European heart journal. Digital health最新文献

{"title":"Sudden cardiac arrest prediction via deep learning electrocardiogram analysis.","authors":"Matt T Oberdier, Luca Neri, Alessandro Orro, Richard T Carrick, Marco S Nobile, Sujai Jaipalli, Mariam Khan, Stefano Diciotti, Claudio Borghi, Henry R Halperin","doi":"10.1093/ehjdh/ztae088","DOIUrl":"10.1093/ehjdh/ztae088","url":null,"abstract":"<p><strong>Aims: </strong>Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool.</p><p><strong>Methods and results: </strong>A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities.</p><p><strong>Conclusion: </strong>Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"170-179"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.","authors":"Jui-Tzu Huang, Chih-Hsueh Tseng, Wei-Ming Huang, Wen-Chung Yu, Hao-Min Cheng, Hsi-Lu Chao, Chern-En Chiang, Chen-Huan Chen, Albert C Yang, Shih-Hsien Sung","doi":"10.1093/ehjdh/ztaf003","DOIUrl":"10.1093/ehjdh/ztaf003","url":null,"abstract":"<p><strong>Aims: </strong>Left ventricular hypertrophy (LVH) is clinically important; current electrocardiography (ECG) diagnostic criteria are inadequate for early detection. This study aimed to develop an artificial intelligence (AI)-based algorithm to improve the accuracy and prognostic value of ECG criteria for LVH detection.</p><p><strong>Methods and results: </strong>A total of 42 016 patients (64.3 ± 16.5 years, 55.3% male) were enrolled. LV mass index was calculated from echocardiographic measurements. Left ventricular hypertrophy screening utilized ECG criteria, including Sokolow-Lyon, Cornell product, Cornell/strain index, Framingham criterion, and Peguero-Lo Presti. An AI algorithm using CatBoost was developed and validated (training dataset 80% and testing dataset 20%). F1 scores, reflecting the harmonic mean of precision and recall, were calculated. Mortality data were obtained through linkage with the National Death Registry. The CatBoost-based AI algorithm outperformed conventional ECG criteria in detecting LVH, achieving superior sensitivity, specificity, positive predictive value, F1 score, and area under curve. Significant features to predict LVH involved QRS and P-wave morphology. During a median follow-up duration of 10.1 years, 1655 deaths occurred in the testing dataset. Cox regression analyses showed that LVH identified by AI algorithm (hazard ratio and 95% confidence interval: 1.587, 1.309-1.924), Sokolow-Lyon (1.19, 1.038-1.365), Cornell product (1.301, 1.124-1.505), Cornell/strain index (1.306, 1.185-1.439), Framingham criterion (1.174, 1.062-1.298), and echocardiography-confirmed LVH (1.124, 1.019-1.239) were all significantly associated with mortality. Notably, AI-diagnosed LVH was more predictive of mortality than echocardiography-confirmed LVH.</p><p><strong>Conclusion: </strong>Artificial intelligence-based LVH diagnosis outperformed conventional ECG criteria and was a superior predictor of mortality compared to echocardiography-confirmed LVH.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"252-260"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized app-based coaching for improving physical activity in heart failure with preserved ejection fraction patients compared with standard care: rationale and design of the MyoMobile Study.","authors":"Silav Zeid, Jürgen H Prochaska, Alexander Schuch, Sven Oliver Tröbs, Andreas Schulz, Thomas Münzel, Tanja Pies, Wilfried Dinh, Matthias Michal, Perikles Simon, Philipp Sebastian Wild","doi":"10.1093/ehjdh/ztae096","DOIUrl":"10.1093/ehjdh/ztae096","url":null,"abstract":"<p><strong>Aims: </strong>Patients suffering from heart failure with preserved ejection fraction (HFpEF) often exhibit a sedentary lifestyle, contributing to the worsening of their condition. Although there is an inverse relationship between physical activity (PA) and adverse cardiovascular outcomes, the implementation of Class Ia PA guidelines is hindered by low participation in supervised and structured programmes, which are not suitable for a diverse population of HFpEF patients. The MyoMobile study has been designed to assess the effect of a 12-week, app-based coaching programme on promoting PA in patients with HFpEF.</p><p><strong>Methods and results: </strong>The MyoMobile study was a single-centre, randomized, controlled three-armed parallel group clinical trial with prospective data collection to investigate the effect of a personalized mobile app health intervention compared with usual care on PA levels in patients with HFpEF. Major inclusion criteria were age ≥ 45 years, a diagnosis of HFpEF, LVEF > 40%, and current HF symptoms (NYHA Class I-III). Major exclusion criteria included acute decompensated HF, non-ambulatory status, recent acute coronary syndrome or cardiac surgery, alternative diagnoses for HF symptoms, active cancer treatment, and physical or medical conditions affecting mobility. Participants were recruited from hospitals, general practices, and practices specialized in internal medicine and cardiology in the Rhine-Main area, Germany. Participants underwent an objective 7-day PA measurement with a 3D accelerometer (Dynaport, McRoberts) at screening and after the 12-week intervention period. Following the screening, eligible participants were randomized into one of three groups: standard care (PA consulting), the intervention arm with app-based PA tracking and coaching, or the intervention arm with tracking but without coaching. The primary efficacy endpoint was the change in average daily step count between the average step count at baseline and at the end of the intervention, comparing standard care to a 12-week app-based PA coaching intervention.</p><p><strong>Conclusion: </strong>Exercise intolerance is a primary symptom in HFpEF patients, leading to poor quality of life and HF-related adverse outcomes due to physical inactivity. The MyoMobile study was designed to investigate the use of app-based coaching to improve PA in patients with HFpEF with a personalized, home-based intervention, focusing on simple step counts for flexibility and ease of integration into daily routines.</p><p><strong>Clinical trial registration: </strong>URL: https://clinicaltrials.gov/ct2/show/NCT04940312.</p><p><strong>Unique identifier: </strong>NCT04940312.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"298-309"},"PeriodicalIF":3.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review.","authors":"Ruben G A van der Waerden, Rick H J A Volleberg, Thijs J Luttikholt, Pierandrea Cancian, Joske L van der Zande, Gregg W Stone, Niels R Holm, Elvin Kedhi, Javier Escaned, Dario Pellegrini, Giulio Guagliumi, Shamir R Mehta, Natalia Pinilla-Echeverri, Raúl Moreno, Lorenz Räber, Tomasz Roleder, Bram van Ginneken, Clara I Sánchez, Ivana Išgum, Niels van Royen, Jos Thannhauser","doi":"10.1093/ehjdh/ztaf005","DOIUrl":"10.1093/ehjdh/ztaf005","url":null,"abstract":"<p><p>Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"270-284"},"PeriodicalIF":3.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile/wearable digital devices for care of active cancer patients: a survey from the ESC Council of Cardio-Oncology.","authors":"Giuseppe Boriani, Jacopo F Imberti, Riccardo Asteggiano, Pietro Ameri, Davide A Mei, Michał Farkowski, Julian Chun, Josè Luis Merino, Teresa Lopez-Fernandez, Alexander R Lyon","doi":"10.1093/ehjdh/ztae082","DOIUrl":"10.1093/ehjdh/ztae082","url":null,"abstract":"<p><strong>Aims: </strong>The Council of Cardio-Oncology of the European Society of Cardiology developed an on-line anonymous survey to provide an overall picture of the current practice on the use of mobile and wearable digital devices in cardio-oncology and the potential barriers to their large-scale applicability.</p><p><strong>Methods and results: </strong>Between June 2023 and January 2024, an online anonymous questionnaire was completed by 220 healthcare professionals from 55 countries. The greatest number of respondents reported that mobile/wearable digital devices have a role in all active cancer patients for measuring heart rate (33.9%), blood pressure (34.4%), body temperature (32.0%), physical activity (42.4%), and sleep (31.2%). In the setting of atrial fibrillation detection, respondents were evenly split between applying these technologies in all patients (33.0%) or only in selected patients (33.0%). Regarding QTc interval monitoring, 30.6% reported that mobile/wearable digital devices play a role only in selected patients. The decision to use the device was taken by the patient in 56.6% of cases and the physician in 43.4%. The most important barrier reported to mobile/wearable device implementation in the setting of cardiac rhythm monitoring and QTc measurement was their cost (weighted average: 3.38 and 3.39, respectively).</p><p><strong>Conclusion: </strong>Mobile/wearable digital devices are considered to play an important role in different settings of cardio-oncology, including monitoring of patients' parameters and arrhythmia detection. Their role in monitoring physical activity and QTc interval appears more nuanced. The most important perceived barrier to mobile/wearable digital device implementation is considered their high cost.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"162-169"},"PeriodicalIF":3.9,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models.","authors":"Muhammad Ibtsaam Qadir, Ravi S Hira, Fiona R Kolbinger","doi":"10.1093/ehjdh/ztaf001","DOIUrl":"10.1093/ehjdh/ztaf001","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"159-161"},"PeriodicalIF":3.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble learning model for detection of pulmonary hypertension using electrocardiogram, chest X-ray, and brain natriuretic peptide.","authors":"Risa Kishikawa, Satoshi Kodera, Naoto Setoguchi, Kengo Tanabe, Shunichi Kushida, Mamoru Nanasato, Hisataka Maki, Hideo Fujita, Nahoko Kato, Hiroyuki Watanabe, Masao Takahashi, Naoko Sawada, Jiro Ando, Masataka Sato, Shinnosuke Sawano, Hiroki Shinohara, Koki Nakanishi, Shun Minatsuki, Junichi Ishida, Katsuhito Fujiu, Hiroshi Akazawa, Hiroyuki Morita, Norihiko Takeda","doi":"10.1093/ehjdh/ztae097","DOIUrl":"10.1093/ehjdh/ztae097","url":null,"abstract":"<p><strong>Aims: </strong>Delayed diagnosis of pulmonary hypertension (PH) is a known cause of poor patient prognosis. We aimed to develop an artificial intelligence (AI) model, using ensemble learning method to detect PH using electrocardiography (ECG), chest X-ray (CXR), and brain natriuretic peptide (BNP), facilitating accurate detection and prompting further examinations.</p><p><strong>Methods and results: </strong>We developed a convolutional neural network model using ECG data to predict PH, labelled by ECG from seven institutions. Logistic regression was used for the BNP prediction model. We referenced a CXR deep learning model using ResNet18. Outputs from each of the three models were integrated into a three-layer fully connected multimodal model. Ten cardiologists participated in an interpretation test, detecting PH from patients' ECG, CXR, and BNP data both with and without the ensemble learning model. The area under the receiver operating characteristic curves of the ECG, CXR, BNP, and ensemble learning model were 0.818 [95% confidence interval (CI), 0.808-0.828], 0.823 (95% CI, 0.780-0.866), 0.724 (95% CI, 0.668-0.780), and 0.872 (95% CI, 0.829-0.915). Cardiologists' average accuracy rates were 65.0 ± 4.7% for test without AI model and 74.0 ± 2.7% for test with AI model, a statistically significant improvement (<i>P</i> < 0.01).</p><p><strong>Conclusion: </strong>Our ensemble learning model improved doctors' accuracy in detecting PH from ECG, CXR, and BNP examinations. This suggests that earlier and more accurate PH diagnosis is possible, potentially improving patient prognosis.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"209-217"},"PeriodicalIF":3.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health programme following rhythm control in patients with atrial fibrillation: comprehensive disease management by self-monitoring, coaching, and telemedicine.","authors":"Georges von Degenfeld, Anke Langbein, Alessandra Boscheri, Maximilian O Ziegler, Jonas Demlehner, Paul Weyh, Alexander Leber, Sandra Schreier, Stefan G Spitzer","doi":"10.1093/ehjdh/ztae099","DOIUrl":"10.1093/ehjdh/ztae099","url":null,"abstract":"<p><strong>Aims: </strong>Digital health is becoming increasingly powerful and available but is frequently not effectively integrated into daily practice. A hybrid programme was developed to provide holistic diagnostic and therapeutic patient care in atrial fibrillation.</p><p><strong>Methods and results: </strong>Patients (<i>n</i> = 68) were recruited at the electrophysiology centre following successful interventional restoration of sinus rhythm. The 12-month programme consists of the key modalities: (i) self-recording of one-lead electrocardiograms (ECGs), (ii) short-term remote ECG diagnosis and medical advice by video consultation, and (iii) App-based education on lifestyle and risk factor optimization with video consultation. Patients recorded 29 092 ECGs, averaging 1.42 ECGs/day. Recurrent arrhythmia was found and confirmed in 39 patients. In all cases, arrhythmia was first diagnosed based on wearable ECG over the platform, rather than by standard in-office ECG/Holter. No false positive occurred. Patients with recurred arrhythmia were treated by pulmonary vein isolation (<i>n</i> = 17), electric cardioversion (<i>n</i> = 17), antiarrhythmic medication (<i>n</i> = 5), or other interventional procedures (<i>n</i> = 1). Most patients (<i>n</i> = 30) scheduled a video consultation over the App as the first medical touchpoint after arrhythmia occurrence. In 21 patients with arterial hypertension, systolic blood pressure was reduced by 8.0 ± 8.6 mmHg (mean ± SD), <i>P</i> < 0.01. In 25 patients with obesity (body mass index ≥ 30), body weight was reduced by 3.6 ± 5.5 kg (mean ± SD), <i>P</i> < 0.01.</p><p><strong>Conclusion: </strong>This real-world analysis indicates that the hybrid holistic programme is applicable in daily practice and is actively followed by patients and improves diagnostic and therapeutic outcomes. These promising data need to be confirmed in a controlled randomized study.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"261-269"},"PeriodicalIF":3.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome.","authors":"Allan Böhm, Amitai Segev, Nikola Jajcay, Konstantin A Krychtiuk, Guido Tavazzi, Michael Spartalis, Marta Kollarova, Imrich Berta, Jana Jankova, Frederico Guerra, Edita Pogran, Andrej Remak, Milana Jarakovic, Viera Sebenova Jerigova, Katarina Petrikova, Shlomi Matetzky, Carsten Skurk, Kurt Huber, Branislav Bezak","doi":"10.1093/ehjdh/ztaf002","DOIUrl":"10.1093/ehjdh/ztaf002","url":null,"abstract":"<p><strong>Aims: </strong>Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality rates approaching 50%. The ability to identify high-risk patients prior to the development of CS may allow for pre-emptive measures to prevent the development of CS. The objective was to derive and externally validate a simple, machine learning (ML)-based scoring system using variables readily available at first medical contact to predict the risk of developing CS during hospitalization in patients with ACS.</p><p><strong>Methods and results: </strong>Observational multicentre study on ACS patients hospitalized at intensive care units. Derivation cohort included over 40 000 patients from Beth Israel Deaconess Medical Center, Boston, USA. Validation cohort included 5123 patients from the Sheba Medical Center, Ramat Gan, Israel. The final derivation cohort consisted of 3228 and the final validation cohort of 4904 ACS patients without CS at hospital admission. Development of CS was adjudicated manually based on the patients' reports. From nine ML models based on 13 variables (heart rate, respiratory rate, oxygen saturation, blood glucose level, systolic blood pressure, age, sex, shock index, heart rhythm, type of ACS, history of hypertension, congestive heart failure, and hypercholesterolaemia), logistic regression with elastic net regularization had the highest externally validated predictive performance (<i>c</i>-statistics: 0.844, 95% CI, 0.841-0.847).</p><p><strong>Conclusion: </strong>STOP SHOCK score is a simple ML-based tool available at first medical contact showing high performance for prediction of developing CS during hospitalization in ACS patients. The web application is available at https://stopshock.org/#calculator.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"240-251"},"PeriodicalIF":3.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of a smartphone-connected remote monitoring system on self-management continuity and health awareness in cardiovascular outpatients: an exploratory survey.","authors":"Masanobu Ishii, Masahiro Yamamoto, Yasuhiro Otsuka, So Ikebe, Yoshinori Yamanouchi, Kenichi Tsujita, Taishi Nakamura","doi":"10.1093/ehjdh/ztae101","DOIUrl":"10.1093/ehjdh/ztae101","url":null,"abstract":"<p><strong>Aims: </strong>Cardiovascular diseases are a leading cause of death globally, and effective self-management is critical for patient outcomes. Integrating Internet of Things-enabled devices with smartphone applications presents a novel approach to enhancing self-management, yet challenges with digital literacy and device usability persist, especially among the elderly. This study aimed to evaluate the adherence, ease of use, and impact on health awareness of a smartphone-connected remote monitoring system among cardiovascular outpatients in Japan.</p><p><strong>Methods and results: </strong>We conducted a single-centre, prospective survey at Kumamoto University Hospital involving 10 cardiovascular outpatients (median age: 72.5 years) including heart failure (<i>n</i> = 2), hypertension (<i>n</i> = 3), post-cardiac surgery (<i>n</i> = 2), and others (<i>n</i> = 3). Participants received Bluetooth-enabled monitoring devices and a smartphone app for automatic data synchronization. Adherence, ease of use, and changes in health awareness were assessed through a structured questionnaire. The study found that 8 of 10 participants adhered to daily monitoring, with an average usage period of 48 days. Nine of 10 required minimal support with device use and 8 of 10 reported increase in health awareness. Seven of 10 indicated they could continue using it long term. The average recommendation score was 8.8/10. The timely detection of asymptomatic paroxysmal atrial fibrillation in one patient highlighted the system's potential clinical benefits.</p><p><strong>Conclusion: </strong>This pilot study suggests that a smartphone-connected remote monitoring system may enhance self-management practices and health awareness among cardiovascular outpatients. While the findings are promising, larger studies with longer follow-up periods are needed to confirm these results and evaluate the system's impact on clinical outcomes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"289-292"},"PeriodicalIF":3.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}