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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

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Abstract

Effective and efficient behavioral interventions are important and of high interest today. Due to shortcomings of related approaches, we introduce MobileCoach (mobile-coach.eu) as novel open source behavioral intervention platform. With its modular architecture, its rule-based engine that monitors behavioral states and triggers state transitions, we assume MobileCoach to lay a fruitful ground for evidence-based, scalable and low-cost behavioral interventions in various application domains. The code basis is made open source and thus, MobileCoach can be used and revised not only by interdisciplinary research teams but also by public bodies or business organizations without any legal constraints. Technical details of the platform are presented as well as preliminary empirical findings regarding the acceptance of one particular intervention in the public health context. Future work will integrate Internet of Things services that sense and process data streams in a way that MobileCoach interventions can be further tailored to the needs and characteristics of individual participants.

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... Until now, it is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial effects on the diabetes management in romantic couple's everyday life when considered together. Therefore, the aim of this study is to systematically investigate the effects of social support and CDC on health behaviors involved in diabetes management (eg, physical activity, healthy diet, and medication adherence) and well-being using a novel ambulatory assessment application for smartphones for the open-source behavioral intervention platform MobileCoach (AAMC [13]) [14,15] that allows the objective evaluation of the core study constructs and outcomes in romantic couples' everyday lives. ...
... The AAMC consists of a smartphone app, a smartwatch app, and server system built on top of MobileCoach [92]. MobileCoach is a server-client system that allows both the collection of sensor and self-report data (eg, for EMA studies or for health monitoring purposes) and the delivery of health interventions [14,15]. On the server side, the data collection and intervention logic are defined (eg, when to collect which information), whereas short message service text messages and mobile phone applications for Apple's iOS and Google's Android operating systems are used to actually collect that data and deliver the interventions. ...
... The social environment has been found to be highly influential in the illness management process [10,11] although effects of social support and CDC on health behavior change and well-being in the context of T2DM management are not yet well understood. This study is the first to systematically investigate couples' dyadic illness management by investigating visible and invisible support and CDC in T2DM patients and their partners in daily life by applying an experience sampling approach and an observational approach on the basis of the new open-source behavioral intervention platform MobileCoach [14,15]. This combined experience sampling and observational approach is highly relevant for the following reasons: analyzing visible and invisible support and CDC in daily life has thus far not been done with a focus on a dyadic perspective by considering T2DM patients and their partners. ...
Article
Full-text available
Background: Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple’s everyday life. Objective: The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). Methods: This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. Results: The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. Conclusions: For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. International Registered Report Identifier (IRRID): PRR1-10.2196/13685
... Until now, it is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial effects on the diabetes management in romantic couple's everyday life when considered together. Therefore, the aim of this study is to systematically investigate the effects of social support and CDC on health behaviors involved in diabetes management (eg, physical activity, healthy diet, and medication adherence) and well-being using a novel ambulatory assessment application for smartphones for the open-source behavioral intervention platform MobileCoach (AAMC [13]) [14,15] that allows the objective evaluation of the core study constructs and outcomes in romantic couples' everyday lives. ...
... The AAMC consists of a smartphone app, a smartwatch app, and server system built on top of MobileCoach [92]. MobileCoach is a server-client system that allows both the collection of sensor and self-report data (eg, for EMA studies or for health monitoring purposes) and the delivery of health interventions [14,15]. On the server side, the data collection and intervention logic are defined (eg, when to collect which information), whereas short message service text messages and mobile phone applications for Apple's iOS and Google's Android operating systems are used to actually collect that data and deliver the interventions. ...
... The social environment has been found to be highly influential in the illness management process [10,11] although effects of social support and CDC on health behavior change and well-being in the context of T2DM management are not yet well understood. This study is the first to systematically investigate couples' dyadic illness management by investigating visible and invisible support and CDC in T2DM patients and their partners in daily life by applying an experience sampling approach and an observational approach on the basis of the new open-source behavioral intervention platform MobileCoach [14,15]. This combined experience sampling and observational approach is highly relevant for the following reasons: analyzing visible and invisible support and CDC in daily life has thus far not been done with a focus on a dyadic perspective by considering T2DM patients and their partners. ...
Preprint
BACKGROUND Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple’s everyday life. OBJECTIVE The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). METHODS This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. RESULTS The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. CONCLUSIONS For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/13685
... In an effort to understand receptivity to JITAIs, in our prior work we developed a mobile JITAI system to promote physical activity. The "Ally" app -based on the open-source MobileCoach framework -was a chat-based digital coach (for Android and iOS phones) that delivered an actual behavior-change intervention aimed at increasing the participant's daily step count [10,18]. The previous study was conducted with 189 participants in Switzerland, over a period of 6 weeks. ...
... < 0.001). 10 On post-hoc analysis with Dunnett's Test, we observed that the static model showed a significant improvement of over 38% in just-in-time receptivity when compared to the control model ( < 0.001). The adaptive model had an improvement of more than 15% over the control model, but the improvement was not significant ( = 0.271). ...
... Such population level analyses is consistent with prior works in receptivity [22] and interruptibility [25,27]. 10 The Chi Square test compares the GLM to a null model. Next, we discuss the secondary metrics; we observed that the type of machine-learning model had a significant effect on the likelihood of "response", i.e., if the participant ever responded to the initiating message (irrespective of time), 2 (2) = 15.001, ...
Article
Full-text available
Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user’s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach – Ally – that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.
... A domain similar to feedback systems for sporting applications is the domain of applications for mobile health (mHealth). In the field of behavioural change interventions, an approach similar to MC is followed by the MobileCoach framework [16]. Its main target audience is creators of digital health interventions that are modelled-using automata theory-as state machines. ...
... Due to their ubiquity, smartphones are often used as mobile recording devices in mHealth. While it is most likely that most mHealth applications can be modelled using DMC, it has to be pointed out that models specialised for this area, such as MobileCoach [16] and RADAR [17], are likely better suited. ...
Article
Full-text available
In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.
... While many studies mainly focus on one specific domain, some projects aim at developing a reusable framework for ecoaching. Examples are the work by [7,12,28,31]. In particular, e-coaches of [7,12] provided fully automated Talk-based communication, albeit the latter used only text messages instead of a dialog interface. ...
... Examples are the work by [7,12,28,31]. In particular, e-coaches of [7,12] provided fully automated Talk-based communication, albeit the latter used only text messages instead of a dialog interface. In [7], various theory-driven computational models are introduced to develop a knowledge representation for behavior change counseling and focus on modeling counseling knowledge from which dialog actions can be inferred. ...
Article
Full-text available
In this paper, a user interface paradigm, called Talk-and-Tools, is presented for automated e-coaching. The paradigm is based on the idea that people interact in two ways with their environment: symbolically and physically. The main goal is to show how the paradigm can be applied in the design of interactive systems that offer an acceptable coaching process. As a proof of concept, an e-coaching system is implemented that supports an insomnia therapy on a smartphone. A human coach was replaced by a cooperative virtual coach that is able to interact with a human coachee. In the interface of the system, we distinguish between a set of personalized conversations (“Talk”) and specialized modules that form a coherent structure of input and output facilities (“Tools”). Conversations contained a minimum of variation to exclude unpredictable behavior but included the necessary mechanisms for variation to offer personalized consults and support. A variety of system and user tests was conducted to validate the use of the system. After a 6-week therapy, some users spontaneously reported the experience of building a relationship with the e-coach. It is concluded that the addition of a conversational component fills an important gap in the design of current mobile systems.
... Specifically, SELMA instructs participants on how to integrate mindfulness into their daily routine and provides users with a relaxation exercise. Figure 1 shows an overview of the intervention schedule, and Multimedia Appendix 1 provides an overview of the weekly core themes and tasks to complete (including 19 references [10,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]). The program closes with a brief summary and farewell. ...
... Technically, SELMA was developed with MobileCoach [56], an open-source software platform for the design and evaluation of mobile TBHCs [31,55]. This platform allows the intervention authors to design (fully automated and script-based) digital health interventions consistent with the talk-and-tools paradigm [57]. ...
Preprint
BACKGROUND Ongoing pain is one of the most common diseases and has a major physical psychological, social and economic impact. A mobile health intervention utilizing a fully-automated text-based healthcare chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between participant and the THCB. OBJECTIVE The objectives of this paper are twofold: (1) to describe the design and implementation of SELMA (painSELfMAnagement), a 2-month smartphone-based Cognitive Behavior Therapy (CBT) TBHC intervention for pain self-management of patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior and pain duration, working alliance, acceptance and adherence were evaluated. METHODS Participants were recruited online and in collaboration with pain experts and randomized to interact with SELMA for 8 weeks, either every day or every other day, concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity and the bond scale of working alliance were measured at baseline and post-intervention, intention to change behavior and pain duration at baseline only, and acceptance post- intervention via self-report instruments. Adherence was assessed objectively via usage data. RESULTS From May 2018 till August 2018, 311 adults downloaded the SELMA app, 102 consented to participate and met the inclusion criteria. The average age of the 88 female (86.4%) and 14 male (13.6%) participants was 43.7 (SD=12.7) years. Baseline group comparison did not differ in any demographic or clinical variables. Pain intensity was reduced significantly (P=.05) and general well-being increased significantly (P=.01) in both groups. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the wait-list control group post-intervention. The intention to change behavior was related positively to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the THCB SELMA was comparable to working alliance in guided internet therapies with human coaches. Participants enjoyed using the app, perceived it as useful and easy to use, and would recommend it to others. Overall, 52% adhered to the program by self-selecting coaching modules actively. Participants’ comments revealed an appreciation of the empathic and responsible interaction with the THCB SELMA. A main criticism was that there was no option to enter free text for patients’ own comments. CONCLUSIONS SELMA is feasible, revealed mainly positive feedback and valuable suggestions for future revisions. For example, participants’ intention to change behavior or a more homogenous sample (e.g. with a specific type of chronic pain) should be considered in further tailoring SELMA. CLINICALTRIAL German Clinical Trials Register DRKS00017147; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017147, Swiss National Clinical Trial Portal: SNCTP000002712.
... Specifically, SELMA instructs participants on how to integrate mindfulness into their daily routine and provides users with a relaxation exercise. Figure 1 shows an overview of the intervention schedule, and Multimedia Appendix 1 provides an overview of the weekly core themes and tasks to complete (including 19 references [10,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]). The program closes with a brief summary and farewell. ...
... Technically, SELMA was developed with MobileCoach [56], an open-source software platform for the design and evaluation of mobile TBHCs [31,55]. This platform allows the intervention authors to design (fully automated and script-based) digital health interventions consistent with the talk-and-tools paradigm [57]. ...
Experiment Findings
Chronische Schmerzen sind weit verbreitet, stellen für Betroffene meist eine hohe soziale, psychische und körperliche Beeinträchtigung dar und generieren hohe Gesundheitskosten. Interventionen mit einem textbasierten Dialogsystem (Chatbot) zählen zu den neuesten Health-Tech Entwicklungen. Die vorliegende Studie ist die erste, die mittels einer Prä- (T1) und Postmessung (T2) anhand der Schmerzintensität, dem allg. Wohlbefinden sowie der schmerzbedingten Beeinträchtigung (= primärer Endpunkt) die Wirksamkeit eines Chatbot-Coaching-App in der Schmerztherapie testet. Die Veränderungsbereitschaft und Schmerzdauer werden als moderierende Faktoren miteinbezogen. Die App SELMA (Schmerz-sELbstMAnagement) ermöglicht ein skalierbares Assessment und eine massgeschneiderte Intervention für Schmerzbetroffene. Ein Gesprächsagent wird als digitaler Coach verwendet, um das Schmerzmanagement durch Vermittlung von Psychoedukation und Bewältigungsstrategien zu verbessern. 102 Schmerzbetroffene wurden randomisiert der Gruppe «intervene» (n = 59) und «wait» (n = 43) zugeteilt. Die Daten wurden deskriptiv und mit einem Linear Mixed Model nach dem Intention-to-Treat Prinzip ausgewertet.
... Specifically, SELMA instructs participants on how to integrate mindfulness into their daily routine and provides users with a relaxation exercise. Figure 1 shows an overview of the intervention schedule, and Multimedia Appendix 1 provides an overview of the weekly core themes and tasks to complete (including 19 references [10,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]). The program closes with a brief summary and farewell. ...
... Technically, SELMA was developed with MobileCoach [56], an open-source software platform for the design and evaluation of mobile TBHCs [31,55]. This platform allows the intervention authors to design (fully automated and script-based) digital health interventions consistent with the talk-and-tools paradigm [57]. ...
Article
Full-text available
Background: Ongoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. Objective: The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Methods: Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. Results: From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants’ comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients’ own comments. Conclusions: SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants’ intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA. Trial Registration: German Clinical Trials Register DRKS00017147; https://tinyurl.com/vx6n6sx, Swiss National Clinical Trial Portal: SNCTP000002712; https://www.kofam.ch/de/studienportal/suche/70582/studie/46326.
... The stressOUT app is a cross-platform Java program that runs on several operating systems. It is planned as a module for the open source behavioral intervention platform MobileCoach (www.mobile-coach.eu)[10,15]. A schematic overview of stressOUT is shown inFig. ...
Chapter
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Work-related stress has the potential to increase the risk of chronic stress, major depression and other non-communicable diseases. Organizational stress monitoring usually applies long-term self-report instruments that are designed in a retrospective manner, and thus, is obtrusive, time-consuming and, most important, fails to detect and predict short-term episodes of stress. To address this shortcoming, we apply design science research with the goal to design, implement and evaluate a stress management service for knowledge workers (stressOUT) that senses the degree of work-related stress solely based on mouse movements. Using stress theory as justificatory knowledge, we implemented stressOUT that tracks mouse movements and perceived stress levels randomly twice a day with the goal to learn features of mouse movements that are related to stress perceptions. Results of a first longitudinal field study indicate that mouse cursor speed is negatively related to perceived stress. Future work is discussed.
... eu). 19 It records a participant's sensor data in the night (eg, audio data via a smartphone's microphone) and delivers the daily questionnaires to the patients. Figure 2 illustrates the app's user experience. ...
Article
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Introduction: Nocturnal cough is a burdensome asthma symptom. However, knowledge about the prevalence of nocturnal cough in asthma is limited. Furthermore, prior research has shown that nocturnal cough and impaired sleep quality are associated with asthma control, but the association between these two symptoms remains unclear. This study further investigates the potential of these symptoms as markers for asthma control and the accuracy of automated, smartphone-based passive monitoring for nocturnal cough detection and sleep quality assessment. Methods and analysis: The study is a multicentre, longitudinal observational study with two stages. Sensor and questionnaire data of 94 individuals with asthma will be recorded for 28 nights by means of a smartphone. On the first and the last study day, a participant’s asthma will be clinically assessed, including spirometry and fractionated exhaled nitric oxide levels. Asthma control will be assessed by the Asthma Control Test and sleep quality by means of the Pittsburgh Sleep Quality Index. In addition, nocturnal coughs from smartphone microphone recordings will be labelled and counted by human annotators. Relatively unrestrictive eligibility criteria for study participation are set to support external validity of study results. Analysis of the first stage is concerned with the prevalence and trends of nocturnal cough and the accuracies of smartphone-based automated detection of nocturnal cough and sleep quality. In the second stage, patient-reported asthma control will be predicted in a mixed effects regression model with nocturnal cough frequencies and sleep quality of past nights as the main predictors. Ethics and dissemination: The study was reviewed and approved by the ethics commission responsible for research involving humans in eastern Switzerland (BASEC ID: 2017–01872). All study data will be anonymised on study termination. Results will be published in medical and technical peer-reviewed journals.
... WhatsApp. The open source behavioral intervention platform MobileCoach [18] was used to build the chatbot and deliver the interactive coaching dialogues. In previous studies, MobileCoach-based interventions have successfully reduced problem drinking in adolescents [19] and engaged the majority of participants of a 3-month smoking cessation program [20]. ...
Article
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BACKGROUND: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. OBJECTIVE: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. METHODS: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. RESULTS: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. CONCLUSIONS: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. TRIAL REGISTRATION: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d).
... The DyMand system as shown in Fig. 1 consists of a smartwatch app 1 , smartphone app 2 and a cloud-server system [3,5]. In developing DyMand, experts from the field of computer science, information system and health psychology used justificatory knowledge from prior work [1,4,6,13] about social support, CDC, health behavior and well-being to derive a list of design requirements (DR) that are important for collecting corresponding data in-situ. ...
Conference Paper
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Married adults share illness management with spouses and it involves social support and common dyadic coping (CDC). Social support and CDC have an impact on health behavior and well-being or emotions in couples' dyadic management of diabetes in daily life. Hence, understanding dyadic interactions in-situ in chronic disease management could inform behavioral interventions to help the dyadic management of chronic diseases. It is however not clear how well social support and CDC can be assessed in daily life among couples who are managing chronic diseases. In this ongoing work, we describe the development of DyMand, a novel open-source mobile and wearable system for ambulatory assessment of couples' dyadic management of chronic diseases. Our first prototype is used in the context of diabetes mellitus Type II. Additionally, we briefly describe our experience deploying the prototype in two pre-pilot tests with five subjects and our plans for future deployments.
... To this end, we created a chat-bot based digital coach, which would deliver interventions for improving physical activity by setting daily step goals. The coach was based on the opensource MobileCoach framework [2], and was available on iOS and Android. We conducted our 6-week study with 189 participants, representative of the German-speaking part of Switzerland. ...
Conference Paper
Advances in mobile, wearable and embedded sensing technology have created new opportunities for research into a variety of health conditions. This has led to the field of mobile health (mHealth), which covers a full spectrum of works, including but not limited to disease surveillance, treatment support, epidemic outbreak tracking, and chronic disease management. An important sub-field which has been rising in the past years is the application of mHealth technology in the field of mental and behavioral health, enabling researchers to study stress, depression, mood, personality change, schizophrenia, physical activity and addictive behavior, among other things.
... The intervention program will be developed using the MobileCoach system. Technical details of the system are described elsewhere [34,35]. The MobileCoach system is available as an open source project on http://www.mobile-coach.eu. ...
Article
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Background: Life-skills trainings conducted within the school curriculum are effective in preventing the onset and escalation of substance use among adolescents. However, their dissemination is impeded due to their large resource requirements. Life-skills training provided via mobile phones might represent a more economic and scalable approach. The main objective of the planned study is to test the efficacy of a mobile phone-based life-skills training to prevent substance use among adolescents within a controlled trial. Methods/design: The efficacy of a mobile phone-based life-skills training to prevent substance use among adolescents will be tested in comparison to an assessment only control group, within a cluster-randomised controlled trial with two follow-up assessments after 6 and 18 months. The fully automated program is based on social cognitive theory and addresses self-management skills, social skills, and substance use resistance skills. Participants of the intervention group will receive up to 4 weekly text messages over 6 months in order to stimulate (1) positive outcome expectations, e.g., on using self-management skills to cope with stress, (2) self-efficacy, e.g., to resist social pressure, (3) observational learning, e.g. of interpersonal competences, (4) facilitation, e.g., of strategies to cope with negative emotions, and (5) self-regulation, e.g., by self-monitoring of stress and emotions. Active program engagement will be stimulated by interactive features such as quiz questions, message- and picture-contests, and integration of a friendly competition with prizes in which program users collect credits with each interaction. Study participants will be 1312 students between the ages of 14 and 16 years from approximately 100 secondary school classes. Primary outcome criteria will be problem drinking according to the short form of the Alcohol Use Disorders Identification Test and cigarette smoking within the last 30 days preceding the follow-up assessment at month 18. Discussion: This is the first study testing the efficacy of a mobile phone-based life-skills training for substance use prevention among adolescents within a controlled trial. Given that this intervention approach proves to be effective, it could be easily implemented in various settings and would reach large numbers of young people in a cost-effective way. Trial registration: ISRCTN41347061 (registration date: 21/07/2018).
... To administer the intervention components evaluated in this study, the Ally app includes a chatbot (Ally) that provides interactive coaching dialogues similar to other messaging Apps such as Apple's iMessage, Facebook's Messenger or WhatsApp. The open source behavioral intervention platform MobileCoach [18] was used to build the chatbot and deliver the interactive coaching dialogues. In previous studies, MobileCoach-based interventions have successfully reduced problem drinking in adolescents [19] and engaged the majority of participants of a three-month smoking cessation program [20]. ...
Article
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Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. Conclusions: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. Trial registration: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d). International registered report identifier (irrid): DERR1-10.2196/11540.
... The smartphone-based coaching intervention is based on the MobileCoach (www.mobile-coach.eu). The Mobile-Coach is an open source platform for the design, delivery and evaluation of scalable smartphone-based interventions [40,43,78]. It is available via the research and industry-friendly Apache 2 license and follows a client-server model. ...
Article
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Background: This protocol describes a study that will test the effectiveness of a 10-week non-clinical psychological coaching intervention for intentional personality change using a smartphone application. The goal of the intervention is to coach individuals who are willing and motivated to change some aspects of their personality, i.e., the Big Five personality traits. The intervention is based on empirically derived general change mechanisms from psychotherapy process-outcome research. It uses the smartphone application PEACH (PErsonality coACH) to allow for a scalable assessment and tailored interventions in the everyday life of participants. A conversational agent will be used as a digital coach to support participants to achieve their personality change goals. The goal of the study is to examine the effectiveness of the intervention at post-test assessment and three-month follow-up. Methods/Design: A 2x2 factorial between-subject randomized, wait-list controlled trial with intensive longitudinal methods will be conducted to examine the effectiveness of the intervention. Participants will be randomized to one of four conditions. One experimental condition includes a conversational agent with high self-awareness to deliver the coaching program. The other experimental condition includes a conversational agent with low self-awareness. Two wait-list conditions refer to the same two experimental conditions, albeit with four weeks without intervention at the beginning of the study. The 10-week intervention includes different types of micro-interventions: (a) individualized implementation intentions, (b) psychoeducation, (c) behavioral activation tasks, (d) self-reflection, (e) resource activation, and (f) individualized progress feedback. Study participants will be at least 900 German-speaking adults (18 years and older) who install the PEACH application on their smartphones, give their informed consent, pass the screening assessment, take part in the pre-test assessment and are motivated to change or modify some aspects of their personality. Discussion: This is the first study testing the effectiveness of a smartphone- and conversational agent-based coaching intervention for intended personality change. Given that this novel intervention approach proves effective, it could be implemented in various non-clinical settings and could reach large numbers of people due to its low-threshold character and technical scalability.
... MobileCoach is an open source, behavioural intervention platform for fully automatic digital intervention. It was developed in Zürich, Switzerland and it is designed to monitor behavioural states and trigger transition states to achieve the final intervention goals (Filler et al. 2015). Figure 3 shows the overall system architecture of Mobi-leCoach. ...
Article
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Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term ‘Internet addiction’ (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient’s Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.
... The open source behavioral intervention platform MobileCoach (www.mobile-coach.eu) [14] was used to build the chatbot and deliver the interactive coaching dialogues. In previous studies, MobileCoach-based interventions have successfully reduced problem drinking in adolescents [15] and engaged the majority of participants of a three-month smoking cessation program [16]. ...
Preprint
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BACKGROUND Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring. OBJECTIVE The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. METHODS In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. RESULTS Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. CONCLUSIONS This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. CLINICALTRIAL ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d) INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11540
... Beide Programmvarianten (MCT und MCT+) wurden auf dem MobileCoach System entwickelt. Details dieses Open-Source Systems sind bei und Filler et al. (2015) beschrieben. Sicherheitsvorkehrungen, wie Passwortschutz und SSLverschlüsselte Datenübertragung wurden realisiert, um einen Missbrauch der online eingegebenen Daten zu verhindern. ...
... The intervention program was developed using the MobileCoach system. Technical details of the system are described elsewhere[30,31]. The MobileCoach system is available as an open-source project. ...
Article
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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: Thefullyautomatedprogram,namedready4life,isbasedonsocialcognitivetheoryandaddressesself-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.
... The intervention was designed with, and triggered by, the open-source behavioral intervention platform MobileCoach version 1.1 [26]. The original study protocol was approved by the ethics committee of the Faculty of Philosophy at the University of Zurich, Switzerland (date of approval: June 24, 2014). ...
Article
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Background: Although mobile phone–delivered smoking cessation programs are a promising way to promote smoking cessation among adolescents, little is known about how adolescents might actually use them. Objective: The aim of this study was to determine adolescents’ trajectories of engagement with a mobile phone–delivered smoking cessation program over time and the associations these trajectories have with baseline characteristics and treatment outcomes. Methods: We performed secondary data analysis on a dataset from a study that compared a mobile phone–delivered integrated smoking cessation and alcohol intervention with a smoking cessation only intervention for adolescents recruited in vocational and upper secondary school classes (N=1418). Throughout the 3-month intervention, participants in both intervention groups received one text message prompt per week that either assessed smoking-related target behaviors or encouraged participation in a quiz or a message contest. Sequence analyses were performed to identify engagement trajectories. Analyses were conducted to identify predictors of engagement trajectory and associations between engagement trajectories and treatment outcomes. Results: Three engagement trajectories emerged: (1) stable engagement (646/1418, 45.56%), (2) decreasing engagement (501/1418, 35.33%), and (3) stable nonengagement (271/1418, 19.11%). Adolescents who were younger, had no immigrant background, perceived more benefits of quitting smoking, and reported binge drinking preceding the baseline assessment were more likely to exhibit stable engagement. Due to different reach of more engaged and less engaged participants at follow-up, three statistical models (complete-cases, last-observation-carried-forward, and multiple imputation) for the associations of engagement trajectory and smoking outcome were tested. For 7-point smoking abstinence, no association was revealed to be statistically significant over all three models. However, decreasing engagement with the program was associated over all three models, with greater reductions in daily tobacco use than nonengagement. Conclusions: The majority of tobacco-smoking adolescents engaged extensively with a mobile phone–based smoking cessation program. However, not only stable engagement but also decreasing engagement with a program might be an indicator of behavioral change. Measures to avoid nonengagement among adolescents appear especially necessary for older smokers with an immigrant background who do not drink excessively. In addition, future studies should not only examine the use of specific program components but also users’ engagement trajectories to better understand the mechanisms behind behavioral change.
... The THCB is part of the open source behavioral intervention platform MobileCoach www.mobile-coach.eu[12,27]. It has already been evaluated in the public health context[14,37]and provides a modular architecture and rule engine for the design of fullyautomated digital health interventions. ...
Conference Paper
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Health professionals have limited resources and are not able to personally monitor and support patients in their everyday life. Against this background and due to the increasing number of self-service channels and digital health interventions, we investigate how text-based healthcare chatbots (THCB) can be designed to effectively support patients and health professionals in therapeutic settings beyond on-site consultations. We present an open source THCB system and how the THCP was designed for a childhood obesity intervention. Preliminary results with 15 patients indicate promising results with respect to intervention adherence (ca. 13.000 conversational turns over the course of 4 months or ca. 8 per day and patient), scalability of the THCB approach (ca. 99.5% of all conversational turns were THCB-driven) and over-average scores on perceived enjoyment and attachment bond between patient and THCB. Future work is discussed.
... Likewise, safety aspects must be considered (RQ5) when the VC is used for tasks that may affect the user's health condition (e.g., rehabilitation). To accelerate VCs' implementation, Filler et al. (2015) developed an open-source platform called ''MobileCoach'' 1 that can be extended or revised by application developers and serve as a starting point to build proprietary systems. In addition, the platform could motivate the study of generic VCs for facilitating other application scenarios by ''simply'' replacing the coaching plans and could give rise to new platform business models in the future (RQ6). ...
... The app, available on both iOS and Android, is based on the MobileCoach platform, developed by Filler et al. [10,17]. The app consists of a chat interface where the users get diferent intervention messages and can respond from a set of choices. ...
Article
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Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classifier.
... Ally was developed using the MobileCoach platform (www.mobile-coach.eu), an open-source server-client software for the design of ecological momentary assessments and digital health interventions (27). We supported both common mobile platforms, that is, Google's Android and Apple's iOS, to reach a market share of 99.3% in Switzerland (28). ...
Article
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Background The Assistant to Lift your Level of activitY (Ally) app is a smartphone application that combines financial incentives with chatbot-guided interventions to encourage users to reach personalized daily step goals. Purpose To evaluate the effects of incentives, weekly planning, and daily self-monitoring prompts that were used as intervention components as part of the Ally app. Methods We conducted an 8 week optimization trial with n = 274 insurees of a health insurance company in Switzerland. At baseline, participants were randomized to different incentive conditions (cash incentives vs. charity incentives vs. no incentives). Over the course of the study, participants were randomized weekly to different planning conditions (action planning vs. coping planning vs. no planning) and daily to receiving or not receiving a self-monitoring prompt. Primary outcome was the achievement of personalized daily step goals. Results Study participants were more active and healthier than the general Swiss population. Daily cash incentives increased step-goal achievement by 8.1%, 95% confidence interval (CI): [2.1, 14.1] and, only in the no-incentive control group, action planning increased step-goal achievement by 5.8%, 95% CI: [1.2, 10.4]. Charity incentives, self-monitoring prompts, and coping planning did not affect physical activity. Engagement with planning interventions and self-monitoring prompts was low and 30% of participants stopped using the app over the course of the study. Conclusions Daily cash incentives increased physical activity in the short term. Planning interventions and self-monitoring prompts require revision before they can be included in future versions of the app. Selection effects and engagement can be important challenges for physical-activity apps. Clinical Trial Information This study was registered on ClinicalTrials.gov, NCT03384550.
... The study app was developed with MobileCoach (www.mobile-coach.eu) [19,20], an open-source software platform for smartphone-based and chatbot-delivered behavioral health interventions and ecological momentary assessments. MobileCoach-based interventions have been used in various studies, for example for stress management [21], personality change [22], promotion of health literacy [23], or physical activity [24]. ...
Preprint
Background: Electronic Health (eHealth) interventions have a potential to increase physical activity of their users. However, their effectiveness varies and they often have only short-lasting effects. One possible way to enhance their effectiveness, is increasing positive outcome expectations of the users by giving them positive suggestions regarding the effectiveness of the intervention. It has been shown that when individuals have positive expectations regarding various types of interventions, they tend to benefit from these interventions more. Objective: The main objective of this web-based study was to investigate whether positive suggestions can change the expectations of the participants regarding the effectiveness of a smartphone physical activity intervention and subsequently enhance the number of steps participants take during the intervention. Additionally, we studied if suggestions affect perceived app effectiveness, engagement with the app, self-reported vitality and fatigue of the participants. Methods: A 21-day physical fully automated activity intervention aimed at helping participants to walk more steps. The intervention was delivered via a smartphone-based application (app), that deliver specific tasks to participants (e.g., setting activity goals or looking for social support) and recorded daily step count of the participants. Participants were randomized to either a positive suggestions group (n = 69) or a control group (n = 64). Positive suggestions emphasizing the effectiveness of the intervention were implemented in an online flyer sent to the participants before the intervention. Suggestions were repeated on day 8 and 15 of the intervention via the app. Results: Participants significantly increased their daily step count from baseline compared to 21 days of the intervention (t (107) = -8.62, p < .001) regardless of the suggestions. Participants in the positive suggestions group had more positive expectations regarding the app (B= -1.61, SE= 0.47, p < 0.001) and higher expected engagement with the app (B= 3.80, SE= 0.63, p < .001) compared to the participants in the control group. No effect of suggestions on the step count (B = -22.05, SE = 334.90, p = .95), perceived effectiveness of the app (B= 0.78, SE= 0.69, p= 0.26), engagement with the app (B= 0.78, SE= 0.75, p= 0.29), and vitality (B= 0.01, SE= 0.11, p= 0.95) were found. Positive suggestions decreased the fatigue of participants during the three weeks of the intervention (B= 0.11, SE= 0.02, p< 0.001). Conclusions: Even though the suggestions did not affect the number of daily steps, they increased the positive expectations of the participants and decreased their fatigue. These results indicate that adding positive suggestions to eHealth physical activity interventions might be a promising way to influence subjective, but not objective, outcomes of interventions. Future research should focus on finding ways to strengthen the suggestions as they have a potential to boost effectiveness of eHealth interventions. Clinical Trial: osf.io/cwjes
... The smartphone-based intervention and assessments were implemented using MobileCoach (mobile-coach.eu; Filler et al., 2015). MobileCoach is an open source platform for the design, delivery, and evaluation of scalable smartphone-based interventions. ...
Article
Research indicates that it might be possible to change personality traits through intervention, but this clinical research has primarily focused on changing neuroticism. To date, there are no established, proven techniques for changing other domains of personality, such as conscientiousness and openness. This research examined the effects of a two‐week smartphone‐based intervention to either change one facet of conscientiousness (i.e. self‐discipline) or one facet of openness to experience (i.e. openness to action). Two intervention studies (total N = 255) with two active intervention groups for mutual comparisons were conducted. Results of self‐reports and observer reports showed that people who wanted to become more self‐disciplined were less self‐disciplined at pretest. Similarly, people who wanted to become more open to action were less open to action at pretest. The results showed that people who chose the self‐discipline intervention showed greater increases in self‐discipline, and people who chose the openness to action intervention showed greater increases in openness to action compared with the other group. Changes were maintained until follow‐up two and six weeks after the end of the intervention. Future work is needed to examine whether these personality changes are enduring or reflect temporary accentuation as a result of participation in the intervention. © 2020 European Association of Personality Psychology
... The intervention was developed with the open-source software platform MobileCoach (www.mobile-coach.eu) [68,84], which has been already used successfully for various clinical and public health interventions [17,67,77,79,80,85,86] and ecological momentary assessments [87][88][89]. ...
Preprint
BACKGROUND Successful management of chronic diseases requires a trustful collaboration between healthcare professionals, patients, and family members. Scalable conversational agents (CAs), designed to assist healthcare professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out into the everyday lives of patients and their family members. Until now, however, it has not been clear whether CAs, in such a role, would be accepted and whether they can support this multi-stakeholder collaboration. OBJECTIVE With asthma in children representing a relevant target of chronic disease management, this work has two objectives: (1) To describe the design of MAX, a CA-delivered asthma intervention that supports healthcare professionals targeting child-parent teams in their everyday lives; (2) To assess the (a) reach of MAX, (b) CA-patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of healthcare professionals in primary and secondary care settings. METHODS MAX was designed to increase cognitive skills (i.e. knowledge about asthma) and behavioral skills (i.e. inhalation technique) in 10-15-year-olds with asthma and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) To build a CA-patient working alliance; (2) To offer hybrid (human- and CA-supported) ubiquitous coaching; (3) To provide an intervention with a high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The CA communicates with healthcare professionals via email, with patients via a mobile chat app and with a family member via SMS. A single-arm feasibility study in primary and secondary care settings was conducted to assess MAX. RESULTS Results indicate an overall positive evaluation of MAX with respect to its reach (49.5% (49 out of 99) of recruited and eligible patient-family member teams participated), a strong patient-CA working alliance, and a high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the CA as opposed to between patient and healthcare professional, thus indicating the scalability of MAX. In addition, it took healthcare professionals less than four minutes to assess the inhalation technique and three days to deliver that feedback to the patients. Several suggestions for improvement were made. CONCLUSIONS For the first time, this work provides evidence that CAs, designed as mediating social actors involving healthcare professionals, patients and family members, are not only accepted in such a “team player” role, but also show potential to improve health-relevant outcomes in chronic disease management.
... We provided all participants with tablet computers equipped with sim cards in order to maintain a permanent internet connection and thus allow the completion of the daily questionnaires that patients were instructed to complete every evening. For this purpose, a study app was developed for the tablet with the open-source software MobileCoach [16,17]. The timing of the questionnaires was individualized, and daily notifications were sent to remind patients to fill out the survey before going to bed. ...
Article
Background: Medication adherence is an indispensable prerequisite for the long-term management of many chronic diseases. However, published literature suggests that non-adherence is widely prevalent. Health behavior change theories can help understand the underlying processes and allow the accumulation of knowledge in the field. The present study applied the health action process approach (HAPA) in an intensive longitudinal research design to investigate medication adherence in patients after discharge from inpatient cardiac rehabilitation. Method: In total, n = 139 patients (84.9% male, Mage = 62.2 years) completed n = 2,699 daily diaries in the 22 days following discharge from inpatient cardiac rehabilitation. Patients’ intentions to take medication and predictors were assessed in daily end-of-day questionnaires. Adherence to medication was measured subjectively (self-report) and objectively. Multilevel modeling was applied to disentangle the between- and within-person level. Results: Higher levels of risk awareness and self-efficacy were positively associated with intentions to take medication at both levels of analysis. Contrary to theoretical assumptions, positive outcome expectations were not associated with intention, neither between- nor within-person. In contrast to published literature, patients showed very high medication adherence (95.2% self-report, 92.2% objectively). Conclusion: In line with the theoretical assumptions, the results showed that risk awareness and self-efficacy are promising modifiable factors that could be targeted to motivate patients to take medication as prescribed. Daily measurements revealed that patients took their medication as prescribed; thus, future studies should make every effort to recruit patients vulnerable to non-adherence to avoid ceiling effects.
... In the time between study center appointments, questionnaire and sensor data were collected in situ with a smartphone (Samsung Galaxy A3 2017, SM-A320FL) application based on MobileCoach. 16,17 This work has four objectives: (1) to examine the statistical associations of both markers (ie, nocturnal cough and sleep quality) with asthma control, in particular with regard to within-patient associations; to investigate whether they enable detection of weeks, in which (2) patients had uncontrolled asthma or (3) clinically significant deteriorations in asthma control occurred; and (4) to explore whether they can be used to predict asthma attacks in advance. ...
Article
Full-text available
Introduction: Objective markers for asthma, that can be measured without extra patient effort, could mitigate current shortcomings in asthma monitoring. We investigated whether smartphone-recorded nocturnal cough and sleep quality can be utilized for the detection of periods with uncontrolled asthma or meaningful changes in asthma control and for the prediction of asthma attacks. Methods: We analyzed questionnaire and sensor data of 79 adults with asthma. Data were collected in situ for 29 days by means of a smartphone. Sleep quality and nocturnal cough frequencies were measured every night with the Pittsburgh Sleep Quality Index and by manually annotating coughs from smartphone audio recordings. Primary endpoint was asthma control assessed with a weekly version of the Asthma Control Test. Secondary endpoint was self-reported asthma attacks. Results: Mixed-effects regression analyses showed that nocturnal cough and sleep quality were statistically significantly associated with asthma control on a between- and within-patient level (p < 0.05). Decision trees indicated that sleep quality was more useful for detecting weeks with uncontrolled asthma (balanced accuracy (BAC) 68% vs 61%; Δ sensitivity -12%; Δ specificity -2%), while nocturnal cough better detected weeks with asthma control deteriorations (BAC 71% vs 56%; Δ sensitivity 3%; Δ specificity -34%). Cut-offs using both markers predicted asthma attacks up to five days ahead with BACs between 70% and 75% (sensitivities 75 - 88% and specificities 57 - 72%). Conclusion: Nocturnal cough and sleep quality have useful properties as markers for asthma control and seem to have prognostic value for the early detection of asthma attacks. Due to the limited study duration per patient and the pragmatic nature of the study, future research is needed to comprehensively evaluate and externally validate the performance of both biomarkers and their utility for asthma self-management.
... The intervention was developed with the open-source software platform MobileCoach [67,83,84], which has already been used successfully for various clinical and public health interventions [17,68,[77][78][79]85,86] and ecological momentary assessments [87][88][89]. MobileCoach is available under the academia-and industry-friendly open-source Apache 2.0 license. ...
Article
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Background: Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration. Objective: With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent–delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent–patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings. Methods: MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent–patient working alliance; (2) to offer hybrid (human- and conversational agent–supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX. Results: Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made. Conclusions: This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a “team player” role but also show potential to improve health-relevant outcomes in chronic disease management.
... The intervention was designed with, and initiated by, the open-source behavioral intervention platform MobileCoach [38,39]. The original study protocol was approved by the ethics committee of the Faculty of Arts and Sciences at the University of Zurich, Switzerland (approval number 18.6.5; ...
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BACKGROUND Mobile phone–delivered life skills programs are an emerging and promising way to promote mental health and prevent substance use among adolescents, but little is known about how adolescents actually use them. OBJECTIVE The aim of this study is to determine engagement with a mobile phone–based life skills program and its different components, as well as the associations of engagement with adolescent characteristics and intended substance use and mental health outcomes. METHODS We performed secondary data analysis on data from the intervention group (n=750) from a study that compared a mobile phone–based life skills intervention for adolescents recruited in secondary and upper secondary school classes with an assessment-only control group. Throughout the 6-month intervention, participants received 1 SMS text message prompt per week that introduced a life skills topic or encouraged participation in a quiz or individual life skills training or stimulated sharing messages with other program participants through a friendly contest. Decision trees were used to identify predictors of engagement (use and subjective experience). The stability of these decision trees was assessed using a resampling method and by graphical representation. Finally, associations between engagement and intended substance use and mental health outcomes were examined using logistic and linear regression analyses. RESULTS The adolescents took part in half of the 50 interactions (mean 23.6, SD 15.9) prompted by the program, with SMS text messages being the most used and contests being the least used components. Adolescents who did not drink in a problematic manner and attended an upper secondary school were the ones to use the program the most. Regarding associations between engagement and intended outcomes, adolescents who used the contests more frequently were more likely to be nonsmokers at follow-up than those who did not (odds ratio 0.86, 95% CI 0.76-0.98; P =.02). In addition, adolescents who read the SMS text messages more attentively were less likely to drink in a problematic manner at follow-up (odds ratio 0.43, 95% CI 1.29-3.41; P =.003). Finally, participants who used the program the most and least were more likely to increase their well-being from baseline to 6-month follow-up compared with those with average engagement ( βs =.39; t<sub>586</sub>=2.66; P =.008; R<sup>2</sup> =0.24). CONCLUSIONS Most of the adolescents participating in a digital life skills program that aimed to prevent substance use and promote mental health engaged with the intervention. However, measures to increase engagement in problem drinkers should be considered. Furthermore, efforts must be made to ensure that interventions are engaging and powerful across different educational levels. First results indicate that higher engagement with digital life skills programs could be associated with intended outcomes. Future studies should apply further measures to improve the reach of lower-engaged participants at follow-up to establish such associations with certainty.
... There is, however, a lack of a clear implementation framework for VCAs. For instance, text-based and embodied conversational agents can currently be implemented using existing frameworks dedicated to digital health interventions [72][73][74][75]; however, to the best of our knowledge, there is no such framework for VCAs. A platform for the development of VCAs dedicated to specific chronic or mental health conditions could encourage standardized implementation, which would be more comparable in their development and evaluation processes. ...
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Background: This systematic literature review aims to provide a better understanding of the current methods on VCAs delivering interventions for the prevention and management of chronic and mental conditions. Objective: This systematic literature review aims to provide a better understanding of the current methods on VCAs delivering interventions for the prevention and management of chronic and mental conditions. Methods: We conducted a systematic literature review using PubMed Medline, EMBASE, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention and/or management of chronic or mental conditions through a VCA and reporting an empirical evaluation of the system in terms of system accuracy and/or in terms of technology acceptance. Two independent reviewers conducted screening and data extraction and measured their agreement with Cohen’s kappa. A narrative approach was applied to synthesize the selected records. Results: Twelve out of 7’170 articles met the inclusion criteria. All studies were non-experimental. The VCAs provided behavioral support (N=5), health monitoring services (N=3), or both (N=4). The interventions were delivered via smartphone (N=5), tablet (N=2), or smart speakers (N=3). In two cases, no device was specified. Three VCAs targeted cancer, while two VCAs each targeted diabetes and heart failure. The other VCAs targeted hearing-impairment, asthma, Parkinson's disease, dementia and autism, “intellectual disability”, and depression. The majority of the studies (N=7) assessed technology acceptance but only a minority (N=3) used validated instruments. Half of the studies (N=6) reported either performance measures on speech recognition or on the ability of VCA’s to respond to health-related queries. Only a minority of the studies (N=2) reported behavioral measure or a measure of attitudes towards intervention-related health behavior. Moreover, only a minority of studies (N=4) reported controlling for participant’s previous experience with technology. Finally, risk bias varied markedly. Conclusions: The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias, show that research on VCAs for chronic and mental conditions is still in its infancy. Although results in system accuracy and technology acceptance are encouraging, there still is a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental conditions, both in absolute terms and in comparison to standard healthcare.
... The intervention was designed with, and initiated by, the open-source behavioral intervention platform MobileCoach [38,39]. The original study protocol was approved by the ethics committee of the Faculty of Arts and Sciences at the University of Zurich, Switzerland (approval number 18.6.5; ...
Article
Full-text available
Background: Mobile phone–delivered life skills programs are an emerging and promising way to promote mental health and prevent substance use among adolescents, but little is known about how adolescents actually use them. Objective: The aim of this study is to determine engagement with a mobile phone–based life skills program and its different components, as well as the associations of engagement with adolescent characteristics and intended substance use and mental health outcomes. Methods: We performed secondary data analysis on data from the intervention group (n=750) from a study that compared a mobile phone–based life skills intervention for adolescents recruited in secondary and upper secondary school classes with an assessment-only control group. Throughout the 6-month intervention, participants received 1 SMS text message prompt per week that introduced a life skills topic or encouraged participation in a quiz or individual life skills training or stimulated sharing messages with other program participants through a friendly contest. Decision trees were used to identify predictors of engagement (use and subjective experience). The stability of these decision trees was assessed using a resampling method and by graphical representation. Finally, associations between engagement and intended substance use and mental health outcomes were examined using logistic and linear regression analyses. Results: The adolescents took part in half of the 50 interactions (mean 23.6, SD 15.9) prompted by the program, with SMS text messages being the most used and contests being the least used components. Adolescents who did not drink in a problematic manner and attended an upper secondary school were the ones to use the program the most. Regarding associations between engagement and intended outcomes, adolescents who used the contests more frequently were more likely to be nonsmokers at follow-up than those who did not (odds ratio 0.86, 95% CI 0.76-0.98; P=.02). In addition, adolescents who read the SMS text messages more attentively were less likely to drink in a problematic manner at follow-up (odds ratio 0.43, 95% CI 1.29-3.41; P=.003). Finally, participants who used the program the most and least were more likely to increase their well-being from baseline to 6-month follow-up compared with those with average engagement (βs=.39; t586=2.66; P=.008; R2=0.24). Conclusions: Most of the adolescents participating in a digital life skills program that aimed to prevent substance use and promote mental health engaged with the intervention. However, measures to increase engagement in problem drinkers should be considered. Furthermore, efforts must be made to ensure that interventions are engaging and powerful across different educational levels. First results indicate that higher engagement with digital life skills programs could be associated with intended outcomes. Future studies should apply further measures to improve the reach of lower-engaged participants at follow-up to establish such associations with certainty.
... At the start of the study, participants were equipped with a smartphone (Samsung Galaxy A3 2017, SM-A320FL) on which Clara-the study's chat-based app-was installed. This app was a study-specific enhancement of the mobile app in the open-source MobileCoach behavioral intervention platform [44,45]. At night, the app recorded audio data using the smartphone's microphone. ...
Article
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Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.
... Elena+ was developed during Summer 2020 using MobileCoach (www.mobile-coach.eu), an open source software platform, available under the industry and academic-friendly Apache 2 license (137) for smartphone-based and CA-delivered digital health interventions and ecological momentary assessments (138,139). MobileCoach allows intervention authors to design fully automated data collection protocols and interventions consistent with the talk-and-tools paradigm (140). It offers a chat-based interface with free text/number input and predefined answer options that are used to simulate conversational turns commonly applied in counseling sessions with health professionals and their clients (the "talk"). ...
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Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.
... During the intensive intervention phase, the PM group had an individual multi-component BCI following Swiss guidelines [4], including handouts on nutritional education and physical activity [16], and was equipped with a smartphone with the PathMate2 (PM) app [17]. The PM app was developed with the MobileCoach open-source software for health interventions [18,19], aiming to support behavioural changes in accordance with state-ofthe-art multi-component interventions. The effects of earlier versions of the PM app have been evaluated in longitudinal studies [20,21]. ...
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Background: Less than 2% of overweight children and adolescents in Switzerland can participate in multi-component behaviour changing interventions (BCI), due to costs and lack of time. Stress often hinders positive health outcomes in youth with obesity. Digital health interventions, with fewer on-site visits, promise health care access in remote regions; however, evidence for their effectiveness is scarce. Methods: This randomized controlled not blinded trial (1:1) was conducted in a specialized childhood obesity center in Switzerland. Forty-one youth aged 10-18 years old with body mass index (BMI) >P.90 with risk factors or co-morbidities or BMI>P.97 were recruited. During 5.5 months, the PathMate2 group (PM) received daily conversational agent counselling via mobile app, combined with standardized counselling (4 on-site visits). Controls (CON) participated in a BCI (7 on-site visits). We compared the outcomes of both groups after 5.5 (T1) and 12 (T2) months. Primary outcome was reduction in BMI-SDS (BMI standard deviation score). Secondary outcomes were changes in body composition and further physical parameters. Additionally, we hypothesized that less stressed children would lose more weight. Thus, children performed biofeedback relaxation exercises while cortisol and other stress parameters were evaluated. Results: After randomization and dropouts before intervention start (n=10), the median BMI-SDS of all patients (18 PM, 13 CON) at T0 was 2.61 (range 1.7 to 3.5). BMI-SDS decreased significantly at T1 in CON (median change -0.35, -1.6 to 0.1, p=0.002) compared to PM ( 0.08, -0.4 to 0.3, p=0.15), but not at T2. Muscle mass, strength and agility improved significantly in both groups at T2; only PM reduced significantly their body fat at T1 and T2. Average daily PM app usage rate was 71.5%. Cortisol serum levels reduced significantly after biofeedback but with no association between stress parameters and BMI-SDS. No side effects were observed. Conclusions: Equally to BCI, PathMate2 intervention resulted in significant and lasting improvements of physical capacities and body composition, but not in sustained BMI-SDS decrease. This youth-appealing mobile health intervention provides an interesting approach for youth with obesity who have limited access to health care. Biofeedback reduces acute stress and could be an innovative adjunct to usual care.
Conference Paper
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The open source platform MobileCoach (mobile-coach.eu) has been used for various behavioral health interventions in the public health context. However, so far, MobileCoach is limited to text message-based interactions. That is, participants use error-prone and laborious text-input fields and have to bear the SMS costs. Moreover, MobileCoach does not provide a dedicated chat channel for individual requests beyond the processing capabilities of its chatbot. Intervention designers are also limited to text-based self-report data. In this paper, we thus present a mobile chat app with pre-defined answer options, a dedicated chat channel for patients and health professionals and sensor data integration for the MobileCoach platform. Results of a pretest (N = 11) and preliminary findings of a randomized controlled clinical trial (N = 14) with young patients, who participate in an intervention for the treatment of obesity, are promising with respect to the utility of the chat app.
Article
Background: eHealth interventions have the potential to increase the physical activity of users. However, their effectiveness varies, and they often have only short-term effects. A possible way of enhancing their effectiveness is to increase the positive outcome expectations of users by giving them positive suggestions regarding the effectiveness of the intervention. It has been shown that when individuals have positive expectations regarding various types of interventions, they tend to benefit from these interventions more. Objective: The main objective of this web-based study is to investigate whether positive suggestions can change the expectations of participants regarding the effectiveness of a smartphone physical activity intervention and subsequently enhance the number of steps the participants take during the intervention. In addition, we study whether suggestions affect perceived app effectiveness, engagement with the app, self-reported vitality, and fatigue of the participants. Methods: This study involved a 21-day fully automated physical activity intervention aimed at helping participants to walk more steps. The intervention was delivered via a smartphone-based app that delivered specific tasks to participants (eg, setting activity goals or looking for social support) and recorded their daily step count. Participants were randomized to either a positive suggestions group (69/133, 51.9%) or a control group (64/133, 48.1%). Positive suggestions emphasizing the effectiveness of the intervention were implemented in a web-based flyer sent to the participants before the intervention. Suggestions were repeated on days 8 and 15 of the intervention via the app. Results: Participants significantly increased their daily step count from baseline compared with 21 days of the intervention (t107=-8.62; P<.001) regardless of the suggestions. Participants in the positive suggestions group had more positive expectations regarding the app (B=-1.61, SE 0.47; P<.001) and higher expected engagement with the app (B=3.80, SE 0.63; P<.001) than the participants in the control group. No effects of suggestions on the step count (B=-22.05, SE 334.90; P=.95), perceived effectiveness of the app (B=0.78, SE 0.69; P=.26), engagement with the app (B=0.78, SE 0.75; P=.29), and vitality (B=0.01, SE 0.11; P=.95) were found. Positive suggestions decreased the fatigue of the participants during the 3 weeks of the intervention (B=0.11, SE 0.02; P<.001). Conclusions: Although the suggestions did not affect the number of daily steps, they increased the positive expectations of the participants and decreased their fatigue. These results indicate that adding positive suggestions to eHealth physical activity interventions might be a promising way of influencing subjective but not objective outcomes of interventions. Future research should focus on finding ways of strengthening the suggestions, as they have the potential to boost the effectiveness of eHealth interventions. Trial registration: Open Science Framework 10.17605/OSF.IO/CWJES; https://osf.io/cwjes.
Article
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Background Life skills are abilities for adaptive and positive behavior that enable individuals to deal effectively with the demands and challenges of everyday life. Life-skills training programs conducted within the school curriculum are effective in preventing the onset and escalation of substance use among adolescents. However, their dissemination is impeded due to their large resource requirements. Life-skills training provided via mobile phones may provide a more economic and scalable approach. Objective The goal of this study was to test the appropriateness (ie, acceptance, use, and evaluation) and short-term efficacy of a mobile phone–based life-skills training program to prevent substance use among adolescents within a controlled trial. Methods The study design was a two-arm, parallel-group, cluster-randomized controlled trial with assessments at baseline and follow-up assessments after 6 and 18 months. This report includes outcomes measured up to the 6-month follow-up. The efficacy of the intervention was tested in comparison to an assessment-only control group. The automated intervention program SmartCoach included online feedback and individually tailored text messages provided over 22 weeks. The contents were based on social cognitive theory and addressed self-management skills, social skills, and substance use resistance skills. Linear mixed models and generalized linear mixed models, as well as logistic or linear regressions, were used to investigate changes between baseline and 6-month follow-up in the following outcomes: 30-day prevalence rates of problem drinking, tobacco use, and cannabis use as well as quantity of alcohol use, quantity of cigarettes smoked, cannabis use days, perceived stress, well-being, and social skills. Results A total of 1759 students from 89 Swiss secondary and upper secondary school classes were invited to participate in the study. Of these, 1473 (83.7%) students participated in the study; the mean age was 15.4 years (SD 1.0) and 55.2% (813/1473) were female. Follow-up assessments at 6 months were completed by 1233 (83.7%) study participants. On average, program participants responded to half (23.6 out of 50) of the prompted activities. Program evaluations underlined its appropriateness for the target group of secondary school students, with the majority rating the program as helpful and individually tailored. The results concerning the initial effectiveness of this program based on 6-month follow-up data are promising, with three of nine outcomes of the intention-to-treat analyses showing beneficial developments of statistical significance (ie, quantity of alcohol use, quantity of tobacco use, and perceived stress; P<.05) and another three outcomes (ie, problem drinking prevalence, cannabis use days, and social skills) showing beneficial developments of borderline significance (P<.10). Conclusions The results showed good acceptance of this intervention program that could be easily and economically implemented in school classes. Initial results on program efficacy indicate that it might be effective in both preventing or reducing substance use and fostering life skills; however, data from the final 18-month follow-up assessments will be more conclusive. Trial Registration ISRCTN Registry ISRCTN41347061; https://doi.org/10.1186/ISRCTN41347061
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Each year, more than 800 000 persons die by suicide, making it a leading cause of death worldwide. Recent innovations in information and communication technology may offer new opportunities in suicide prevention in individuals, hereby potentially reducing this number. In our project, we design digital indices based on both self-reports and passive mobile sensing and test their ability to predict suicidal ideation, a major predictor for suicide, and psychiatric hospital readmission in high-risk individuals: psychiatric patients after discharge who were admitted in the context of suicidal ideation or a suicidal attempt, or expressed suicidal ideations during their intake. Specifically, two smartphone applications -one for self-reports (SIMON-SELF) and one for passive mobile sensing (SIMON-SENSE)- are installed on participants’ smartphones. SIMON-SELF uses a text-based chatbot, called Simon, to guide participants along the study protocol and to ask participants questions about suicidal ideation and relevant other psychological variables five times a day. These self-report data are collected for four consecutive weeks after study participants are discharged from the hospital. SIMON-SENSE collects behavioral variables -such as physical activity, location, and social connectedness- parallel to the first application. We aim to include 100 patients over 12 months to test whether (1) implementation of the digital protocol in such a high-risk population is feasible, and (2) if suicidal ideation and psychiatric hospital readmission can be predicted using a combination of psychological indices and passive sensor information. To this end, a predictive algorithm for suicidal ideation and psychiatric hospital readmission using various learning algorithms (e.g. random forest and support vector machines) and multilevel models will be constructed. Data collected on the basis of psychological theory and digital phenotyping may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time and cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives and significantly reduce economic impact by decreasing inpatient treatment and days lost to inability.
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Context: In situ patient data over multiple weeks are needed to explore the potential of nocturnal cough and sleep quality as digital biomarkers for asthma. Methods: Ninety-four asthmatics need to complete a 29-day EMA study in which nocturnal smartphone sensor data is recorded and daily questionnaires of 13 to 45 items are delivered by an adapted version of the MobileCoach app. Patients are withdrawn from the study in case of non-adherence on more than five days. Adherence is not financially incentivized. Appointments with health professionals take place on the first and last day. Intervention: Engagement, operationalized as response rates to the questionnaires, is promoted using the following strategies: first, patients discuss with health professionals how they will integrate the study app tasks in their daily routine. Second, working alliance is established through the chat-based interaction with the app’s virtual study nurse. Third, non-adherence is illustrated as lost hearts to elicit loss aversion. Finally, in case of non-adherence (on consecutive days) a notification system sends out reminder SMS to patients (prompts calls from health professionals). Results: The first 29 patients successfully completed 791 of the 810 daily questionnaires (97.65%). 58 reminder SMS were sent to patients and 13 calls by health professionals were triggered. One patient lost all hearts and was withdrawn from the study. The remaining patients completed the study with an average of 4.61/5 hearts (SD = 0.83). Conclusion: The preliminary results suggest that the employed strategies successfully promoted engagement in a population known for non-adherence in clinical practice.
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BACKGROUND Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. OBJECTIVE In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a “core” intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (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. METHODS A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A<sub>1c</sub> ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A<sub>1c</sub>. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A<sub>1c</sub> improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. RESULTS Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. CONCLUSIONS 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. CLINICALTRIAL ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/27109
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Background Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. Objective In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a “core” intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (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. Methods A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A1c ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. Results Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. Conclusions 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. Trial Registration ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473 International Registered Report Identifier (IRRID) PRR1-10.2196/27109
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Background: Chronic and mental conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable way. VCAs allow for a more natural interaction compared to text-based conversational agents, facilitate input for users who cannot type, allow for routine monitoring and support when in-person healthcare is not possible, and open the doors to voice and speech analysis. The state of the art of VCAs for chronic and mental conditions is, however, unclear. Objective: This systematic literature review aims to provide a better understanding of state-of-the-art research on VCAs delivering interventions for the prevention and management of chronic and mental conditions. Methods: We conducted a systematic literature review using PubMed Medline, EMBASE, PsycINFO, Scopus, and Web of Science databases. We included primary research that involved the prevention or management of chronic or mental conditions, where the voice was the primary interaction modality of the conversational agent, and where an empirical evaluation of the system in terms of system accuracy and/or in terms of technology acceptance was included. Two independent reviewers conducted screening and data extraction and measured their agreement with Cohen’s kappa. A narrative approach was applied to synthesize the selected records. Results: Twelve out of 7’170 articles met the inclusion criteria. The majority of the studies (N=10) were non-experimental, while the remainder (N=2) were quasi-experimental. The VCAs provided behavioral support (N=5), a health monitoring service (N=3), or both (N=4). The VCA services were delivered via smartphone (N=5), tablet (N=2), or smart speakers (N=3). In two cases, no device was specified. Three VCAs targeted cancer, while two VCAs each targeted diabetes and heart failure. The other VCAs targeted hearing-impairment, asthma, Parkinson's disease, dementia and autism, “intellectual disability”, and depression. The majority of the studies (N=7) assessed technology acceptance but only a minority (N=3) used validated instruments. Half of the studies (N=6) reported either performance measures on speech recognition or on the ability of VCA’s to respond to health-related queries. Only a minority of the studies (N=2) reported behavioral measure or a measure of attitudes towards intervention-related health behavior. Moreover, only a minority of studies (N=4) reported controlling for participant’s previous experience with technology. Conclusions: Considering the heterogeneity of the methods and the limited number of studies identified, it seems that research on VCAs for chronic and mental conditions is still in its infancy. Although results in system accuracy and technology acceptance are encouraging, there still is a need to establish evidence on the efficacy of VCAs for the prevention and management of chronic and mental conditions, both in absolute terms and in comparison to standard healthcare.
Chapter
Laut WHO sind chronische Krankheiten wie Herz-Kreislauf-Erkrankungen, Krebs, Diabetes oder Asthma weltweit für circa 70 % aller Todesfälle verantwortlich. Leistungserbringer haben allerdings nur beschränkte Ressourcen und können den Gesundheitszustand im Alltag von Patienten nicht kontinuierlich erheben und daher auch nicht immer rechtzeitig intervenieren, bevor es zu einer allfälligen Hospitalisierung kommt. Vor diesem Hintergrund diskutiert dieser interdisziplinäre Beitrag das Potenzial digitaler Pillen. Das Ziel digitaler Pillen besteht darin, Gesundheitszustände mithilfe von Informations- und Kommunikationstechnologie möglichst kontinuierlich, zweckdienlich und bequem zu erheben und nur dann zu intervenieren, wenn es unbedingt sein muss, kurzum Patienten den Umgang mit ihrer chronischen Krankheit im Alltag zu erleichtern. Nach einer Einleitung wird das Konzept digitaler Pillen näher erläutert. Danach werden fünf digitale Pillen aus den Bereichen Gesundheitskompetenz, Prävention und Therapie näher vorgestellt. Abschließend wird das Konzept der digitalen Pille kritisch reflektiert und Potenziale sowie Herausforderungen werden diskutiert.
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