For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation.
Objective
The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters.
Methods
The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model.
Results
Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy.
Conclusion
The proposed method has the potential to become an alternative to computationally expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.
{"title":"Variational autoencoder–based neural electrocardiogram synthesis trained by FEM-based heart simulator","authors":"Ryo Nishikimi PhD , Masahiro Nakano MS , Kunio Kashino PhD , Shingo Tsukada MD, PhD","doi":"10.1016/j.cvdhj.2023.12.002","DOIUrl":"10.1016/j.cvdhj.2023.12.002","url":null,"abstract":"<div><h3>Background</h3><p>For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation.</p></div><div><h3>Objective</h3><p>The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters.</p></div><div><h3>Methods</h3><p>The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model.</p></div><div><h3>Results</h3><p>Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy.</p></div><div><h3>Conclusion</h3><p>The proposed method has the potential to become an alternative to computationally expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 1","pages":"Pages 19-28"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266669362300110X/pdfft?md5=f34ed708c317b76ab2e5df72a322606b&pid=1-s2.0-S266669362300110X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013261","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}
Remote monitoring (RM) of cardiac implantable electronic device (CIED) patients is now considered standard of care. However, a fundamental requirement of RM is continuous connectivity between the patient’s implanted device and the CIED manufacturer’s central server. This study examined the rate of RM disconnections in CIED recipients and the impact of short message service (SMS) to facilitate reconnections.
Methods
Using a platform that collects RM data from CIED manufacturers, we retrospectively examined the disconnection and reconnection events in 6085 patients from 20 medical centers. Each medical center reported their usual practice regarding RM disconnections, which consisted of either an automatic SMS from the platform to patients who were disconnected for 2 weeks or the standard of care (SC) of a phone call to patients.
Results
During a 1-year period, 43% of patients had at least 1 disconnection. Half of these patients experienced multiple disconnections. The use of SMS reduced the time to reconnection by 43% in comparison to SC. The median time to reconnect a disconnected patient was 11.0 [3.2, 29.0] days for SC vs 6.3 [1.3, 22.0] days for SMS (P < .0001). Furthermore, there was a high rate of reconnections within the first 48 hours of the SMS message, which was nearly double that in the SC arm.
Conclusion
This study demonstrates the feasibility of an automatic system to deliver an SMS to patients with a disconnected CIED to facilitate early reconnection to RM.
{"title":"Using technology to improve reconnection to remote monitoring in cardiac implantable electronic device patients","authors":"Julien Durand MSc , Jean-Luc Bonnet PhD , Arnaud Lazarus MD , Jérôme Taieb MD , Arnaud Rosier MD, PhD , Suneet Mittal MD","doi":"10.1016/j.cvdhj.2023.11.020","DOIUrl":"10.1016/j.cvdhj.2023.11.020","url":null,"abstract":"<div><h3>Background</h3><p>Remote monitoring (RM) of cardiac implantable electronic device (CIED) patients is now considered standard of care. However, a fundamental requirement of RM is continuous connectivity between the patient’s implanted device and the CIED manufacturer’s central server. This study examined the rate of RM disconnections in CIED recipients and the impact of short message service (SMS) to facilitate reconnections.</p></div><div><h3>Methods</h3><p>Using a platform that collects RM data from CIED manufacturers, we retrospectively examined the disconnection and reconnection events in 6085 patients from 20 medical centers. Each medical center reported their usual practice regarding RM disconnections, which consisted of either an automatic SMS from the platform to patients who were disconnected for 2 weeks or the standard of care (SC) of a phone call to patients.</p></div><div><h3>Results</h3><p>During a 1-year period, 43% of patients had at least 1 disconnection. Half of these patients experienced multiple disconnections. The use of SMS reduced the time to reconnection by 43% in comparison to SC. The median time to reconnect a disconnected patient was 11.0 [3.2, 29.0] days for SC vs 6.3 [1.3, 22.0] days for SMS (<em>P</em> < .0001). Furthermore, there was a high rate of reconnections within the first 48 hours of the SMS message, which was nearly double that in the SC arm.</p></div><div><h3>Conclusion</h3><p>This study demonstrates the feasibility of an automatic system to deliver an SMS to patients with a disconnected CIED to facilitate early reconnection to RM.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"5 1","pages":"Pages 1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000932/pdfft?md5=e0449394c89c96631ec8e27ce2dac42c&pid=1-s2.0-S2666693623000932-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139293910","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.011
Ali R. Roghanizad , Lauren E. Lederer , T. Clark Howell , Shelley Hwang , Jessilyn P. Dunn
{"title":"POINT OF CARE AI DRIVEN ER2EP REFERRALS FOR NON-VALVULAR ATRIAL FIBRILLATION","authors":"Ali R. Roghanizad , Lauren E. Lederer , T. Clark Howell , Shelley Hwang , Jessilyn P. Dunn","doi":"10.1016/j.cvdhj.2023.11.011","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2023.11.011","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 200-201"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000841/pdfft?md5=67a96aa44bd4b15c6508df4443b79189&pid=1-s2.0-S2666693623000841-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138769580","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.017
Yosra Bouchoucha , Dorsaf Omri , Taoufik Aguili
{"title":"EFFICIENT RF ENERGY HARVESTING FOR IOT CARDIO STIMULATOR DEVICES IN THE ISM BAND THROUGH ENHANCING RECTENNA PERFORMANCE AND POWER MANAGEMENT","authors":"Yosra Bouchoucha , Dorsaf Omri , Taoufik Aguili","doi":"10.1016/j.cvdhj.2023.11.017","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2023.11.017","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Page 204"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000907/pdfft?md5=016df8ff6b475e9ee8dd19b59528e593&pid=1-s2.0-S2666693623000907-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138769675","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.09.001
Arman Naseri MSc , David Tax PhD , Pim van der Harst MD, PhD , Marcel Reinders PhD , Ivo van der Bilt MD, PhD
Background
Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly useful to physicians, a machine learning (ML) model could find clinically relevant patterns. Unfortunately, ML models typically need supervised (ie, annotated) data, and labeling of large amounts of continuous data is very labor intensive. Therefore, ML methods that are data efficient, ie, needing a low number of labels, are required to detect potential clinical value in patterns found in wearable data.
Objective
The primary study objective of the ME-TIME (Machine Learning Enabled Time Series Analysis in Medicine) study is to design an ML model that can detect atrial fibrillation (AF) and heart failure (HF) from wearable data in a data-efficient manner. To achieve this, self-supervised and weakly supervised learning techniques are used.
Methods
Two hundred subjects (100 reference, 50 AF, and 50 HF) are being invited to participate in wearing a Fitbit fitness tracker for 3 months. Interested volunteers are sent a questionnaire to determine their health, in particular cardiovascular health. Volunteers without any (history of) serious illness are assigned to the reference group. Participants with AF and HF are recruited in the Haga teaching hospital in The Hague, The Netherlands.
Results
Enrollment commenced on May 1, 2022, and as of the time of this report, 62 subjects have been included in the study. Preliminary analysis of the data reveals significant inter-subject variability. Notably, we identified heart rate recovery curves and time-delayed correlations between heart rate and step count as potential strong indicators for heart disease.
Conclusion
Using self-supervised and multiple-instance learning techniques, we hypothesize that patterns specific to AF and HF can be found in continuous data obtained from smartwatches.
背景智能手表能够对心率（来自光速图）、步数计数器、皮肤温度等心血管生物标记进行连续、无创的时间序列监测；因此，它们有望协助早期检测和预防心血管疾病。虽然这些生物标志物对医生可能没有直接用处，但机器学习（ML）模型可以找到与临床相关的模式。遗憾的是，ML 模型通常需要有监督（即注释）的数据，而对大量连续数据进行标注非常耗费人力。因此，需要数据效率高的 ML 方法（即只需少量标签）来检测可穿戴数据中发现的模式是否具有潜在的临床价值。目标ME-TIME（Machine Learning Enabled Time Series Analysis in Medicine）研究的主要研究目标是设计一种 ML 模型，以数据效率高的方式从可穿戴数据中检测心房颤动（AF）和心力衰竭（HF）。为此，研究人员采用了自监督和弱监督学习技术。方法研究人员邀请 200 名受试者（100 名参照者、50 名房颤受试者和 50 名心衰受试者）佩戴 Fitbit 健身追踪器 3 个月。有兴趣的志愿者会收到一份调查问卷，以确定其健康状况，尤其是心血管健康状况。没有任何严重疾病（病史）的志愿者被分配到参照组。患有房颤和高血压的受试者在荷兰海牙的哈加教学医院招募。结果2022年5月1日开始招募，截至本报告发布时，已有62名受试者被纳入研究。对数据的初步分析表明，受试者之间的差异很大。值得注意的是，我们发现心率恢复曲线和心率与步数之间的延时相关性是心脏病的潜在有力指标。结论利用自监督和多实例学习技术，我们假设可以从智能手表获得的连续数据中发现房颤和高血压的特定模式。
{"title":"Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables","authors":"Arman Naseri MSc , David Tax PhD , Pim van der Harst MD, PhD , Marcel Reinders PhD , Ivo van der Bilt MD, PhD","doi":"10.1016/j.cvdhj.2023.09.001","DOIUrl":"10.1016/j.cvdhj.2023.09.001","url":null,"abstract":"<div><h3>Background</h3><p>Smartwatches enable continuous and noninvasive time series monitoring of cardiovascular biomarkers like heart rate (from photoplethysmograms), step counter, skin temperature, et cetera; as such, they have promise in assisting in early detection and prevention of cardiovascular disease. Although these biomarkers may not be directly useful to physicians, a machine learning (ML) model could find clinically relevant patterns. Unfortunately, ML models typically need supervised (ie, annotated) data, and labeling of large amounts of continuous data is very labor intensive. Therefore, ML methods that are data efficient, ie, needing a low number of labels, are required to detect potential clinical value in patterns found in wearable data.</p></div><div><h3>Objective</h3><p>The primary study objective of the ME-TIME (Machine Learning Enabled Time Series Analysis in Medicine) study is to design an ML model that can detect atrial fibrillation (AF) and heart failure (HF) from wearable data in a data-efficient manner. To achieve this, self-supervised and weakly supervised learning techniques are used.</p></div><div><h3>Methods</h3><p>Two hundred subjects (100 reference, 50 AF, and 50 HF) are being invited to participate in wearing a Fitbit fitness tracker for 3 months. Interested volunteers are sent a questionnaire to determine their health, in particular cardiovascular health. Volunteers without any (history of) serious illness are assigned to the reference group. Participants with AF and HF are recruited in the Haga teaching hospital in The Hague, The Netherlands.</p></div><div><h3>Results</h3><p>Enrollment commenced on May 1, 2022, and as of the time of this report, 62 subjects have been included in the study. Preliminary analysis of the data reveals significant inter-subject variability. Notably, we identified heart rate recovery curves and time-delayed correlations between heart rate and step count as potential strong indicators for heart disease.</p></div><div><h3>Conclusion</h3><p>Using self-supervised and multiple-instance learning techniques, we hypothesize that patterns specific to AF and HF can be found in continuous data obtained from smartwatches.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 165-172"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000737/pdfft?md5=8336905ff497def43550b2ab9ed5a430&pid=1-s2.0-S2666693623000737-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934685","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.003
Liam Butler PhD , Ibrahim Karabayir PhD , Dalane W. Kitzman MD , Alvaro Alonso MD, PhD , Geoffrey H. Tison MD, MPH , Lin Yee Chen MD, MS , Patricia P. Chang MD, MHS , Gari Clifford PhD , Elsayed Z. Soliman MD, MS , Oguz Akbilgic DBA, PhD
Background
Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF risk early to allow for risk modification.
Objective
The main objectives were to validate HF risk prediction models using Multi-Ethnic Study of Atherosclerosis (MESA) data and assess performance on HFpEF and HFrEF classification.
Methods
There were six models in comparision derived using ARIC data. 1) The ECG-AI model predicting HF risk was developed using raw 12-lead ECGs with a convolutional neural network. The clinical models from 2) ARIC (ARIC-HF) and 3) Framingham Heart Study (FHS-HF) used 9 and 8 variables, respectively. 4) Cox proportional hazards (CPH) model developed using the clinical risk factors in ARIC-HF or FHS-HF. 5) CPH model using the outcome of ECG-AI and the clinical risk factors used in CPH model (ECG-AI-Cox) and 6) A Light Gradient Boosting Machine model using 288 ECG Characteristics (ECG-Chars). All the models were validated on MESA. The performances of these models were evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.
Results
ECG-AI, ECG-Chars, and ECG-AI-Cox resulted in validation AUCs of 0.77, 0.73, and 0.84, respectively. ARIC-HF and FHS-HF yielded AUCs of 0.76 and 0.74, respectively, and CPH resulted in AUC = 0.78. ECG-AI-Cox outperformed all other models. ECG-AI-Cox provided an AUC of 0.85 for HFrEF and 0.83 for HFpEF.
Conclusion
ECG-AI using ECGs provides better-validated predictions when compared to HF risk calculators, and the ECG feature model and also works well with HFpEF and HFrEF classification.
{"title":"A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction","authors":"Liam Butler PhD , Ibrahim Karabayir PhD , Dalane W. Kitzman MD , Alvaro Alonso MD, PhD , Geoffrey H. Tison MD, MPH , Lin Yee Chen MD, MS , Patricia P. Chang MD, MHS , Gari Clifford PhD , Elsayed Z. Soliman MD, MS , Oguz Akbilgic DBA, PhD","doi":"10.1016/j.cvdhj.2023.11.003","DOIUrl":"10.1016/j.cvdhj.2023.11.003","url":null,"abstract":"<div><h3>Background</h3><p>Heart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective electrocardiogram (ECG)-based artificial intelligence (AI; ECG-AI) models that can predict HF risk early to allow for risk modification.</p></div><div><h3>Objective</h3><p>The main objectives were to validate HF risk prediction models using Multi-Ethnic Study of Atherosclerosis (MESA) data and assess performance on HFpEF and HFrEF classification.</p></div><div><h3>Methods</h3><p>There were six models in comparision derived using ARIC data. 1) The ECG-AI model predicting HF risk was developed using raw 12-lead ECGs with a convolutional neural network. The clinical models from 2) ARIC (ARIC-HF) and 3) Framingham Heart Study (FHS-HF) used 9 and 8 variables, respectively. 4) Cox proportional hazards (CPH) model developed using the clinical risk factors in ARIC-HF or FHS-HF. 5) CPH model using the outcome of ECG-AI and the clinical risk factors used in CPH model (ECG-AI-Cox) and 6) A Light Gradient Boosting Machine model using 288 ECG Characteristics (ECG-Chars). All the models were validated on MESA. The performances of these models were evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.</p></div><div><h3>Results</h3><p>ECG-AI, ECG-Chars, and ECG-AI-Cox resulted in validation AUCs of 0.77, 0.73, and 0.84, respectively. ARIC-HF and FHS-HF yielded AUCs of 0.76 and 0.74, respectively, and CPH resulted in AUC = 0.78. ECG-AI-Cox outperformed all other models. ECG-AI-Cox provided an AUC of 0.85 for HFrEF and 0.83 for HFpEF.</p></div><div><h3>Conclusion</h3><p>ECG-AI using ECGs provides better-validated predictions when compared to HF risk calculators, and the ECG feature model and also works well with HFpEF and HFrEF classification.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 183-190"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000762/pdfft?md5=8ef34da86fd7e31cf9150c7011f22cef&pid=1-s2.0-S2666693623000762-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515031","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.007
Karen K. Giuliano , Tuomas Laine , Matthew Pekarske , Marco Luchetti MD , John W. Beard MD
{"title":"BYNDR™ COMMUNICATION PROTOCOL PROVIDES RELIABLE WIRELESS PHYSIOLOGIC DATA FLOW","authors":"Karen K. Giuliano , Tuomas Laine , Matthew Pekarske , Marco Luchetti MD , John W. Beard MD","doi":"10.1016/j.cvdhj.2023.11.007","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2023.11.007","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 198-199"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000804/pdfft?md5=b3c48045bc2b961ad9132f0922cf60d9&pid=1-s2.0-S2666693623000804-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138769724","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.005
{"title":"IEEE-EMBS International Conference on Body Sensor Networks – Sensors and Systems for Digital Health","authors":"","doi":"10.1016/j.cvdhj.2023.11.005","DOIUrl":"https://doi.org/10.1016/j.cvdhj.2023.11.005","url":null,"abstract":"","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 198-205"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000786/pdfft?md5=405e12e1ab32c595bfa3f85d7002273d&pid=1-s2.0-S2666693623000786-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138769760","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}
Pub Date : 2023-12-01DOI: 10.1016/j.cvdhj.2023.11.002
Megan M. McLaughlin MD, MPH , Priscilla Y. Hsue MD , Dylan A. Lowe PhD , Jeffrey E. Olgin MD , Alexis L. Beatty MD, MAS
Objective
Persons with HIV (PWH) have increased risk for atherosclerotic cardiovascular disease (CVD). Despite this increased risk, perceived cardiovascular risk among PWH is low, and interventions that are known to be beneficial in the general population, such as statins, have low uptake in this population. We sought to develop a bank of text messages about (1) the association between HIV and CVD and (2) advice on reducing cardiovascular risk.
Methods
We developed an initial bank of 162 messages. We solicited feedback from 29 PWH recruited from outpatient clinics providing HIV care at a large urban tertiary medical center and a public hospital in San Francisco, California. Participants reviewed 7–10 messages each and rated message usefulness, readability, and potential impact on behavior on a scale from 1 (least) to 5 (most). We also collected open-ended feedback on the messages and data on preferences about message timing.
Results
The average score for the messages was 4.4/5 for usefulness, 4.4/5 for readability, and 4.0/5 for potential impact on behavior. The text messages were iteratively revised based on participant feedback, and lowest-rated messages were removed from the message bank. The final message bank included 116 messages on diet (30.2%), physical activity (24.8%), tobacco (11.2%), the association between HIV and cardiovascular disease (9.5%), general heart health (6.9%), cholesterol (5.2%), blood pressure (4.3%), blood sugar (2.6%), sleep (2.6%), and weight (2.6%).
Conclusion
We describe an approach for developing educational text messages on primary prevention of cardiovascular disease among PWH.
{"title":"Development of text messages for primary prevention of cardiovascular disease in persons with HIV","authors":"Megan M. McLaughlin MD, MPH , Priscilla Y. Hsue MD , Dylan A. Lowe PhD , Jeffrey E. Olgin MD , Alexis L. Beatty MD, MAS","doi":"10.1016/j.cvdhj.2023.11.002","DOIUrl":"10.1016/j.cvdhj.2023.11.002","url":null,"abstract":"<div><h3>Objective</h3><p>Persons with HIV (PWH) have increased risk for atherosclerotic cardiovascular disease (CVD). Despite this increased risk, perceived cardiovascular risk among PWH is low, and interventions that are known to be beneficial in the general population, such as statins, have low uptake in this population. We sought to develop a bank of text messages about (1) the association between HIV and CVD and (2) advice on reducing cardiovascular risk.</p></div><div><h3>Methods</h3><p>We developed an initial bank of 162 messages. We solicited feedback from 29 PWH recruited from outpatient clinics providing HIV care at a large urban tertiary medical center and a public hospital in San Francisco, California. Participants reviewed 7–10 messages each and rated message usefulness, readability, and potential impact on behavior on a scale from 1 (least) to 5 (most). We also collected open-ended feedback on the messages and data on preferences about message timing.</p></div><div><h3>Results</h3><p>The average score for the messages was 4.4/5 for usefulness, 4.4/5 for readability, and 4.0/5 for potential impact on behavior. The text messages were iteratively revised based on participant feedback, and lowest-rated messages were removed from the message bank. The final message bank included 116 messages on diet (30.2%), physical activity (24.8%), tobacco (11.2%), the association between HIV and cardiovascular disease (9.5%), general heart health (6.9%), cholesterol (5.2%), blood pressure (4.3%), blood sugar (2.6%), sleep (2.6%), and weight (2.6%).</p></div><div><h3>Conclusion</h3><p>We describe an approach for developing educational text messages on primary prevention of cardiovascular disease among PWH.</p></div>","PeriodicalId":72527,"journal":{"name":"Cardiovascular digital health journal","volume":"4 6","pages":"Pages 191-197"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666693623000750/pdfft?md5=9b6eb52bde1724c63a3d1df2b20856d4&pid=1-s2.0-S2666693623000750-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515815","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号