Digital health interventions using mobile technologies such as mHealth apps and wearables have the potential to promote healthy behavior change in individuals at a scale larger than traditional interventions. However, the lack of user retention with these interventions still limits their long-term effectiveness in health outcomes. Gamification increases user engagement, however, gamification does not increase the entertainment value of the content, which is often perceived as unengaging by users. In contrast, popular entertainment focused mobile games do foster retention due to their engaging content, and often augmented through the use of persuasive dark patterns. Although dark patterns are ethically questionable techniques for driving engagement, there is potential for ethically enabled dark patterns to increase engagement in health interventions. In this feasibility study, we present the Health Intervention Minigame Framework that enables the embedding of entertainment games into digital health interventions. We demonstrate a proof of concept prototype that supports three entertainment games with ethically enabled dark patterns. Although the framework is a first step towards the implementation of ethically-enabled dark patterns in entertainment games, it is still unclear whether the potential increased engagement with real-world health activities of such implementations outweighs their psychological cost.

Abstract Powered by smartphones and wearable devices, the Experience Sampling Method (ESM) has increased in popularity for studying behaviors, thoughts, and experiences over time and in situ. Participants in ESM studies receive several notifications a day to self-report but often disengage due to intrusive and poorly timed notifications. Consequently, the response rate drops over time, hampering data collection and degrading ecological validity. Researchers have experimented with various strategies to optimize notification scheduling, including personalization, context sensing, and machine learning (ML). Edge computing can facilitate the training of ML models without the need for server communications, which is especially convenient for in-the-wild studies with unreliable network connectivity. Complementary logical evaluations on edge devices can minimize participant burden by accounting for sampling density, i.e., ensuring a minimum number of well-distributed daily notifications. However, these efforts raise engineering and scientific challenges related to avoiding cold start and training models on smartwatches. To overcome these challenges, we propose an open-source architecture and software that facilitates online learning to optimize notification delivery. Our feasibility study with $$N=37$$ N = 37 participants resulted in a response rate of 10.2% higher and a reaction time of 9.6% lower on average compared to the classical interval-based sampling.

Publicly displaying personal tracking data can promote healthier lifestyles, with most existing research focusing on digital visualizations. However, public physicalizations have been shown to attract more attention and foster greater engagement. Despite this, few studies in this area have utilized personal physical activity data as input or explored its impact on motivating data generators’ physical activity. To address this gap, we designed PlanetWalker, a system that visualizes users’ walking steps on their phone and shows the visualization publicly through a digital display, as well as medium- and large-scale public physicalizations as probes. The probes were deployed following the Wizard of Oz method in sequence during a six-week in-the-wild study. Through evaluations with users generating data and passersby, our findings show how public physicalizations can improve motivation, facilitate social interaction, and increase engagement. We conclude by discussing the design directions for supporting the public presentation of personal data through public physicalization to motivate physical activity and provide design trade-offs and suggestions on the physicality and scale of the medium.

The Experience Sampling Method (ESM) is commonly used to understand behaviors, thoughts, and feelings in the wild by collecting self-reports. Sustaining sufficient response rates, especially in long-running studies remains challenging. To avoid low response rates and dropouts, experimenters rely on their experience, proposed methodologies from earlier studies, trial and error, or the scarcely available participant behavior data from previous ESM protocols. This approach often fails in finding the acceptable study parameters, resulting in redesigning the protocol and repeating the experiment. Research has shown the potential of machine learning to personalize ESM protocols such that ESM prompts are delivered at opportune moments, leading to higher response rates. The corresponding training process is hindered due to the scarcity of open data in the ESM domain, causing a cold start, which could be mitigated by simulating participant behavior. Such simulations provide training data and insights for the experimenters to update their study design choices. Creating this simulation requires behavioral science, psychology, and programming expertise. Large language models (LLMs) have emerged as facilitators for information inquiry and programming, albeit random and occasionally unreliable. We aspire to assess the readiness of LLMs in an ESM use case. We conducted research using GPT-3.5 turbo-16k to tackle an ESM simulation problem. We explored several prompt design alternatives to generate ESM simulation programs, evaluated the output code in terms of semantics and syntax, and interviewed ESM practitioners. We found that engineering LLM-enabled ESM simulations have the potential to facilitate data generation, but they perpetuate trust and reliability challenges.

Smartwatches are emerging as an increasingly popular platform for longitudinal in situ data collection with methods often referred to as experience sampling and ecological momentary assessment. Their small size challenges designers of relevant applications to ensure usability and a positive user experience. This paper investigates the usability of different input techniques for responding to in situ surveys administered on smartwatches. In this paper, we classify different input techniques that can support this task. Then, we report on two user studies that compared different input techniques and their suitability at two levels of user activity: while sitting and while walking. A pilot study (N = 18) examined numeric input with three input techniques that utilize common features of smartwatches with a touchscreen: Multi-Step Tapping, Bezel Rotation, and Swiping. The main study (N = 80) examined numeric input and list selection including in the comparison two more techniques: Long-List Tapping and Virtual Buttons to scroll through options. Overall, we found that whether users are seated or walking did not affect the speed or accuracy of input. Bezel rotation was the slowest input technique but also the most accurate. Swiping resulted in most errors. Long-List Tapping yielded the shortest reaction times. Future research should examine different form factors for the smartwatch and diverse usage contexts.

Although a vast number of mobile health interventions has been discussed in the scientific literature, the scientific evidence for engineering gamified health interventions is too meager. Depending on the context, this leads to immature investments on one side of the spectrum and delayed implementations on the other side. GameBus is a web platform for increasing the rigor of gamified health experiments in living lab settings. This article clarifies the scientific design rationale of this platform and provides an eight-year evaluation of its performance. We demonstrate that a large variety of studies has been performed, leading to a promising data set for analyzing the impact of gamification techniques in the context of health promotion. While various articles on such studies have been published, along with data sets, this article shares novel information to enable other scholars to scale up such efforts. We also highlight critical gaps to guide future work on the platform.

Mobile health (mHealth) tools are regularly used in a wide range of mental health domains to assess and monitor patients, potentially increasing patient engagement. Recent studies demonstrated that tailored approaches provide better results than generic approaches. However, the effectiveness of tailoring has not yet been investigated empirically for patients with severe mental illnesses (SMIs). It also remains unclear how personalized goals, which are critical from a treatment point of view, impact engagement. Therefore, we designed a novel mHealth tool to increase SMI patient engagement with their personal goals which we evaluated empirically. We designed a two-period, two-arm within-subject crossover study in which 4 participants were exposed to personalized and non-personalized behavioral goals. Contrary to expectations, personalized behavioral goals did not have a significant impact on engagement levels. When considering our participant feedback and also in the context of flow theory, we rationalized that our goal personalization strategy was too static for SMI patients. Therefore, in our future work, we will investigate dynamic strategies that adapt goal difficulty over time.

An important purpose of science is building and advancing general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some fields. Particularly, the systematic linking of empirical data with theoretical constructs is currently lacking. Within this article, we propose a prototypical solution (i.e., a metamodel and graph database) for consolidating scientific knowledge by linking theoretical constructs with empirical data. We conducted a case study within the field of health behavior change where the system is used to record three scientific theories and three empirical studies as well as their mutual links. Finally, we demonstrate how the system can be queried to accumulate knowledge.

In recent years, it has been demonstrated that checklists can improve patient safety significantly. To facilitate the effective use of checklists in daily practice, both the medical community and the informatics community propose to implement checklists in dynamic checklist applications that can be integrated into the clinical workflow and that is specific to the patient context. However, it is difficult to develop such applications because they are tightly intertwined with the content of specific checklists. We propose a platform that enables access to dynamic checklist applications by configuring the infrastructures provided in the platform. Then, the applications can be developed without time-consuming programming work. We define a number of design criteria regarding point of care and clinical processes by analyzing the existing checklist applications and the lessons learned from implementations. Then, by applying rule-based clinical decision support and workflow management technologies, we design technical mechanisms to satisfy the design criteria. A dynamic checklist application platform is designed based on these mechanisms. Finally, we build a platform in various design cycle iterations, driven by multiple clinical cases. By applying the platform, we develop nine comprehensive dynamic checklist applications with 242 dynamic checklists. The results demonstrate both the feasibility and the overall generic nature of the proposed approach. We propose a novel platform for configuring dynamic checklist applications. This platform satisfies the general requirements and can be easily configured to satisfy different scenarios in which safety checklists are used.

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement.

The Business Process Management field addresses design, improvement, management, support, and execution of business processes. In doing so, we argue that it focuses more on developing modeling notations and process design approaches than on the needs and preferences of the individual who is modeling (i.e., the user). New data-centric process modeling approaches are taken as a relevant and timely stream of process design approaches to test our argument. First, we provide a review of existing data-centric process approaches, culminating in a theoretical classification framework. Next, we empirically evaluate three specific approaches with regard to the claims they make. We had participants representative of actual users try out these approaches on realistic scenarios via a series of workshops. Participants assessed to what extent quality claims from the literature could be recognized within the workshop sessions. The results of this evaluation substantiate a number of claims behind the approaches, but also identify opportunities to further improve them. Most prominently, we found that the usability aspects of all considered approaches are a source of concern. This leads us to the insight that usability aspects of process design approaches are crucial and, in the perception of groups representative of actual users, leave much to be desired. In that sense, our research can be seen as a wake-up call for process modeling notation designers to consider the usability side—and as such, the interest of the human modeler—more than is currently the case.

Clinical safety checklists receive much research attention since they can reduce medical errors and improve patient safety. Computerized checklist support systems are also being developed actively. Such systems should individualize checklists based on information from the patient’s medical record while also considering the context of the clinical workflows. Unfortunately, the form definitions, database queries and workflow definitions related to dynamic checklists are too often hard-coded in the source code of the support systems. This increases the cognitive effort for the clinical stakeholders in the design process, it complicates the sharing of dynamic checklist definitions as well as the interoperability with other information systems. In this paper, we address these issues by contributing the DCCSS meta-model which enables the model-based development of dynamic checklist support systems. DCCSS was designed as an incremental extension of standard meta-models, which enables the reuse of generic model editors in a novel setting. In particular, DCCSS integrates the Business Process Model and Notation (BPMN) and the Guideline Interchange Format (GLIF), which represent best of breed languages for clinical workflow modeling and clinical rule modeling respectively. We also demonstrate one of the use cases where DCCSS has already been applied in a clinical setting.

It has recently been demonstrated that checklists can enable significant improvements to patient safety. However, their clinical acceptance is significantly lower than expected. This is due to the lack of good support systems. Specifically, support systems are too static: this holds for paper-based support as well as for electronic systems that digitize paper-based support naively. Both approaches are independent from clinical process and clinical context. In this paper, we propose a process-oriented and context-aware dynamic checklist support system: Trace book. This system supports the execution of complex clinical processes and rules involving data from Electronic Medical Record systems. Workflow activities and forms are specific to individual patients based on clinical rules and they are dispatched to the right user automatically based on a process model. Besides describing the Trace book functionality in general, this paper demonstrates the support system specifically on an example application that we are preparing for a controlled clinical evaluation. At last we discuss the limitations of Trace book.

This inaugural Special Issue on Experimental Software Engineering in the Cloud (ESEiC) contributes to the further development of experimental software tools while also demonstrating ways in which the empirical evaluation of software engineering results can be improved. The open call for papers to this issue invited academic software developers to publish entire software environments together with a paper that focuses on the empirical evaluation of the related engineering techniques. Out of five submissions, two have been accepted. Both papers are the result of long term research efforts by multiple authors, and are supplemented by a multitude of software environments: all of the software engineering tools under study are available via virtual machines (VMs) in the academic SHARE cloud. The first paper cites 13 VMs, each containing one tool, while the second one cites one VM containing five tools under study. Both papers contribute new frameworks for comparing the tools under study. To the best of our knowledge, this is the first informatics journal issue that offers reproducible comparisons of such depth and breadth. In this editorial, we clarify the background of the special issue, introduce the SHARE cloud platform, and summarize the two papers.

Personal Health Records (PHRs) should remain the lifelong property of patients, who should be able to show them conveniently and securely to selected caregivers and institutions. In this paper, we present MyPHRMachines, a cloud-based PHR system taking a radically new architectural solution to health record portability. In MyPHRMachines, health-related data and the application software to view and/or analyze it are separately deployed in the PHR system. After uploading their medical data to MyPHRMachines, patients can access them again from remote virtual machines that contain the right software to visualize and analyze them without any need for conversion. Patients can share their remote virtual machine session with selected caregivers, who will need only a Web browser to access the pre-loaded fragments of their lifelong PHR. We discuss a prototype of MyPHRMachines applied to two use cases, i.e., radiology image sharing and personalized medicine.

Clinical protocols are a popular instrument to document how clinicians are expected to behave under specific conditions. Protocols are typically based on internationally peer reviewed clinical guidelines as well as on hospital-local agreements. Existing techniques for monitoring protocol adherence only support protocol descriptions involving simple sequences and local decision rules. As care and cure processes are becoming increasingly complex, the need for more advanced techniques naturally emerges. In this paper we present a novel approach to defining and monitoring complex clinical protocols. By using BPMN to document protocols we enable the concise specification of protocols that involve multiple stakeholders that operate in parallel and under uncertainty. Uncertainty relates to the fact that protocols may involve complex loops and choices. While this specification style was becoming increasingly popular in the literature and practice of hospital management and operations management in general, corresponding conformance analysis techniques were still lacking. This paper contributes the first such technique and evaluate it on a complex compliance pattern from the cardiology domain.

Personal Health Records (PHRs) should remain the lifelong property of patients and should be showable conveniently and securely to selected caregivers. Regarding interoperability, current solutions for PHRsfocus on standard data exchange formats and transformations to move data across health information systems. In this paper we propose MyPHRMachines, a patient-centric system that takes a radically new architectural solution to health record interoperability. We propose to deploy besides the medical data also the related software to the PHR system. After uploading their medical data to MyPHRMachines, patients can access them again from remote virtual machines that contain the right software to visualize and analyze them without any conversion. Patients can share their remote virtual machine session with a selected health provider, who will need only a Web browser to access the pre-loaded fragments of the lifelong PHR. We illustrate how our prototype already supports the use case of a real-world patient and discuss the research agenda required to translate this prototype into a viable solution for the international healthcare industry.

Patient Health Records (PHRs) shift the ownership of health data from health providers to patients. Such a shift poses important challenges from the data privacy point of view. Patients would like to be able to selectively reveal information to other stakeholders and, at the same time, be assured that their health information will not be used improperly once shared. Current PHR systems partially fail to satisfy these requirements. In this paper, we show that both requirements can be satisfied fully when adopting a novel cloud-based PHR system architecture.We expain the role of remote virtual machines in this architecture and use interaction models to reason about privacy implications. Finally, we evaluate MyPHRMachines, a prototypical implementation of the architecture: we demonstrate that the system enables the execution of third party genome analysis services on patientowned genome data while ensuring that (1) such services cannot maliciously store this data and (2) patients can show the analysis results to experts without sharing along their full genome.

Due to license restrictions and installation issues, it is often not feasible to experiment with software without making substantial investments. Especially in the case of legacy tools, it turns out that even free software is often too costly (i.e., time-consuming) to be installed for evaluating the quality of a research contribution. After organizing a series of events related to software modeling, we have constructed (and started to use) SHARE, a system for sharing practically any type of software artifact to reviewers and to other participants who have very limited time available. The system relies on cloud-computing technologies to provide online access to interactive environments containing all the tools, documentation, input and output models to reproduce alleged research results. The system also enables one to clone such an environment and add additional models or tools in order to extend a contribution or pinpoint a problem. In retrospect, we observe that the approach is not limited to software modeling and SHARE is in fact gaining acceptance in other fields already.

Today's model-driven engineering tools focus on the automatic transformation of software models and lack essential support for interacting with developers. This paper presents some lessons learned from building a standard compliant platform for the visual development of interactive consistency maintenance software. Based on an established requirements engineering case study, the paper illustrates the need for developer interaction and the controlled tolerance of inconsistencies. This motivates the role of traceability links in two dimensions: links between application models allow one to maintain consistency incrementally and tolerate inconsistencies in a controlled manner. In the other dimension, links between transformation models enable the refinement of declarative descriptions of consistency contracts into constructive transformations. Such transformations can be generated automatically from the contracts but tend to be optimized subtly by a transformation expert