1,721,658 research outputs found
Hybride 2D/3D mens-machine interactietechnieken in immersieve virtuele moddeleeromgevingen
No full textHybride 2D/3D mens-machine interactietechnieken in immersieve virtuele moddeleeromgevingen
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Evaluating the Impact of the HeartHab App on Motivation, Physical Activity, Quality of Life, and Risk Factors of Coronary Artery Disease Patients: Multidisciplinary Crossover Study
No full textBackground: Telerehabilitation approaches have been successful in supporting coronary artery disease (CAD) patients to rehabilitate at home after hospital-based rehabilitation. However, on completing a telerehabilitation program, the effects are not sustained beyond the intervention period because of the lack of lifestyle adaptations. Furthermore, decline in patients’ motivation lead to recurrence of disease and increased rehospitalization rates. We developed HeartHab, using persuasive design principles and personalization, to enable sustenance of rehabilitation effects beyond the intervention period. HeartHab promotes patients’ understanding, motivates them to reach personalized rehabilitation goals, and helps to maintain positive lifestyle adaptations during telerehabilitation.
Objective: This study aimed to investigate the impact of the HeartHab app on patients’ overall motivation, increasing physical activities, reaching exercise targets, quality of life, and modifiable risk factors in patients with CAD during telerehabilitation. The study also investigated carryover effects to determine the maintenance of effects after the conclusion of the intervention.
Methods: A total of 32 CAD patients were randomized on a 1:1 ratio to telerehabilitation or usual care. We conducted a 4-month crossover study with a crossover point at 2 months using a mixed-methods approach for evaluation. We collected qualitative data on users’ motivation, user experience, and quality of life using questionnaires, semistructured interviews and context-based sentiment analysis. Quantitative data on health parameters, exercise capacity, and risk factors were gathered from blood tests and ergo-spirometry tests. Data procured during the app usage phase were compared against baseline values to assess the impact of the app on parameters such as motivation, physical activity, quality of life, and risk factors. Carryover effects were used to gather insights on the maintenance of effects.
Results: The qualitative data showed that 75% (21/28) of patients found the HeartHab app motivating and felt encouraged to achieve their rehabilitation targets. 84% (21/25) of patients either reached or exceeded their prescribed physical activity targets. We found positive significant effects on glycated hemoglobin (P=.01; d=1.03; 95% CI 0.24-1.82) with a mean decrease of 1.5 mg/dL and high-density lipoprotein (HDL) cholesterol (P=.04; d=0.78; 95% CI 0.02-1.55) with a mean increase of 0.61 mg/dL after patients used the HeartHab app. We observed significant carryover effects on weight, HDL cholesterol, and maximal oxygen consumption (VO2 max), indicating the maintenance of effects.
Conclusions: Persuasive design techniques integrated in HeartHab and tailoring of exercise targets were effective in motivating patients to reach their telerehabilitation targets. This study demonstrated significant effects on glucose and HDL cholesterol and positive carryover effects on weight, HDL cholesterol, and VO2 max. There was also a perceived improvement in quality of life. A longer-term evaluation with more patients could possibly reveal effectiveness on other risk factors and maintenance of the positive health behavior change
Task-based prediction of interaction patterns for ambient intelligence environments
No full textIn this paper we introduce a monitoring system to support the user executing tasks in an ambient intelligence environment. In contrast with traditional environments, the goal of the user can not always be defined beforehand, but is determined while the user interacts with the environment. The monitor observes the user's activities and learns to correlate a set of user actions with a goal. The system maps activities to a task model and reuses these models to take appropriate actions in later similar user actions that are observed
Investigating Motivations and Patient Profiles for Personalization of Health Applications for Behaviour Change
No full textPersonalization is a key aspect when developing applications targeting health behaviour change. However, the use of personalized mobile interventions for lifestyle behaviour is still in its infancy. Based on our former research on mobile applications to support cardiac patients in health behaviour change, we identified four key motivations to enhance the personalization offered in applications targeting health behaviour change. In this paper, we propose a mixed-methods approach, using both qualitative and quantitative data collected in prior studies, to apply personalization in the design of health applications. Our approach consists of five steps: 1) collecting data for personalization, 2) detecting patient profiles using clustering methods, 3) understanding patient profiles using a graph-ical representation, 4) describing patient profiles using personas, and 5) personalizing a health application according to patient profiles. One of the major strengths of our approach is that it combines established HCI techniques such as personas and data visualization techniques with methods from big data analytics and artificial intelligence to identify ways to personalize health applications. We conclude by presenting future directions to apply personalization in the domain of health technologies.This research was funded by the Special Research Fund (BOF) of Hasselt University (BOF18DOC26) and the EU funded project H2020 IA CoroPrevention (848056)
The digital profile of cardiac patients anno 2021
No full textType of funding sources: Public grant(s) – EU funding. Main funding source(s): EU funded project H2020 IA CoroPrevention,Special Research Fund (BOF) of Hasselt UniversityH2020 IA CoroPrevention (848056)
Computerized decision support for exercise prescription in cardiovascular rehabilitation: high hopes. . .but still a long way to go
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