Electronic Health Physical Activity Behavior Change Intervention to Self-Manage Cardiovascular Disease: Qualitative Exploration of Patient and Health Professional Requirements
Background: Cardiovascular diseases are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation. Uptake of traditional cardiac rehabilitation remains suboptimal, as attendance at formal hospital-based cardiac rehabilitation programs is low, with community-based cardiac rehabilitation rates and individual long-term exercise maintenance even lower. Home-based cardiac rehabilitation programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available.
Objective: Given the potential that home-based cardiac rehabilitation programs have, it is important to explore how to appropriately design any such intervention in conjunction with key stakeholders. The aim of this study was to engage with individuals with cardiovascular disease and other professionals within the health ecosystem to (1) understand the personal, social, and physical factors that inhibit or promote their capacity to engage with physical activity and (2) explore their technology competencies, needs, and wants in relation to an eHealth intervention.
Methods: Fifty-four semistructured interviews were conducted across two countries. Interviews were audiotaped, transcribed verbatim, and analyzed using thematic analysis. Barriers to the implementation of PATHway were also explored specifically in relation to physical capability and safety as well as technology readiness and further mapped onto the COM-B model for future intervention design.
Results: Key recommendations included collection of patient data and use of measurements, harnessing hospital based social connections, and advice to utilize a patient-centered approach with personalization and tailoring to facilitate optimal engagement.
Conclusions: In summary, a multifaceted, personalizable intervention with an inclusively designed interface was deemed desirable for use among cardiovascular disease patients both by end users and key stakeholders. In-depth understanding of core needs of the population can aid intervention development and acceptability.
Deirdre Walsh, Kieran Moran, Véronique Cornelissen, Roselien Buys, Nils Cornelis, Catherine Woods.(2018). Electronic Health Physical Activity Behavior Change Intervention to Self-Manage Cardiovascular Disease: Qualitative Exploration of Patient and Health Professional Requirements, Journal of Medical Internet Research, 20(5):e163
Computerized Decision Support for Beneficial Home-based Exercise Rehabilitation in Patients with Cardiovascular Disease
Background: Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes.
Objectives: We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs.
Methods: The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems.
Results: The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance.
Conclusions: Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.
Andreas Triantafyllidis, Dimitris Filos, Roselien Buys, Jomme Claes, Veronique Cornelissen, Evangelia Kouidi, Anargyros Chatzitofis, Dimitris Zarpalas, Petros Daras, Deirdre Walsh, Catherine Woods, Kieran Moran, Nicos Maglaveras, Ioanna Chouvarda (2018). Computerized Decision Support for Beneficial Home-based Exercise Rehabilitation in Patients with Cardiovascular Disease, Computer Methods and Programs in Biomedicine
Motion Analysis: Action Detection, Recognition and Evaluation Based on Motion Capture Data
A novel framework, for real-time action detection, recognition and evaluation of motion capture data, is presented in this paper. Pose and kinematics information is used for data description. Automatic and dynamic weighting, altering joint data significance based on action involvement, and Kinetic energy-based descriptor sampling are employed for efficient action segmentation and labelling. The automatically segmented and recognized action instances are subsequently fed to the framework action evaluation component, which compares them with the corresponding reference ones, estimating their similarity. Exploiting fuzzy logic, the framework subsequently gives semantic feedback with instructions on performing the actions more accurately. Experimental results on MSR-Action3D and MSRC12 benchmarking datasets and a new, publicly available one, provide evidence that the proposed framework compares favourably to state-of-the-art methods by 0.5–6% in all three datasets, showing that the proposed method can be effectively used for unsupervised gesture/action training.
Fotini Patrona, Anargyros Chatzitofis, Dimitrios Zarpalas, Petros Daras (2018). Motion analysis: Action detection, recognition and evaluation based on motion capture data, Pattern Recognition, 76, pp 612–622