Search

This poster showcases the use of JSON structure to create software rules based on expert knowledge to generate alerts based on patient data. We utilize JsonLogic to implement such rules. The JsonLogic format is designed to allow you to share rules (logic) between front-end and back-end code (regardless of language difference), even to store logic along with a record in a database (https://jsonlogic.com/). We expanded the functionality of JsonLogic by adding custom operators enabling advanced data processing and analytics. The following system is designed in the context of the assessment of the feasibility of recommending diuretics to heart failure patients, based on monitoring weight, blood oxygen saturation, blood pressure, and self-report by patients.

Human Digital Twins (HDTs) are specifically designed to represent humans virtually. Despite their recency, HDTs are considered to be powerful tools for enhancing personalized healthcare, early disease detection, improving patient outcomes, and optimizing lifestyle. Building HDTs is challenging especially due to the complexity of modeling human behavior. To unravel such complexity, in this paper we focus on specific aspects of human behavior and emotions (valence and arousal) to enable a more informed HTD behavior modeling. We assessed the feasibility and performance of our data collection infrastructure with N=112 participants. During a science festival, our participants wore a smartwatch to self-report their emotions while simultaneously their physiological data were collected through smartwatch sensors. We explored predictive modeling possibilities once with all the collected data and then with only the sensor-related data. The former performed best with 25 features, modeled with the Quadratic Discriminant Analysis classifier, resulting in 72.4% accuracy, however, with only 4 features derived from the self-reports, the K-Nearest Neighbors Classifier was able to achieve an accuracy of 71.6%. The latter used 17 sensor-related features and the Quadratic Discriminant Analysis classifier estimator reaching a 51.3% accuracy.

Data-enabled design (DED) is a novel design methodology that is gaining momentum given the growing availability and maturity of IoT technologies. Experiences with this method are limited, for which it is interesting to study how different design teams apply and appropriate the method in different contexts. Our case concerns the design of a smartwatch app and an online dashboard to facilitate self-monitoring in cardiac health to prevent heart failure. We report on the design and evaluation of contextual experiences with the overall system, offering an accessible foundation for future iterative improvements. Our case demonstrates how the DED method can facilitate the participation of stakeholders and especially end-users, in co-designing within a heavily regulated and technologically rich context.

Background: Currently, in the majority of patients with stable angina pectoris (SAP) treatment consists of optimal medical treatment, potentially followed by coronary angiography and subsequent coronary revascularisation if necessary”. Recent work questioned the effectiveness of these invasive procedures in reducing re-events and improving prognosis. The potential of exercise-based cardiac rehabilitation on clinical outcomes in patients with coronary artery disease is well-known. However, in the modern era, no studies compared the effects of cardiac rehabilitation versus coronary revascularisation in patients with SAP. Methods: In this multicentre randomised controlled trial, 216 patients with stable angina pectoris and residual anginal complaints under optimal medical treatment will be randomised to: 1) usual care (i.e., coronary revascularisation), or 2) a 12-month cardiac rehabilitation (CR) programme. CR consists of a multidisciplinary intervention, including education, exercise training, lifestyle coaching and a dietary intervention with a stepped decline in supervision. The primary outcome will be anginal complaints (Seattle Angina Questionnaire-7) following the 12-month intervention. Secondary outcomes include cost-effectiveness, ischemic threshold during exercise, cardiovascular events, exercise capacity, quality of life and psychosocial wellbeing. Discussion: In this study, we will examine the hypothesis that multidisciplinary CR is at least equally effective in reducing anginal complaints as the contemporary invasive approach at 12-months follow-up for patients with SAP. If proven successful, this study will have significant impact on the treatment of patients with SAP as multidisciplinary CR is a less invasive and potentially less costly and better sustainable treatment than coronary revascularisations. Trial registration: Netherlands Trial Register, NL9537. Registered 14 June 2021.

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.

Science aims to build and advance general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some domains, for example in the field of health behavior change. Especially, a systematic mapping between empirical data and theoretical constructs is lacking. We propose a toolbox to establish that mapping. We adopted a modeling approach based on literature surveys to elicit requirements and to derive a metamodel. We adopted a graph-based database system to implement the metamodel, and designed a web-based tool for importing data from annotated text documents. To evaluate that toolbox (named SciModeler), we have conducted a case study within the field of health behavior change to record three scientific theories, three empirical studies, and the mapping in-between. We have documented how SciModeler aids closing gaps between empirical data and theoretical constructs. We have demonstrated that this enables new types of analyses by sharing example queries for (1) refining scientific theories, (2) exploring promising intervention strategies for a specific context, and (3) checking the potential impact of an intervention platform in a specific context. Our supplementary materials promote replication of these results. SciModeler can support the consolidation of scientific knowledge in the field of health behavior change, and we suggest that it may be applied within other fields, as well. An important direction for future work is promoting online collaboration on SciModeler graphs.

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.

In general, individuals with lower socioeconomic status (SES) are less physically active and adhere to poorer diets than higher SES individuals. To promote healthier lifestyles in lower SES populations, we hosted a digital health promotion program among male vocational students at a school in The Netherlands. In a pilot study, we evaluated whether this target audience could be engaged with an mHealth app using lottery-based incentives that trigger feelings of anticipated regret. Especially, we studied the social and interpersonal aspects of regret lotteries in a within-subject experimental design. In this design, subjects either participated in a social variant (i.e., with students competing against their peers for a chance at a regret lottery), or an individual variant (i.e., with subjects solely individually engaged in a lottery). Additionally, we studied the impact of different payout schedules in a between-subject experimental design. In this design, participants were assigned to either a short-term, low-value payout schedule, or a long-term, high-value payout schedule. From a population of 72 male students, only half voluntarily participated in our 10-week program. From interviews, we learned that the main reason for neglecting the program was not related to the lottery-based incentives, nor to the prizes that were awarded. Instead, non-enrolled subjects did not join the program, because their peers were not joining. Paradoxically, it was suggested that students withheld their active participation until a larger portion of the sample was actively participating. From the subjects that enrolled in the program (N = 36, males, between 15 and 25 years of age), we found that a large proportion stopped interacting with the program over time (e.g., after roughly 4 weeks). Our results also indicated that students performed significantly more health-related activities when assigned to the social regret lottery, as opposed to the individual variant. This result was suppor

Background: Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. mHealth interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored towards an individual's preferences and goals. Yet, it remains unclear how goals are most effectively tailored in a gamified health promotion context. Objective: In this study, we evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. We hypothesized that interventions that suggest health goals tailored towards users’ own self-perceived capabilities and preferences will be more engaging than interventions with generic goals. Methods: The study was designed as a two-arm randomized intervention trial. Participants were recruited among staff members of seven governmental organizations. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides and sports sessions. Using an mHealth application, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants, and a leaderboard displaying the average scores per organizational department. Participants could score points by performing any of the six healthy tasks they got assigned (e.g., walk for at least 2000 m). The level of complexity of three tasks was updated every two weeks; either by changing the suggested task intensity, or the suggested frequency of the task. The two intervention arms—with participants randomly assigned—consisted of: 1) a personalized treatment that tailored the complexity parameters based on the self-perceived capabilities and self-selected goals of participants, and 2) a control treatment whe

Gathering diabetes-related data requires effort from the patient side to log specific events throughout experiments. This is an error-prone task that the patients usually handle by following a prescribed protocol. However, patients often do not follow the protocol, causing missing or imperfect data. This study investigates the possibility of generating Markov models from existing/logged data and using them for imputation. The models are used to infer information related to missing events (types/activities) in data recorded by diabetes patients, allowing for improvements in the quality and continuity of such data. Our results indicate that such an approach can help improve the quality of the data collected.