Triantafyllidis A, Filos D, Buys R, Claes J, Cornelissen V, Kouidi E, Chatzitofis A, Zarpalas D, Daras P, Walsh D, Woods C, Moran K, Maglaveras N, Chouvarda I. Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018;
162:1-10. [PMID:
29903475 DOI:
10.1016/j.cmpb.2018.04.030]
[Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 03/28/2018] [Accepted: 04/17/2018] [Indexed: 06/08/2023]
Abstract
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.
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