JMIR Cardio最新文献

{"title":"Comparing the Efficacy of Targeted and Blast Portal Messaging in Message Opening Rate and Anticoagulation (AC) Initiation in Patients with Atrial Fibrillation in Preventing Preventable Strokes Study II: Prospective Cohort Study (Preprint)","authors":"Alok Kapoor, Parth Patel, Soumya Chennupati, Daniel Mbusa, H. Sadiq, Sanjeev Rampam, Robert Leung, Megan Miller, Kevin Rivera Vargas, Patrick Fry, M. Lowe, Christina Catalano, Charles Harrison, John Catanzaro, Sybil Crawford, Anne Marie Smith","doi":"10.2196/49590","DOIUrl":"https://doi.org/10.2196/49590","url":null,"abstract":"","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139370576","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":"Outcomes of a Virtual Cardiac Rehabilitation Program for Patients Undergoing Atrial Fibrillation Ablation: A Pilot Study (Preprint)","authors":"Satish Misra, Karen Niazi, Kamala Swayampakala, Amanda Brown, Melissa Lang, Elizabeth Davenport, Sherry Saxonhouse, John Fedor, Brian Powell, Joseph Thompson, John Holshouser, Rohit Mehta","doi":"10.2196/49345","DOIUrl":"https://doi.org/10.2196/49345","url":null,"abstract":"","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136345719","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":"Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development.","authors":"Ruben S Zoodsma,&nbsp;Rian Bosch,&nbsp;Thomas Alderliesten,&nbsp;Casper W Bollen,&nbsp;Teus H Kappen,&nbsp;Erik Koomen,&nbsp;Arno Siebes,&nbsp;Joppe Nijman","doi":"10.2196/45190","DOIUrl":"https://doi.org/10.2196/45190","url":null,"abstract":"<p><strong>Background: </strong>Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.</p><p><strong>Objective: </strong>This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.</p><p><strong>Methods: </strong>Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO₂) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.</p><p><strong>Results: </strong>A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.</p><p><strong>Conclusions: </strong>In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45190"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9613630","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":"The Association Between Mobile App Use and Change in Functional Capacity Among Cardiac Rehabilitation Participants: Cohort Study.","authors":"Janah May Oclaman, Michelle L Murray, Donald J Grandis, Alexis L Beatty","doi":"10.2196/44433","DOIUrl":"10.2196/44433","url":null,"abstract":"<p><strong>Background: </strong>Cardiac rehabilitation (CR) is underused in the United States and globally, with participation disparities across gender, socioeconomic status, race, and ethnicities. The pandemic led to greater adoption of telehealth CR and mobile app use.</p><p><strong>Objective: </strong>Our primary objective was to estimate the association between CR mobile app use and change in functional capacity from enrollment to completion in patients participating in a CR program that offered in-person, hybrid, and telehealth CR. Our secondary objectives were to study the association between mobile app use and changes in blood pressure (BP) or program completion.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study of participants enrolled in CR at an urban CR program in the United States. Participants were English speaking, at least 18 years of age, participated in the program between May 22, 2020, and May 21, 2022, and downloaded the CR mobile app. Mobile app use was quantified by number of exercise logs, vitals logs, and education material views. The primary outcome was change in functional capacity, measured by change in 6-minute walk distance (6MWD) from enrollment to completion. The secondary outcome was change in BP from enrollment to completion. We estimated associations using multivariable linear or logistic regression models adjusted for age, sex, race, ethnicity, socioeconomic status by ZIP code, insurance, and primary diagnosis for CR referral.</p><p><strong>Results: </strong>A total of 107 participants (mean age 62.9, SD 13.02 years; 90/107, 84.1% male; and 57/105, 53.3% self-declared as White Caucasian) used the mobile app and completed the CR program. Participants had a mean 64.0 (SD 54.1) meter increase in 6MWD between enrollment and completion (P<.001). From enrollment to completion, participants with an elevated BP at baseline (≥130/80 mmHg) experienced a significant decrease in BP (systolic BP -11.5 mmHg; P=.002 and diastolic BP -7.7 mmHg; P=.003). We found no significant association between total app interactions and change in 6MWD (coefficient -0.03, 95% CI -0.1 to 0.07; P=.59) or change in BP (systolic coefficient 0.002, 95% CI -0.03 to 0.03; P=.87 and diastolic coefficient -0.005, 95% CI -0.03 to 0.02; P=.65). There was no significant association between total exercise logs and change in 6MWD (coefficient 0.1, 95% CI -0.3 to 0.4; P=.57) or total BP logs and change in BP (systolic coefficient -0.02, 95% CI -0.1 to 0.06; P=.63 and diastolic coefficient -0.02, 95% CI -0.09 to 0.04; P=.50). There was no significant association between total app interactions and completion of CR (adjusted odds ratio 1.00, 95% CI 0.99-1.01; P=.44).</p><p><strong>Conclusions: </strong>CR mobile app use as part of an in-person, hybrid, or telehealth CR program was not associated with greater improvement in functional capacity or BP or with program completion.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e44433"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10227697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9555396","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":"Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation.","authors":"Jason Hearn,&nbsp;Jef Van den Eynde,&nbsp;Bhargava Chinni,&nbsp;Ari Cedars,&nbsp;Danielle Gottlieb Sen,&nbsp;Shelby Kutty,&nbsp;Cedric Manlhiot","doi":"10.2196/40524","DOIUrl":"https://doi.org/10.2196/40524","url":null,"abstract":"<p><strong>Background: </strong>Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.</p><p><strong>Objective: </strong>The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.</p><p><strong>Methods: </strong>Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set.</p><p><strong>Results: </strong>The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE.</p><p><strong>Conclusions: </strong>In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e40524"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9860679","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 Artificial Intelligence-Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study.","authors":"Jiesuck Park,&nbsp;Yeonyee Yoon,&nbsp;Youngjin Cho,&nbsp;Joonghee Kim","doi":"10.2196/44791","DOIUrl":"https://doi.org/10.2196/44791","url":null,"abstract":"<p><strong>Background: </strong>Despite accumulating research on artificial intelligence-based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated.</p><p><strong>Objective: </strong>We evaluated the utility of an existing artificial intelligence-based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina.</p><p><strong>Methods: </strong>This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)-related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score.</p><p><strong>Results: </strong>Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001).</p><p><strong>Conclusions: </strong>QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e44791"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9477097","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 of Artificial Intelligence-Based Automated Quantitative Coronary Angiography Compared to Intravascular Ultrasound: Retrospective Cohort Study.","authors":"In Tae Moon,&nbsp;Sun-Hwa Kim,&nbsp;Jung Yeon Chin,&nbsp;Sung Hun Park,&nbsp;Chang-Hwan Yoon,&nbsp;Tae-Jin Youn,&nbsp;In-Ho Chae,&nbsp;Si-Hyuck Kang","doi":"10.2196/45299","DOIUrl":"https://doi.org/10.2196/45299","url":null,"abstract":"<p><strong>Background: </strong>An accurate quantitative analysis of coronary artery stenotic lesions is essential to make optimal clinical decisions. Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography.</p><p><strong>Objective: </strong>The aim of this paper is to validate the performance of artificial intelligence-based quantitative coronary angiography (AI-QCA) in comparison with that of intravascular ultrasound (IVUS).</p><p><strong>Methods: </strong>This retrospective study included patients who underwent IVUS-guided coronary intervention at a single tertiary center in Korea. Proximal and distal reference areas, minimal luminal area, percent plaque burden, and lesion length were measured by AI-QCA and human experts using IVUS. First, fully automated QCA analysis was compared with IVUS analysis. Next, we adjusted the proximal and distal margins of AI-QCA to avoid geographic mismatch. Scatter plots, Pearson correlation coefficients, and Bland-Altman were used to analyze the data.</p><p><strong>Results: </strong>A total of 54 significant lesions were analyzed in 47 patients. The proximal and distal reference areas, as well as the minimal luminal area, showed moderate to strong correlation between the 2 modalities (correlation coefficients of 0.57, 0.80, and 0.52, respectively; P<.001). The correlation was weaker for percent area stenosis and lesion length, although statistically significant (correlation coefficients of 0.29 and 0.33, respectively). AI-QCA tended to measure reference vessel areas smaller and lesion lengths shorter than IVUS did. Systemic proportional bias was not observed in Bland-Altman plots. The biggest cause of bias originated from the geographic mismatch of AI-QCA with IVUS. Discrepancies in the proximal or distal lesion margins were observed between the 2 modalities, which were more frequent at the distal margins. After the adjustment of proximal or distal margins, there was a stronger correlation of proximal and distal reference areas between AI-QCA and IVUS (correlation coefficients of 0.70 and 0.83, respectively).</p><p><strong>Conclusions: </strong>AI-QCA showed a moderate to strong correlation compared with IVUS in analyzing coronary lesions with significant stenosis. The main discrepancy was in the perception of the distal margins by AI-QCA, and the correction of margins improved the correlation coefficients. We believe that this novel tool could provide confidence to treating physicians and help in making optimal clinical decisions.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e45299"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9820286","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":"Cardio4Health Study, a Cardiac Telerehabilitation Pilot Program Aimed at Patients After an Ischemic Event: Cross-sectional Study.","authors":"Margarita Calvo-López,&nbsp;Raquel Arranz Tolós,&nbsp;Josefa Marin Expósito,&nbsp;Domenico Gruosso,&nbsp;Rut Andrea,&nbsp;Mercè Roque,&nbsp;Carles Falces,&nbsp;Gemma Yago,&nbsp;Judith Saura Araguas,&nbsp;Nuria Pastor,&nbsp;Marta Sitges,&nbsp;Maria Sanz-de la Garza","doi":"10.2196/44179","DOIUrl":"https://doi.org/10.2196/44179","url":null,"abstract":"<p><strong>Background: </strong>Center-based cardiac rehabilitation programs (CRPs) reduce morbidity and mortality after an ischemic cardiac event; however, they are widely underused. Home-based CRP has emerged as an alternative to improve patient adherence; however, its safety and efficacy remain unclear, especially for older patients and female patients.</p><p><strong>Objective: </strong>This study aimed to develop a holistic home-based CRP for patients with ischemic heart disease and evaluate its safety and impact on functional capacity, adherence to a healthy lifestyle, and quality of life.</p><p><strong>Methods: </strong>The 8-week home-based CRP included patients of both sexes, with no age limit, who had overcome an acute myocardial infarction in the previous 3 months, had a left ventricular ejection fraction of ≥40%, and had access to a tablet or mobile device. The CRP was developed using a dedicated platform designed explicitly for this purpose and included 3 weekly exercise sessions combining tailored aerobic and strength training and 2 weekly educational session focused on lifestyle habits, therapeutic adherence, and patient empowerment.</p><p><strong>Results: </strong>We initially included 62 patients, of whom 1 was excluded for presenting with ventricular arrhythmias during the initial stress test, 5 were excluded because of incompatibility, and 6 dropped out because of a technological barrier. Ultimately, 50 patients completed the program: 85% (42/50) were male, with a mean age of 58.9 (SD 10.3) years, a mean left ventricular ejection fraction of 52.1% (SD 6.72%), and 25 (50%) New York Heart Association functional class I and 25 (50%) New York Heart Association II-III. The CRP significantly improved functional capacity (+1.6 metabolic equivalent tasks), muscle strength (arm curl test +15.5% and sit-to-stand test +19.7%), weekly training volume (+803 metabolic equivalent tasks), adherence to the Mediterranean diet, emotional state (anxiety), and quality of life. No major complications occurred, and adherence was excellent (>80%) in both the exercise and educational sessions. In the subgroup analysis, CRP showed equivalent beneficial effects irrespective of sex and age. In addition, patient preferences for CRP approaches were equally distributed, with one-third (14/50, 29%) of the patients preferring a face-to-face CRP, one-third (17/50, 34%) preferring a telematic CRP, and one-third (18/50, 37%) preferring a hybrid approach. Regarding CRP duration, 63% (31/50) of the patients considered it adequate, whereas the remaining 37% (19/50) preferred a longer program.</p><p><strong>Conclusions: </strong>A holistic telematic CRP dedicated to patients after an ischemic cardiac event, irrespective of sex and age, is safe and, in our population, has achieved positive results in improving maximal aerobic capacity, weekly training volume, muscle strength, quality of life, compliance with diet, and anxiety symptoms. The preference for a center- or h","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e44179"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10299135","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":"Engagement in Self-measured Blood Pressure Monitoring Among Medically Underresourced Participants (the Reach Out Trial): Digital Framework Qualitative Study.","authors":"Abby Katherine Hellem,&nbsp;Candace Whitfield,&nbsp;Amanda Casetti,&nbsp;Maria Cielito Robles,&nbsp;Mackenzie Dinh,&nbsp;William Meurer,&nbsp;Lesli Skolarus","doi":"10.2196/38900","DOIUrl":"https://doi.org/10.2196/38900","url":null,"abstract":"<p><strong>Background: </strong>Mobile health (mHealth) interventions serve as a scalable opportunity to engage people with hypertension in self-measured blood pressure (SMBP) monitoring, an evidence-based approach to lowering blood pressure (BP) and improving BP control. Reach Out is an SMS text messaging-based SMBP mHealth trial that aims to reduce BP among hypertensive patients recruited from the emergency department of a safety net hospital in a low-income, predominately Black city.</p><p><strong>Objective: </strong>As the benefits of Reach Out are predicated on participants' engagement with the intervention, we sought to understand participants' determinants of engagement via prompted SMBP with personalized feedback (SMBP+feedback).</p><p><strong>Methods: </strong>We conducted semistructured telephone interviews based on the digital behavior change interventions framework. Participants were purposively sampled from 3 engagement categories: high engagers (≥80% response to SMBP prompts), low engagers (≤20% response to BP prompts), and early enders (participants who withdrew from the trial).</p><p><strong>Results: </strong>We conducted interviews with 13 participants, of whom 7 (54%) were Black, with a mean age of 53.6 (SD 13.25) years. Early enders were less likely to be diagnosed with hypertension prior to Reach Out, less likely to have a primary care provider, and less likely to be taking antihypertensive medications than their counterparts. Overall, participants liked the SMS text messaging design of the intervention, including the SMBP+feedback. Several participants across all levels of engagement expressed interest in and identified the benefit of enrolling in the intervention with a partner of their choice. High engagers expressed the greatest understanding of the intervention, the least number of health-related social needs, and the greatest social support to engage in SMBP. Low engagers and early enders shared a mixed understanding of the intervention and less social support compared to high engagers. Participation decreased as social needs increased, with early enders sharing the greatest amount of resource insecurity apart from a notable exception of a high engager with high health-related social needs.</p><p><strong>Conclusions: </strong>Prompted SMBP+feedback was perceived favorably by all participants. To enhance SMBP engagement, future studies could consider greater support in the initiation of SMBP, evaluating and addressing participants' unmet health-related social needs, as well as strategies to cultivate social norms.</p>","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e38900"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9725279","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":"Implementation and User Evaluation of an eHealth Technology Platform Supporting Patients With Cardiovascular Disease in Managing Their Health After a Cardiac Event: Mixed Methods Study.","authors":"Britt E Bente,&nbsp;Jobke Wentzel,&nbsp;Celina Schepers,&nbsp;Linda D Breeman,&nbsp;Veronica R Janssen,&nbsp;Marcel E Pieterse,&nbsp;Andrea W M Evers,&nbsp;Lisette van Gemert-Pijnen","doi":"10.2196/43781","DOIUrl":"https://doi.org/10.2196/43781","url":null,"abstract":"Background eHealth technology can help patients with cardiovascular disease adopt and maintain a healthy lifestyle by supporting self-management and offering guidance, coaching, and tailored information. However, to support patients over time, eHealth needs to blend in with their needs, treatment, and daily lives. Just as needs can differ between patients, needs can change within patients over time. To better adapt technology features to patients’ needs, it is necessary to account for these changes in needs and contexts of use. Objective This study aimed to identify and monitor patients’ needs for support from a web-based health management platform and how these needs change over time. It aimed to answer the following research questions: “How do novice and more advanced users experience an online health management platform?” “What user expectations support or hinder the adoption of an online health management platform, from a user perspective?” and “How does actual usage relate to user experiences and adoption?” Methods A mixed methods design was adopted. The first method involved 2 rounds of usability testing, followed by interviews, with 10 patients at 0 months (round 1) and 12 patients at 6 months (round 2). In the second method, log data were collected to describe the actual platform use. Results After starting cardiac rehabilitation, the platform was used frequently. The patients mentioned that they need to have an incentive, set goals, self-monitor their health data, and feel empowered by the platform. However, soon after the rehabilitation program stopped, use of the platform declined or patients even quit because of the lack of continued tailored or personalized advice. The reward system motivated them to log data, but most participants indicated that being healthy should be the main focus, not receiving gifts. A web-based platform is flexible, accessible, and does not have any obligations; however, it should be implemented as an addition to regular care. Conclusions Although use of the platform declined in the longer term, patients quitting the technology did not directly indicate that the technology was not functioning well or that patients no longer focused on achieving their values. The key to success should not be user adherence to a platform but adherence to healthy lifestyle habits. Therefore, the implementation of eHealth should include the transition to a stage where patients might no longer need support from a technology platform to be independently and sustainably adherent to their healthy lifestyle habits. This emphasizes the importance of conducting multi-iterative evaluations to continuously monitor whether and how patients’ needs and contexts of use change over time. Future research should focus on how this transition can be identified and monitored and how these insights can inform the design and implementation of the technology.","PeriodicalId":14706,"journal":{"name":"JMIR Cardio","volume":"7 ","pages":"e43781"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9710119","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}