eSano – An eHealth Platform for Internet- and Mobile-based Interventions [original]

eSano – An eHealth Platform for

Internet- and Mobile-based Interventions*

Robin Kraft 1,2,3, Abdul Rahman Idrees 1,2, Lena Stenzel 1, Tran Nguyen 1, Manfred Reichert 2,

R¨

udiger Pryss 3and Harald Baumeister 1

Abstract— The prevention and treatment of mental disorders

and chronic somatic diseases is a core challenge for health

care systems of the 21th century. Mental- and behavioral

health interventions provide the means for lowering the public

health burden. However, structural deficits, reluctance to use

existing services, perceived stigma and further personal and

environmental reasons restrict the uptake of these evidence-

based approaches. Internet- and mobile-based interventions

(IMIs) might overcome some of the limitations of on-site

interventions by providing an anonymous, scalable, time- and

location-independent, yet evidence-based approach. In order

to implement digital mental and behavioral health concepts

across the life-span into practice, a technical solution to

support the design, creation, and execution of IMIs is needed.

However, there are various conceptual, technical as well as legal

challenges to implementing a corresponding software solution

in the healthcare domain. Therefore, the work at hand (1)

identifies these challenges and derives a number of respective

requirements, (2) introduces the eHealth platform eSano, a

software project developed by an interdisciplinary team of

computer scientists, psychologists, therapists, and other domain

experts, with the aim to serve as a flexible basis for mental and

behavioral research and health care, and (3) provides technical

insights into the developed platform and its approach to address

the aforementioned requirements.

I. INTRODUCTION

Behavioral risk factors for the development and progres-

sion of chronic somatic diseases such as diet, physical

activity, and drug use [1] as well as an at least high

as ever mental burden of disease [2] stresses health care

system resources around the globe. Structural health care

system deficits (e.g., lack of resources, fragmented systems),

reluctance to use existing services, perceived stigma, and

further personal and environmental reasons restrict uptake of

evidence-based mental and behavioral health approaches [3].

Internet- and mobile-based interventions (IMIs) have the po-

tential to overcome some of the limitations of on-site mental

and behavioral health measures by providing a (partially)

anonymous, scalable, time- and location independent, yet

evidence-based alternative [3], [4].

*This research was partially funded by PSYCHOnlineTHERAPY and

CHIMPS-NET funded by the Innovation Committee of the Joint Federal

Committee (Gemeinsamer Bundesausschuss, gBA, no: 01NVF18036 and

01NVF18003) as well as ProTransition funded by the Ministry of Social

Affairs and Integration, Baden-W¨

urttemberg.

1Department of Clinical Psychology and Psychotherapy, Ulm University,

Germany

2Institute of Databases and Information Systems, Ulm University, Ger-

many

3Institute of Clinical Epidemiology and Biometry, University of

W¨

urzburg, Germany

Since individually programming every single intervention

would be very time and resource consuming and would also

require extensive collaboration with software developers for

every small change, a more generic approach is desirable.

Following the principles of End-User Development [5], re-

searchers and health care professionals should be empowered

to create their own interventions, which can then be executed

on participants’ devices without the involvement of a soft-

ware developer. Furthermore, as shown for eHealth platforms

before [6], there are various conceptual and technical chal-

lenges to address as well as high legal and ethical healthcare

standards and regulations to comply with.

The paper at hand introduces the eHealth platform eSano1,

which aims to provide the desired functionality and meet the

technical, ethical, and regulatory requirements of internet-

and mobile-based interventions, as well as provide the basis

to cope with future emerging requirements. It consists of a)

a web-based content management system (CMS) to create

and design interventions, b) a web-based platform for thera-

pists or other health care professionals providing therapeutic

guidance (so-called eCoaches [7]), and c) a cross-platform

application for patients or other participants to execute the

interventions.

The remainder of this work is organized as follows. In

Section II, related work on IT infrastructures and platforms

delivering IMIs is discussed. Furthermore, in Section III,

background information about eMental and eBehavioral

health, internet interventions, IMIs and the eSano project

are provided. The functional requirements and non-functional

requirements for the eSano eHealth platform are described

in Section IV. How these requirements are addressed by the

proposed approach is outlined in Section V. Finally, the paper

is concluded with a summary and an outlook in Section VI.

II. RELATED WORK

IT infrastructures and platforms delivering IMIs and other

online treatments have been considered by research and

government-funded projects in the past.

The online platform Iterapi [8] provides internet-based

psychological interventions for a range of mental disorders

and other health-related issues and has been used in many

randomized controlled trials and outpatient treatments. Each

study or treatment has its own website with a custom layout,

language, and separated data storage. Interventions include

self-help treatment modules with questionnaires, information

1sano = health in Esperanto

collection forms (e.g., worksheets or diaries), and a com-

munication system (audio, video and text messages as well

as discussion forums). Therapists and editors have access

to an administrative area from which they can review and

export answers to questionnaires and worksheets, progress

with modules, as well as activity logs. In addition, the

administration of users and groups can be performed in this

area [8].

The platform Moodbuster2was originally developed for

the treatment of depression [9], but has been expanded

into a more generic framework for mental health interven-

tions [10]. Its features include flexible unguided as well as

guided self-help treatment modules, ecological momentary

assessments (EMA) with automated reminders, mobile (bio)

sensor integration (e.g., to measure daily physical activity),

and automated feedback based on a reasoning engine. The

platform consists of a central database, a web-based platform

for patients and therapists and a mobile application. Users

can use the mobile application to follow shortened versions

of the treatment modules, complete EMA questionnaires, and

monitor their progress [9].

The MindSpot Clinic3is an Australian government-

funded service, which provides online assessment and treat-

ment for anxiety, stress, depression and low mood [11].

The features of the ”online clinic” include self-report

questionnaires, therapist-guided Internet-delivered cognitive-

behavioral therapy (iCBT) lessons, automated e-mail re-

minders, an overview of symptoms, and information about

local mental health services. Intervention lessons are com-

posed of text, images and supplementary material [11].

There are other commercial service providers like Mind-

district4,SilverCloud5, and Online-Therapy.com6. However,

no information about their internal technical details are

publicly available.

In order to allow for the research of therapeutically guided

and unguided IMIs as well as the development and evaluation

of innovative Internet- and mobile-based approaches in the

fields of mental disorders, chronic somatic diseases and

health behaviour risk factors, a flexible eHealth platform was

desired that is free from any conflicting commercial goals

or boundaries of existing projects and could be adapted to

our rapidly changing requirements. Thus, the eSano project

presented in the work at hand was launched.

III. BACKGROUND INFORMATION

eMental and eBehavioral health is the use of internet

related technologies to support the mental and physical health

of people. It relies on internet technologies as an instrument

of communication to deliver prevention, treatment, health

promotion interventions and other courses. Many benefits can

be gained by adopting eMental and eBehavioral health tools,

such as a wider scope of delivery, with which people can

2https://www.moodbuster.science (accessed: 2021-01-21)

3https://mindspot.org.au (accessed: 2021-01-21)

4https://www.minddistrict.com (accessed: 2021-01-21)

5https://www.silvercloudhealth.com (accessed: 2021-01-21)

6https://www.online-therapy.com (accessed: 2021-01-21)

access such tools from the comfort of their own homes [12].

Another positive aspect are the potentially reduced costs of

using eMental and eBehavioral health tools over traditional

treatments [13]. Moreover, there is a high potential for

flexibility by enabling users to access their treatment at any

time, and from any place [3].

One eMental and eBehavioral Health concept that has

garnered more and more importance is constituted by inter-

net interventions. Internet interventions are typically related

to psychotherapy (most often Cognitive Behavior Therapy

(CBT)), behavioral medicine and/or health behavior change

interventions that have been operationalized and transformed

for delivery on the Internet, often with the goal of behavior

changes and subsequent symptom improvements [14]. They

are usually self-paced, interactive, tailored to the user, and

use multimedia content (e.g., images, audio, or video) [14].

Internet interventions can be categorized into Internet-and or

mobile-based internet interventions (with or without human

support), online counseling (e.g., email, chat, or video-

based), and internet-operated therapeutic software (e.g., rule-

based expert systems) [15]. In the scope of this work, we

will focus on Internet- and mobile-based internet interven-

tions [3], [4].

At its core, IMIs are often based on evidence-based

interactive self-help lessons [3]. Moreover, IMIs aim to

take full advantage of the technical capabilities of mod-

ern mobile devices (e.g., smartphones) by not only allow-

ing users to execute interventions virtually anywhere, but

also to incorporate mobile functionalities, such as real-time

messaging, feedback, reminders, and other reinforcement

automations, as well as the potential to leverage mobile

sensing features [16], [17]. IMIs can be implemented with

varying degrees of human support ranging from unguided

self-help modules, with which participants can work on

tasks completely independently, to guided interventions, with

which an accompanying psychologist (eCoach) can provide

regular feedback and guidance on the tasks [7]. In addition,

IMIs can be carried out as stand-alone approach or integrated

in conventional on-site interventions (blended therapy) [3].

The effectiveness of IMIs has been shown multiple times

in the past [18], [3], [19], [20], [4]. Interventions are thereby

particularly effective when guided by an eCoach [7].

In 2017, the Department of Clinical Psychology and

Psychotherapy (KLIPS) and the Institute of Databases and

Information Systems (DBIS) at Ulm University launched

an IT project called eSano with the goal to provide a

technological infrastructure for the creation and delivery of

generic unguided as well as guided IMIs. The platform

is already being used in multiple ongoing studies at Ulm

University and its cooperation partners.

IV. REQUIREMENTS

In collaboration with domain experts (e.g., psychologists,

computer scientists, regulatory consultants) and gained expe-

riences with other similar projects in the field [6], a number

of requirements were derived and continuously adapted for

the eSano eHealth platform.

The functional requirements are listed in Tables I and

II, including the provision of a user identity, a role sys-

tem, groups, generic interventions, a customizable interven-

tion configuration, generic diaries, guidance, a preview for

CMS editors and eCoaches, chat messages between different

users on the platform, and automatic reminders. The non-

functional requirements are listed in Table III, including

safety, security & privacy, availability, performance & scal-

ability, offline availability, interoperability, multilingualism,

modularity, maintainability & extensibility, and an open-

source software development approach.

TABLE I

MOST IMPORTANT FUNCTIONAL REQUIREMENTS OF THE ESANO

EHEALTH PLATFORM (PART 1)

ID Title Description

FR1 User Identity There should be authentication and autho-

rization mechanisms allowing only privileged

users (c.f. Role System) to access the CMS

and eCoach platforms. All users should only

be able to access their own data or data to

which they have been granted access.

FR2 Role System There should be a role system in order to

allow certain users to access different areas

of the platform (e.g., roles for content editors,

eCoaches and admins).

FR3 Groups There should be the option to group users

(content editors, eCoaches, participants) and

content (interventions, diaries) in order to im-

plement studies and enable isolated work en-

vironments for privileged users.

FR4 Generic

Interventions

The platform should enable the composition

of generic interventions for content editors

(using end-user development), which can then

be assigned by eCoaches and executed by

participants. Interventions should be structured

into modules (called lessons, e.g., Psychoe-

ducation: development of anxiety [21]), and

these in turn into pages. Each lesson/page can

contain different static multimedia elements

(e.g., headline, text, image, audio, video) as

well as interactive elements (e.g., questions).

Content might only be displayed under certain

conditions (conditional content, e.g., if a ques-

tion was answered with a certain option).

FR5 Intervention

Configuration

The process of interventions should be config-

urable. For each lesson it should be definable

whether it should be unlocked a) always, b)

after completion of the previous lesson, c) at

a specific date & time, or d) manually by the

eCoach (see Guidance).

V. APPROACH

The eSano eHealth platform attempts to provide a software

solution that addresses the requirements described previ-

ously. The following describes how the software architecture

as well as the data model of the platform are composed.

Furthermore, the guidance-related features are described in

more detail. Finally, the process of developing and validating

the software in regulated environments is addressed.

A. Software Architecture

eSano is mainly composed of three applications that

rely on one central backend, as shown in Fig.1. Inter-

TABLE II

MOST IMPORTANT FUNCTIONAL REQUIREMENTS OF THE ESANO

EHEALTH PLATFORM (PART 2)

ID Title Description

FR6 Generic

Diaries

The platform should enable the composition

of generic diaries for content editors, which

can then be assigned by eCoaches and exe-

cuted by participants. Diaries can contain the

same elements as an intervention lesson, but

can be completed as often as desired.

FR7 Guidance Participants might be assigned to an eCoach.

This eCoach should then be able to assign in-

terventions and diaries to the participant, ac-

cess the given answers and provide feedback

in form of text messages. Additionally, the

eCoach might change the intervention con-

figuration and manually unlock new lessons.

FR8 Preview The intervention and diary contents should

be viewable in a preview in the CMS and

eCoach platform that simulates in what way

the content shall look like in the partici-

pant app, but allows to skip content through

lessons.

FR9 Chat

Messages

There should be the option for users of the

platform to communicate via chat messages

(e.g., eCoaches with their participants).

FR10 Automatic

Reminders

The platform should offer the ability to au-

tomatically remind users of the platform and

notify them about certain activities via emails

and push notifications. For example, partic-

ipants should be reminded to finish their

lessons or read feedback from their eCoach.

Database

Content Management

System (CMS)

eCoach

Platform

Cross-platform

Participant App

REST

API

Fig. 1. Architecture of the eSano eHealth platform

vention content is designed and created by domain ex-

perts in the web-based Content Management System (CMS).

The interventions can then be published to the web-based

eCoach platform. eCoaches can manage their participants

and assign interventions to them. Participants are then

able to work on these interventions in the cross-platform

Participant Application (web-based, Android, iOS). Dur-

ing the execution of an intervention, therapeutic guid-

ance can be provided by the eCoach. All applications

are built with web technologies, using frameworks such

as Laravel (PHP), Vue.js (JavaScript/TypeScript), Angular

(JavaScript/TypeScript), and Ionic (JavaScript/TypeScript)

and use HTML, CSS, and SASS to display content to its

users. The communication between the three applications and

TABLE III

MOST IMPORTANT NON-FUNCTIONAL REQUIREMENTS OF THE ESANO

EHEALTH PLATFORM

ID Title Description

NR1 Safety, Security

& Privacy

The platform should meet high safety, security

and data protection standards. This includes

development under consideration of the EU

General Data Protection Regulation (GDPR)

and the Medical Device Regulation (MDR).

The software validation process should be

oriented towards relevant standards and guid-

ance documents (e.g., IEC 62304 [22]). All

confidential data should be stored securely and

transmitted in encrypted form. User data and

credentials should be stored separately from

application data. Health risks should be iden-

tified and addressed early and transparently

presented to users. A security model should

exist for the entire platform.

NR2 Availability,

Performance

& Scalability

The platform should always be available for

its users, if possible. There should be no

noticeable performance drops at higher load.

Scalability aspects for mHealth platforms [23]

should be considered.

NR3 Offline

Availability

If possible, the mobile participant app should

also be usable when there is no internet con-

nection (or more generally: no connection to

the backend). All data should be stored on the

device and synchronized with the backend, if

necessary.

NR4 Interoperability The platform should provide good interop-

erability with other (external) systems. This

includes the implementation of common data

exchange format standards and communica-

tion protocols as well as the provision of

uniform, understandable and well-documented

interfaces.

NR5 Multilingualism The platform should be available in multiple

languages at the same time. The language of

the applications as well as the content should

be configurable by the user.

NR6 Modularity,

Maintainability

& Extensibility

The platform should be modular, so that it

can be easily maintained and extended to meet

future changing or new requirements.

NR7 Open-Source The software should be open-source. Any

software developments and used software li-

braries should be under licenses which al-

low everyone to use, study, change, and re-

distribute the software.

the backend is based on the use of the Representational State

Transfer (REST) architecture. The overall communication is

encrypted end-to-end with TLS and transmitted via Hyper-

text Transfer Protocol Secure (HTTPS). The separation of the

eSano platform into three independent applications provides

several benefits, e.g., each application can be developed

independently with completely different toolsets based on its

specific requirements. Moreover, errors in one application do

not necessarily affect the other applications, which makes the

platform more resilient and simplifies maintenance because

these errors can be isolated from the rest of the platform.

B. Data model & representation

A simplified Entity Relationship (ER) model diagram is

shown in Fig. 2. The main entity in the diagram is the study

1..N

1 0..N

Intervention

1..N

Module

10..NElement

0..N

Study

Participant

eCoach

0..N

supervision

in study

1 0..N 0..N 1

Answersheet

0..N

Intervention

Instance

Intervention

Configuration

Default

Intervention

Configuration

0..N

Answer

Fig. 2. Simplified entity-relationship model (ERM) of the eSano data model

entity. It can be seen that almost every other entity is a

descendent of it, either directly or indirectly. An eCoach can

be a supervisor for a specific participant in a study and a

study can have one or more supervisors and participants.

Furthermore, interventions are grouped under studies, where

any intervention can belong to one study. These interven-

tions hold the different modules that were designed in the

CMS and each module could contain one or more elements

(e.g., headline, text, image, audio, video). Additionally, the

default intervention configuration is stored together with the

intervention (see Section V-C). The Intervention Instance

represents a specific instance of an intervention that has been

adjusted to suit the specific needs of each participant. This

includes the intervention configuration (see Section V-C) as

well as the current progress of the participant within the

respective intervention. An eCoach can create an instance

of an intervention and modify it so that it works according

to the needs and requirements of a certain user and then

this instance can be assigned to this participant. Whenever

a participant goes through a module and submits answers,

these answers are stored in an answersheet. This can be seen

in Fig. 2 with the entity Answersheet, where an answersheet

can contain multiple answers and can belong to exactly one

module.

As outlined in Section V-A, RESTful interfaces are used

to exchange data between different applications of the eSano

platform. In this process, the requested entities are trans-

formed into the JavaScript Object Notation (JSON), which

is a human-readable and easy-to-understand format, but can

also be directly processed by many JavaScript-based clients.

Since the interfaces are independent of the data storage, it is

possible to extend the API with additional interfaces and data

formats to serve other needs and applications in the future.

C. Guidance

eSano offers therapists and other domain experts a number

of choices and a significant degree of freedom when super-

vising the users of the platform. Communication between

participants and their supervisors can easily be initiated by

both parties. Whenever a participant finishes a module, a

notification is sent to the user’s supervisor. From the eCoach

platform, the supervisor can go through the answers of

Module 1

Module 2

Module 3

User

eCoach

CMS

Default Intervention Configuration

Module 3

Module 1

Module 2

Module 1

Module 3

Module 4

User-specific Configuration

Run-Time

Changes

Publish

Module 4

eCoach

Platform

Assign

Editor

Module 1

Module 3

User-specific Configuration

Fig. 3. Guidance process in eSano that spans the different subsystems

the user and, when needed, also provide suitable feedback.

Additionally, when there is a need for a module to be

repeated, the supervisor can simply run such a command.

On top of that, modules can be added to the intervention

and can also be removed or exchanged for other modules.

This flexibility helps therapists, healthcare providers and

researchers to supervise their participants closely and to

easily update the modules, if the situation or therapy progress

requires it.

The workflow of the guidance process is described in

Fig. 3. Domain experts can use the CMS to 1

create and

design an intervention with a number of modules (e.g., for

depression or anxiety [21]). They can then decide which

modules shall be included in the intervention default config-

uration, how these modules should be unlocked (i.e., always,

after completion of the previous module, at a specific date

and time, or manually by the eCoach), and in which order

they should be processed by the user. Once the default

configuration of the intervention is set up, the intervention

can be 2

published, so that it is accessible from the eCoach

platform. At this point, an eCoach (e.g., a therapist) can

further modify the default configuration of the intervention

into a user-specific configuration using the eCoach platform

and then 3

assign it to a participant. This stage helps

eCoaches to tailor the intervention to the individual needs

and therapy plan of each participant. After an intervention

has been assigned to a participant, it can be accessed from the

participant app. As described in Section V-A, the participant

app is a cross-platform application, so users can access their

interventions through both a web browser and a mobile

device. While the user is working through the modules of

the intervention, the eCoach can supervise this process and

monitor the progress by checking the activities as well as the

given answers of the user. The eCoach can then 4

provide

feedback to the participant based on the user’s progress, and

the user is also able to contact the eCoach whenever needed.

Moreover, the eCoach has the option to further 5

apply

run-time changes to the user-specific configuration, e.g., by

adding another module from the intervention repository as

indicated by Fig 3, which helps to make the process more

flexible and more responsive to new developments in the

user’s needs and requirements.

D. Software development & validation process

The platform is developed tailored to the requirements of

the German Medical Devices Act and the Medical Device

Regulation (MDR). Thus, the software development, docu-

mentation and validation processes take into account the IEC

62304 [22] (safety class B), the GAMP5 [24] (category 4),

the General Principles of Software Validation [25] of the

FDA, as well as the Pharmaceutical Inspection Cooperation

Scheme (PIC/S) 11-3 [26]. Requirements are documented

in detail and updated on an ongoing basis. Risks regarding

patient safety, data integrity, and product functionality are

assessed and rated in terms of their severity and estimated

frequency of occurrence. For each risk, mitigation measures

are taken, documented and continuously monitored. The

software is not released until all risks are within acceptable

levels. Validation of all requirements is performed and doc-

umented for every release of the platform that is used in

regulated environments.

Furthermore, the platform is currently being used in the

three-armed multicenter cluster-randomized controlled non-

inferiority trial PSYCHOnlineTHERAPY [21] to assess its

feasibility and usability. The study compares two implemen-

tations of blended psychotherapy with CBT. 75 outpatient

psychotherapists are recruited and each of them asked to

include 12 patients with depressive or anxiety disorders,

yielding a total sample size of N = 900.

VI. SUMMARY & OUTLOOK

In this work, we introduced eSano, an eHealth platform

for Internet- and mobile-based interventions. The functional

and non-functional requirements, which were elaborated in

collaboration with various domain experts, were described.

Furthermore, several aspects of the approach that have been

selected for the eSano platform in order to cope with these

requirements were highlighted. In this context, the software

architecture and data model of the platform were described.

In addition, the guidance process was outlined in more detail.

Finally, the software development and validation process

used in order to cope with legal requirements and guidelines

in regulated environments of the healthcare domain was

described.

As eMental and eBehavioral Health in general and internet

interventions in specific are gaining more and more attention

and acceptance, and the research in this field is advancing,

new innovative eHealth and mHealth approaches will emerge

on the one hand. On the other hand, they are required with

respect to many needs of the 21th century. Furthermore, tech-

nological advances are opening up even more possibilities,

e.g., chatbots that mimic therapeutic conversations [27], [28]

or just-in-time interventions that intervene at the moment of

need by analyzing sensor and other contextual information

captured by mobile devices [17]. The respective software

solutions, in turn, must on the one hand be able to deal flex-

ibly with these changing requirements and adapt accordingly.

On the other hand, software in regulated environments must

comply with legal regulations, which leads to increased doc-

umentation and validation efforts that prevent rapid changes.

In the future, it is planned to split the eSano project into

a validated version intended for regulated environments and

one version that allows for flexible changes used for cutting-

edge research.

REFERENCES

[1] C. J. Murray, A. Y. Aravkin, P. Zheng, C. Abbafati, K. M. Abbas,

M. Abbasi-Kangevari, F. Abd-Allah, A. Abdelalim, M. Abdollahi,

I. Abdollahpour et al., “Global burden of 87 risk factors in 204

countries and territories, 1990–2019: a systematic analysis for the

global burden of disease study 2019,” The Lancet, vol. 396, no. 10258,

pp. 1223–1249, 2020.

[2] R. C. Kessler, S. Aguilar-Gaxiola, J. Alonso, S. Chatterji, S. Lee,

J. Ormel, T. B. ¨

Ust¨

un, and P. S. Wang, “The global burden of

mental disorders: an update from the who world mental health (wmh)

surveys,” Epidemiologia e psichiatria sociale, vol. 18, no. 1, p. 23,

2009.

[3] D. D. Ebert, T. Van Daele, T. Nordgreen, M. Karekla, A. Compare,

C. Zarbo, A. Brugnera, S. Øverland, G. Trebbi, K. L. Jensen et al.,

“Internet-and mobile-based psychological interventions: applications,

efficacy, and potential for improving mental health,” European Psy-

chologist, 2018.

[4] E. Bendig, N. Bauereiß, D. D. Ebert, F. Snoek, G. Andersson,

and H. Baumeister, “Internet-based interventions in chronic somatic

disease,” Deutsches ¨

Arzteblatt International, vol. 115, no. 40, p. 659,

2018.

[5] H. Lieberman, F. Patern`

o, M. Klann, and V. Wulf, “End-user develop-

ment: An emerging paradigm,” in End user development. Springer,

2006, pp. 1–8.

[6] R. Kraft, W. Schlee, M. Stach, M. Reichert, B. Langguth, H. Baumeis-

ter, T. Probst, R. Hannemann, and R. Pryss, “Combining mobile

crowdsensing and ecological momentary assessments in the healthcare

domain,” Frontiers in Neuroscience, vol. 14, p. 164, 2020.

[7] H. Baumeister, L. Reichler, M. Munzinger, and J. Lin, “The impact of

guidance on internet-based mental health interventions—a systematic

review,” internet Interventions, vol. 1, no. 4, pp. 205–215, 2014.

[8] G. Vlaescu, A. Alasj¨

o, A. Miloff, P. Carlbring, and G. Andersson,

“Features and functionality of the iterapi platform for internet-based

psychological treatment,” Internet Interventions, vol. 6, pp. 107–114,

2016.

[9] L. Warmerdam, H. Riper, M. C. Klein, P. van de Ven, A. Rocha,

M. R. Henriques, E. Tousset, H. Silva, G. Andersson, and P. Cuijpers,

“Innovative ict solutions to improve treatment outcomes for depres-

sion: The ict4depression project.” Annual Review of Cybertherapy and

Telemedicine, vol. 181, no. 1, pp. 339–343, 2012.

[10] P. Van De Ven, H. O’Brien, R. Henriques, M. Klein, R. Msetfi, J. Nel-

son, A. Rocha, J. Ruwaard, D. O’Sullivan, H. Riper et al., “Ultemat:

A mobile framework for smart ecological momentary assessments and

interventions,” Internet interventions, vol. 9, pp. 74–81, 2017.

[11] N. Titov, B. F. Dear, L. G. Staples, J. Bennett-Levy, B. Klein,

R. M. Rapee, C. Shann, D. Richards, G. Andersson, L. Ritterband

et al., “Mindspot clinic: an accessible, efficient, and effective online

treatment service for anxiety and depression,” Psychiatric Services,

vol. 66, no. 10, pp. 1043–1050, 2015.

[12] M. A. Feijt, Y. A. de Kort, I. M. Bongers, and W. A. IJsselsteijn,

“Perceived drivers and barriers to the adoption of emental health by

psychologists: the construction of the levels of adoption of emental

health model,” Journal of medical Internet research, vol. 20, no. 4, p.

e153, 2018.

[13] S. Lal and C. E. Adair, “E-mental health: a rapid review of the

literature,” Psychiatric Services, vol. 65, no. 1, pp. 24–32, 2014.

[14] L. M. Ritterband, L. A. Gonder-Frederick, D. J. Cox, A. D. Clifton,

R. W. West, and S. M. Borowitz, “Internet interventions: In review,

in use, and into the future.” Professional Psychology: Research and

Practice, vol. 34, no. 5, p. 527, 2003.

[15] A. Barak, B. Klein, and J. G. Proudfoot, “Defining internet-supported

therapeutic interventions,” Annals of behavioral medicine, vol. 38,

no. 1, pp. 4–17, 2009.

[16] H. Baumeister, R. Kraft, A. Baumel, R. Pryss, and E.-M. Messner,

“Persuasive e-health design for behavior change,” in Digital Pheno-

typing and Mobile Sensing. Springer, 2019, pp. 261–276.

[17] M. Rabbi, P. Klasnja, T. Choudhury, A. Tewari, and S. Murphy, “Op-

timizing mhealth interventions with a bandit,” in Digital Phenotyping

and Mobile Sensing. Springer, 2019, pp. 277–291.

[18] P. Carlbring, G. Andersson, P. Cuijpers, H. Riper, and E. Hedman-

Lagerl¨

of, “Internet-based vs. face-to-face cognitive behavior therapy

for psychiatric and somatic disorders: an updated systematic review

and meta-analysis,” Cognitive Behaviour Therapy, vol. 47, no. 1, pp.

1–18, 2018.

[19] J. Koenigbauer, J. Letsch, P. Doebler, D. Ebert, and H. Baumeister,

“Internet-and mobile-based depression interventions for people with

diagnosed depression: a systematic review and meta-analysis,” Journal

of affective disorders, vol. 223, pp. 28–40, 2017.

[20] M. Domhardt, J. Letsch, J. Kybelka, J. Koenigbauer, P. Doebler, and

H. Baumeister, “Are internet-and mobile-based interventions effective

in adults with diagnosed panic disorder and/or agoraphobia? a system-

atic review and meta-analysis,” Journal of Affective Disorders, 2020.

[21] H. Baumeister, N. Bauereiss, A.-C. Zarski, L. Braun, C. Buntrock,

C. Hoherz, A. R. Idrees, R. Kraft, P. Meyer, T. B. D. Nguyen et al.,

“Clinical and cost-effectiveness of psychonlinetherapy: study protocol

of a multicenter blended outpatient psychotherapy cluster randomized

controlled trial for patients with depressive and anxiety disorders,”

Frontiers in Psychiatry, vol. 12, p. 656, 2021.

[22] “IEC 62304: Medical device software – Software life cycle processes,”

International Electrotechnical Commission (IEC), Geneva, CH, Stan-

dard, 2006.

[23] R. Kraft, F. Birk, M. Reichert, A. Deshpande, W. Schlee, B. Langguth,

H. Baumeister, T. Probst, M. Spiliopoulou, and R. Pryss, “Efficient

processing of geospatial mhealth data using a scalable crowdsensing

platform,” Sensors, vol. 20, no. 12, p. 3456, 2020.

[24] GAMP 5 - A Risk-based Approach to Compliant GxP Computerized

Systems, 5th ed. North Bethesda, USA: International Society for

Pharmaceutical Engineering, 2008.

[25] General Principles of Software Validation; Final Guidance for Indus-

try and FDA Staff. Rockville, USA: U.S. Department Of Health and

Human Services, Food and Drug Administration (FDA), 2002.

[26] PIC/S Secretariat, Ed., Good Practices for Computerised Systems in

Regulated ”GXP” Environments (PI 011-3). Geneva, CH: Phar-

maceutical Inspection Convention and Pharmaceutical Inspection Co-

operation Scheme (PIC/S), 2007.

[27] E. Bendig, B. Erb, L. Schulze-Thuesing, and H. Baumeister, “The

next generation: chatbots in clinical psychology and psychotherapy to

foster mental health–a scoping review,” Verhaltenstherapie, pp. 1–13,

2019.

[28] R. Pryss, R. Kraft, H. Baumeister, J. Winkler, T. Probst, M. Reichert,

B. Langguth, M. Spiliopoulou, and W. Schlee, “Using chatbots to

support medical and psychological treatment procedures: Challenges,

opportunities, technologies, reference architecture,” in Digital Pheno-

typing and Mobile Sensing. Springer, 2019, pp. 249–260.