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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
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.
... 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, ...
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.
... The study prototype apps were developed with the open-source (Apache-2.0 license) software platform MobileCoach [66][67][68]. This platform was used for various other digital interventions, for example, to prevent depression and type-2 diabetes with a holistic lifestyle intervention [69], to reduce distress in cancer patients [70] or students [71], to follow a balanced diet [72], to change a specific personality trait [73], to reduce headaches [74], to improve hypertension management [75] or sleep [76] or type-1 diabetes management [77]. ...
... The apps could be operated on Android as well as iOS operating systems. The system ensures data privacy and security through password-protected access to the MobileCoach Designer and Server, inapp encryption, and SSL encryption for data transfers between the mobile apps, MobileCoach Designer, and MobileCoach Server [66][67][68]. ...
... The mobile application for our self-help intervention was developed with use of the open-source software of MobileCoach (www.mobile-coach.eu) (Filler et al., 2015;Kowatsch et al., 2017), which has been previously used for smartphonebased and chatbot-delivered behavioral interventions (eg, (Tinschert et al., 2019;Lee et al., 2011)). See Appendix 2 for more information about the technical implication. ...
... -coach.eu) (Filler et al., 2015;Kowatsch et al., 2017), an open-source software platform for smartphone-based and chatbot-delivered behavioral interventions (eg, (Stieger et al., 2021)) and ecological momentary assessments (eg, (Tinschert et al., 2019)). The Mobile Coach platform provided the researchers with a web-based graphical user interface and allowed us to implement the needed intervention logic and content. ...
eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = −3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.
... In the field of behavioural change interventions, an approach similar to DMC is followed by the MobileCoach framework [27]. It provides a layered architecture for creating behavioural interventions and thus is tailored for collecting data in survey-like form. ...
... The majority of analysed studies (27) provided terminal feedback. They are listed in Table 4 together with the feedback provided. ...
Feedback is essential for athletes in order to improve their sport performance. Feedback systems try to provide athletes and coaches not only with visualisations of acquired data, but moreover, with insights into—possibly—invisible aspects of their performance. With the widespread adoption of smartphones and the increase in their capabilities, their use as a device for applications of feedback systems is becoming increasingly popular. However, developing mobile feedback systems requires a high level of expertise from researchers and practitioners. The Direct Mobile Coaching model is a design-paradigm for mobile feedback systems. In order to reduce programming efforts, PEGASOS, a framework for creating feedback systems implementing the so-called Direct Mobile Coaching model, is introduced. The paper compares this framework with state-of-the-art research with regard to their ability of providing different variants feedback and offering multimodality to users.
... 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. ...
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.
... 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]. ...
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.
... As an empirical case, we selected the open-source software platform MobileCoach 7 (Filler et al., 2015;Kowatsch et al., 2017) with two of its conversational IA instances that supports interdisciplinary teams of experts in their efforts to change people's health, lifestyle, and personality. Achieving such changes in the lives of individuals requires an interdisciplinary team of clinical or psychological experts, that work together. ...
Studies regularly demonstrate how well intelligent agents (IAs) can support humans or are demonstrably superior to them in some areas. Given that some tasks likely remain unsuitable for even the most intelligent machines in the mid-future, work in hybrid teams of humans and IAs—where the capabilities of both are effectively combined—will most likely shape the way we work in the coming decades. In an abductive study, we investigate an early example of hybrid teams, consisting of a conversational intelligent agent (IA) and humans, that aims to improve health behavior or change personality traits. We theorize Transactive Intelligent Memory System (TIMS) as a new vision of collaboration between humans and IAs in hybrid teams, based on our empirical insights and our literature review on transactive memory systems theory. Our empirical evidence shows that IAs can develop a form of individual and external memory, and hybrid teams of humans and IAs can realize joint systems of transactive memory—a competence that current literature only ascribes to humans. We further find that whether individuals view IAs merely as external memory aids or as part of their teams’ transactive memory is moderated by the tasks’ complexity and knowledge intensity, as well as the IA’s ability to complete the task. This theorizing helps to better understand the role of IAs in future team-based working processes. Developers of IAs can use TIMS as a tool for requirements formulation to prepare their software agents for collaboration in hybrid teams.
... The smartphone-based JITAI was developed with the latest version of the MobileCoach software platform (April 2024) [28,29] which has already been used for various health interventions [23,[30][31][32][33][34][35][36]. MobileCoach offers data collection capabilities (e.g., via self-reports and interaction logs) and the delivery of JITAIs with the help of a rule-based CA. ...
Background:
Dementia is projected to impact 152 million people by 2050, making it one of the most pressing global health challenges. The neurodegenerative process initiates well before clinical symptoms manifest, advancing from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and ultimately to dementia. Despite the growing prevalence, awareness of dementia prevention is limited, and many individuals express a desire to cease living upon diagnosis. Lifestyle interventions can mitigate cognitive decline, but there is a need for effective, scalable approaches to deliver these interventions to older adults. Digital health interventions, such as app-based just-in-time adaptive interventions (JITAIs), offer a promising solution, but their application in cognitively impaired older populations remains underexplored.
Objective:
This study evaluated the feasibility, acceptability, and adherence to a smartphone-based JITAI delivered by a rule-based conversational agent (CA) among older adults with SCD or MCI. The primary focus was on adherence to the CA-initiated conversational turns (measured objectively via interaction logs), with secondary objectives including the perceptions of technology acceptance, the working alliance with the CA, self-reported adherence to the suggested health-promoting activity, and feedback for future improvements (through a questionnaire and short interview).
Methods:
This monocentric study investigated 15 participants (mean age = 70.3 years; 66.7% female and 33.3% male) with SCD (n=12) or MCI (n=3). Participants used the study app that delivered daily health-promoting activities through a CA over two weeks. Participants received notifications to engage in seven health-related activities, and adherence to the activity was self-reported. Post-intervention, participants rated their experience with the app and assessed their working alliance with the CA through the 6-item session alliance inventory. Data on smartphone usage, demographic information, and cognitive performance (via MoCA) were collected during a pre-intervention visit.
Results:
Participants rated the study app positively, especially regarding ease of use and a subset of the working alliance. Adherence to the CA-initiated conversational turn was measured at an average of 80.95% across 14 days. 27.14% of participants indicated as being vulnerable and 100% then responded with their state of receptivity, of which 82.65% were receptive to completing the activity, and 69.13% self-reporting adherence to the activity. There was no significant decline in adherence across the study period. Qualitative results support these findings and present two emerging themes: app enjoyment and enhancing engagement.
Conclusions:
This study demonstrates that smartphone-based JITAIs are feasible and generally well-accepted by older adults with SCD or MCI. However, the findings underscore the need for robust technological infrastructure and potential personal assistance to optimize adherence. Future interventions could benefit from integrating wearables to improve real-time engagement and accurately monitor adherence, ultimately supporting healthy aging and cognitive health in older populations.
... mobile-coach.eu), an open-source software platform for behavioral health interventions and data collection purposes (Filler et al., 2015;Kowatsch et al., 2017). ...
... BalanceUP, developed for iOS (Apple Inc) and Android (Google LLC) platforms using MobileCoach [54,69,70], provides a chat-based interface for communicating with the CA ( Figure 1B). The communication between users and the server is encrypted. ...
... The app, which is available in German, French, and Italian, is based on Swiss dietary recommendations (Die Schweizer Lebensmittelpyramide -ausgewogene Ernährung) and was developed through a collaboration between Pathmate Technologies AG (Pathmate Technologies), the Centre for Digital Health Interventions (Centre for Digital Health interventions, 2017), and the Swiss Federal Food Safety and Veterinary Office (BLV). It was developed with a commercial version of the open-source software MobileCoach (Filler et al., 2015;Kowatsch et al., 2017). ...
Background
Obesity is a global health issue affecting over 2 billion people. Mobile health apps, specifically nutrition apps, have been identified as promising solutions to combat obesity. However, research on adherence to nutrition apps is scarce, especially for publicly available apps without monetary incentives and personal onboarding. Understanding factors associated with adherence is essential to improve the efficacy of these apps. This study aims to identify such factors by analyzing a large dataset of a free and publicly available app (“MySwissFoodPyramid”) that promotes healthy eating through dietary self-monitoring and nutrition literacy delivered via a conversational agent.
Methods
A retrospective analysis was conducted on 19,805 users who used the app for at least two days between November 2018 and May 2022. Adherence was defined as completing a food diary by tracking dietary intake over a suggested period of three days. Users who finished multiple diaries were considered long-term adherent. The associations between the day and time of installation, tutorial use, reminder use, and conversational agent choice were examined regarding adherence, long-term adherence, and the number of completed diaries.
Results
Overall, 66.8% of included users were adherent, and 8.5% were long-term adherent. Users who started the intervention during the day (5 am – 7 pm) were more likely to be adherent and completed more diaries. Starting to use the intervention between Sunday and Wednesday was associated with better adherence and a higher number of completed diaries. Users who chose the female conversational agent were more likely to be adherent, long-term adherent, and completed more diaries. Users who skipped the tutorial were less adherent and completed fewer diaries. Users who set a follow-up reminder were more likely to be long-term adherent and completed more diaries.
Conclusions
This study demonstrates the potential of digital health interventions to achieve comparably high adherence rates, even without monetary incentives or human-delivered support. It also reveals factors associated with adherence highlighting the importance of app tutorials, customizable reminders, tailored content, and the date and time of user onboarding for improving adherence to mHealth apps. Ultimately, these findings may help improve the effectiveness of digital health interventions in promoting healthy behaviors.
... The app was built using MobileCoach (version 21.9.1), an open-source software platform for digital biomarker and health intervention research [32,33]. Conceptually, the app implements the Talk-and-Tools paradigm, which was applied successfully in the domain of mHealth behavior change interventions [34]. ...
Background
Mobile health (mHealth) apps offer unique opportunities to support self-care and behavior change, but poor user engagement limits their effectiveness. This is particularly true for fully automated mHealth apps without any human support. Human support in mHealth apps is associated with better engagement but at the cost of reduced scalability.
Objective
This work aimed to (1) describe the theory-informed development of a fully automated relaxation and mindfulness app to reduce distress in people with cancer (CanRelax app 2.0), (2) describe engagement with the app on multiple levels within a fully automated randomized controlled trial over 10 weeks, and (3) examine whether engagement was related to user characteristics.
Methods
The CanRelax app 2.0 was developed in iterative processes involving input from people with cancer and relevant experts. The app includes evidence-based relaxation exercises, personalized weekly coaching sessions with a rule-based conversational agent, 39 self-enactable behavior change techniques, a self-monitoring dashboard with gamification elements, highly tailored reminder notifications, an educational video clip, and personalized in-app letters. For the larger study, German-speaking adults diagnosed with cancer within the last 5 years were recruited via the web in Switzerland, Austria, and Germany. Engagement was analyzed in a sample of 100 study participants with multiple measures on a micro level (completed coaching sessions, relaxation exercises practiced with the app, and feedback on the app) and a macro level (relaxation exercises practiced without the app and self-efficacy toward self-set weekly relaxation goals).
Results
In week 10, a total of 62% (62/100) of the participants were actively using the CanRelax app 2.0. No associations were identified between engagement and level of distress at baseline, sex assigned at birth, educational attainment, or age. At the micro level, 71.88% (3520/4897) of all relaxation exercises and 714 coaching sessions were completed in the app, and all participants who provided feedback (52/100, 52%) expressed positive app experiences. At the macro level, 28.12% (1377/4897) of relaxation exercises were completed without the app, and participants’ self-efficacy remained stable at a high level. At the same time, participants raised their weekly relaxation goals, which indicates a potential relative increase in self-efficacy.
Conclusions
The CanRelax app 2.0 achieved promising engagement even though it provided no human support. Fully automated social components might have compensated for the lack of human involvement and should be investigated further. More than one-quarter (1377/4897, 28.12%) of all relaxation exercises were practiced without the app, highlighting the importance of assessing engagement on multiple levels.
... In some interventions, the coaching activity was executed via text messaging services (Olaru et al., 2022;Stieger et al., 2020). These kinds of interventions may evolve in an interesting direction because they could use smartphone-based intervention and dedicated apps to interact with the adolescent (Filler et al., 2015;Kowatsch et al., 2017;Stieger et al., 2020). This seems very important given that mobile phones are ecological instruments for middle school adolescents and may motivate their intentions and behavior in personality change and school improvement. ...
... These rates are impressive for this minimally guided intervention aimed at improving help-seeking, which did not involve direct, verbal interaction with the facilitator except during the delivery of one/two components. Studies have revealed a wide range of engagement rates in online mental health interventions [21,22]. Additionally, engagement rates across studies are not easily comparable due to differences in the nature of the target population, nature of intervention, target variables, etc. Engagement can become even more challenging when a technology-based intervention targets distressed non-treatment seekers with the aim of improving help-seeking. ...
Purpose: There is a pressing need for interventions with the potential for scalability to enhance help-seeking inclination and behavior among individuals experiencing common mental health concerns. These interventions are important for addressing the widespread treatment gap. This study aimed to test the effectiveness, feasibility, and acceptability of a newly developed simple technology-based multi-component help-seeking intervention ("ReachOut") for common mental health concerns among distressed, non-treatment-seeking young adults.
Methods: "ReachOut" was delivered to 172 young adults aged 20-35 years, scoring above the cutoff on the Kessler Psychological Distress scale. Effectiveness was studied using a single-group short-term prospective study design to examine changes in help-seeking barriers, inclination, and behavior. We assessed intervention feasibility in terms of demand, implementation, practicality, and limited efficacy and acceptability was determined based on the rate of participation consent, the extent of pro-active initiation of contact with the facilitator during the intervention, feedback obtained on various "ReachOut" components and ratings on the likelihood of recommending the intervention to a person in distress.
Results: Significant reductions in the mean barriers and improvement in mean help-seeking inclination from mental health professionals (MHPs) were observed on the Friedman test from baseline to the two-month follow-up period after the intervention. Thirty-eight percent of participants (N=41) reported seeking help from MHPs by two-month follow-up. Feedback from participants, assessments, and observations indicated that "ReachOut" was feasible and acceptable among the target sample.
Conclusions: The study provides preliminary evidence of the effectiveness, feasibility, and acceptability of the help-seeking intervention "ReachOut" in reducing barriers and improving help-seeking inclination and behavior for common mental health concerns among distressed non-treatment-seeking young adults.
... All questionnaires were programmed using LimeSurvey (LimeSurvey Project, 2012). A study app was developed for the tablet with the open-source software MobileCoach (Filler et al., 2015;Kowatsch et al., 2017). The app delivered the questionnaires to the patients via a conversational agent, that is, a computer program that imitates communication with a human being (Cassell, 2000). ...
Purpose: The adoption of a healthy lifestyle is crucial for patients with established cardiac diseases. However, many patients do not engage in regular physical activity in their everyday life. Research Method: The present study applied the health action process approach (HAPA) in an intensive longitudinal research design (n = 3,354 daily surveys) investigating intention towards physical activity and objectively measured physical activity in 137 cardiac patients (Mage = 62.1 years) during and after inpatient rehabilitation across 28 days. Self-reported HAPA variables were measured daily in online questionnaires at the end of each day. Theory-driven hypotheses were tested using linear multilevel models. Results: One-third of the sample did not reach the recommended physical activity levels in the first weeks after discharge from rehabilitation. Results are mostly in line with the motivational HAPA phase at both levels of analysis; outcome expectations and self-efficacy were positively associated with intentions. Results for the volitional phase were partly in line with the HAPA. Daily deviations in previous-day planning and concurrent action control were positively associated with physical activity during and after cardiac rehabilitation. Conclusion: The results of this study partly speak towards the HAPA in predicting physical activity in cardiac patients, thereby replicating prior research. The HAPA framework offers guidance for motivating and empowering cardiac patients to be more active in their everyday life.
... The DHI is implemented in a mobile app using the MobileCoach intervention platform [40,41], an open-source platform for the design and deployment of DHIs based on rule-based CA. Here, the participant chooses 1 of 4 coaches before the intervention, and the intervention information is provided as text messages, graphics, and videos or by gamification and storytelling approaches by emulating human-like interactions (Figure 1). ...
Background
Long-term unemployed have poor nutritional and physical activity statuses, and, therefore, special health promotion needs. Particularly in rural areas, however, they often do not have access to health promotion service. Thus, new promising strategies to improve the health of long-term unemployed are needed. Hence, a digital health intervention to promote nutritional and physical health behaviors was conceived, and the effectiveness of the intervention in combination with face-to-face sessions will be evaluated in a randomized controlled trial.
Objective
The aim of this study is to elucidate the effectiveness of a mobile digital health intervention to promote the nutritional and physical activity behaviors of long-term unemployed in the rural areas of Germany.
Methods
The 9-week intervention aims to promote nutritional or physical activity behavior by improving drinking habits, increasing the consumption of fruits, vegetables, and whole grains, increasing daily step count, strengthening muscles, and improving endurance. The intervention design is based on the transtheoretical model and is implemented in a mobile app using the MobileCoach open-source platform. The effectiveness of the intervention will be elucidated by a 9-week, 2-armed, parallel-designed trial. Therefore, long-term unemployed will be recruited by employees of the German social sector institutions and randomized either to receive information brochures; the digital intervention in the form of a mobile app; and 3 face-to-face sessions regarding technical support, healthy eating, and physical activity (n=100) or to receive a control treatment consisting of solely the hand over of information brochures (n=100). The effectiveness of the intervention will be assessed using questionnaires at baseline, after 9 weeks in face-to-face appointments, and after a 3-month follow-up period by postal contact. The use of the mobile app will be monitored, and qualitative interviews or focus groups with the participants will be conducted. Incentives of €50 (US $49.7) will be paid to the participants and are tied to the completion of the questionnaires and not to the use of the mobile app or progress in the intervention.
Results
The effectiveness of the intervention in promoting the nutritional and physical activity behaviors of long-term unemployed participants will be elucidated. The adherence of the participants to and the acceptance and usability of the mobile device app will be evaluated. Recruitment started in March 2022, and the final publication of the results is expected in the first half of 2023.
Conclusions
Positive health-related changes made by the intervention would display the potency of digital health interventions to promote nutritional and physical activity behaviors among long-term unemployed in the rural areas of Germany, which would also contribute to an improved health status of the German population in general.
Trial Registration
German Clinical Trials Register DRKS00024805; https://www.drks.de/DRKS00024805
International Registered Report Identifier (IRRID)
PRR1-10.2196/40321
... The intervention for this study was delivered entirely on the web via the Benefit Move app, which the participants downloaded on their smartphones. The Benefit Move app was implemented using MobileCoach [42,43], an open-source software platform for smartphone-based and chatbot-delivered behavioral interventions (eg, study by Kowatsch et al [44]) and ecological momentary assessments (eg, study by Tinschert et al [45]). MobileCoach was developed by the Centre for Digital Health Interventions at Eidgenössische Technische Hochschule Zürich and the University of St. Gallen in Switzerland [46]. ...
Background
Financial incentive interventions for improving physical activity have proven to be effective but costly. Deposit contracts (in which participants pledge their own money) could be an affordable alternative. In addition, deposit contracts may have superior effects by exploiting the power of loss aversion. Previous research has often operationalized deposit contracts through loss framing a financial reward (without requiring a deposit) to mimic the feelings of loss involved in a deposit contract.
Objective
This study aimed to disentangle the effects of incurring actual losses (through self-funding a deposit contract) and loss framing. We investigated whether incentive conditions are more effective than a no-incentive control condition, whether deposit contracts have a lower uptake than financial rewards, whether deposit contracts are more effective than financial rewards, and whether loss frames are more effective than gain frames.
Methods
Healthy participants (N=126) with an average age of 22.7 (SD 2.84) years participated in a 20-day physical activity intervention. They downloaded a smartphone app that provided them with a personalized physical activity goal and either required a €10 (at the time of writing: €1=US $0.98) deposit up front (which could be lost) or provided €10 as a reward, contingent on performance. Daily feedback on incentive earnings was provided and framed as either a loss or gain. We used a 2 (incentive type: deposit or reward) × 2 (feedback frame: gain or loss) between-subjects factorial design with a no-incentive control condition. Our primary outcome was the number of days participants achieved their goals. The uptake of the intervention was a secondary outcome.
ResultsOverall, financial incentive conditions (mean 13.10, SD 6.33 days goal achieved) had higher effectiveness than the control condition (mean 8.00, SD 5.65 days goal achieved; P=.002; ηp2=0.147). Deposit contracts had lower uptake (29/47, 62%) than rewards (50/50, 100%; P
... 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). ...
... 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. ...
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.
... 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; ...
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.
... The study app was developed with MobileCoach [21,22], an open-source software platform for smartphone-based and chatbot-delivered behavioral health interventions and ecological momentary assessments. MobileCoach-based interventions [23,24] have been used in various studies for, for example, stress management [25], personality change [26], promotion of health literacy [23], or physical activity [24]. ...
BackgroundeHealth 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.
ResultsParticipants significantly increased their daily step count from baseline compared with 21 days of the intervention (t107=−8.62; P
... 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; ...
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.
... 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"). ...
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.
... 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]. ...
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
... 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]. ...
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.
... 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. ...
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 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. ...
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.
... 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. ...
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 (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]. ...
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.
... 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. ...
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
... 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. ...
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.
... 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). ...
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 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. ...
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.
... 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]. ...
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. ...
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.
This paper presents the development of a Flask-based web application designed to predict diseases based on user-reported symptoms and provide relevant health information. Leveraging machine learning techniques, the system utilizes a dataset of diseases and their associated symptoms to generate predictions through cosine similarity and a pre-trained Random Forest model. The application features a user-friendly interface for registration, login, and symptom reporting. Additionally, it integrates the DuckDuckGo search API to fetch detailed information about predicted diseases, enhancing the user experience with comprehensive health insights. The application also includes an interactive chatbot to guide users through the symptom input process, ensuring accurate data collection for reliable disease prediction. The system is built with Python, utilizing libraries such as pandas, numpy, and scikit-learn for data processing and model deployment, and is powered by SQLAlchemy for database management. This work aims to provide an accessible tool for preliminary health assessment, potentially aiding in early diagnosis and prompt medical
Background
Digital cognitive behavioral therapy for insomnia (dCBTi) is an effective intervention for treating insomnia. The findings regarding its efficacy compared to face-to-face cognitive behavioral therapy for insomnia are inconclusive but suggest that dCBTi might be inferior. The lack of human support and low treatment adherence are believed to be barriers to dCBTi achieving its optimal efficacy. However, there has yet to be a direct comparative trial of dCBTi with different types of coaching support.
Objective
This study examines whether adding chatbot-based and human coaching would improve the treatment efficacy of, and adherence to, dCBTi.
Methods
Overall, 129 participants (n=98, 76% women; age: mean 34.09, SD 12.05 y) whose scores on the Insomnia Severity Index [ISI] were greater than 9 were recruited. A randomized controlled comparative trial with 5 arms was conducted: dCBTi with chatbot-based coaching and therapist support (dCBTi-therapist), dCBTi with chatbot-based coaching and research assistant support, dCBTi with chatbot-based coaching only, dCBTi without any coaching, and digital sleep hygiene and self-monitoring control. Participants were blinded to the condition assignment and study hypotheses, and the outcomes were self-assessed using questionnaires administered on the web. The outcomes included measures of insomnia (the ISI and the Sleep Condition Indicator), mood disturbances, fatigue, daytime sleepiness, quality of life, dysfunctional beliefs about sleep, and sleep-related safety behaviors administered at baseline, after treatment, and at 4-week follow-up. Treatment adherence was measured by the completion of video sessions and sleep diaries. An intention-to-treat analysis was conducted.
Results
Significant condition-by-time interaction effects showed that dCBTi recipients, regardless of having any coaching, had greater improvements in insomnia measured by the Sleep Condition Indicator (P=.003; d=0.45) but not the ISI (P=.86; d=–0.28), depressive symptoms (P<.001; d=–0.62), anxiety (P=.01; d=–0.40), fatigue (P=.02; d=–0.35), dysfunctional beliefs about sleep (P<.001; d=–0.53), and safety behaviors related to sleep (P=.001; d=–0.50) than those who received digital sleep hygiene and self-monitoring control. The addition of chatbot-based coaching and human support did not improve treatment efficacy. However, adding human support promoted greater reductions in fatigue (P=.03; d=–0.33) and sleep-related safety behaviors (P=.05; d=–0.30) than dCBTi with chatbot-based coaching only at 4-week follow-up. dCBTi-therapist had the highest video and diary completion rates compared to other conditions (video: 16/25, 60% in dCBTi-therapist vs <3/21, <25% in dCBTi without any coaching), indicating greater treatment adherence.
Conclusions
Our findings support the efficacy of dCBTi in treating insomnia, reducing thoughts and behaviors that perpetuate insomnia, reducing mood disturbances and fatigue, and improving quality of life. Adding chatbot-based coaching and human support did not significantly improve the efficacy of dCBTi after treatment. However, adding human support had incremental benefits on reducing fatigue and behaviors that could perpetuate insomnia, and hence may improve long-term efficacy.
Trial Registration
ClinicalTrials.gov NCT05136638; https://www.clinicaltrials.gov/study/NCT05136638
Self-efficacy is a key construct in behavioral science affecting mental health and psychopathology. Here, we expand on previously demonstrated between-persons self-efficacy effects. We prompted 66 patients five times daily for 14 days before starting cognitive behavioral therapy (CBT) to provide avoidance, hope, and perceived psychophysiological-arousal ratings. Multilevel logistic regression analyses confirmed self-efficacy’s significant effects on avoidance in daily life (odds ratio [ OR] = 0.53, 95% confidence interval [CI] = [0.34, 0.84], p = .008) and interaction effects with anxiety in predicting perceived psychophysiological arousal ( OR = 0.79, 95% CI = [0.62, 1.00], p = .046) and hope ( OR = 1.21, 95% CI = [1.03, 1.42], p = .02). More self-efficacious patients also reported greater anxiety-symptom reduction early in treatment. Our findings assign a key role to self-efficacy for daily anxiety-symptom experiences and for early CBT success. Self-efficacy interventions delivered in patients’ daily lives could help improve treatment outcome.
Background
Primary headaches, including migraine and tension-type headaches, are widespread and have a social, physical, mental, and economic impact. Among the key components of treatment are behavior interventions such as lifestyle modification. Scalable conversational agents (CAs) have the potential to deliver behavior interventions at a low threshold. To our knowledge, there is no evidence of behavioral interventions delivered by CAs for the treatment of headaches.
Objective
This study has 2 aims. The first aim was to develop and test a smartphone-based coaching intervention (BalanceUP) for people experiencing frequent headaches, delivered by a CA and designed to improve mental well-being using various behavior change techniques. The second aim was to evaluate the effectiveness of BalanceUP by comparing the intervention and waitlist control groups and assess the engagement and acceptance of participants using BalanceUP.
Methods
In an unblinded randomized controlled trial, adults with frequent headaches were recruited on the web and in collaboration with experts and allocated to either a CA intervention (BalanceUP) or a control condition. The effects of the treatment on changes in the primary outcome of the study, that is, mental well-being (as measured by the Patient Health Questionnaire Anxiety and Depression Scale), and secondary outcomes (eg, psychosomatic symptoms, stress, headache-related self-efficacy, intention to change behavior, presenteeism and absenteeism, and pain coping) were analyzed using linear mixed models and Cohen d. Primary and secondary outcomes were self-assessed before and after the intervention, and acceptance was assessed after the intervention. Engagement was measured during the intervention using self-reports and usage data.
Results
A total of 198 participants (mean age 38.7, SD 12.14 y; n=172, 86.9% women) participated in the study (intervention group: n=110; waitlist control group: n=88). After the intervention, the intention-to-treat analysis revealed evidence for improved well-being (treatment: β estimate=–3.28, 95% CI –5.07 to –1.48) with moderate between-group effects (Cohen d=–0.66, 95% CI –0.99 to –0.33) in favor of the intervention group. We also found evidence of reduced somatic symptoms, perceived stress, and absenteeism and presenteeism, as well as improved headache management self-efficacy, application of behavior change techniques, and pain coping skills, with effects ranging from medium to large (Cohen d=0.43-1.05). Overall, 64.8% (118/182) of the participants used coaching as intended by engaging throughout the coaching and completing the outro.
Conclusions
BalanceUP was well accepted, and the results suggest that coaching delivered by a CA can be effective in reducing the burden of people who experience headaches by improving their well-being.
Trial Registration
German Clinical Trials Register DRKS00017422; https://trialsearch.who.int/Trial2.aspx?TrialID=DRKS00017422
Background
Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices.
Objective
This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses.
Methods
This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response.
Results
The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations.
Conclusions
The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment.
Trial Registration
ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617
International Registered Report Identifier (IRRID)
DERR1-10.2196/42547
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
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.
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.
CLINICALTRIAL
ClinicalTrials.gov NCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473
INTERNATIONAL REGISTERED REPORT
PRR1-10.2196/27109
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
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.
The globally increasing prevalence of childhood obesity is one of the most serious public health challenges of the twenty-first century. Du to the need for multi-professional therapies that require a high amount of personnel and financial resources, IT-supported interventions promise help. So far, meta-studies show their limited impact on health outcomes. This work presents therefore a design theory that helps constructing health information systems (HIS) that positively impact the performance of obesity expert and children teams. Team performance is measured through self-reports, patients´ adherence to therapy and positive health outcomes. In order to assess the utility of the proposed design theory, its underlying design process was adopted by an interdisciplinary team of therapists, patients, their parents, IS researcher and computer scientists. This team developed and evaluated several HIS services collaboratively over the course of two years. Results of this design process show first evidence of the utility of the HIS design theory. However, challenges with regard to the design process still exist and are discussed.
Obesity is a major health concern caused by unhealthy eating behaviours. Digital weight loss interventions have adopted mobile technology primarily in order to support self-monitoring. However many available apps are not designed as part of dietetic practice; therefore a distinct gap in the research exists relating to technology that supports the patient-practitioner relationship. This paper presents myPace, which is a complete weight loss and management system that is deployed via a smartphone and a PC. It connects dietitians and patients between face-to-face consultations and extends the relationship through patients' regular progress updates and dietitians' tailored and timely advice, for sustained behaviour change. The prototype was developed from research into behavior change for weight loss, which furthermore was underpinned by theory and tenets of human support models, such as the supportive accountability framework. We report on early phase system design goals via a formative research process, which aimed to implement theoretical principles and match practical dietetic practice. To that end only the clinical end user's perspective was sought through a coaching think-aloud protocol on the first iteration of the prototype and interviews with dietitians. Findings show that the system has many positive design features, but which require further development in order for the system to be fully acceptable within dietetic practice and motivate patient engagement.
BACKGROUND: Tobacco smoking prevalence continues to be high, particularly among adolescents and young adults with lower educational levels, and is therefore a serious public health problem. Tobacco smoking and problem drinking often co-occur and relapses after successful smoking cessation are often associated with alcohol use. This study aims at testing the efficacy of an integrated smoking cessation and alcohol intervention by comparing it to a smoking cessation only intervention for young people, delivered via the Internet and mobile phone.
We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted.
Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I ² = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I ² = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude.
Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.
Smartphone sensing enables inference of physical context, while online social networks (OSNs) allow mobile applications to harness users’ interpersonal relationships. However, OSNs and smartphone sensing remain disconnected, since obstacles, including the synchronization of mobile sensing and OSN monitoring, inefficiency of smartphone sensors, and privacy concerns, stand in the way of merging the information from these two sources.
In this paper we present the design, implementation and evaluation of SenSocial, a middleware that automates the process of obtaining and joining OSN and physical context data streams for the development of ubiquitous computing applications. SenSocial enables instantiation, management and aggregation of context streams from multiple remote devices. Through micro-benchmarks we show that SenSocial successfully and efficiently captures OSN and mobile sensed data streams. We developed two prototype applications in order to evaluate our middleware and we demonstrate that SenSocial significantly reduces the amount of programming effort needed for building social sensing applications.
Background
Problem drinking, particularly risky single-occasion drinking (RSOD), also called “binge drinking”, is widespread among adolescents and young adults in most Western countries. Few studies have tested the effectiveness of interventions to reduce RSOD in young people with heterogeneous and particularly lower educational background.
Objective
To test the appropriateness and initial effectiveness of a combined, individually tailored Web- and text messaging (SMS)–based intervention program to reduce problem drinking in vocational school students.
Methods
The fully automated program provided: (1) online feedback about an individual’s drinking pattern compared to the drinking norms of an age- and gender-specific reference group, and (2) recurrent individualized SMS messages over a time period of 3 months. Generalized Estimating Equation (GEE) analyses were used to investigate the longitudinal courses of the following outcomes over the study period of 3 months: RSOD, alcohol-related problems, mean number of standard drinks per week, and maximum number of standard drinks on an occasion.
ResultsThe program was tested in 36 school classes at 7 vocational schools in Switzerland. Regardless of their drinking behavior, 477 vocational school students who owned a mobile phone were invited to participate in the program. Of these, 364 (76.3%) participated in the program. During the intervention period, 23 out of 364 (6.3%) persons unsubscribed from participating in the program. The GEE analyses revealed decreases in the percentage of persons with RSOD from baseline (75.5%, 210/278) to follow-up assessment (67.6%, 188/278, P
If prevention initiatives are to get beyond their current marginalized and fragmented status, they must be framed in a comprehensive context. This article places primary prevention at one end of a comprehensive continuum of interventions and explores the continuum in terms of a component for addressing barriers to development and learning. Such a component is conceptualized as primary and essential to successful school reform. Current concerns and emerging trends related to policy, research, practice, and training are highlighted, and general implications for systemic changes are suggested.
This study investigates the role of information systems in stimulating energy-efficient behavior in private households. We present the example of Velix, a web portal designed to motivate customers of a utility company to reduce their electricity consumption. In particular, we consider the effectiveness of goal setting functionality and defaults in influencing energy conservation behavior. For this purpose, we use the web portal as a test of the theoretical propositions underlying its design. Based on data collected from a field experiment with 1,791 electricity consumers, we test hypotheses regarding the structural relations between defaults and goals, the impact of defaults and goals on consumption behavior, and the moderating role of feedback on goal choice. Our results confirm the positive impact of goal setting on energy conservation. We show that default goals lead to statistically significant savings by affecting goal choice. However, if the default goals are set too low or too high with respect to a self-set goal, the defaults will detrimentally affect behavior. We also show that feedback on goal attainment moderates the effect of default goals on goal choice. The results extend the knowledge on goal setting and defaults and have implications for the design of effective energy feedback systems. The study's approach, which combines hypothesis-driven work and design-oriented IS research, could serve as a blueprint for further research endeavors of this kind, particularly with regard to feedback systems based on future smart metering infrastructures.
Previously, behavioural scientists seeking to create Internet-based behaviour change interventions have had to rely on computer scientists to actually develop and modify web interventions. The LifeGuide software was designed to enable behavioural researchers to develop and adapt Internet-based behavioural interventions themselves. This article reports a qualitative case study of users' experiences and perceptions of the LifeGuide software. The aim was to explore users' experiences and their perceptions of the benefits and limitations of this approach to intervention development. Twenty LifeGuide users took part in semi-structured interviews and one provided feedback via email. Thematic analysis identified three overarching themes: 'Recognising LifeGuide's potential', 'I'm not a programmer' and 'Knowledge sharing - the future of LifeGuide'. Users valued LifeGuide's potential to allow them to flexibly develop and modify interventions at little cost. However, users noted that their lack of programming experience meant that they needed to learn new skills for using the software, and they varied in the extent to which they felt able to develop interventions without any input from programmers. Respondents saw the potential of using the LifeGuide Community Website to share technical support and examples of intervention components to support their use of LifeGuide.
A meta-analysis of 58 experimental and quasi-experimental studies of the effects of cognitive-behavioral therapy (CBT) on
the recidivism of adult and juvenile offenders confirmed prior positive findings and explored a range of potential moderators
to identify factors associated with variation in treatment effects. With method variables controlled, the factors independently
associated with larger recidivism reductions were treatment of higher risk offenders, high quality treatment implementation,
and a CBT program that included anger control and interpersonal problem solving but not victim impact or behavior modification
components. With these factors accounted for, there was no difference in the effectiveness of different brand name CBT programs
or generic forms of CBT.
What is the role of face-to-face interactions in the diffusion of health-related behaviors- diet choices, exercise habits, and long-term weight changes? We use co-location and communication sensors in mass-market mobile phones to model the diffusion of health-related behaviors via face-to-face interactions amongst the residents of an undergraduate residence hall during the academic year of 2008--09. The dataset used in this analysis includes bluetooth proximity scans, 802.11 WLAN AP scans, calling and SMS networks and self-reported diet, exercise and weight-related information collected periodically over a nine month period. We find that the health behaviors of participants are correlated with the behaviors of peers that they are exposed to over long durations. Such exposure can be estimated using automatically captured social interactions between individuals. To better understand this adoption mechanism, we contrast the role of exposure to different sub-behaviors, i.e., exposure to peers that are obese, are inactive, have unhealthy dietary habits and those that display similar weight changes in the observation period. These results suggest that it is possible to design self-feedback tools and real-time interventions in the future. In stark contrast to previous work, we find that self-reported friends and social acquaintances do not show similar predictive ability for these social health behaviors.
Hand-washing is regarded as a potentially important behavior for preventing transmission of respiratory infection, particularly during a pandemic.
The objective of our study was to evaluate whether a Web-based intervention can encourage more frequent hand-washing in the home, and to examine potential mediators and moderators of outcomes, as a necessary first step before testing effects of the intervention on infection rates in the PRIMIT trial (PRimary care trial of a website based Infection control intervention to Modify Influenza-like illness and respiratory infection Transmission).
In a parallel-group pragmatic exploratory trial design, 517 nonblinded adults recruited through primary care were automatically randomly assigned to a fully automated intervention comprising 4 sessions of tailored motivational messages and self-regulation support (n = 324) or to a no-intervention control group (n = 179; ratio 2:1). Hand-washing frequency and theory of planned behavior cognitions relating to hand-washing were assessed by online questionnaires at baseline (in only half of the control participants, to permit evaluation of effects of baseline assessment on effect sizes), at 4 weeks (postintervention; all participants), and at 12 weeks.
Hand-washing rates in the intervention group were higher at 4 weeks than in the control group (mean 4.40, n = 285 and mean 4.04, n = 157, respectively; P < .001, Cohen d = 0.42) and remained higher at 12 weeks (mean 4.45, n = 282 and mean 4.12, n = 154, respectively; P < .001, Cohen d = 0.34). Hand-washing intentions and positive attitudes toward hand-washing increased more from baseline to 4 weeks in the intervention group than in the control group. Mediation analyses revealed positive indirect effects of the intervention on change in hand-washing via intentions (coefficient = .15, 95% confidence interval [CI], .08-.26) and attitudes (coefficient = 0.16, 95% CI, .09-.26). Moderator analyses confirmed that the intervention was similarly effective for men and women, those of higher and lower socioeconomic status, and those with higher and lower levels of perceived risk.
This study provides promising evidence that Web-based interventions could potentially provide an effective method of promoting hand hygiene in the home. Data were collected during the 2010 influenza pandemic, when participants in both groups had already been exposed to extensive publicity about the need for hand hygiene, suggesting that our intervention could add to existing public health campaigns. However, further research is required to determine the effects of the intervention on actual infection rates. TRIAL: International Standard Randomized Controlled Trial Number (ISRCTN): 75058295; http://www.controlled-trials.com/ISRCTN75058295 (Archived by WebCite at http://www.webcitation.org/62KSbkNmm).
To develop more efficient programmes for promoting dietary and/or physical activity change (in order to prevent type 2 diabetes) it is critical to ensure that the intervention components and characteristics most strongly associated with effectiveness are included. The aim of this systematic review of reviews was to identify intervention components that are associated with increased change in diet and/or physical activity in individuals at risk of type 2 diabetes.
MEDLINE, EMBASE, CINAHL, PsycInfo, and the Cochrane Library were searched for systematic reviews of interventions targeting diet and/or physical activity in adults at risk of developing type 2 diabetes from 1998 to 2008. Two reviewers independently selected reviews and rated methodological quality. Individual analyses from reviews relating effectiveness to intervention components were extracted, graded for evidence quality and summarised.
Of 3856 identified articles, 30 met the inclusion criteria and 129 analyses related intervention components to effectiveness. These included causal analyses (based on randomisation of participants to different intervention conditions) and associative analyses (e.g. meta-regression). Overall, interventions produced clinically meaningful weight loss (3-5 kg at 12 months; 2-3 kg at 36 months) and increased physical activity (30-60 mins/week of moderate activity at 12-18 months). Based on causal analyses, intervention effectiveness was increased by engaging social support, targeting both diet and physical activity, and using well-defined/established behaviour change techniques. Increased effectiveness was also associated with increased contact frequency and using a specific cluster of "self-regulatory" behaviour change techniques (e.g. goal-setting, self-monitoring). No clear relationships were found between effectiveness and intervention setting, delivery mode, study population or delivery provider. Evidence on long-term effectiveness suggested the need for greater consideration of behaviour maintenance strategies.
This comprehensive review of reviews identifies specific components which are associated with increased effectiveness in interventions to promote change in diet and/or physical activity. To maximise the efficiency of programmes for diabetes prevention, practitioners and commissioning organisations should consider including these components.
Innovative effective smoking cessation interventions are required to appeal to those who are not accessing traditional cessation services. Mobile phones are widely used and are now well integrated into the daily lives of many, particularly young adults. Mobile phones are a potential medium for the delivery of health programmes such as smoking cessation.
To determine whether mobile phone-based interventions are effective at helping people who smoke, to quit.
We searched MEDLINE, EMBASE, Cinahl, PsycINFO, The Cochrane Library, the National Research Register and the ClinicalTrials register, with no restrictions placed on language or publication date.
We included randomized or quasi-randomized trials. Participants were smokers of any age who wanted to quit. Studies were those examining any type of mobile phone-based intervention. This included any intervention aimed at mobile phone users, based around delivery via mobile phone, and using any functions or applications that can be used or sent via a mobile phone.
Information on the specified quality criteria and methodological details was extracted using a standardised form. Participants who dropped out of the trials or were lost to follow up were considered to be smoking. Meta-analysis of the included studies was undertaken using the Mantel-Haenszel Risk Ratio fixed-effect method provided that there was no evidence of substantial statistical heterogeneity as assessed by the I(2) statistic. Where meta-analysis was not possible, summary and descriptive statistics are presented.
Four studies were excluded as they were small non-randomized feasibility studies, and two studies were excluded because follow up was less than six months. Four trials (reported in five papers) are included: a text message programme in New Zealand; a text message programme in the UK; and an Internet and mobile phone programme involving two different groups in Norway. The different types of interventions are analysed separately. When combined by meta-analysis the text message programme trials showed a significant increase in short-term self-reported quitting (RR 2.18, 95% CI 1.80 to 2.65). However, there was considerable heterogeneity in long-term outcomes, with the much larger trial having problems with misclassification of outcomes; therefore these data were not combined. When the data from the Internet and mobile phone programmes were pooled we found statistically significant increases in both short and long-term self-reported quitting (RR 2.03, 95% CI 1.40 to 2.94).
The current evidence shows no effect of mobile phone-based smoking cessation interventions on long-term outcome. While short-term results are positive, more rigorous studies of the long-term effects of mobile phone-based smoking cessation interventions are needed.
The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked these to effectiveness.
The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also to link different modes to effect sizes.
We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken after the intervention.
We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On average, interventions had a statistically small but significant effect on health-related behavior (d(+) = 0.16, 95% CI 0.09 to 0.23). More extensive use of theory was associated with increases in effect size (P = .049), and, in particular, interventions based on the theory of planned behavior tended to have substantial effects on behavior (d(+) = 0.36, 95% CI 0.15 to 0.56). Interventions that incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated fewer techniques (P < .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional methods of communicating with participants, especially the use of short message service (SMS), or text, messages.
The review provides a framework for the development of a science of Internet-based interventions, and our findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change techniques and modes of delivery.
In this paper, an empirical review of 64 teen tobacco use cessation studies is provided. Examined include program contents, delivery modalities, number of contacts, and expected quit rates. In addition, means of recruitment and retention of smokers in programming are discussed. Also, promising contemporary methods of teen smoking cessation are examined, including use of pharmacologic adjuncts, electronic technology, and cigarette price increases (and no smoking policy). Conclusions are made regarding implications for developing and implementing teen tobacco use cessation programs.
The mobile phone represents a unique platform for interactive applications that can harness the opportunity of an immediate contact with a user in order to increase the impact of the delivered information. However, this accessibility does not necessarily translate to reachability, as recipients might refuse an initiated contact or disfavor a message that comes in an inappropriate moment.
In this paper we seek to answer whether, and how, suitable moments for interruption can be identified and utilized in a mobile system. We gather and analyze a real-world smartphone data trace and show that users' broader context, including their activity, location, time of day, emotions and engagement, determine different aspects of interruptibility. We then design and implement InterruptMe, an interruption management library for Android smartphones. An extensive experiment shows that, compared to a context-unaware approach, interruptions elicited through our library result in increased user satisfaction and shorter response times.
This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social ...
BACKGROUND: Innovative and effective smoking cessation interventions are required to appeal to those who are not accessing traditional cessation services. Mobile phones are widely used and are now well-integrated into the daily lives of many, particularly young adults. Mobile phones are a potential medium for the delivery of health programmes such as smoking cessation. OBJECTIVES: To determine whether mobile phone-based interventions are effective at helping people who smoke, to quit. SEARCH METHODS: For the most recent update, we searched the Cochrane Tobacco Addiction Group Specialised Register in May 2012. We also searched UK Clinical Research Network Portfolio for current projects in the UK and the ClinicalTrials register for on-going or recently completed studies. We searched through the reference lists of identified studies and attempted to contact the authors of ongoing studies, with no restrictions placed on language or publication date. SELECTION CRITERIA: We included randomized or quasi-randomized trials. Participants were smokers of any age who wanted to quit. Studies were those examining any type of mobile phone-based intervention. This included any intervention aimed at mobile phone users, based around delivery via mobile phone, and using any functions or applications that can be used or sent via a mobile phone. DATA COLLECTION AND ANALYSIS: Information on risk of bias and methodological details was extracted using a standardised form. Participants who dropped out of the trials or were lost to follow-up were considered to be smoking. We calculated risk ratios (RR) for each included study. Meta-analysis of the included studies was undertaken using the Mantel-Haenszel fixed-effect method. Where meta-analysis was not possible, summary and descriptive statistics are presented. MAIN RESULTS: Five studies with at least six month cessation outcomes were included in this review. Three studies involve a purely text messaging intervention that has been adapted over the course of these three studies for different populations and contexts. One study is a multi-arm study of a text messaging intervention and an internet QuitCoach separately and in combination. The final study involves a video messaging intervention delivered via the mobile phone. When all five studies were pooled, mobile phone interventions were shown to increase the long term quit rates compared with control programmes (RR 1.71, 95% CI 1.47 to 1.99, over 9000 participants), using a definition of abstinence of no smoking at six months since quit day but allowing up to three lapses or up to five cigarettes. Statistical heterogeneity was substantial as indicated by the I² statistic (I² = 79%), but as all included studies were similar in design, intervention and primary outcome measure, we have presented the meta-analysis in this review. AUTHORS' CONCLUSIONS: The current evidence shows a benefit of mobile phone-based smoking cessation interventions on long-term outcomes, though results were heterogenous with findings from three of five included studies crossing the line of no effect. The studies included were predominantly of text messaging interventions. More research is required into other forms of mobile phone-based interventions for smoking cessation, other contexts such as low income countries, and cost-effectiveness.
This article demonstrates the usefulness of two theories for the development of effective health communication campaigns. The integrative model of behavioral prediction focuses on changing beliefs about consequences, normative issues, and efficacy with respect to a particular behavior. Media priming theory focuses on strengthening the association between a belief and its outcomes, such as attitude and intention toward performing the behavior. Both the integrative model of behavioral prediction and media priming theory provide guidance with respect to the selection of beliefs to target in an intervention. The article describes the theories, shows how they can be applied to the selection of target beliefs, and, for each theory, defines the criteria for belief selection. The two theories as well as their appropriate analytic strategies are complementary rather than conflicting.
Sleep and mood problems have a considerable public health impact with serious societal and significant financial effects. In this work, we study the relationship between these factors in the everyday life of healthy young adults. More importantly, we look at these factors from a social perspective, studying the impact that couples have on each other and the role that face-to-face interactions play. We find that there is a significant bi-directional relationship between mood and sleep. More interestingly, we find that the spouse's sleep and mood may have an effect on the subject's mood and sleep. Further, we find that subjects whose sleep is significantly correlated with mood tend to be more sociable. Finally, we observe that less sociable subjects show poor mood more often than their more sociable contemporaries. These novel insights, especially those involving sociability, measured from quantified face-to-face interaction data gathered through smartphones, open up several avenues to enhance public health research through the use of latest wireless sensing technologies.
Exposure and adoption of opinions in social networks are important questions in education, business, and government. We de- scribe a novel application of pervasive computing based on using mobile phone sensors to measure and model the face-to-face interactions and subsequent opinion changes amongst undergraduates, during the 2008 US presidential election campaign. We nd that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individ- ual exposure to dierent opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as elec- tion debates and election day. To our knowledge, this is the rst time such dynamic homophily eects have been measured. Automatically esti- mated exposure explains individual opinions on election day. Finally, we report statistically signicant dierences
As the United States expends extraordinary efforts toward the digitization of its health-care system, and as policy makers across the globe look to information technology (IT) as a means of making health-care systems safer, more affordable, and more accessible, a rare and remarkable opportunity has emerged for the information systems research community to leverage its in-depth knowledge to both advance theory and influence practice and policy. Although health IT (HIT) has tremendous potential to improve quality and reduce costs in healthcare, significant challenges need to be overcome to fully realize this potential. In this commentary, we survey the landscape of existing studies on HIT to provide an overview of the current status of HIT research. We then identify three major areas that warrant further research: (1) HIT design, implementation, and meaningful use; (2) measurement and quantification of HIT payoff and impact; and (3) extending the traditional realm of HIT. We discuss specific research questions in each domain and suggest appropriate methods to approach them. We encourage information systems scholars to become active participants in the global discourse on health-care transformation through IT.
This was an exploratory pilot study forming part of a programme of work to develop and trial an effective web-based intervention to reduce the risk of transmission of respiratory infections by promoting hand washing and other preventive behaviours in pandemic and non-pandemic contexts. The main purpose of this study was to confirm that the behavioural determinants we had identified from theory were related as predicted to intentions and to establish the validity of our measures of behavioural intentions. Participants (N = 84) completed a self-report web-delivered questionnaire measuring intentions to engage in hand washing and the hypothesised behavioural determinants of intentions, based on the theory of planned behaviour and protection motivation theory. In a factorial 2 × 2 design, half of the participants were first randomised to receive messages about potential negative consequences of pandemic flu (the "high-threat" condition) and half were assigned to receive "coping" messages describing the rationale and effectiveness of hand washing for reducing the risk of infection. A substantial proportion of variance in intentions was explained by measures of attitudes (instrumental and affective), social norms (descriptive and injunctive), perceived behavioural control (especially, access to hand gel) and perceived risk (in particular, the likelihood of catching pandemic flu). Our measures of intentions were sensitive to between-group differences, and although our design did not permit causal inference (particularly in view of selective dropout among those required to read most web pages), the pattern of differences was in the expected direction, that is, hand-washing intentions tended to be stronger in those receiving the high-threat message and coping messages. This study provided encouraging confirmation that our intervention development was proceeding correctly. Measures of intentions proved sensitive to group differences, and the behavioural determinants included in the study explained a substantial proportion of the variance in intentions. The study also provided useful indications that our high-threat message might increase hand-washing intentions, that providing hand gel might be beneficial and that it would be necessary to actively manage the risk of selective dropout in the intervention group.
Within the context of general practice, continuity of care creates an opportunity for a personal doctor-patient relationship to develop which has been associated with significant benefits for patients and general practitioners (GPs). Continuity of care is, however, threatened by trends in the organisational development of primary health care in the United Kingdom and its intrinsic role within general practice is currently the subject of debate.
To determine how many patients report having a personal doctor and when this is most valued, to compare the value of a personal doctor-patient relationship with that of convenience, and to relate these findings to a range of patient, GP, and practice variables.
Cross sectional postal questionnaire study.
Nine hundred and ninety-six randomly selected adult patients from a stratified random sample of 18 practices and 284 GP principals in Oxfordshire.
Qualitative interviews with patients and GPs were conducted and used to derive a parallel patient and GP questionnaire. Each patient (100 from each practice) was invited to complete a questionnaire to evaluate their experience and views concerning personal care. All GP principals currently practising in Oxfordshire were sent a similar questionnaire, which also included demographic variables.
Overall, 75% of patients reported having at least one personal GP. The number of patients reporting a personal GP in each practice varied from 53% to 92%. Having a personal doctor-patient relationship was highly valued by patients and GPs, in particular for more serious, psychological and family issues when 77-88% of patients and 80-98% of GPs valued a personal relationship more than a convenient appointment. For minor illness it had much less value.
Patients and GPs particularly value a personal doctor-patient relationship for more serious or for psychological problems. Whether a patient has a personal GP is associated with their perception of its importance and with factors which create an opportunity for a relationship to evolve.
Predicting Adverse Behavior with Early Warning Health Information Systems by Mining Association Rules on Multi-dimensional Behavior: A Proposal (Poster)
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Draft comprehensive global monitoring framework and targets for the prevention and control of noncommunicable diseases. Genéve, Switzerland: World Health Organization
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