Aim We aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management. Methods This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review. Results We found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population. Conclusion While the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome. Graphic abstract Mobile health solutions for atrial fibrillation detection and management: a systematic review.
Aims In atrial fibrillation (AF) patients, untreated sleep-disordered breathing (SDB) is associated with lower success rates of rhythm control strategies and as such structured SDB testing is recommended. Herein, we describe the implementation of a virtual SDB management pathway in an AF outpatient clinic and examine the utility and feasibility of this new approach. Methods and results Prospectively, consecutive AF patients accepted for AF catheter ablation procedures without previous diagnosis of SDB were digitally referred to a virtual SDB management pathway and instructed to use WatchPAT-ONE (ITAMAR) for one night. Results were automatically transferred to a virtual sleep laboratory, upon which a teleconsultation with a sleep physician was planned. Patient experience was measured using surveys. SDB testing was performed in 119 consecutive patients scheduled for AF catheter ablation procedures. The median time from digital referral to finalization of the sleep study report was 18 [11–24] days. In total, 65 patients (55%) were diagnosed with moderate-to-severe SDB. Patients with SDB were prescribed more cardiovascular drugs and had higher body mass indices (BMI, 29 ± 3.3 vs. 27 ± 4.4kg/m2, P < 0.01). Patients agreed that WatchPAT-ONE was easy to use (91%) and recommended future use of this virtual pathway in AF outpatient clinics (86%). Based on this remote SDB testing, SDB treatment was recommended in the majority of patients. Conclusion This novel virtual AF management pathway allowed remote SDB testing in AF outpatient clinics with a short time to diagnosis and high patient satisfaction. Structured SDB testing results in a high detection of previously unknown SDB in AF patients scheduled for AF ablation.
Aim Within the TeleCheck-AF project, numerous centers in Europe used on-demand photoplethysmography (PPG) technology to remotely assess heart rate and rhythm in conjunction with teleconsultations. Based on the TeleCheck-AF investigator experiences, we aimed to develop an educational structured stepwise practical guide on how to interpret PPG signals and to introduce typical clinical scenarios how on-demand PPG was used. Methods During an online conference, the structured stepwise practical guide on how to interpret PPG signals was discussed and further refined during an internal review process. We provide the number of respective PPG recordings (FibriCheck®) and number of patients managed within a clinical scenario during the TeleCheck-AF project. Results To interpret PPG recordings, we introduce a structured stepwise practical guide and provide representative PPG recordings. In the TeleCheck-AF project, 2522 subjects collected 90616 recordings in total. The majority of these recordings was classified by the PPG algorithm as sinus rhythm (57.6%), followed by AF (23.6%). In 9.7% of recordings the quality was too low to interpret. The most frequently clinical scenarios where PPG technology was used in the TeleCheck-AF project was follow-up after AF ablation (1110 patients) followed by heart rate and rhythm assessment around (tele)consultation (966 patients). Conclusion We introduce a newly developed structured stepwise practical guide on PPG signal interpretation developed based on presented experiences from TeleCheck-AF. The present clinical scenarios for the use of on-demand PPG technology derived from the TeleCheck-AF project will help to implement PPG technology in the management of AF patients.
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