Lancet Digital Health最新文献

{"title":"AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study","authors":"Jirong Yi PhD ,&nbsp;Anna M Marcinkiewicz MD ,&nbsp;Aakash Shanbhag MSc ,&nbsp;Robert J H Miller MD ,&nbsp;Jolien Geers MD ,&nbsp;Wenhao Zhang PhD ,&nbsp;Aditya Killekar MSc ,&nbsp;Nipun Manral MSc ,&nbsp;Mark Lemley BSc ,&nbsp;Mikolaj Buchwald PhD ,&nbsp;Jacek Kwiecinski MD ,&nbsp;Jianhang Zhou MSc ,&nbsp;Paul B Kavanagh MSc ,&nbsp;Joanna X Liang MPH ,&nbsp;Valerie Builoff BSc ,&nbsp;Prof Terrence D Ruddy MD ,&nbsp;Prof Andrew J Einstein MD ,&nbsp;Attila Feher MD ,&nbsp;Edward J Miller Prof ,&nbsp;Prof Albert J Sinusas MD ,&nbsp;Piotr J Slomka PhD","doi":"10.1016/j.landig.2025.02.002","DOIUrl":"10.1016/j.landig.2025.02.002","url":null,"abstract":"<div><h3>Background</h3><div>CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.</div></div><div><h3>Methods</h3><div>We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves.</div></div><div><h3>Findings</h3><div>The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5–T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46−3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92–2·96; p&lt;0·0001, 1·55, 1·26–1·90; p&lt;0·0001, and 1·30, 1·06–1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62–0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44–0·71; p&lt;0·0001).</div></div><div><h3>Interpretation</h3><div>CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value.</div></div><div><h3>Funding</h3><div>The National Heart, Lung, and Blood Institute, National Institutes of Health.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100862"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?","authors":"Prof Mohan Pammi MD ,&nbsp;Prakesh S Shah MD ,&nbsp;Liu K Yang PhD ,&nbsp;Joseph Hagan ScD ,&nbsp;Nima Aghaeepour PhD ,&nbsp;Prof Josef Neu MD","doi":"10.1016/j.landig.2025.01.007","DOIUrl":"10.1016/j.landig.2025.01.007","url":null,"abstract":"<div><div>Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data—namely, digital twins, synthetic patient data, and in-silico trials—are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100851"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of mobile health technology combining telemonitoring and teleintervention versus usual care in vulnerable-phase heart failure management (HERMeS): a multicentre, randomised controlled trial","authors":"Sergi Yun MD ,&nbsp;Prof Josep Comín-Colet PhD ,&nbsp;Esther Calero-Molina RN ,&nbsp;Encarnación Hidalgo RN ,&nbsp;Núria José-Bazán RN ,&nbsp;Marta Cobo Marcos MD ,&nbsp;Teresa Soria RN ,&nbsp;Pau Llàcer PhD ,&nbsp;Cristina Fernández MSc ,&nbsp;José Manuel García-Pinilla PhD ,&nbsp;Concepción Cruzado RN ,&nbsp;Álvaro González-Franco MD ,&nbsp;Eva María García-Marina RN ,&nbsp;José Luis Morales-Rull PhD ,&nbsp;Cristina Solé MD ,&nbsp;Elena García-Romero MD ,&nbsp;Julio Núñez PhD ,&nbsp;José Civera RN ,&nbsp;Coral Fernández RN ,&nbsp;Mercedes Faraudo RN ,&nbsp;Jordi Fernández","doi":"10.1016/j.landig.2025.02.006","DOIUrl":"10.1016/j.landig.2025.02.006","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The potential of mobile health (mHealth) technology combining telemonitoring and teleintervention as a non-invasive intervention to reduce the risk of cardiovascular events in patients with heart failure during the early post-discharge period (ie, the vulnerable phase) has not been evaluated to our knowledge. We investigated the efficacy of incorporating mHealth into routine heart failure management in vulnerable-phase patients.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;The Heart Failure Events Reduction with Remote Monitoring and eHealth Support (HERMeS) trial was a 24-week, randomised, controlled, open-label with masked endpoint adjudication, phase 3 trial conducted in ten centres (hospitals [n=9] and a primary care service [n=1]) experienced in heart failure management in Spain. We enrolled adults (aged ≥18 years) with heart failure diagnosed according to the 2016 European Society of Cardiology criteria (then-current clinical practice guidelines at the initiation of the trial) who had recently been discharged (within the preceding 30 days of enrolment) from a hospital admission that was due to heart failure decompensation, or who were in the process of discharge planning. After discharge, participants were centrally randomly assigned (1:1) via a web-based system to mHealth, comprising telemonitoring and preplanned structured health-care follow-up via videoconference, or usual care according to each centre’s heart failure care framework including a nurse-led educational programme. The primary outcome was a composite of the occurrence of cardiovascular death or worsening heart failure events during the 6-month follow-up period, assessed by time-to-first-event analysis in the full analysis set by the intention-to-treat principle. No prospective systematic collection of harms information was planned. The HERMeS trial is registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT03663907&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, and is completed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;From May 15, 2018, to April 4, 2022, 506 participants (207 [41%] women and 299 [59%] men) were randomly assigned: 255 to mHealth and 251 to usual care. The mean age of participants was 73 years (SD 13). Follow-up ended prematurely in 51 (20%) of 255 participants in the mHealth group and 36 (14%) of 251 in the usual care group. During follow-up in the mHealth group, cardiovascular death or a worsening heart failure event occurred in 43 (17%) of 255 participants, compared with 102 (41%) of 251 in the usual care group (hazard ratio for time to first event 0·35 [95% CI 0·24–0·50]; p&lt;0·0001; relative risk reduction 65% [95% CI 50–76]). No spontaneously reported harms were reported in either group during follow-up.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;mHealth-based heart failure care combining teleintervention and telemonitoring reduced the risk of new fatal and non-fatal cardiovascular events compared ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100866"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential effects of the social media age ban in Australia for children younger than 16 years","authors":"Jasmine Fardouly","doi":"10.1016/j.landig.2025.01.016","DOIUrl":"10.1016/j.landig.2025.01.016","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e235-e236"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Snapshot artificial intelligence—determination of ejection fraction from a single frame still image: a multi-institutional, retrospective model development and validation study","authors":"Jeffrey G Malins PhD ,&nbsp;D M Anisuzzaman PhD ,&nbsp;John I Jackson PhD ,&nbsp;Eunjung Lee PhD ,&nbsp;Jwan A Naser MBBS ,&nbsp;Behrouz Rostami PhD ,&nbsp;Jared G Bird MD ,&nbsp;Dan Spiegelstein MD ,&nbsp;Talia Amar MSc ,&nbsp;Prof Jae K Oh MD ,&nbsp;Prof Patricia A Pellikka MD ,&nbsp;Jeremy J Thaden MD ,&nbsp;Prof Francisco Lopez-Jimenez MD MSc ,&nbsp;Prof Sorin V Pislaru MD PhD ,&nbsp;Prof Paul A Friedman MD ,&nbsp;Prof Garvan C Kane MD PhD ,&nbsp;Zachi I Attia PhD","doi":"10.1016/j.landig.2025.02.003","DOIUrl":"10.1016/j.landig.2025.02.003","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is poised to transform point-of-care practice by providing rapid snapshots of cardiac functioning. Although previous AI models have been developed to estimate left ventricular ejection fraction (LVEF), they have typically used video clips as input, which can be computationally intensive. In the current study, we aimed to develop an LVEF estimation model that takes in static frames as input.</div></div><div><h3>Methods</h3><div>Using retrospective transthoracic echocardiography (TTE) data from Mayo Clinic Rochester and Mayo Clinic Health System sites (training: n=19 627; interval validation: n=862), we developed a two-dimensional convolutional neural network model that provides an LVEF estimate associated with an input frame from an echocardiogram video. We then evaluated model performance for Mayo Clinic TTE data (Rochester, n=1890; Arizona, n=1695; Florida, n=1862), the EchoNet-Dynamic TTE dataset (n=10 015), a prospective cohort of patients from whom TTE and handheld cardiac ultrasound (HCU) were simultaneously collected (n=625), and a prospective cohort of patients from whom HCU clips were collected by expert sonographers and novice users (n=100, distributed across three external sites).</div></div><div><h3>Findings</h3><div>We observed consistently strong model performance when estimates from single frames were averaged across multiple video clips, even when only one frame was taken per video (for classifying LVEF ≤40% <em>vs</em> LVEF&gt;40%, area under the receiver operating characteristic curve [AUC]&gt;0·90 for all datasets except for HCU clips collected by novice users, for which AUC&gt;0·85). We also observed that LVEF estimates differed slightly depending on the phase of the cardiac cycle when images were captured.</div></div><div><h3>Interpretation</h3><div>When aiming to rapidly deploy such models, single frames from multiple videos might be sufficient for LVEF classification. Furthermore, the observed sensitivity to the cardiac cycle offers some insights on model performance from an explainability perspective.</div></div><div><h3>Funding</h3><div>Internal institutional funds provided by the Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e255-e263"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study","authors":"Joshua Mayourian MD ,&nbsp;Ivor B Asztalos MD ,&nbsp;Amr El-Bokl MD ,&nbsp;Platon Lukyanenko PhD ,&nbsp;Ryan L Kobayashi MD ,&nbsp;William G La Cava MD ,&nbsp;Sunil J Ghelani MD ,&nbsp;Prof Victoria L Vetter MD ,&nbsp;Prof John K Triedman MD","doi":"10.1016/j.landig.2025.01.001","DOIUrl":"10.1016/j.landig.2025.01.001","url":null,"abstract":"<div><h3>Background</h3><div>Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of LVSD in the general adult population, it has yet to be applied comprehensively across congenital heart disease lesions.</div></div><div><h3>Methods</h3><div>We trained a convolutional neural network on paired ECG–echocardiograms (≤2 days apart) across the lifespan of a wide range of congenital heart disease lesions to detect left ventricular ejection fraction (LVEF) of 40% or less. Model performance was evaluated on single ECG–echocardiogram pairs per patient at Boston Children's Hospital (Boston, MA, USA) and externally at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves.</div></div><div><h3>Findings</h3><div>The training cohort comprised 124 265 ECG–echocardiogram pairs (49 158 patients; median age 10·5 years [IQR 3·5–16·8]; 3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%). Test groups included internal testing (21 068 patients; median age 10·9 years [IQR 3·7–17·0]; 3381 [2·7%] of 124 265 ECG–echocardiogram pairs with LVEF ≤40%) and external validation (42 984 patients; median age 10·8 years [IQR 4·9–15·0]; 1313 [1·7%] of 76 400 ECG–echocardiogram pairs with LVEF ≤40%) cohorts. High model performance was achieved during internal testing (AUROC 0·95, AUPRC 0·33) and external validation (AUROC 0·96, AUPRC 0·25) for a wide range of congenital heart disease lesions. Patients with LVEF greater than 40% by echocardiogram who were deemed high risk by AI-ECG were more likely to have future dysfunction compared with low-risk patients (hazard ratio 12·1 [95% CI 8·4–17·3]; p&lt;0·0001). High-risk patients by AI-ECG were at increased risk of mortality in the overall cohort and lesion-specific subgroups. Common salient features highlighted across congenital heart disaese lesions include precordial QRS complexes and T waves, with common high-risk ECG features including deep V2 S waves and lateral precordial T wave inversion. A case study on patients with ventricular pacing showed similar findings.</div></div><div><h3>Interpretation</h3><div>Our externally validated algorithm shows promise in prediction of current and future LVSD in patients with congenital heart disease, providing a clinically impactful, inexpensive, and convenient cardiovascular health tool in this population.</div></div><div><h3>Funding</h3><div>Kostin Innovation Fund, Thrasher Research Fund Early Career Award, Boston Children's Hospital Electrophysiology Research Education Fund, National Institutes of Health, National Institute of Childhood Diseases and Human Development, and National Library of Medicine.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e264-e274"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighing the benefits and risks of collecting race and ethnicity data in clinical settings for medical artificial intelligence","authors":"Amelia Fiske PhD ,&nbsp;Sarah Blacker PhD ,&nbsp;Lester Darryl Geneviève PhD ,&nbsp;Theresa Willem MA ,&nbsp;Marie-Christine Fritzsche ,&nbsp;Alena Buyx MD ,&nbsp;Leo Anthony Celi MD ,&nbsp;Stuart McLennan PhD","doi":"10.1016/j.landig.2025.01.003","DOIUrl":"10.1016/j.landig.2025.01.003","url":null,"abstract":"<div><div>Many countries around the world do not collect race and ethnicity data in clinical settings. Without such identified data, it is difficult to identify biases in the training data or output of a given artificial intelligence (AI) algorithm, and to work towards medical AI tools that do not exclude or further harm marginalised groups. However, the collection of these data also poses specific risks to racially minoritised populations and other marginalised groups. This Viewpoint weighs the risks of collecting race and ethnicity data in clinical settings against the risks of not collecting those data. The collection of more comprehensive identified data (ie, data that include personal attributes such as race, ethnicity, and sex) has the possibility to benefit racially minoritised populations that have historically faced worse health outcomes and health-care access, and inadequate representation in research. However, the collection of extensive demographic data raises important concerns that include the construction of intersectional social categories (ie, race and its shifting meaning in different sociopolitical contexts), the risks of biological reductionism, and the potential for misuse, particularly in situations of historical exclusion, violence, conflict, genocide, and colonialism. Careful navigation of identified data collection is key to building better AI algorithms and to work towards medicine that does not exclude or harm marginalised groups.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e286-e294"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Lancet Digit Health 2025; 7: e161–66","authors":"","doi":"10.1016/j.landig.2025.03.004","DOIUrl":"10.1016/j.landig.2025.03.004","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Page e237"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the social media ban","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.03.003","DOIUrl":"10.1016/j.landig.2025.03.003","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Page e232"},"PeriodicalIF":23.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of home-based cardiac rehabilitation interventions delivered via mHealth technologies: a systematic review and meta-analysis","authors":"Leah Li MSN ,&nbsp;Mickaël Ringeval PhD ,&nbsp;Gerit Wagner PhD ,&nbsp;Prof Guy Paré PhD ,&nbsp;Cemal Ozemek PhD ,&nbsp;Spyros Kitsiou PhD","doi":"10.1016/j.landig.2025.01.011","DOIUrl":"10.1016/j.landig.2025.01.011","url":null,"abstract":"<div><h3>Background</h3><div>Centre-based cardiac rehabilitation (CBCR) is underused due to low referral rates, accessibility barriers, and socioeconomic constraints. mHealth technologies have the potential to address some of these challenges through remote delivery of home-based cardiac rehabilitation (HBCR). This study aims to assess the effects of mHealth HBCR interventions compared with usual care and CBCR in patients with heart disease.</div></div><div><h3>Methods</h3><div>We conducted a systematic review and meta-analysis of randomised controlled trials of mHealth HBCR interventions. Four electronic databases (MEDLINE, CENTRAL, CINAHL, and Embase) were searched from inception to March 31, 2023, with no restrictions on language or publication type. Eligible studies were randomised controlled trials of adult patients (age ≥18 years) with heart disease, comparing mHealth interventions with usual care or CBCR. The primary outcome of interest was aerobic exercise capacity, assessed with VO₂ peak or 6-min walk test (6MWT). Quality of evidence was assessed using the GRADE system. This review was registered with PROSPERO, CRD42024544087.</div></div><div><h3>Findings</h3><div>Our search yielded 9164 references, of which 135 were retained for full-text review. 13 randomised controlled trials met eligibility criteria and were included in the systematic review, involving 1508 adults with myocardial infarction, angina pectoris, or heart failure, or who had undergone revascularisation. Intervention duration ranged from 6 weeks to 24 weeks. Random-effects meta-analysis showed that, compared with usual care, mHealth HBCR significantly improved 6MWT (mean difference 24·74, 95% CI 9·88–39·60; 532 patients) and VO₂ peak (1·77, 1·19–2·35; 359 patients). No significant differences were found between mHealth HBCR and CBCR. Quality of evidence ranged from low to very low across outcomes due to risk of bias and imprecision (small sample size).</div></div><div><h3>Interpretation</h3><div>mHealth HBCR could improve access and health outcomes in patients who are unable to attend CBCR. Further research is needed to build a robust evidence base on the clinical effectiveness and cost-effectiveness of mHealth HBCR, particularly in comparison with CBCR, to inform clinical practice and policy.</div></div><div><h3>Funding</h3><div>None.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 4","pages":"Pages e238-e254"},"PeriodicalIF":23.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}