Mobile App and Digital System for Patients after Myocardial Infarction (afterAMI): Results from a Randomized Trial.
Krzowski BartoszMaria BoszkoMichał PellerPaulina HoffmanNatalia ŻurawskaKamila SkoczylasGabriela OsakŁukasz KołtowskiMarcin GrabowskiGrzegorz OpolskiPaweł BalsamPublished in: Journal of clinical medicine (2023)
Cardiac rehabilitation after acute myocardial infarction is crucial and improves patients' prognosis. It aims to optimize cardiovascular risk factors' control. Providing additional support via mobile applications has been previously suggested. However, data from prospective, randomized trials evaluating digital solutions are scarce. In this study, we aimed to evaluate a mobile application-afterAMI-in the clinical setting and to investigate the impact of a digitally-supported model of care in comparison with standard rehabilitation. A total of 100 patients after myocardial infarction were enrolled. Patients were randomized into groups with either a rehabilitation program and access to afterAMI or standard rehabilitation alone. The primary endpoint was rehospitalizations and/or urgent outpatient visits after 6 months. Cardiovascular risk factors' control was also analyzed. Median age was 61 years; 65% of the participants were male. This study failed to limit the number of primary endpoint events (8% with app vs. 27% without app; p = 0.064). However, patients in the interventional group had lower NT-proBNP levels ( p = 0.0231) and better knowledge regarding cardiovascular disease risk factors ( p = 0.0009), despite no differences at baseline. This study showcases how a telemedical tool can be used in the clinical setting.
Keyphrases
- cardiovascular risk factors
- cardiovascular disease
- end stage renal disease
- ejection fraction
- newly diagnosed
- risk factors
- acute myocardial infarction
- prognostic factors
- metabolic syndrome
- healthcare
- randomized controlled trial
- palliative care
- coronary artery disease
- clinical trial
- heart failure
- percutaneous coronary intervention
- cardiovascular events
- deep learning
- atrial fibrillation
- chronic pain
- pain management