Dataset Information

Mobile health technology-supported atrial fibrillation screening and integrated care: A report from the mAFA-II trial Long-term Extension Cohort.

ABSTRACT:

Background

In the mobile Atrial Fibrillation App (mAFA)-II trial, the use of mobile health (mHealth) technology, incorporating AF screening and integrated management strategy, was associated with improved short-term clinical outcomes. The aim of this study was to report adherence/persistence and long term (?1 year) clinical outcomes of the mAFA-II trial, with mHealth-supported optimised stroke prevention, symptom control and comorbidity management.

Methods

We studied an adult population screened for AF, where identified patients could enter a structured program of holistic and integrated care based on the ABC (Atrial fibrillation Better Care) pathway using mHealth with a mAFA intervention. In this cluster randomised trial, comparing mHeath intervention to usual care, the primary composite outcome was 'stroke/thromboembolism, all-cause death and rehospitalization'.

Results

The 1261 subjects (mean age 67.0 years, 38.0% female) who were followed up over one year (mean follow-up 687 (standard deviation, SD 191) days) in the intervention arm, had a lower risk of the composite outcome of 'ischaemic stroke/systemic thromboembolism, death, and rehospitalization' (hazard ratio, HR 0.18, 95% confidence interval, CI: 0.13-0.25, P < 0.001), compared to usual care (1212 subjects, mean age 70.1 years, 42.1% female). Of 842 patients using their smart devices for 'Better symptom management', 70.8% had good management adherence (monitoring time/follow-up since initial monitoring ? 70%), with the persistence of use of 91.7%.

Conclusion

Amongst AF patients with long term use (?1 year) of mHealth technology for optimising stroke prevention, symptom control and comorbidity management, adherence/persistence was good and associated with a reduction in adverse clinical outcomes.

SUBMITTER: Guo Y

PROVIDER: S-EPMC7553102 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Mobile health technology-supported atrial fibrillation screening and integrated care: A report from the mAFA-II trial Long-term Extension Cohort.

Guo Yutao Y Guo Jun J Shi Xiangmin X Yao Yuan Y Sun Yihong Y Xia Yunlong Y Yu Bo B Liu Tong T Chen Yundai Y Lip Gregory Y H GYH

European journal of internal medicine 20201013

<h4>Background</h4>In the mobile Atrial Fibrillation App (mAFA)-II trial, the use of mobile health (mHealth) technology, incorporating AF screening and integrated management strategy, was associated with improved short-term clinical outcomes. The aim of this study was to report adherence/persistence and long term (≥1 year) clinical outcomes of the mAFA-II trial, with mHealth-supported optimised stroke prevention, symptom control and comorbidity management.<h4>Methods</h4>We studied an adult popu ...[more]

PMID: 33067121

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Project description:ImportanceThe Mobile Health Technology for Improved Screening and Optimized Integrated Care in Atrial Fibrillation (mAFA-II) trial is a prospective cluster randomized trial that found a significant reduction in the composite clinical outcome of stroke or thromboembolism, all-cause death, and rehospitalization among patients with atrial fibrillation (AF) who used a mobile health (mHealth) technology that implemented the Atrial Fibrillation Better Care (ABC) pathway (ie, A, anticoagulation/avoid stroke; B, better symptom control; and C, cardiovascular disease and comorbidity management) compared with those receiving usual care. Multimorbidity (defined as ≥2 chronic long-term conditions) is common in older patients with AF, but the impact of integrated or holistic care (based on the ABC pathway) on clinical outcomes in this population is uncertain.ObjectiveTo evaluate whether implementation of the integrated ABC pathway, supported by mHealth technology, would reduce AF-related adverse events in patients with multimorbidity.Design, setting, and participantsThis prespecified ancillary analysis of data from the extended follow-up of the mAFA II trial was conducted between June 2018 and April 2021. Adult patients with AF were included in the analysis if they had at least 2 comorbidities. Participants were enrolled across 40 centers in China.InterventionIntegrated care supported by mHealth technology (mAFA intervention) vs usual care.Main outcomes and measuresThe main outcome was the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization. Cox proportional hazard modeling was performed for adverse outcomes after adjusting for cluster effect and baseline risk factors.ResultsOf 1890 patients, 833 (mean [SD] age, 72.0 [12.0] years; 278 [33.4%] women) with multimorbidity were allocated to the intervention group (ABC pathway), with a mean (SD) follow-up of 419 (257) days, and 1057 patients (mean [SD] age, 72.8 [13.0] years; 443 [41.9%] women) with multimorbidity were allocated to usual care, with a mean (SD) follow-up of 457 (154) days. Compared with usual care, the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization was significantly reduced in the intervention group (hazard ratio [HR], 0.37; 95% CI, 0.26-0.53; P < .001), as were rehospitalizations alone (HR, 0.42; 95% CI, 0.27-0.64; P < .001). For the C criterion of the ABC pathway, rates of acute coronary syndrome, heart failure, and uncontrolled blood pressure during follow-up were lower in the intervention group than the usual care group (27 patients [3.2%] vs 145 patients [13.7%]; HR, 0.29; 95% CI, 0.19-0.45; P < .001). Subgroup analyses by age, prior stroke, and sex demonstrated consistently lower HRs for the primary composite outcome and rehospitalization for patients with AF allocated to the intervention group compared with patients receiving usual care.Conclusions and relevanceIn this study, mHealth technology-based integrated care that facilitated the implementation of the ABC pathway reduced meaningful clinical adverse events in older patients with AF and multimorbidity vs usual care.Trial registrationWHO International Clinical Trials Registry Platform (ICTRP) Registration number: ChiCTR-OOC-17014138.

Project description:BackgroundAtrial fibrillation (AF) is a common arrhythmia that adversely affects health-related quality of life (HRQoL). We conducted a pilot trial of individuals with AF using a smartphone to provide a relational agent as well as rhythm monitoring. We employed our pilot to measure acceptability and adherence and to assess its effectiveness in improving HRQoL and adherence.ObjectiveThis study aims to measure acceptability and adherence and to assess its effectiveness to improve HRQoL and adherence.MethodsParticipants were recruited from ambulatory clinics and randomized to a 30-day intervention or usual care. We collected baseline characteristics and conducted baseline and 30-day assessments of HRQoL using the Atrial Fibrillation Effect on Quality of Life (AFEQT) measure and self-reported adherence to anticoagulation. The intervention consisted of a smartphone-based relational agent, which simulates face-to-face counseling and delivered content on AF education, adherence, and symptom monitoring with prompted rhythm monitoring. We compared differences in AFEQT and adherence at 30 days, adjusted for baseline values. We quantified participants' use and acceptability of the intervention.ResultsA total of 120 participants were recruited and randomized (59 to control and 61 to intervention) to the pilot trial (mean age 72.1 years, SD 9.10; 62/120, 51.7% women). The control group had a 95% follow-up, and the intervention group had a 93% follow-up. The intervention group demonstrated significantly higher improvement in total AFEQT scores (adjusted mean difference 4.5; 95% CI 0.6-8.3; P=.03) and in daily activity (adjusted mean difference 7.1; 95% CI 1.8-12.4; P=.009) compared with the control between baseline and 30 days. The intervention group showed significantly improved self-reported adherence to anticoagulation therapy at 30 days (intervention 3.5%; control 23.2%; adjusted difference 16.6%; 95% CI 2.8%-30.4%; P<.001). Qualitative assessments of acceptability identified that participants found the relational agent useful, informative, and trustworthy.ConclusionsIndividuals randomized to a 30-day smartphone intervention with a relational agent and rhythm monitoring showed significant improvement in HRQoL and adherence. Participants had favorable acceptability of the intervention with both objective use and qualitative assessments of acceptability.

Project description:BACKGROUND:Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). OBJECTIVE:We investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. METHODS:A total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. RESULTS:In all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). CONCLUSIONS:The algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. TRIAL REGISTRATION:Chinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6).

Project description:BackgroundAtrial fibrillation (AF) is an increasingly common chronic health condition for which integrated care that is multidisciplinary and patient-centric is recommended yet challenging to implement.ObjectiveThe aim of Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is to evaluate the feasibility and potential efficacy of a digital intervention (AF-Support) comprising preprogrammed automated telephone calls (artificial intelligence conversational technology), SMS text messages, and emails, as well as an educational website, to support patients with AF in self-managing their condition and coordinate primary and secondary care follow-up.MethodsCoordinating Health Care With Artificial Intelligence-Supported Technology in AF is a 6-month randomized controlled trial of adult patients with AF (n=385), who will be allocated in a ratio of 4:1 to AF-Support or usual care, with postintervention semistructured interviews. The primary outcome is AF-related quality of life, and the secondary outcomes include cardiovascular risk factors, outcomes, and health care use. The 4:1 allocation design enables a detailed examination of the feasibility, uptake, and process of the implementation of AF-Support. Participants with new or ongoing AF will be recruited from hospitals and specialist-led clinics in Sydney, New South Wales, Australia. AF-Support has been co-designed with clinicians, researchers, information technologists, and patients. Automated telephone calls will occur 7 times, with the first call triggered to commence 24 to 48 hours after enrollment. Calls follow a standard flow but are customized to vary depending on patients' responses. Calls assess AF symptoms, and participants' responses will trigger different system responses based on prespecified protocols, including the identification of red flags requiring escalation. Randomization will be performed electronically, and allocation concealment will be ensured. Because of the nature of this trial, only outcome assessors and data analysts will be blinded. For the primary outcome, groups will be compared using an analysis of covariance adjusted for corresponding baseline values. Randomized trial data analysis will be performed according to the intention-to-treat principle, and qualitative data will be thematically analyzed.ResultsEthics approval was granted by the Western Sydney Local Health District Human Ethics Research Committee, and recruitment started in December 2020. As of December 2021, a total of 103 patients had been recruited.ConclusionsThis study will address the gap in knowledge with respect to the role of postdischarge digital care models for supporting patients with AF.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886.International registered report identifier (irrid)DERR1-10.2196/34470.

Project description:BACKGROUND:Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. METHODS:We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study. RESULTS:The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%). CONCLUSIONS:An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.

Dataset Information

Mobile health technology-supported atrial fibrillation screening and integrated care: A report from the mAFA-II trial Long-term Extension Cohort.

Background

Methods

Results

Conclusion

Publications

Mobile health technology-supported atrial fibrillation screening and integrated care: A report from the mAFA-II trial Long-term Extension Cohort.

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