JMIR Cardio最新文献

{"title":"User Engagement, Acceptability, and Clinical Markers in a Digital Health Program for Nonalcoholic Fatty Liver Disease: Prospective, Single-Arm Feasibility Study.","authors":"Sigridur Björnsdottir, Hildigunnur Ulfsdottir, Elias Freyr Gudmundsson, Kolbrun Sveinsdottir, Ari Pall Isberg, Bartosz Dobies, Gudlaug Erla Akerlie Magnusdottir, Thrudur Gunnarsdottir, Tekla Karlsdottir, Gudlaug Bjornsdottir, Sigurdur Sigurdsson, Saemundur Oddsson, Vilmundur Gudnason","doi":"10.2196/52576","DOIUrl":"10.2196/52576","url":null,"abstract":"<p><strong>Background: </strong>Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world. Common comorbidities are central obesity, type 2 diabetes mellitus, dyslipidemia, and metabolic syndrome. Cardiovascular disease is the most common cause of death among people with NAFLD, and lifestyle changes can improve health outcomes.</p><p><strong>Objective: </strong>This study aims to explore the acceptability of a digital health program in terms of engagement, retention, and user satisfaction in addition to exploring changes in clinical outcomes, such as weight, cardiometabolic risk factors, and health-related quality of life.</p><p><strong>Methods: </strong>We conducted a prospective, open-label, single-arm, 12-week study including 38 individuals with either a BMI >30, metabolic syndrome, or type 2 diabetes mellitus and NAFLD screened by FibroScan. An NAFLD-specific digital health program focused on disease education, lowering carbohydrates in the diet, food logging, increasing activity level, reducing stress, and healthy lifestyle coaching was offered to participants. The coach provided weekly feedback on food logs and other in-app activities and opportunities for participants to ask questions. The coaching was active throughout the 12-week intervention period. The primary outcome was feasibility and acceptability of the 12-week program, assessed through patient engagement, retention, and satisfaction with the program. Secondary outcomes included changes in weight, liver fat, body composition, and other cardiometabolic clinical parameters at baseline and 12 weeks.</p><p><strong>Results: </strong>In total, 38 individuals were included in the study (median age 59.5, IQR 46.3-68.8 years; n=23, 61% female). Overall, 34 (89%) participants completed the program and 29 (76%) were active during the 12-week program period. The median satisfaction score was 6.3 (IQR 5.8-6.7) of 7. Mean weight loss was 3.5 (SD 3.7) kg (P<.001) or 3.2% (SD 3.4%), with a 2.2 (SD 2.7) kg reduction in fat mass (P<.001). Relative liver fat reduction was 19.4% (SD 23.9%). Systolic blood pressure was reduced by 6.0 (SD 13.5) mmHg (P=.009). The median reduction was 0.14 (IQR 0-0.47) mmol/L for triglyceride levels (P=.003), 3.2 (IQR 0.0-5.4) µU/ml for serum insulin (s-insulin) levels (P=.003), and 0.5 (IQR -0.7 to 3.8) mmol/mol for hemoglobin A_1c (HbA_1c) levels (P=.03). Participants who were highly engaged (ie, who used the app at least 5 days per week) had greater weight loss and liver fat reduction.</p><p><strong>Conclusions: </strong>The 12-week-long digital health program was feasible for individuals with NAFLD, receiving high user engagement, retention, and satisfaction. Improved liver-specific and cardiometabolic health was observed, and more engaged participants showed greater improvements. This digital health program could provide a new tool to improve health outcomes in people with NAFLD.</p><p><strong>Trial r","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":" ","pages":"e52576"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10905363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139048808","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":"Feasibility of Using Text Messaging to Identify and Assist Patients With Hypertension With Health-Related Social Needs: Cross-Sectional Study.","authors":"Aryn Kormanis, Selina Quinones, Corey Obermiller, Nancy Denizard-Thompson, Deepak Palakshappa","doi":"10.2196/54530","DOIUrl":"10.2196/54530","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Health-related social needs are associated with poor health outcomes, increased acute health care use, and impaired chronic disease management. Given these negative outcomes, an increasing number of national health care organizations have recommended that the health system screen and address unmet health-related social needs as a routine part of clinical care, but there are limited data on how to implement social needs screening in clinical settings to improve the management of chronic diseases such as hypertension. SMS text messaging could be an effective and efficient approach to screen patients; however, there are limited data on the feasibility of using it.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We conducted a cross-sectional study of patients with hypertension to determine the feasibility of using SMS text messaging to screen patients for unmet health-related social needs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We randomly selected 200 patients (≥18 years) from 1 academic health system. Patients were included if they were seen at one of 17 primary care clinics that were part of the academic health system and located in Forsyth County, North Carolina. We limited the sample to patients seen in one of these clinics to provide tailored information about local community-based resources. To ensure that the participants were still patients within the clinic, we only included those who had a visit in the previous 3 months. The SMS text message included a link to 6 questions regarding food, housing, and transportation. Patients who screened positive and were interested received a subsequent message with information about local resources. We assessed the proportion of patients who completed the questions. We also evaluated for the differences in the demographics between patients who completed the questions and those who did not using bivariate analyses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 200 patients, the majority were female (n=109, 54.5%), non-Hispanic White (n=114, 57.0%), and received commercial insurance (n=105, 52.5%). There were no significant differences in demographics between the 4446 patients who were eligible and the 200 randomly selected patients. Of the 200 patients included, the SMS text message was unable to be delivered to 9 (4.5%) patients and 17 (8.5%) completed the social needs questionnaire. We did not observe a significant difference in the demographic characteristics of patients who did versus did not complete the questionnaire. Of the 17, a total of 5 (29.4%) reported at least 1 unmet need, but only 2 chose to receive resource information.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We found that only 8.5% (n=17) of patients completed a SMS text message-based health-related social needs questionnaire. SMS text messaging may not be feasible as a single modality to screen patients in this population. Future research should evaluate if SMS text message-based social needs screening is feasible in other populations o","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"8 ","pages":"e54530"},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10900090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139722705","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":"Physical Activity, Heart Rate Variability, and Ventricular Arrhythmia During the COVID-19 Lockdown: Retrospective Cohort Study.","authors":"Sikander Z Texiwala, Russell J de Souza, Suzette Turner, Sheldon M Singh","doi":"10.2196/51399","DOIUrl":"10.2196/51399","url":null,"abstract":"<p><strong>Background: </strong>Ventricular arrhythmias (VAs) increase with stress and national disasters. Prior research has reported that VA did not increase during the onset of the COVID-19 lockdown in March 2020, and the mechanism for this is unknown.</p><p><strong>Objective: </strong>This study aimed to report the presence of VA and changes in 2 factors associated with VA (physical activity and heart rate variability [HRV]) at the onset of COVID-19 lockdown measures in Ontario, Canada.</p><p><strong>Methods: </strong>Patients with implantable cardioverter defibrillator (ICD) followed at a regional cardiac center in Ontario, Canada with data available for both HRV and physical activity between March 1 and 31, 2020, were included. HRV, physical activity, and the presence of VA were determined during the pre- (March 1-10, 2020) and immediate postlockdown (March 11-31) period. When available, these data were determined for the same period in 2019.</p><p><strong>Results: </strong>In total, 68 patients had complete data for 2020, and 40 patients had complete data for 2019. Three (7.5%) patients had VA in March 2019, whereas none had VA in March 2020 (P=.048). Physical activity was reduced during the postlockdown period (mean 2.3, SD 1.6 hours vs mean 2.1, SD 1.6 hours; P=.003). HRV was unchanged during the pre- and postlockdown period (mean 91, SD 30 ms vs mean 92, SD 28 ms; P=.84).</p><p><strong>Conclusions: </strong>VA was infrequent during the COVID-19 pandemic. A reduction in physical activity with lockdown maneuvers may explain this observation.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"8 ","pages":"e51399"},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10877486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691850","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":"Efficacy of eHealth Technologies on Medication Adherence in Patients With Acute Coronary Syndrome: Systematic Review and Meta-Analysis.","authors":"Akshaya Srikanth Bhagavathula, Wafa Ali Aldhaleei, Tesfay Mehari Atey, Solomon Assefa, Wubshet Tesfaye","doi":"10.2196/52697","DOIUrl":"10.2196/52697","url":null,"abstract":"<p><strong>Background: </strong>Suboptimal adherence to cardiac pharmacotherapy, recommended by the guidelines after acute coronary syndrome (ACS) has been recognized and is associated with adverse outcomes. Several randomized controlled trials (RCTs) have shown that eHealth technologies are useful in reducing cardiovascular risk factors. However, little is known about the effect of eHealth interventions on medication adherence in patients following ACS.</p><p><strong>Objective: </strong>The aim of this study is to examine the efficacy of the eHealth interventions on medication adherence to selected 5 cardioprotective medication classes in patients with ACS.</p><p><strong>Methods: </strong>A systematic literature search of PubMed, Embase, Scopus, and Web of Science was conducted between May and October 2022, with an update in October 2023 to identify RCTs that evaluated the effectiveness of eHealth technologies, including texting, smartphone apps, or web-based apps, to improve medication adherence in patients after ACS. The risk of bias was evaluated using the modified Cochrane risk-of-bias tool for RCTs. A pooled meta-analysis was performed using a fixed-effect Mantel-Haenszel model and assessed the medication adherence to the medications of statins, aspirin, P2Y12 inhibitors, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and β-blockers.</p><p><strong>Results: </strong>We identified 5 RCTs, applicable to 4100 participants (2093 intervention vs 2007 control), for inclusion in the meta-analysis. In patients who recently had an ACS, compared to the control group, the use of eHealth intervention was not associated with improved adherence to statins at different time points (risk difference [RD] -0.01, 95% CI -0.03 to 0.03 at 6 months and RD -0.02, 95% CI -0.05 to 0.02 at 12 months), P2Y12 inhibitors (RD -0.01, 95% CI -0.04 to 0.02 and RD -0.01, 95% CI -0.03 to 0.02), aspirin (RD 0.00, 95% CI -0.06 to 0.07 and RD -0.00, 95% CI -0.07 to 0.06), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (RD -0.01, 95% CI -0.04 to 0.02 and RD 0.01, 95% CI -0.04 to 0.05), and β-blockers (RD 0.00, 95% CI -0.03 to 0.03 and RD -0.01, 95% CI -0.05 to 0.03). The intervention was also not associated with improved adherence irrespective of the adherence assessment method used (self-report or objective).</p><p><strong>Conclusions: </strong>This review identified limited evidence on the effectiveness of eHealth interventions on adherence to guideline-recommended medications after ACS. While the pooled analyses suggested a lack of effectiveness of such interventions on adherence improvement, further studies are warranted to better understand the role of different eHealth approaches in the post-ACS context.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e52697"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10762619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138803499","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":"Guideline-Based Cardiovascular Risk Assessment Delivered by an mHealth App: Development Study","authors":"Fabian Starnecker, Lara Marie Reimer, Leon Nissen, Marko Jovanović, Maximilian Kapsecker, S. Rospleszcz, M. von Scheidt, J. Krefting, Nils Krüger, Benedikt Perl, Jens Wiehler, Ruoyu Sun, Stephan Jonas, H. Schunkert","doi":"10.2196/50813","DOIUrl":"https://doi.org/10.2196/50813","url":null,"abstract":"\u0000 \u0000 Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment. The respective test results need to be interpretable for laypersons.\u0000 \u0000 \u0000 \u0000 Together with a patient organization, we aimed to design a digital risk calculator that allows people to individually assess and optimize their CVD risk. The risk calculator was integrated into the mobile health app HerzFit, which provides the respective background information.\u0000 \u0000 \u0000 \u0000 To cover a broad spectrum of individuals for both primary and secondary prevention, we integrated the respective scores (Framingham 10-year CVD, Systematic Coronary Risk Evaluation 2, Systematic Coronary Risk Evaluation 2 in Older Persons, and Secondary Manifestations Of Arterial Disease) into a single risk calculator that was recalibrated for the German population. In primary prevention, an individual’s heart age is estimated, which gives the user an easy-to-understand metric for assessing cardiac health. For secondary prevention, the risk of recurrence was assessed. In addition, a comparison of expected to mean and optimal risk levels was determined. The risk calculator is available free of charge. Data safety is ensured by processing the data locally on the users’ smartphones.\u0000 \u0000 \u0000 \u0000 Offering a risk calculator to the general population requires the use of multiple instruments, as each provides only a limited spectrum in terms of age and risk distribution. The integration of 4 internationally recommended scores allows risk calculation in individuals aged 30 to 90 years with and without CVD. Such integration requires recalibration and harmonization to provide consistent and plausible estimates. In the first 14 months after the launch, the HerzFit calculator was downloaded more than 96,000 times, indicating great demand. Public information campaigns proved effective in publicizing the risk calculator and contributed significantly to download numbers.\u0000 \u0000 \u0000 \u0000 The HerzFit calculator provides CVD risk assessment for the general population. The public demonstrated great demand for such a risk calculator as it was downloaded up to 10,000 times per month, depending on campaigns creating awareness for the instrument.\u0000","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"17 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study.","authors":"Lindsay Dryden, Jacquelin Song, Teresa J Valenzano, Zhen Yang, Meggie Debnath, Rebecca Lin, Jane Topolovec-Vranic, Muhammad Mamdani, Tony Antoniou","doi":"10.2196/47262","DOIUrl":"10.2196/47262","url":null,"abstract":"<p><strong>Background: </strong>Warfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients.</p><p><strong>Objective: </strong>This study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients.</p><p><strong>Methods: </strong>We abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael's Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care.</p><p><strong>Results: </strong>Random forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively.</p><p><strong>Conclusions: </strong>Machine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in c","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e47262"},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10733832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138487586","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":"Accuracy, Usability, and Adherence of Smartwatches for Atrial Fibrillation Detection in Older Adults After Stroke: Randomized Controlled Trial.","authors":"Eric Y Ding, Khanh-Van Tran, Darleen Lessard, Ziyue Wang, Dong Han, Fahimeh Mohagheghian, Edith Mensah Otabil, Kamran Noorishirazi, Jordy Mehawej, Andreas Filippaios, Syed Naeem, Matthew F Gottbrecht, Timothy P Fitzgibbons, Jane S Saczynski, Bruce Barton, Ki Chon, David D McManus","doi":"10.2196/45137","DOIUrl":"10.2196/45137","url":null,"abstract":"<p><strong>Background: </strong>Atrial fibrillation (AF) is a common cause of stroke, and timely diagnosis is critical for secondary prevention. Little is known about smartwatches for AF detection among stroke survivors. We aimed to examine accuracy, usability, and adherence to a smartwatch-based AF monitoring system designed by older stroke survivors and their caregivers.</p><p><strong>Objective: </strong>This study aims to examine the feasibility of smartwatches for AF detection in older stroke survivors.</p><p><strong>Methods: </strong>Pulsewatch is a randomized controlled trial (RCT) in which stroke survivors received either a smartwatch-smartphone dyad for AF detection (Pulsewatch system) plus an electrocardiogram patch or the patch alone for 14 days to assess the accuracy and usability of the system (phase 1). Participants were subsequently rerandomized to potentially 30 additional days of system use to examine adherence to watch wear (phase 2). Participants were aged 50 years or older, had survived an ischemic stroke, and had no major contraindications to oral anticoagulants. The accuracy for AF detection was determined by comparing it to cardiologist-overread electrocardiogram patch, and the usability was assessed with the System Usability Scale (SUS). Adherence was operationalized as daily watch wear time over the 30-day monitoring period.</p><p><strong>Results: </strong>A total of 120 participants were enrolled (mean age 65 years; 50/120, 41% female; 106/120, 88% White). The Pulsewatch system demonstrated 92.9% (95% CI 85.3%-97.4%) accuracy for AF detection. Mean usability score was 65 out of 100, and on average, participants wore the watch for 21.2 (SD 8.3) of the 30 days.</p><p><strong>Conclusions: </strong>Our findings demonstrate that a smartwatch system designed by and for stroke survivors is a viable option for long-term arrhythmia detection among older adults at risk for AF, though it may benefit from strategies to enhance adherence to watch wear.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03761394; https://clinicaltrials.gov/study/NCT03761394.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.1016/j.cvdhj.2021.07.002.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45137"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10716742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138444724","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":"Diagnostic Accuracy of Single-Lead Electrocardiograms Using the Kardia Mobile App and the Apple Watch 4: Validation Study.","authors":"Kristina Klier, Lucas Koch, Lisa Graf, Timo Schinköthe, Annette Schmidt","doi":"10.2196/50701","DOIUrl":"10.2196/50701","url":null,"abstract":"<p><strong>Background: </strong>To date, the 12-lead electrocardiogram (ECG) is the gold standard for cardiological diagnosis in clinical settings. With the advancements in technology, a growing number of smartphone apps and gadgets for recording, visualizing, and evaluating physical performance as well as health data is available. Although this new smart technology is innovative and time- and cost-efficient, less is known about its diagnostic accuracy and reliability.</p><p><strong>Objective: </strong>This study aimed to examine the agreement between the mobile single-lead ECG measurements of the Kardia Mobile App and the Apple Watch 4 compared to the 12-lead gold standard ECG in healthy adults under laboratory conditions. Furthermore, it assessed whether the measurement error of the devices increases with an increasing heart rate.</p><p><strong>Methods: </strong>This study was designed as a prospective quasi-experimental 1-sample measurement, in which no randomization of the sampling was carried out. In total, ECGs at rest from 81 participants (average age 24.89, SD 8.58 years; n=58, 72% male) were recorded and statistically analyzed. Bland-Altman plots were created to graphically illustrate measurement differences. To analyze the agreement between the single-lead ECGs and the 12-lead ECG, Pearson correlation coefficient (r) and Lin concordance correlation coefficient (CCC_Lin) were calculated.</p><p><strong>Results: </strong>The results showed a higher agreement for the Apple Watch (mean deviation QT: 6.85%; QT interval corrected for heart rate using Fridericia formula [QTcF]: 7.43%) than Kardia Mobile (mean deviation QT: 9.53%; QTcF: 9.78%) even if both tend to underestimate QT and QTcF intervals. For Kardia Mobile, the QT and QTcF intervals correlated significantly with the gold standard (r_QT=0.857 and r_QTcF=0.727; P<.001). CCC_Lin corresponded to an almost complete heuristic agreement for the QT interval (0.835), whereas the QTcF interval was in the range of strong agreement (0.682). Further, for the Apple Watch, Pearson correlations were highly significant and in the range of a large effect (r_QT=0.793 and r_QTcF=0.649; P<.001). CCC_Lin corresponded to a strong heuristic agreement for both the QT (0.779) and QTcF (0.615) intervals. A small negative correlation between the measurement error and increasing heart rate could be found of each the devices and the reference.</p><p><strong>Conclusions: </strong>Smart technology seems to be a promising and reliable approach for nonclinical health monitoring. Further research is needed to broaden the evidence regarding its validity and usability in different target groups.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e50701"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10704323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138295221","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":"Barriers and Facilitators Associated With Remote Monitoring Adherence Among Veterans With Pacemakers and Implantable Cardioverter-Defibrillators: Qualitative Cross-Sectional Study.","authors":"Sanket S Dhruva, Merritt H Raitt, Scott Munson, Hans J Moore, Pamela Steele, Lindsey Rosman, Mary A Whooley","doi":"10.2196/50973","DOIUrl":"10.2196/50973","url":null,"abstract":"<p><strong>Background: </strong>The Heart Rhythm Society strongly recommends remote monitoring (RM) of cardiovascular implantable electronic devices (CIEDs) because of the clinical outcome benefits to patients. However, many patients do not adhere to RM and, thus, do not achieve these benefits. There has been limited study of patient-level barriers and facilitators to RM adherence; understanding patient perspectives is essential to developing solutions to improve adherence.</p><p><strong>Objective: </strong>We sought to identify barriers and facilitators associated with adherence to RM among veterans with CIEDs followed by the Veterans Health Administration.</p><p><strong>Methods: </strong>We interviewed 40 veterans with CIEDs regarding their experiences with RM. Veterans were stratified into 3 groups based on their adherence to scheduled RM transmissions over the past 2 years: 6 fully adherent (≥95%), 25 partially adherent (≥65% but <95%), and 9 nonadherent (<65%). As the focus was to understand challenges with RM adherence, partially adherent and nonadherent veterans were preferentially weighted for selection. Veterans were mailed a letter stating they would be called to understand their experiences and perspectives of RM and possible barriers, and then contacted beginning 1 week after the letter was mailed. Interviews were structured (some questions allowing for open-ended responses to dive deeper into themes) and focused on 4 predetermined domains: knowledge of RM, satisfaction with RM, reasons for nonadherence, and preferences for health care engagement.</p><p><strong>Results: </strong>Of the 44 veterans contacted, 40 (91%) agreed to participate. The mean veteran age was 75.3 (SD 7.6) years, and 98% (39/40) were men. Veterans had been implanted with their current CIED for an average of 4.4 (SD 2.8) years. A total of 58% (23/40) of veterans recalled a discussion of home monitoring, and 45% (18/40) reported a good understanding of RM; however, when asked to describe RM, their understanding was sometimes incomplete or not correct. Among the 31 fully or partially adherent veterans, nearly all were satisfied with RM. Approximately one-third recalled ever being told the results of a remote transmission. Among partially or nonadherent veterans, only one-fourth reported being contacted by a Department of Veterans Affairs health care professional regarding not having sent a remote transmission; among those who had troubleshooted to ensure they could send remote transmissions, they often relied on the CIED manufacturer for help (this experience was nearly always positive). Most nonadherent veterans felt more comfortable engaging in RM if they received more information or education. Most veterans were interested in being notified of a successful remote transmission and learning the results of their remote transmissions.</p><p><strong>Conclusions: </strong>Veterans with CIEDs often had limited knowledge about RM and did not recall being contacted about ","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e50973"},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10698647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138176230","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":"Physician- and Patient-Elicited Barriers and Facilitators to Implementation of a Machine Learning-Based Screening Tool for Peripheral Arterial Disease: Preimplementation Study With Physician and Patient Stakeholders.","authors":"Vy Ho, Cati Brown Johnson, Ilies Ghanzouri, Saeed Amal, Steven Asch, Elsie Ross","doi":"10.2196/44732","DOIUrl":"10.2196/44732","url":null,"abstract":"<p><strong>Background: </strong>Peripheral arterial disease (PAD) is underdiagnosed, partially due to a high prevalence of atypical symptoms and a lack of physician and patient awareness. Implementing clinical decision support tools powered by machine learning algorithms may help physicians identify high-risk patients for diagnostic workup.</p><p><strong>Objective: </strong>This study aims to evaluate barriers and facilitators to the implementation of a novel machine learning-based screening tool for PAD among physician and patient stakeholders using the Consolidated Framework for Implementation Research (CFIR).</p><p><strong>Methods: </strong>We performed semistructured interviews with physicians and patients from the Stanford University Department of Primary Care and Population Health, Division of Cardiology, and Division of Vascular Medicine. Participants answered questions regarding their perceptions toward machine learning and clinical decision support for PAD detection. Rapid thematic analysis was performed using templates incorporating codes from CFIR constructs.</p><p><strong>Results: </strong>A total of 12 physicians (6 primary care physicians and 6 cardiovascular specialists) and 14 patients were interviewed. Barriers to implementation arose from 6 CFIR constructs: complexity, evidence strength and quality, relative priority, external policies and incentives, knowledge and beliefs about intervention, and individual identification with the organization. Facilitators arose from 5 CFIR constructs: intervention source, relative advantage, learning climate, patient needs and resources, and knowledge and beliefs about intervention. Physicians felt that a machine learning-powered diagnostic tool for PAD would improve patient care but cited limited time and authority in asking patients to undergo additional screening procedures. Patients were interested in having their physicians use this tool but raised concerns about such technologies replacing human decision-making.</p><p><strong>Conclusions: </strong>Patient- and physician-reported barriers toward the implementation of a machine learning-powered PAD diagnostic tool followed four interdependent themes: (1) low familiarity or urgency in detecting PAD; (2) concerns regarding the reliability of machine learning; (3) differential perceptions of responsibility for PAD care among primary care versus specialty physicians; and (4) patient preference for physicians to remain primary interpreters of health care data. Facilitators followed two interdependent themes: (1) enthusiasm for clinical use of the predictive model and (2) willingness to incorporate machine learning into clinical care. Implementation of machine learning-powered diagnostic tools for PAD should leverage provider support while simultaneously educating stakeholders on the importance of early PAD diagnosis. High predictive validity is necessary for machine learning models but not sufficient for implementation.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":" ","pages":"e44732"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42723950","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}