management

technology in order to enable the complex coordination tasks

that become necessary in connection with homework. For

example, a process might enable remote monitoring of home-

work, giving therapists the opportunity of timely adjustments.

In addition, the approach allows involving researchers by

providing them with valuable data (e.g., heart rate) gathered

during and after homework. This paper presents an approach

for creating processes that run on smart mobile devices and

enable flexible remote therapeutic intervention support. Such

mobile approach significantly enhances therapy assistance on

one hand and mobile homework-related scenarios on the other.

Keywords-Mobile service, therapeutic intervention, mobile

context, mobile process, mobile assistance, mobile therapy

I. INTRODUCTION

According to numerous clinical studies, therapeutic in-

terventions constitute a fundamental part of most therapies

to increase their efficacy [1], [2]. The variety of therapeutic

interventions, in turn, ranges from simple medication intakes

up to complex homework, which are assigned by a therapist

to the patient. Taking the diversity of useful homework

into account, therapists crave for a proper IT support. In

line with this trend, recent studies (e.g., [3]) revealed that

the use of smart mobile devices is particularly demanded

by therapists [4]. Unfortunately, existing mobile approaches

still show drawbacks. First, they mostly provide specifically

tailored mobile applications and, hence, do not deal with the

diversity of therapeutic interventions. Second, they focus on

the therapist-patient relationship, neglecting other promising

opportunities. Third, features provided by current smart

mobile devices (e.g., sensors) have not been broadly used

for therapeutic interventions yet.

Figure 1: Pursued Objectives

Considering these drawbacks, Fig. 1 illustrates three

fundamental objectives relevant for a proper therapeutic

intervention support. First of all, in addition to smartphones

and tablets, emerging technologies like smartwatches need

to be integrated to address current demands of mobile users.

Second, the opportunities provided by smart mobile device

sensors should be utilized as well. Third, the therapist-

patient relationship needs to be extended to a therapist-

patient-researcher relationship to take benefit from the valu-

able data collected with the smart mobile devices over time.

If these objectives are met, the following advantages will

result:

1) Patients will obtain better treatment as therapeutic

interventions may be personalized, i.e., adjusted to the

specific situation of the patient at hand.

2) Therapists are enabled to provide an optimized and

more efficient treatment as they can monitor and

dynamically adjust therapeutic interventions remotely.

Respective adjustments, for example, might foster

innovative billing models.

3) In the longer term, patients might benefit from new

therapies developed on the basis of the data collected.

4) Therapists can use emerging therapies discovered by

researchers based on the data gathered over time.

5) Researchers discover new therapies or gain new in-

sights into diseases based on the data provided by

patients (e.g., sensor data) as well as by therapists

(e.g., context-based therapy results).

As illustrated, the IT support of therapeutic interventions

is complex. In particular, all (remote) issues not taking place

in the scope of personal meetings (e.g., homework) between

therapist and patients are technically challenging. On the

other, these remote issues require a tight integration with

issues (e.g., general treatment adaptations) that occur during

the personal meetings. Consequently, a holistic view on the

entire procedure is crucial. From a technical perspective,

an overall protocol must be developed to coordinate all

required issues. In the given context, the development of

such a protocol is particularly challenging as both stationary

as well as mobile systems must be considered. Therefore,

we advocate an approach based on mobile processes to

enable the required coordination protocol. If the latter can

be established, the efficacy of therapeutic interventions may

be increased since an effective monitoring of the afore-

mentioned remote issues becomes possible. Based on the

resulting monitoring data, for example, required adaptations

to the therapeutic intervention procedure can be realized in

real time as well as in a more patient-centric manner.

To realize this protocol, first of all, we defined a meta-

model taking the complex organization of therapeutic in-

terventions into account. Based on the meta-model, we

developed a technical solution executing processes on smart

mobile devices. These mobile processes enable us to perform

complex therapeutic interventions for patients assisted by

their smart mobile devices.

This paper shows how mobile processes are realized and

in what way they effectively support therapeutic interven-

tions. In detail, fundamental requirements on one hand,

and the meta-model, main concept, and proof-of-concept

prototype are presented on the other. The remainder of the

paper is structured as follows: Section II discusses elicitated

requirements. Section III presents the meta-model derived

from these requirements. In Section IV, the support of

mobile processes is discussed, whereas Section V deals with

relevant sub-process patterns in more detail. Section VII

gives insights into the proof-of-concept prototype. Finally,

Section VIII discusses related work and Section IX con-

cludes the paper with a summary and outlook.

II. REQUIREMENTS

First of all, we summarize how therapeutic interventions

are traditionally accomplished (i.e., without IT support): The

therapy starts with a first personal meeting of the therapist

and the patient. During this meeting as well as in follow-up

sessions, the therapist creates a comprehensive anamnesis for

the case conceptualization, upon which the remote therapeu-

tic interventions (e.g., homework) are planned. Based on the

anamnesis, the therapist compiles a therapy plan, consisting

of (1) therapeutic interventions, (2) personal appointments

with the patient, and (3) the duration required to achieve

a positive therapy effect for the patient. Concerning the

therapeutic interventions, two intervention types need to be

created by therapists, the one applied during the personal

meetings and the one performed by the patients at home

between the meetings with the therapist. As one of the most

crucial drawbacks of this traditional procedure, homework

cannot be remotely monitored and, hence, therapists are

unable to timely adjust ongoing homework. Additionally,

the exchange of feedback between therapist and patients is

not efficiently supported. As the mobile processes we target

at must cover the variety of therapeutic interventions, pro-

found insights into real-world scenarios are indispensable.

In general, respective insights should be gathered in the

context of large-scale case studies over longer period of

times to better understand actual requirements. Ourselves,

we base the development of the mobile process support

on the experiences we gathered when realizing complex

mobile applications that support therapeutic interventions in

the large scale [5]–[7].

In these case studies, therapists were provided with

specifically tailored mobile applications. Thereby, all studies

revealed that, when using mobile applications, therapeutic

interventions can be supported more efficiently [6]. Regard-

ing the case study in a psychosomatic in-patient clinic, for

example, we learned that the support of homework with

a mobile application is considered as useful in almost all

patient cases [6]. According to a particular feature demanded

in this context, patients shall be notified on their smart

mobile devices about the need to perform their homework.

The latter, in turn, shall be scheduled by a therapist to allow

for a better therapy effect. Table I summarizes the elaborated

requirements that need to be met by mobile processes in

order to efficiently support therapeutic interventions. We

divide the requirements into three categories. (1) Require-

ments related to homework. For example, therapists should

be able to assign media elements to homework, which are

then presented to the patient on his smart mobile device. (2)

Requirements related to the context of a homework. This

context enables us to cope with the demands of therapists on

one hand (e.g., performance of a homework after getting up)

and enables researchers to gather context-sensitive data on

the other. (3) Requirements related to smart mobile devices.

No Title Description

Category I: Homework

1Notification Create notification schema for homework

2Configuration Configure homework (e.g., context, and sensors)

3Versioning Create and manage homework versions

4Feedback Configure feedback schema for homework

5Reuse Use homework for different scenarios

6Media Use media elements for homework (e.g., videos)

Category II: Context

7Notification Bind notification schema to context

8Notification Provide advanced notifications

9Configuration Configure context (e.g., location, etc.)

10 Versioning Create and manage context versions

Category III: Smart Mobile Device

11 Notification Apply notification schema

12 ACR Evaluate context

13 Sensing Use smart mobile device sensors

14 Media Provide media elements

ACR = Automatic Context Recognition

Table I: Elicitated Requirements

For example, the smart mobile device shall provide media

elements to assist patients in performing their homework.

Altogether, 14 fundamental requirements were elicitated.

III. META-MODEL

First of all, the definition of the meta-model needs to

incorporate the lessons learned from the real-world projects.

Second, it needs to capture the complexity of therapeutic

interventions consisting of comprehensive homework, e.g.,

daily back exercises. Homework, in turn, comprise several

activities. For example, the following activities may have to

be performed in the context of a daily back exercise: warm

up, strengthening, and cool down. Therefore, the meta-model

considers homework as well as activities as core entity types.

Furthermore, the meta-model considers the context (e.g.,

being in the living room), in which the homework shall be

performed as well as it takes the sensors (e.g., heart rate) of

smart mobile devices into account.

Accordingly, the meta-model is built up on three levels (cf.

Fig. 2). On the first level, a Therapeutic Intervention consists

of 1up to n Homework, for which an execution order needs

to be defined. On the second level, a particular Homework

encompasses a Notification, an Exercise, and a Feedback. In

addition, the meta-model considers a Context and a Sensor

on the second level. Sensor covers, for example, the sensors

of smart mobile devices gathering data during an exercise

(e.g., heart rate). While a Sensor is related to an Exercise

and a Context, a Context may be related to a Notification

and an Exercise. On the third level, the highlighted entities

encompass activities, which either need to be performed by

a patient (e.g., a warm up activity after getting up) or be

automated (e.g., sensing the heart rate during a warm up),

i.e., run on smart mobile device of a patient.

Figure 2: Meta-Model

IV. MOBILE PROCESS

This section introduces the mobile processes developed

on the basis of the meta-model. Fig. 3 shows a therapeutic

intervention represented in terms of a mobile process. To be

more precise, the mobile process consists of three process

fragments. Each fragment, in turn, corresponds to a particu-

lar homework, whose execution order is determined by the

mobile process. Homework, in turn, always encompasses

three mobile process activities, i.e., a notification activity,

an exercise activity, and a feedback activity. These activities,

in turn, are always executed in the order Notification →

Exercise →F eedback. That means, a patient is notified

that an exercise needs to be accomplished. In turn, the

patient shall provide feedback to the therapist. In practice,

each activity is executed on a smart mobile device. Finally,

homework are always executed in a particular context.

For example, a homework might have to be performed in

the living room after getting up. Note that the concrete

implementation of all activities representing homework and

being executed on the smart mobile device depends on the

concrete homework scenario on one hand and on the used

mobile platform on the other.

The analysis of the real-world scenarios revealed that

activities representing exercises require a further itemization

(cf. Fig. 4, Exercise level). To be more precise, therapists

want to create exercises composed on several activities.

For example, an exercise may comprise a warm-up activity,

a push-up activity, and a cool-down activity. In addition,

therapists may want to use the sensors of smart mobile

devices to collect patient data during these activities. We

therefore integrated a context-based sensing activity with

exercises. Note that our practical insights revealed that both

the notification and the feedback activity must be itemized in

the same way as the exercise activity. Since the notification

and feedback activities are similar to the exercise activity,

we omit a more detailed discussion.

Practical insights further showed that exercise activities

Figure 3: Remote Therapeutic Intervention with three Homework Fragments

require a more flexible concept to meet the requirements

of therapists. Therefore, we allow for the implementation

of exercise activities as sub-processes. For example, activity

effort may be implemented as a sub-process (cf. Fig. 4, Sub-

process level), containing activity swimming, followed by a

decision that evaluates whether swimming shall be followed

by activity biking or activity running. If activity biking is

chosen, the biking speed will be measured by a context-

based sensing activity. Note that the contextual information,

required to evaluate whether biking or running shall be

chosen, needs to be specified by therapists.

Fig. 4 illustrates the described exercise itemization as

well as the possible use of sub-processes. Furthermore,

it is shown that the mobile processes are organized in

five different levels. These levels constitute the basis for

the modeling component that enables therapists to create

therapeutic interventions themselves. More precisely, the

modeling component offers features that consider the differ-

ent levels in order to guide the therapists during the creation

of therapeutic interventions in terms of mobile processes.

Regarding guidance, therapists are restricted to only use

elements that are allowed for a specific level. In particular,

on the exercise level, our modeling component provides

various activity templates. The latter cover frequent sce-

narios occurring in the context of exercises. For example,

the modeling component provides activity templates for

measuring the heart rate or filling in a questionnaire. Note

that therapists may create exercises solely based on the

available activity templates. In practice, however, there exist

scenarios in which the provided activity templates are not

sufficient. For example, therapists may request an activity

template for measuring skin conductance, while filling in

a questionnaire in parallel. In such a case, the application

Figure 4: Mobile Process

developer needs to implement the missing activity template.

After releasing the newly implemented activity template, it

may be used by therapists when modeling the exercise.

Therapeutic interventions created with the modeling com-

ponent are transformed into executable processes running

on smart mobile devices. The corresponding transformation

procedure ensures that the resulting mobile processes can be

correctly executed by a mobile process engine (e.g., the one

we presented in [8]). Finally, the mobile process is deployed

to the mobile application, which relies on the mobile process

engine for executing the mobile processes.

V. SUB-PROCESS PATTERNS

If therapists request new activity templates for defining

concrete therapeutic interventions, these templates must be

realized by an application developer. In several case studies,

we learned that the development of new activity templates

is (1) easily possible and (2) can be mostly accomplished

without involving therapists [9]. Regarding the use of sub-

processes, we further learned that requests from therapists

frequently require the involvement of application developers,

causing challenging debates between application developers

and therapists. For example, two activities may have to

be executed concurrently. However, contemporary mobile

process engines do not provide such concurrency support.

For this purpose, a specifically implemented sub-process is

required, which can then be executed by the mobile process

engine.

Regarding the support of application developers, we iden-

tified four fundamental patterns, which significantly ease the

handling of sub-processes for them. We introduce these sub-

process patterns in the following and discuss how they are

evaluated during the development.

Figure 5: Decision Pattern

A. Decision Pattern

Fig. 5 1

shows a sub-process pattern, called Decision

Pattern, in which two activities may be chosen alternatively

(i.e., exclusive decision); i.e., during run time exactly one

of the two activities may be executed. The decision, which

activity will be actually chosen, is based on data that is

(1) produced by a preceding activity or (2) provided by

the therapist. In Fig. 5 1

, for example, depending on the

number of performed push-ups and determined by the push-

ups, the mobile process engine decides at run time whether

the patient shall perform sit-ups or relax.

When a decision as shown in Fig. 5 1

is required by a

therapist, an application developer must particularly evaluate

if one (e.g., sit-ups in Fig. 5 1

) or more activity templates

not exist. In the latter case, the application developer must

decide whether to provide the missing activity templates or

to replace the entire Decision Pattern with a new activity

template instead of the pattern. To be more precise, an

application developer is supported in deciding whether he

applies the Decision Pattern or uses a specifically imple-

mented activity template. For example, if complex forms

are required for missing activity templates of the Decision

Pattern, in most cases, a new activity template replacing the

entire Decision Pattern fits practical demands best.

B. Parallel Pattern I

The approach of replacing a pattern by a specifically

implemented activity template (cf. Fig. 6 2

) is applied in

the context of the Parallel Pattern I as well (cf. Fig.

6). However, there is another case to be considered by

application developers. In certain situations, it needs to be

evaluated, whether or not the activities shall be executed

concurrently. As existing mobile process engines [9] do not

allow for a concurrent execution, a specifically implemented

Figure 6: Parallel Pattern I

Figure 7: Parallel Pattern II

activity template is required.

Note that the approach of replacing a sub-process pattern

by a specifically implemented activity template constitutes

the basis for the next two sub-process patterns as well.

Therefore, only new aspects are discussed.

C. Parallel Pattern II

This sub-process pattern differs in one respect compared

to the latter pattern. Two of the parallel activities are

associated with an entailment constraint, which is visualized

by a synchronization edge (cf. Fig. 7). The latter expresses

that the target activity may only be started when the source

activity as well as all other preceding activities will have

been finished. In the example of Fig. 7, two sensor activities

shall measure data in parallel, whereas a questionnaire

asking the patient about aspects of the exercise shall only be

provided after completing both measurements. In practice,

the realization of the Parallel Pattern II is challenging on

one hand. On the other, we learned that this pattern is useful

and frequently demanded by therapists. Consequently, we

consider it as an independent sub-process pattern.

D. Loop Pattern

The Loop Pattern is shown in Fig. 8. It covers the case

that activities may be executed multiple times. Again, this

sub-process pattern is practically challenging as well. For

example, in cases where sensor activities shall be executed

multiple times, a specifically implemented activity template

is frequently used. Opposed to that, no specifically imple-

mented activity template is used if human activities shall be

performed multiple times.

VI. DISCUSSION

This section discusses how our mobile processes ad-

dress the requirements presented in Table I. Due to space

limitations, we focus on one particular aspect of each of

the three requirement categories. First, for the homework

category, mobile processes enable the therapist to reuse

homework more easily. For example, a homework containing

a breathing exercise in the context of mental disorders can

be easily customized to reuse it for a chronical back pain

intervention. Therefore, only the context for the chronical

back pain intervention must be changed. Note that the cus-

tomization and reuse approach do not harm the outcome of

therapeutic interventions. Second, for the context category,

mobile processes enable the use of advanced notifications.

As notifications themselves may be represented by a mobile

process fragment, sophisticated procedures become possible.

For example, before a notification is provided, the smart

mobile device measures the moment-to-moment heart rate.

Third, for the smart mobile device category, mobile pro-

cesses enable the flexible use of sensors as their use can be

represented in terms of activity templates. For example, if

a sensor measurement shall take place at the beginning of

an exercise or at its end, can be only determined by placing

the sensor activity to the desired position.

Furthermore, three additional aspects require a brief dis-

cussion. First, regarding the pursued objectives (cf. Fig. 1),

we believe that the involvement of researchers in therapeutic

interventions is promising. Mobile processes, in turn, sup-

port this objective as the entire therapy as well as each single

activity gets a digital footprint. The newly gathered data can

then be automatically evaluated and new medical insights

potentially be revealed in order to enhance existing ther-

apeutic interventions. Second, the mobile processes enable

us to assist an application developer when (1) developing

new activity templates and (2) evaluating the identified

sub-process patterns. Third, legal and privacy aspects are

currently not considered. However, many concepts exist

Figure 8: Loop Pattern

in this context, which are currently evaluated to identify

whether they can be integrated with our approach.

VII. PROOF-OF-CONCEPT PROTOTYPE

We realized a proof-of-concept prototype called Alba-

tros. Note that the prototype is based on a sophisticated

architecure that cannot be presented in more detail due to

space limitations. Inspired by the insights we gained in

the QuestionSys project [10], our modeling component has

been developed. However, the two projects Albatros and

QuestionSys differ significantly with respect to their use of

mobile processes. In the Albatros project, we focus on run

time adaptations of mobile process instances (e.g., adding

an ad-hoc activity) based on monitoring data, whereas in

the QuestionSys project, the monitoring of mobile process

instances is not required. Currently, the prototype of the

Albatros project consists of an HTML5 application and

a mobile application running on Android. The HTML5

application provides the modeling component as well as

the component for monitoring and managing therapeutic

interventions by therapists. The Android application, in turn,

integrates the mobile process engine, which we developed

in the QuestionSys project [8], to enact process models on

smart mobile devices.

Figs. 9 and 10 show selected features of the prototype.

Fig. 9 presents the view provided to therapists for managing

entire patient cases. More precisely, therapeutic interven-

Figure 9: Therapy Details

Figure 10: Mobile Process

tions, personal appointments, and anamnesis are managed

using this view. In turn, Fig. 10 presents the view of a

therapeutic intervention before deploying it to the smart

mobile device. Note that the deployed mobile process may

be visually displayed.

VIII. RELATED WORK

Three categories of related work are relevant in the context

of this paper.

Approaches dealing with mobile therapies. In [11], several

studies are presented that use mobile phones as a motivating

factor for teenagers to record their mental health symptoms.

The recordings, in turn, are discussed in the clinic with the

therapists. In [12], various approaches using smartphones in

the context of personal healthcare are presented. Interest-

ingly, these approaches confirm that contemporary solutions

less focus on mobile therapies, although beneficial use of

interventions is emphasized by most of them. In line with

[12], [3] underlines the benefits of mobile technology regard-

ing the efficacy of psychotherapy. None of these approaches

presents technical solutions for therapeutic interventions as

provided by us.

[13] presents three clinical studies in which mobile

technology was used for mobile interventions. For each

study, a specifically tailored mobile application is realized.

Although [13] reveals that mobile interventions were useful

in the context of these case studies, no generic technical

solution is presented.

Approaches dealing with model-driven guidance in the

context of mobile healthcare users. Recently, approaches

have been introduced that use model-driven techniques.

The QuestionSys approach presented in [8], [14], [15], for

example, allows realizing mobile data collection applica-

tions by healthcare experts (i.e., without any support by

IT experts). Another model-driven approach is presented

in [16]; it presents a framework that automatically creates

healthcare plans based on a model-driven concept. The

plans, in turn, are deployed to smartphones. In [17], a model-

driven approach in connection with Body Area Networks

(BANs) is presented. It considers various BAN scenarios

involving medical expert as well as patients more properly.

Approaches dealing with homework in the context of psy-

chotherapy. Numerous related works deal with homework in

the context of psychotherapy [11], [18], [19]. However, they

mainly focus on user studies instead of technical solutions.

In turn, all of these studies revealed that homework should

be a fundamental pillar of modern psychotherapies.

Altogether, related work has identified homework as being

fundamental in the context of many therapies [6]. The

development of a generic technical solution, which supports

homework in particular and therapeutic interventions in

general, is currently less considered.

IX. SUMMARY AND OUTLOOK

This paper presented a concept based on mobile processes,

which provide the basis for a protocol as well as a technical

solution enabling therapists to provide remote therapeutic

interventions to their patients with the help of smart mobile

devices. We presented fundamental requirements elaborated

in real-world projects in order to capture the practical

demands of therapists on one hand and the expectations

of patients on the other. As a major goal, therapeutic

interventions shall be flexibly applied to patients by the use

of their smart mobile devices. To flexibly cope with the

demands of therapists and to enable personalized treatment,

the support of mobile processes is a decisive step towards

a resilient technical solution. As shown, mobile processes

are built up on different conceptual levels, which are the

fundamental pillar to automatically transform therapeutic

interventions to executable mobile processes. Furthermore,

mobile processes support application developers in realizing

new activity templates and sub-processes for therapeutic

interventions. Especially, in the context of therapeutic inter-

ventions, the support of application developers is of utmost

importance due to non-foreseeable scenarios. We further

sketched our proof-of-concept prototype, which has already

proven its applicability in practice. Overall, the presented

approach is promising to properly support remote therapeutic

interventions on smart mobile devices. In future work, we

focus on two issues. First, the modeling component is to be

evaluated through an experiment. Second, the mobile appli-

cation is implemented on iOS as well as on Windows Phone.

Altogether, the support of therapeutic interventions on smart

mobile devices constitutes an important step towards more

effective therapies.

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