The vision of the MobileCoach team is to provide an open source behavioral intervention platform for fully-automated digital interventions. Due to a modular and extendable structure as well as an architecture rooted in the logics and legal claims of open-source software, the MobileCoach lays a fruitful ground for digital interventions in several application domains of behavior change. For example, the MobileCoach can be used to design and evaluate digital health interventions in the context of smoking cessation, nutrition or physical activity.

Based on personal characteristics and prior behavior assessed when participating in a digital intervention, the MobileCoach uses communication services (e.g. SMS) or sensor services (e.g. physical activity tracker) to monitor central indicators of the particular behavior on a regular basis. Building on individual data, MobileCoach users profit from individualized messages and valuable insights motivating for long-term behavior change.

An overview of a typical MobileCoach intervention is shown in Figure “A typical MobileCoach intervention” below. Following the registration process and an initial behavioral assessment at T0, the target behavior is monitored and according to it, participants receive a tailored intervention with individual feedback and recommendations until T1, for example, three to 12 months after T0. The long-term target behavior is finally evaluated in a follow-up assessment at T2, for example, three months after T1. This long-term target behavior is used as primary outcome variable to test the efficacy of the underlying MobileCoach intervention.

An overview of the various web based and SMS based user interfaces for participants of MobileCoach interventions is shown in Figure “The MobileCoach for participants” below. With the exception of the SMS based user interface, content and layout elements are fully customizable such that the target behavior can be supported as effective and efficient as possible. Native MobileCoach applications for mobile devices, desktop computers or ubiquitous computing environments will be made available as part of upcoming MobileCoach interventions and projects.

Domain experts such as physicians, psychologists or, more generally, behavioral intervention experts can use a lightweight web application to design and evaluate MobileCoach interventions. That is, no technical programming skills are required to design the baseline assessment, the tailored web-based feedback, the intervention rules or content elements. Some screenshots of the web application are shown in Figure “The MobileCoach for Domain Experts” below. Domain experts are also able to import and export interventions. This also includes an export of intervention data in the CSV format that allows further data processing and analyses with statistical packages such as R, Matlab or SPSS. However, real-time monitoring and longitudinal data analyses will be integrated directly in the MobileCoach web application in upcoming projects.

In the pilot phase, the MobileCoach will support two concrete health behaviors related to the public health context. For more information on these interventions, please go to the section Projects and read about our first projects MobileCoach Tobacco and MobileCoach Alcohol. Due to its open-source character, however, future research teams around the globe may contribute by adding features to the software and broadening the number of supported behaviors.

The source code of the open source MobileCoach platform can be downloaded in our section For Developers.

Features of the current stable release - 2.2.14

Updated: April 2, 2019

INTERVENTION FLOW

1. Intervention rules are used to describe the flow of the intervention (e.g. when to send which messages):
   • Rules can send messages at specific points in time and define the time period in which an answer is expected
   • Rules can calculate and create new participant-specific variables
   • Hierarchical structures enable definition of sub-rules (i.e. control structures)
   • Reply rules can be defined to immediately react to answers of the participant
   • No-reply rules can be defined to react if the participant did not answer a question
   • A placeholder system allows using all variables of a participant in all rules
   • Rules support date calculations (e.g. days until my birthday or days until a smoking quit day)
   • Rules support mathematical calculations/equations and regular expressions
   • Rules support lists and sorting operations
   • Specific rule comparators for “select many” variables
   • NEW: Intervention rules to calculate variables, check conditions, send messages, etc. can be executed at any time of the day.
2. NEW: User intention rule executions are triggered by the user.
   • The triggers can be,
     • Starting the App.
     • Finishing a survey.
     • Any sensor (Eg. temperature, location, Bluetooth connectivity) that can be measured through the App.
   • Different user intents can be part of the same intervention and can be uniquely identified.
   • After triggered, user intent rules are similar the regular intervention rules and can be used to perform any task described in the previous part.

MESSAGES and MICRO-DIALOGS

1. Messages are used to start a dialog with a participant:
   • Messages can be sent in a specific or random order
   • Messages can be sorted in question/answer/no-answer triples in order to increase the consistency of the dialog quality
   • Message rules can be used to skip messages for a specific participant
   • A placeholder system allows using all variables of a participant in the messages
   • Messages are assigned to message groups, which allows the reduction of rule complexity in interventions (e.g. message groups for smokers/non-smokers)
   • Message groups can be defined to expect an answer by the participant (or not)
   • Messages can be saved in several languages to enable authors to provide multi-language interventions
   • Messages can include links to intermediate surveys
   • Messages can be sent to the supervisor of a participant under specific circumstances
   • Messages can be sent to the participants vis multiple channels
2. Microdialogs are messages that can be designed specifically for Apps.
   • Messages are grouped together in the form of dialogs.
   • Messages can be designed to send plain text or to ask for information in the form of text, number, date, time or date-time.
   • Participants can also answer with sliders or can select an option.
   • Decision points are also a part of the Microdialogs. Decision points are also rules that are executed dynamically and are used to control the flow of the messages.

DASHBOARD

1. Dashboard for teachers, caretakers, service personnel, etc.
   • NEW: A web-based dashboard allows non-participants to get a summarised view on the current data state of an intervention
   • NEW: Specific REST endpoints allows the anonymized retrieval of accumulated group/intervention values

(SCREENING) SURVEYS & FEEDBACK

1. Design of web-based surveys for screening purposes as part of the interventions:
   • Supported question types:
     • Select one of the provided answers (radio button, e.g. yes/no, Likert-scale type, semantic differential)
     • Select any of the provided answers (checkbox, multiple choice)
     • Number input
     • Text input
     • Multi-line text input
   • Support for multi-item slides (e.g. question page with several Likert-type items)
   • Support for media objects in the slides (e.g. PDF document, video, or audio clip)
   • Specific answers can be pre-selected (e.g. zero value, centered slider)
   • Answers can be stored in variables of the participant
   • Each intervention can contain several surveys (e.g. for branding purposes and survey variants)
   • HTML-templates can be used to layout surveys
   • Slide rules can be used to jump to specific slides based on the former answers of a participant
   • Slide rules can be used to calculate variables based on the answers of a participant
   • A placeholder system allows using all variables of a participant in all survey texts
   • Variable values can be checked for validity (e.g. by use of regular expressions, rules, calculations, etc.)
   • Variables can have default values if a participant leaves a survey before finishing it
   • Surveys can be protected with a password
   • Intermediate surveys enable to send out surveys at any time during an intervention
   • NEW: Starting days of interventions can be defined dynamically
2. Public list of active surveys (e.g. for bookmarking the screening survey)
3. Design of web-based feedback slides
   • Each feedback has a unique URL for each participant to be able to access/communicate the same also in later steps of the intervention
   • Support for media objects in the slides (e.g. PDF document, video or audio clips)
   • Each intervention can contain several feedbacks (e.g. for branding purposes and survey variants)
   • HTML-templates can be used to layout surveys
   • Slide rules can be used to skip/show specific slides to specific participants
   • A placeholder system allows using all variables of a participant in all feedback texts
4. Surveys and feedbacks can be enabled/disabled

MAINTENANCE & RESULTS

1. Unexpected answers will be shown to the intervention author for manual processing (e.g. to set the correct value, to start a personal dialog with the participant)
2. Robust exception handling of answers with the help of regular expressions (e.g. “!!!Yes!!! :-)” can be interpreted as “yes”)
3. Manual messages can be sent to a preselected subset of participants
4. Variables of participants can be directly reviewed or exported to a CSV file for further processing in R, SPSS or Matlab
5. Dialog-based results, i.e. the message dialog for each participant with meta-data such as timestamps, can be reviewed online or exported to a CSV file for further processing in R, SPSS or Matlab
6. Daily generation of a report with aggregated results of all running interventions (e.g. number of sent messages) in a simple property-value format for further processing or inclusion into websites

BASIC FEATURES

1. Access rights management for intervention authors and technical administrators
2. Interventions can be created, imported, exported, enabled, disabled, deleted, assigned to authors
3. Participants can be imported, exported, deleted, assigned to organizations and organization groups
4. Cellphone number is used as a unique identifier, i.e. no additional identifier is required for subjects to participate.
5. Global default values for variables can be set as a backup solution for interventions (if optional questions of the surveys are not answered.)
6. Automatic data and data model adaption for new features or changes in the case of a version update
7. SSL-secured administration interface
8. SSL-secured online surveys and feedbacks
9. Document-based database with separate media storage
10. Reporting feature allows exporting the current state of intervention as an interactive HTML document
11. Dynamically configurable REST-interface allows the reading/writing of specific variables of a participant/group/intervention from a modern website or web application
12. Image storage and caching engine enables user-content to upload and reuse

This is a selection of MobileCoach projects. Please contact Tobias Kowatsch tkowatsch@ethz.ch if your project should be listed here, too.

NRF Singapore Future Health Technologies - Mobile Health Interventions, 2020-2025

Behavioural change is essential in reducing chronic-​disease risk in the general population (e.g. obesity prevention), managing chronic diseases (e.g. diabetes) on a day-​to-day basis and preventing hospitalisations and long-​term complications (e.g. kidney failure). However, it is often difficult to initatiate behavioural change and lifestyle interventions and to maintain them in the long term. While mobile behavioural interventions in the form of smartphone apps that target lifestyle changes are plentiful, low user engagement has limited the effectiveness of many mobile digital health applications. To overcome this challenge, the researchers from ETH Zurich has developed and employed MobileCoach, an open-​source platform that relies on an automated conversational agent (chatbot) to deliver interactive and engaging mobile health interventions. Building on the experience and success in developing MobileCoach, a systematic development process underpinned by empirical studies and clinical trials will be used to develop and evaluate MobileCoach interventions for the prevention and management of non-​communicable chronic diseases in Singapore, together with partners from Singapore. Firstly, the team will apply the Multiphase Optimisation Strategy to identify and overcome individual, cultural, legal and organisational barriers and identify facilitators for the successful implementation of interventions in Singapore. The mobile health interventions developed will be evaluated in optimisation trials, and tested for clinical efficacy in different patient populations (e.g. pre-​diabetic individuals and type 2 diabetes patients) through randomised controlled trials. When the effectiveness of the interventions developed in this research module is demonstrated following the evaluations and tests, they may be scaled up to benefit the Singaporean population thorugh spin-​off companies or through other means. Center for Digital Health Interventions - FHT Mobile Health Interventions Singapore

NIH U.S. Thinking Outside the Clinic: A Digital Health Approach for Young Adults with Type 1 Diabetes, 2020-2025

Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve good glycemic control. However, few interventions have been developed specifically for this age group. In the proposed study, we will provide an app (SweetGoals) to all participants as a “core” intervention. The app prompts participants to upload their diabetes devices weekly to a device-agnostic uploader (Glooko), will automatically retrieve uploaded data and apply decision rules about daily and weekly self-management goals, and will generate feedback messages about goal attainment. Further, the proposed study will test two unique intervention components designed to support and optimize digital self-monitoring, goal setting, and goal achievement. These components include (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic. Instead of relying on the clinic to transform its system of care, our intervention will directly assist young adult patients in developing skills to improve their utilization of the health care system. A prior pilot RCT (N=61) comparing this intervention approach to usual care among adolescents showed significant and sustained impact on A1c. These results were very promising, and we believe this intervention is well suited to young adults. In the proposed study, 300 young adults ages 19-25 with T1D and above target (Hb)A1c (≥ 8.0%) will be recruited via social media. All participants will receive the SweetGoals app built on an open-source intervention platform MobileCoach. The app automates many features of our piloted intervention, greatly increasing disseminability. These include automatic retrieval of diabetes device data “digested” by Glooko and prompting and providing feedback on adherence goals. Adherence targets include (a) daily glucose monitoring; and (b) mealtime behaviors. A 22 factorial design will be used to test the main and interactive effects of (1) modest financial incentives for meeting adherence targets and (2) web health coaching. Coaches will teach a problem-solving approach that generates personalized solutions to adherence challenges. The intervention will last 6 months. The primary outcome will be reduction in A1c. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives. Further, we hypothesize a synergistic interaction between these components, resulting in greater positive outcomes among those receiving both. We will also test four key self-regulation mechanisms and app engagement metrics as predictors of outcomes. Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D. This project addresses a key public health issue with broad applicability to many diseases and health behaviors: how to help people follow a complicated medical regimen and achieve better health outcomes. We will test the role of coaching support focused on problem solving and incentives to help young adults with type 1 diabetes feel better and be healthier. We expect that the things we learn about helping young adults with type 1 diabetes will apply to many other health problems. Center for Technology and Behavioral Health, Dartmouth College - Thinking Outside the Clinic

MAX: A Digital Health Literacy Coach for Children with Asthma, 2017-2020

Health literacy is a crucial ingredient of successful asthma self-management. Studies have shown that a lack of asthma health literacy leads to lower levels of asthma control and thus more severe asthma symptoms, which, in turn, results in a suboptimal course of the disease. However, due to limited personnel and financial resources of healthcare systems, communication of health literacy during on-site consultations does not scale into the everyday life of patients where they need to apply the health literacy knowledge. Ubiquitous technology has the potential to extend the reach of health professionals to the daily life of individuals and could be thus, also a promising channel to bring health literacy content to patients. Nevertheless, it is still open, how such a digital health literacy program must be designed to reach and positively impact a maximum of individuals affected by asthma. This research project has two objectives: (1) to assess the reach and technical feasibility of a novel mobile coach MAX for children with asthma and, in supporting roles, their family members and healthcare practitioners and (2) to assess the impact of MAX on children’s health literacy in dealing with asthma. To reach these goals, we apply the multiphase optimization strategy (MOST) and start with a pilot study (the preparation phase) before follow-up micro-randomized trials, and finally, a fully-powered randomized controlled trial is planned. Center for Digital Health Interventions - MAX

SNF Mobile Couple Support, 2017-2020

The goals of this project are to systematically investigate the unique contributions of visible and invisible social support and common dyadic coping on health behaviors involved in diabetes management (e.g., physical activity, eating, medication adherence) and well-being of diabetes Type II patients and their partners; and to develop an ambulatory assessment application for smartphones for the open source behavioral intervention platform MobileCoach that allows (a) to record speech and recognize the affect of speech based on prosodic, spectral and sentiment analyses, (b) to objectively sense the degree of physical activity by smartphone sensors and (c) to collect self-report data in situ in order to better understand the predictors (visible and invisible social support and common dyadic coping). Center for Digital Health Interventions - SNF Mobile Couple Support

SNF Changing Personality Traits, 2016-2019

The main aim of the planned research program is to test the efficacy of a smartphone-based intervention for intentional personality change using the open source platform MobileCoach that supports the design of evidence-based, scalable and low-cost interventions. The research strategy is threefold: (1) to develop a smartphone-based intervention to change personality traits, (2) to examine its efficacy to change personality traits in the desired direction, and (3) to examine underlying processes and mechanisms in an effort to improve the intervention outcomes. The project website is available here: Center for Digital Health Interventions - SNF Changing Personality Traits

SNF PathMate 2 - Childhood Obesity, 2016-2018

The goal of the research project PathMate2 is to help support obese children, their parents and their physicians with modern technology in creating a healthier lifestyle. Computer scientists, engineers and medical experts are collaborating in this interdisciplinary project in order to develop an information system which will allow obese children and their physicians to stay in touch. Some of the good intentions and plans made during on-site consultations are forgotten soon thereafter. It is therefore especially important that obese children make the right decisions in their lifestyle and diet in their everyday life. With the help of PathMate2, physicians will be able to analyze the data of their patients in real time, provide them with tips or challenges tailored to their needs, and thus motivate them to make healthier choices. The project website is available here: Center for Digital Health Interventions - SNF PathMate 2

CSS Mobile Asthma Companion, 2016-2020

The main objective is to design just-in-time adaptive health interventions for individuals with asthma. Research has conclusively shown that effective disease management interventions can directly affect an asthmatic’s quality of life, enabling a life with almost no symptoms and constraints. To ensure efficacy in our digital asthma interventions, we combine cutting-edge technology with evidence-based medicine and behavioral science. Our vision is to enable people suffering from asthma to gain control over their disease, so that they can live their life to the fullest. The project website is available here: Center for Digital Health Interventions - CSS Mobile Asthma Companion

CSS Predicting States of Receptivity, 2016-2020

Recent advancements in mobile sensing technologies have led to novel intervention concepts, including just-in-time adaptive interventions (JITAI). The JITAI concept shows great potential because it aims to provide the right intervention, at the right time. The right timing of an intervention is crucial so that the content can be received and processed by those being targeted. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notifications, it has not been thoroughly explored for digital health interventions. In this project, we explore the factors affecting users’ states of receptivity towards JITAIs and develop learning algorithms that are able to predict states of recepetivity. To reach this goal, we conducted several field studies, where participants received various health interventions.: Center for Digital Health Interventions - Predicting States of Receptivity

BLV Mobile Diet Coach, 2017-2018

Mobile Diet Coach aims to provide fully-automated interventions to improve food literacy and support health-promoting dietary behaviors. The project website is available here: Center for Digital Health Interventions - Mobile Diet Coach

CSS Physical Activity Interventions, 2015-2017

The aim of the myStep project is to realize the potential of digital health interventions in order to increase physical activity. The vision of myStep is to promote a more active lifestyle and thus contribute to prevent chronic diseases, enhance health and well-being and lower financial burdens of the health care system in a ubiquitous and effective way. The project website is available here: Center for Digital Health Interventions - CSS Physical Activity Interventions

MobileCoach ready4life, 2016-2017

Background: Substance use and misuse often first emerge during adolescence. Generic life skills training that is typically conducted within the school curriculum is effective at preventing the onset and escalation of substance use among adolescents. However, the dissemination of such programs is impeded by their large resource requirements in terms of personnel, money, and time. Life skills training provided via mobile phones might be a more economic and scalable approach, which additionally matches the lifestyle and communication habits of adolescents.

Objective: The aim of this study was to test the acceptance and initial effectiveness of an individually tailored mobile phone–based life skills training program in vocational school students.

Methods: The fully automated program, named ready4life, is based on social cognitive theory and addresses self-management skills, social skills, and substance use resistance skills. Program participants received up to 3 weekly text messages (short message service, SMS) over 6 months. Active program engagement was stimulated by interactive features such as quiz questions, message and picture-contests, and integration of a friendly competition with prizes in which program users collected credits with each interaction. Generalized estimating equation (GEE) analyses were used to investigate for changes between baseline and 6-month follow-up in the following outcomes: perceived stress, self-management skills, social skills, at-risk alcohol use, tobacco smoking, and cannabis use.

Results: The program was tested in 118 school classes at 13 vocational schools in Switzerland. A total of 1067 students who owned a mobile phone and were not regular cigarette smokers were invited to participate in the life skills program. Of these, 877 (82.19%, 877/1067; mean age=17.4 years, standard deviation [SD]=2.7; 58.3% females) participated in the program and the associated study. A total of 43 students (4.9%, 43/877) withdrew their program participation during the intervention period. The mean number of interactive program activities that participants engaged in was 15.5 (SD 13.3) out of a total of 39 possible activities. Follow-up assessments were completed by 436 of the 877 (49.7%) participants. GEE analyses revealed decreased perceived stress (odds ratio, OR=0.93; 95% CI 0.87-0.99; P=.03) and increases in several life skills addressed between baseline and the follow-up assessment. The proportion of adolescents with at-risk alcohol use declined from 20.2% at baseline to 15.5% at follow-up (OR 0.70, 95% CI 0.53-0.93; P=.01), whereas no significant changes were obtained for tobacco (OR 0.94, 95% CI 0.65-1.36; P=.76) or cannabis use (OR 0.91, 95% CI 0.67-1.24; P=.54).

Conclusions: These results reveal high-level acceptance and promising effectiveness of this interventional approach, which could be easily and economically implemented. A reasonable next step would be to test the efficacy of this program within a controlled trial.

Funding: Swiss Lung Association

Contact: Severin Haug severin.haug@isgf.uzh.ch

MobileCoach Tobacco: Efficacy Study, 2014-2016

Objective: To test the efficacy of a technology-based integratedsmoking cessation and alcohol intervention versus a smoking cessation only intervention in adolescents. Methods: This was a two-arm, parallel-group, cluster-randomised controlled trial with assessments at baseline and six months follow-up. Subjects in both groups received tailored mobile phone text messages to support smoking cessation for 3 months, and the option of registering for a programincorporating strategies for smoking cessation centred around a self-defined quit date. Subjects in the integrated intervention group also received tailored feedback regarding their consumption of alcohol and, for binge drinkers, tailored mobile phone text messages encouraging them to maintain their drinking within low-risk limits over a 3-month period. Primary outcomemeasureswere the 7-day point prevalence of smoking abstinence and change in cigarette consumption.

Results: In 360 Swiss vocational and upper secondary school classes, 2127 studentswho smoked tobacco regularly and owned a mobile phonewere invited to participate in the study. Of these, 1471 (69.2%) participated and 6-month follow-up data were obtained for 1116 (75.9%). No significant group differenceswere observed for any of the primary or secondary outcomes. Moderator analyses revealed beneficial intervention effects concerning 7-day smoking abstinence in participants with higher versus lower alcohol consumption.

Conclusions: Overall, the integrated smoking cessation and alcohol intervention exhibited no advantages over a smoking cessation only intervention, but it might be more effective for the subgroup of adolescent smokers with higher alcohol consumption. Providing a combined smoking cessation and alcohol intervention might be recommended for adolescent smokers with higher-level alcohol consumption.

Funding: Swiss Tobacco Prevention Fund

Contact: Severin Haug severin.haug@isgf.uzh.ch

MobileCoach Alcohol: Efficacy Study, 2014-2016

Objective: To test the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people compared to assessment only.

Method: Two-arm, parallel-group, cluster-randomized controlled trial with assessments at baseline and 6-month follow up. The automated intervention included online feedback, based on the social norms approach, and individually tailored text messages addressing social norms, outcome expectations, motivation, self-efficacy, and planning processes, provided over 3 months. The main outcome criterion was the prevalence of risky single-occasion drinking (RSOD, defined as drinking at least 5 standard drinks on a single occasion in men and 4 in women) in the past 30 days. Irrespective of alcohol consumption, 1,355 students from 80 Swiss vocational and upper secondary school classes, all of whom owned a mobile phone, were invited to participate in the study. Of these, 1,041 (76.8%) students participated in the study.

Results: Based on intention-to-treat analyses, RSOD prevalence decreased by 5.9% in the intervention group and increased by 2.6% in the control group, relative to that of baseline assessment (odds ratio [OR] _ 0.62, 95% confidence interval [CI] _ 0.44 –0.87). No significant group differences were observed for the following secondary outcomes: RSOD frequency, quantity of alcohol consumed, estimated peak blood alcohol concentration, and overestimation of peer drinking norms.

Conclusions: The intervention program reduced RSOD, which is a major indicator of problem drinking in young people, effectively.

Funding: Swiss National Science Foundation

Contact: Severin Haug severin.haug@isgf.uzh.ch

Behavioral research teams all around the globe are invited to collaborate with the MobileCoach team or to simply download, setup and use their own instance of the MobileCoach platform. Moreover, healthcare providers or other companies interested in fully-automated behavioral interventions are also invited to contact the MobileCoach team for joint projects.

Academia (selection)

As of today, the following research institutes discuss, design, provide and/or evaluate MobileCoach interventions:

Center for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich
www.c4dhi.org

Prof. Dr. Florian von Wangenheim
fwangenheim@ethz.ch

Center for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich
www.c4dhi.org

Prof. Dr. Elgar Fleisch
efleisch@ethz.ch

Center for Digital Health Interventions, Institute of Technology Management, University of St.Gallen
www.c4dhi.org

Prof. Dr. Elgar Fleisch

Public & Organizational Health Epidemiology, Biostatistics and Prevention Institute, University of Zurich
www.ebpi.uzh.ch

Prof. Dr. med. Georg F. Bauer, DrPH
gfbauer@ifspm.uzh.ch

Department of Psychology, Applied Social and Health Psychology, University of Zurich
www.ebpi.uzh.ch

Prof. Dr. Urte Scholz
urte.scholz@psychologie.uzh.ch

Department of Psychology, Dynamics of Healthy Aging, University of Zurich
www.ebpi.uzh.ch

Prof. Dr. Mathias Allemand
mathias.allemand@uzh.ch

Swiss Research Institute for Public Health and Addiction, University of Zurich
www.isgf.ch

PD Dr. Severin Haug
severin.haug@isgf.uzh.ch

Department of Computer Science & Center for Technology and Behavioral Health, Dartmouth College, USA www.cs.dartmouth.edu | www.c4tbh.org

Prof. David F. Kotz, PhD
kotz@dartmouth.edu

Center for Technology and Behavioral Health, Dartmouth College, USA www.dartmouth.edu | www.c4tbh.org

Joy Gabrielli, PhD
Joy.L.Gabrielli@dartmouth.edu

Companies & Public Institutions (selection)

The following companies or public institutions discuss, design, provide and/or evaluate MobileCoach interventions:

Corporate Health Solutions GmbH, Zurich, Switzerland

Dr. Rebecca Brauchli
Chairwoman of the management board

License

The initiators of the MobileCoach were eager to make this platform freely available to academia, public institutions and industry with the overall goal to promote further development resulting in useful evidence-based behavioral interventions. In this regard, the MobileCoach team has chosen the Apache 2 open source license such that the MobileCoach can even be used in commercial applications without any legal hassles.

Building Blocks of MobileCoach Interventions

Building on the foundations of automata theory, the technical design of the MobileCoach system follows the concepts of a state machine that uses intervention rules for state transitions, which can be referred to as a fully automated expert system. Here, the state is an aggregate of all relevant attributes related to the intervention progress of a participant (e.g. the messages received or answers provided) whereas state transitions triggered by intervention rules lead to a change in these attributes and thus, to a state change.

In particular, each participant of the intervention group is assigned to a particular intervention state based on her answers during the baseline assessment. In response to this assessment, a web-based feedback is generated individually by the system for each participant. Then, depending on a participant’s regular feedback (e.g. via text messages or sensor data from internet of things services) during the subsequent months, intervention rules trigger state transitions and the tailoring of the follow-up communication. In particular, intervention rules are traversed on a regular basis (e.g. once a day) for each participant, and, as a result, update the state of the corresponding participant and start communication (e.g. via text messages) in the form of a question, a feedback or a recommendation.

With upcoming projects, tailoring of interventions and prediction of state transitions with the help of machine learning algorithms will be the primary research focus of the MobileCoach team. As a summary, the three building blocks of MobileCoach interventions are depicted in Figure “Building Blocks of MobileCoaching”.

Technical Architecture

The technical architecture of this rule-based state machine as shown in Figure “The MobileCoach Architecture” is derived from the model-view-controller design pattern. It consists therefore of…

1. an application layer, i.e. the view based on the Vaadin web application framework with the template-engine Mustache for intervention administration and the assessment survey at the baseline,

2. a service layer, i.e. the primary controller that utilizes the Java programming language and the expression evaluator Javaluator for the evaluation of the intervention rules and, finally,

3. a persistence layer, i.e. the model based on the document database mongoDB and plain files for storing the intervention content including a detailed protocol of all incoming and outgoing communication.

Password protection and Secure Sockets Layer (SSL) encoding are used to ensure the privacy and safety of data transfer.

Source Code

Download our MobileCoach source code from our GIT repository or as ZIP/GZ/BZ2 archive:

MobileCoach repository containing all repositories and instructions

MobileCoach DEV CORE component: (required)

• README file
• GIT repository
• ZIP/GZ/BZ2 download

MobileCoach FileServletWrapper component:

• README file
• GIT repository
• ZIP/GZ/BZ2 download

MobileCoach Website component:

• README file
• GIT repository
• ZIP/GZ/BZ2 download

Requirements & Setup

1. With Docker setup, it is easy to set up a pre-built MobileCoach in a Linux server. The instructions can be found here:

• MobileCoach Docker documentation

1. For developers who want to modify the source code, build and set up MobileCoach on your server, the instructions can be found here:

• SlideShare presentation on Requirements & Setup
• README file of MobileCoach DEV CORE
• README file of MobileCoach Website

2020

Hauser-Ulrich, S., Künzli, H., Meier-Peterhans, D., Kowatsch, T. (in press) A Smartphone-based Healthcare Chatbot to Promote Self-Management of Chronic Pain (SELMA): A Pilot Randomized Control Trial, JMIR Mhealth Uhealth.

Haug, S., Paz, R., Scholz, U., Kowatsch, T., Schaub, M.P., Radtke, T. (in press) Efficacy of a mobile phone-delivered just-in-time planning intervention to reduce alcohol use in adolescents: results of a randomized controlled crossover trial, JMIR mHealth and uHealth.

Kramer, J., Künzler, F., Mishra, V., Smith, S.N., Kotz, D.F., Scholz, U., Fleisch, E., Kowatsch, T. (2020) Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results from an Optimization Trial, Annals of Behavioral Medicine. Web

Boateng, G., Lüscher, J., Scholz, U., Kowatsch, T. (2020) Emotion Capture among Real Couples in Everyday Life, 1st Momentary Emotion Elicitation & Capture (MEEC) workshop, co-located with the ACM CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii, USA, April 25th, 2020. Web

Boateng, G., Sels, L., Kuppens, P., Lüscher, J., Scholz, U., Kowatsch, T. (2020) Emotion Elicitation and Capture among Real Couples in the Lab, 1st Momentary Emotion Elicitation & Capture (MEEC) workshop, co-located with the ACM CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii, USA, April 25th, 2020. Web

Presset, B., Kramer, J., Kowatsch, T., Ohl, F. (2020) The social meaning of steps: User reception of a mobile health intervention on physical activity, Critical Public Health. Web

2019

Boateng, G., Santhanam, P., Lüscher, J., Scholz, U., Kowatsch, T. (2019) VADLite: An Open-Source Lightweight System for Real-Time Voice Activity Detection on Smartwatches, 4th International Workshop on Mental Health: Sensing & Intervention, co-located with the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), London, UK. Web

Kowatsch, T., Harperink, S., Dittler, U., Xiao, G., Stanger, C., Oswald, H., Möller, A. (2019) A digital assistant for healthcare providers targeting 10 to 15-year-old patients with asthma and their family: results from a pilot study, Abstract published by the Center for Digital Health Interventions, ETH Zurich & University of St.Gallen. Web

Lüscher, J., Kowatsch, T., Boateng, G., Santhanam, P., Bodemann, G., Scholz, U. (2019) Social Support and Common Dyadic Coping in Couples’ Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application, JMIR Res Protoc 2019;8(10):e13685 DOI: 10.2196/13685. Web

Künzler, F., Mishra, V., Kramer, J., Kotz, D.F., Fleisch, E., Kowatsch, T. (2019) Exploring the State-of-Receptivity for mHealth Interventions, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 3(4): Paper 140. Web

Boateng, G., Santhanam, P., Lüscher, J., Scholz, U., Kowatsch, T. (2019) Poster: DyMand – An Open-Source Mobile and Wearable System for Assessing Couples’ Dyadic Management of Chronic Diseases, The 25th Annual International Conference on Mobile Computing and Networking (MobiCom), Poster & Demo Paper, Los Cabos, Mexico. Web

Künzler, F. (2019) Context-aware notification management systems for just-in-time adaptive interventions, IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2019, Kyoto, Japan. PDF

Barata, F., Kipfer, K., Weber, M., Tinschert, P., Fleisch, E., Kowatsch, T. (2019) Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks, 2019 IEEE International Conference on Healthcare Informatics (ICHI), Xi’an, China, June 10-13. PDF

Boateng, G., Santhanam, P., Lüscher, J., Scholz, U., Kowatsch, T. (2019) DyMand: An Open-Source Mobile and Wearable System for Assessing Couples’ Dyadic Management of Chronic Diseases, 14th International Conference on Design Science Research in Information Systems and Technology (DESRIST), June 4-6, Worcester, MA, USA. PDF

Kramer, J., Künzler, F., Mishra, V., Presset, B., Smith, S.N., Scholz, U., Kotz, D.F., Kowatsch, T. (2019) Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial, JMIR Research Protocols, 8(1), e11540. PDF

Tinschert, P., Rassouli, F., Barata, F., Steurer-Stey, C., Fleisch, E., Puhan, M., Brutsche, M., Kowatsch, T. (2019) Prevalence of Nocturnal Cough in Asthma and its Potential as a Marker for Asthma Control (MAC) in Combination with Sleep Quality: Protocol of a Smartphone-based, Multi-Centre, Longitudinal Observational Study with Two Stages, BMJ Open. PDF

Kowatsch, T., D. Fischer-Taeschler, F. Putzing, P. Bürki, C. Stettler, G. Chiesa-Tanner and E. Fleisch (2019) Die digitale Pille für chronische Krankheiten, in: Digitale Transformation von Dienstleistungen im Gesundheitswesen, M. Pfannstiel, P. Da-Cruz and H. Mehlich (eds.), Springer, Heidelberg, Germany. Web

Tinschert, P., Barata, F., Kramer, J., Rassouli, F., Steurer-Stey, C., Puhan, M., Brutsche, M., Kowatsch, T. (2019) Don’t Lose Heart: Preliminary Engagement Results in an Ecological Momentary Assessment (EMA) Study Evaluating Digital Biomarkers for Asthma, Abstract presented at the International Society for Research on Internet Interventions (ISRII) Meeting 2019, Auckland, New Zealand. PDF

Kramer, J., Künzler, F., Tinschert, P., Kowatsch, T. (2019) Trajectories of Engagement with a Digital Physical Activity Coach: Secondary Analysis of a Micro-Randomized Trial, Abstract presented at the International Society for Research on Internet Interventions (ISRII) Meeting 2019, Auckland, New Zealand. PDF

2018

Boateng, G., Santhanam, P., Lüscher, J., Scholz, U., Kowatsch, T. (2018) Poster: Multimodal Affect Detection among Couples for Diabetes Management. Black in AI Workshop, Neural Information Processing Systems Conference, (NIPS) 2018. PDF

Buhmann, J., Felix, J., Gächter, T., Kowatsch, T., Lehmann, R., von Lutterotti, N., Schedler, K., Steurer, J., Wolfrum, C. (2018) Digitalisierung der Medizin: Konsequenzen für die Ausbildung, Schweizerische Ärztezeitung 99(42), 1441-1444. PDF

Stieger, M., Nißen, M.K., Rüegger, D., Kowatsch, T., Flückiger, C., Allemand, M. (2018) PEACH, a smartphone- and conversational agent-based coaching intervention for intentional personality change: study protocol of a randomized, wait-list controlled trial, BMC Psychology 6(43), 1-15. PDF

Kowatsch, T., M. K. Nißen, D. Rüegger, M. Stieger, C. Flückiger, M. Allemand and F. von Wangenheim (2018) The Impact of Interpersonal Closeness Cues in Text-based Healthcare Chatbots on Attachment Bond and the Desire to Continue Interacting: An Experimental Design In Proceedings of the 26th European Conference on Information Systems (ECIS 2018), Portsmouth, UK. PDF

Shih, I., Nißen, M.K., Büchter, D., Durrer, D., l’Allemand, D., Fleisch, E., Kowatsch, T., Smartphone-based Biofeedback Breathing Training for Stress Management, Poster presented at the Applied Maschine Learning Days, EPFL, Lausanne, Switzerland. PDF

Barata, F., Tinschert, P., Rassouli, F., Baty, F., Brutsche, M., Steurer-Stey, C., Puhan, M., Fleisch, E., Kowatsch, T., Smartphone-based Cough and Sleep Quality Detection, Poster presented at the Applied Machine Learning Days, EPFL, Lausanne, Switzerland. PDF

Haug, S. & Paz, R., Eggli, P. & Schaub, M. (2018) Drinking Goal Trajectories and Their Association with Client Characteristics and Outcomes among Clients in Outpatient Alcohol Treatment, Substance Use & Misuse 53(10):1-12. PDF

Haug, S., Paz, R., Wenger, A. & Schaub M. P. (2018) Efficacy of a mobile phone-based life-skills training program for substance use prevention among adolescents: study protocol of a cluster-randomised controlled trial, BMC Public Health 18:1102. PDF

Haug, S. & Paz, R. (2018) Erreichbarkeit Jugendlicher für ein mobiltelefonbasiertes Programm zur Suchtprävention durch die Förderung von Lebenskompetenzen, Sucht 64(3), 129-139. Web

2017

Kowatsch, T., Barata, F., Tinschert, P., Dittler, U., Egger, J.M., Meyer, F., Schaub, M., Fleisch, E., Oswald, H., Möller, A., Digital Health Literacy Intervention for Children with Asthma, Poster presented at the CSS Health Insurance meets CDHI Event, Lucerne, Switzerland. PDF

Paz, R., Haug, S., Filler, A., Kowatsch, T., Schaub, M.P. (2017) Engagement within a Mobile Phone-based Smoking Cessation Intervention for Adolescents and Its Association with Participant Characteristics and Outcomes, Journal of Medical Internet Research (JMIR) 19(10):e356. PDF

Künzler, F., Kramer, J., Kowatsch, T. (2017) Efficacy of Mobile Context-aware Notification Management Systems: A Systematic Literature Review and Meta-Analysis, 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Rome, 2017, 131-138. doi: 10.1109/WiMOB.2017.8115839 PDF

Haug, S., Paz, R., Meyer, C., Filler, A., Kowatsch, T., Schaub, M.P. (2017) A Mobile Phone-Based Life Skills Training Program for Substance Use Prevention Among Adolescents: Pre-Post Study on the Acceptance and Potential Effectiveness of the Program Ready4life, JMIR Mhealth Uhealth 5(10):e143. PDF

Kowatsch, T., Nißen, M.K., Shih, I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Haug, S., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., l’Allemand, D. (2017) Text-based Healthcare Chatbots Supporting Patient and Health Professional Teams: Preliminary Results of a Randomized Controlled Trial on Childhood Obesity, Persuasive Embodied Agents for Behavior Change (PEACH 2017) Workshop, co-located with the 17th International Conference on Intelligent Virtual Agents (IVA 2017), Stockholm, Sweden. PDF

Haug, S., Paz, R., Kowatsch, T., Filler, A., Schaub, M.P. (2017) Efficacy of a Technology-based Integrated Smoking Cessation and Alcohol Intervention for Smoking Cessation in Adolescents: Results of a Cluster-randomised Controlled Trial, Journal of Substance Abuse Treatment 82(11), 55-66. PDF

Kowatsch, T., Wahle, F., Filler, A. (2017) Design and Lab Experiment of a Stress Detection Service based on Mouse Movements, The 11th Mediterranean Conference on Information Systems (MCIS), Genoa, Italy Best Paper Award PDF

Kowatsch, T., Volland, D., Shih, I., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., l’Allemand, D. (2017) Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach, In: Maedche A., vom Brocke J., Hevner A. (eds) Designing the Digital Transformation. DESRIST 2017. Lecture Notes in Computer Science, vol 10243. Springer: Berlin; Germany, 485-489. Paper Poster Screencast

Haug, S., Paz, R., Kowatsch, T., Filler, A., Dickson-Spillmann, M., Dey, M., Schaub, M.P. (2017) Efficacy of a web- and text messaging-based intervention to reduce problem drinking in adolescents: Results of a cluster-randomised controlled trial, Journal of Consulting and Clinical Psychology, 85(2),147-159. Wilhelm Feuerlein Award 2018 PDF

Paz, R., Haug, S., Kowatsch, T., Filler, A., Schaub, M.P. (2017) Moderators of Outcome in a Technology-based Intervention to Prevent and Reduce Problem Drinking Among Adolescents, Addictive Behaviors 72: 64-71. PDF

Kowatsch, T., Wahle, F., Filler, A. (2017) stressOUT: Design, Implementation and Evaluation of a Mouse-based Stress Management Service, In: Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings, Maedche, A., vom Brocke, J., Hevner, A. (eds), KIT Scientific Working Papers; 64, Karlsruhe, Germany, 37-45. Nominee for the Best Research-in-Progress PDF

Tinschert, P., Barata, F., Kowatsch, T. (2017) Enhancing Asthma Control through IT: Design, Implementation and Planned Evaluation of the Mobile Asthma Companion, in Leimeister, J.M.; Brenner, W. (Hrsg.): Proceedings der 13th International Conference on Wirtschaftsinformatik (WI 2017), St. Gallen, 1291-1294. PDF Video

Künzler, F., Kramer, J., Mishra, V., Presset, B., Smith, S.N., Kotz, D.F., Scholz, U., Fleisch, E., Kowatsch, T., Ally: A Smartphone-based Physical Activity Intervention, Poster presented at the CSS Health Insurance meets CDHI Event, Lucerne, Switzerland. PDF

Shih, I., Volland, D., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., Fleisch, E., l’Allemand, D., Kowatsch, T., Therapy Adherence of Obese Children in a 6-Month High-Frequency Intervention, Poster presented at the CSS Health Insurance meets CDHI Event, Lucerne, Switzerland. PDF

2016

Barata, F., Kowatsch, T., Tinschert, P., Filler, A. (2016). Personal MobileCoach: Tailoring Behavioral Interventions to the Needs of Individual Participants. UBICOMP 2016 Workshop Designing, Developing, and Evaluating The Internet of Personal Health (IoPH), Heidelberg, Germany. PDF

2015

Kowatsch, T., Wahle, F., Filler, A., Kehr, F., Volland, D., Haug, S., Jenny, G., Bauer, G., Fleisch, E., (2015) Towards Short-Term Detection of Job Strain in Knowledge Workers with a Minimal-Invasive Information System Service: Theoretical Foundation and Experimental Design, 23rd European Conference on Information Systems (ECIS), Münster, Germany. Paper-PDF Poster-PDF

Haug, S., Paz, R., Kwon, M., Filler, A., Kowatsch, T., Schaub, M.P. (2015). Smartphone use and Smartphone addiction among young people in Switzerland. Journal of Behavioural Addictions 4(4), pp. 299-307. PDF

Filler, A., Kowatsch, T., Haug, S., Wahle, F., Staake, T. & Fleisch, E. (2015). MobileCoach: A Novel Open Source Platform for the Design of Evidence-based, Scalable and Low-Cost Behavioral Health Interventions - Overview and Preliminary Evaluation in the Public Health Context. Wireless Telecommunications Symposium 2015 (WTS 2015), New York, USA. PDF Technical Focus Outstanding Paper Award & Best Graduate Student Paper Award

2014

Haug, S., Paz Castro, R., Filler, A., Kowatsch, T., Fleisch, E. & Schaub, M.P. (2014). Efficacy of an internet and SMS-based integrated smoking cessation and alcohol intervention for smoking cessation in young people: study protocol of a two-arm cluster randomised controlled trial. BMC Public Health, 14: 1140. PDF Intervention Focus

Filler, A., Haug, S. and Kowatsch, T. (2014). The MobileCoach – An Open Source Solution for Behavioral Change Interventions. Abstract presented at the 7th Scientific Meeting of The International Society for Research on Internet Interventions (ISRII), Valencia, Spain. PDF Technical Focus

Kowatsch, T., Wahle, F., Filler, A. and Fleisch, E. (2014) Predicting Adverse Behavior with Early Warning Health Information Systems by Mining Association Rules on Multi-dimensional Behavior: A Proposal. Poster presented at the 7th Scientific Meeting of The International Society for Research on Internet Interventions (ISRII), Valencia, Spain. PDF Technical Focus

Haug, S., Kowatsch, T., Paz Castro, R., Filler, A. and Schaub, M.P. (2014) Efficacy of a web- and text messaging-based intervention to reduce problem drinking in young people: study protocol of a cluster-randomised controlled trial. BMC Public Health, 14: 809. PDF Intervention Focus

2013

Haug, S. (2013). Mobile phone text messaging to reduce alcohol and tobacco use in young people – a narrative review. Smart Homecare Technology and TeleHealth, 1(1), 11-19. PDF Intervention Focus

Haug, S. Bitter, G., Hanke, M., Ulbricht, S. Meyer, C. & John, U. (2013). Kurzintervention zur Förderung der Tabakabstinenz via Short Message Service (SMS) bei Auszubildenden an beruflichen Schulen: Longitudinale Interventionsstudie zur Ergebnis- und Prozessevaluation. Das Gesundheitswesen, 75(10), 625-631. Intervention Focus

Haug, S., Schaub, M.P., Venzin, V. Meyer, C. & John, U. (2013). Differenzielle Wirksamkeit eines Short Message Service (SMS)-basierten Programms zur Förderung des Rauchstopps bei Jugendlichen. Psychiatrische Praxis, 40(6), 339-346. Intervention Focus

Haug, S., Schaub, M.P., Venzin, V., Meyer, C., John, U. & Gmel, G. (2013). A pre-post study on the appropriateness and effectiveness of a web- and text messaging-based intervention to reduce problem drinking in emerging adults. Journal of Medical Internet Research, 15(9), e196. PDF Intervention Focus

Haug, S., Schaub, M.P., Venzin, V., Meyer, C. & John, U. (2013). Efficacy of a text message-based smoking cessation intervention for young people: a cluster randomized controlled trial. Journal of Medical Internet Research, 15(8), e171. PDF Intervention Focus

2012

Haug, S., Venzin, V. & Meyer, C. (2012). Förderung des Rauchstopps an Berufsfachschulen via SMS. SuchtMagazin, 38(3/4), 38-42. Intervention Focus

The MobileCoach is continuously improved by an interdisciplinary research team. Among the various disciplines, team members are particularly experts in computer science, psychology and the field of public health. Please notice that there are many individuals working on the MobileCoach system or MobileCoach interventions. Here is just the list of individuals that started to work together on MobileCoach back in 2014.

Center for Digital Health Interventions

Prof. Dr. Tobias Kowatsch
Assistant Professor for Digital Health at the University of St.Gallen & Scientific Director of the Center for Digital Health Interventions, University of St.Gallen and ETH Zurich

Doctor of Philosophy in Management (Ph.D.), MSc in Business Informatics, MSc & Dipl.-Inform. (FH) in Computer Science in Media

tkowatsch@ethz.ch

Prabhakaran Santhanam
MobileCoach Software Engineer and Community Manager at the Center for Digital Health Interventions

MSc in Computer Science

psanthanam@ethz.ch

Sascha Gfeller
MobileCoach Software Engineer and Community Manager at the Center for Digital Health Interventions

BSc in Medical Informatics

sgfeller@ethz.ch

Prof. Dr. Florian von Wangenheim
Advisory Board Member, Center for Digital Health Interventions

Chair of Technology Marketing, Department of Management, Technology and Economics (D-MTEC), ETH Zurich

fwangenheim@ethz.ch

Prof. Dr. Elgar Fleisch
Advisory Board Member, Center for Digital Health Interventions

Chair of Information Management, D-MTEC, ETH Zurich, and Chair of Operations Management at the Institute of Technology Management, University of St. Gallen efleisch@ethz.ch

Swiss Research Institute for Public Health and Addiction

PD Dr. Severin Haug
Psychologist and Head of Research at the Swiss Research Institute for Public Health and Addiction

severin.haug@isgf.uzh.ch

Raquel Paz Castro
Psychologist at the Swiss Research Institute for Public Health and Addiction

M.Sc., Psychologist

raquel.paz@isgf.uzh.ch

Former Developers

Andreas Filler
Co-Founder

M.Sc. in Computer Science in Media & Dipl.-Inform. (FH) in Computer Science in Media

filler@pathmate-technologies.com

Dr. Dirk Volland
Co-Founder

Doctor of Philosophy in Management (Ph.D.), Dipl.-Winf (M.Sc.) in Business Informatics

volland@pathmate-technologies.com

Community Members

Jost Schweinfurther
Dipl.-Designer (FH)

jschweinfurther@ethz.ch

Please let us know if you have any further questions or comments regarding the MobileCoach platform. We are eager to learn from your experience to improve the MobileCoach.

• For general research activities or collaboration requests related to the MobileCoach, please contact Tobias Kowatsch tkowatsch@ethz.ch.

• For details on addiction-related interventions or collaborations, please contact our domain expert Severin Haug severin.haug@isgf.uzh.ch.

• For requests related to professional product development of clinical pathways, please contact Pathmate Technologies contact@pathmate-technologies.com.

Imprint

Publisher: Center for Digital Health Interventions, Chair of Information Management, Department of Technology, Management, and Economics, ETH Zurich.

Editor: Dr. Tobias Kowatsch tkowatsch@ethz.ch, Center for Digital Health Interventions

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