Mobile health applications for the detection of atrial fibrillation: a systematic review
Open accessAbstract
Aims: Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. Methods and results: We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0–100% and a specificity of 93.5–99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. Conclusion: Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The two most widely adopted sensor data types are ECG and photoplethysmography (PPG).
“The two most widely adopted sensor data types are ECG and photoplethysmography (PPG) [166–168].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [168]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164 · see citing works
Assessing the quality of reports of randomized clinical trials: Is blinding necessary?
Jadad, A. R., Moore, R. A., Carroll, D., Jenkinson, C., Reynolds, D. J. M., Gavaghan, D. J., & McQuay, H. J. (1996) · Controlled Clinical Trials · doi:10.1016/0197-2456(95)00134-4 · Cited by 17,582 · see citing works
Stang, A. (2010) · European Journal of Epidemiology · doi:10.1007/s10654-010-9491-z · Cited by 17,396 · see citing works
Ruff, C. T., Giugliano, R. P., Braunwald, E., Hoffman, E. B., Deenadayalu, N., Ezekowitz, M. D., Camm, A. J., Weitz, J. I., Lewis, B. S., Parkhomenko, A., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62343-0 · Cited by 5,051 · see citing works
2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS.
Kirchhof, P., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castellá, M., Diener, H., Heidbuchel, H., Hendriks, J., et al. (2016) · Europace · doi:10.1093/europace/euw295 · Cited by 4,465 · see citing works
Newcastle-Ottawa Scale: comparing reviewers’ to authors’ assessments
Lo, C. K.-L., Mertz, D., & Loeb, M. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-45 · Cited by 2,477 · see citing works
Subclinical atrial fibrillation and the risk of stroke
Healey, J. S., Connolly, S. J., Gold, M. R., Israel, C. W., Van Gelder, I. C., Capucci, A., Lau, C. P., Fain, E., Yang, S., Bailleul, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1105575 · Cited by 2,100 · cited 1× · see citing works
“Atrial fibrillation manifestations can range from asymptomatic to highly symptomatic and can negatively affect patients’ quality of life if left untreated.(3) Early diagnosis of AF may have several benefits, including the potential for individualized risk factor evaluation and modification, ablation for symptomatic in…”
BackgroundLarge-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917 · see citing works
Epidemiology of atrial fibrillation: European perspective
Zoni-Berisso, M., Lercari, F., Carazza, T., & Domenicucci, S. (2014) · Clinical Epidemiology · doi:10.2147/clep.s47385 · Cited by 1,275 · see citing works
The impact of mHealth interventions: systematic review of systematic reviews
Marcolino, M. S., Oliveira, J. A. Q., D'Agostino, M., Ribeiro, A. L., Alkmim, M. B. M., & Novillo-Ortiz, D. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8873 · Cited by 1,163 · see citing works
Ghamari, M. (2018) · International Journal of Biosensors & Bioelectronics · doi:10.15406/ijbsbe.2018.04.00125 · Cited by 945 · see citing works
Mass Screening for Untreated Atrial Fibrillation: The STROKESTOP Study
Svennberg, E., Engdahl, J., Al-Khalili, F., Friberg, L., Frykman, V., & Rosenqvist, M. (2015) · Circulation · doi:10.1161/circulationaha.114.014343 · Cited by 605 · see citing works
Halcox, J. P. J., Wareham, K., Cardew, A., Gilmore, M., Barry, J. P., Phillips, C., & Gravenor, M. B. (2017) · Circulation · doi:10.1161/circulationaha.117.030583 · Cited by 580 · see citing works
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.0136 · Cited by 566 · see citing works
Screening for Atrial Fibrillation
Freedman, B., Camm, A. J., Calkins, H. G., Healey, J. S., Rosenqvist, M., Wang, J., Albert, C. M., Anderson, C. S., Antoniou, S., Benjamin, E. J., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.026693 · Cited by 548 · see citing works
Iribarren, S. J., Cato, K., Falzon, L., & Stone, P. W. (2017) · PLOS ONE · doi:10.1371/journal.pone.0170581 · Cited by 519 · cited 1× · see citing works
“Therefore, the overall cost-effectiveness of this technology is currently unclear.(69,70) Policy implications of mobile health development The findings of this review have important policy implications at a population level.”
BackgroundMobile Photoplethysmographic Technology to Detect Atrial Fibrillation
Guo, Y., Wang, H., Zhang, H., Liu, T., Liang, Z., Xia, Y., Yan, L., Xing, Y., Shi, H., Li, S., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.08.019 · Cited by 489 · see citing works
Lowres, N., Neubeck, L., Salkeld, G., Krass, I., McLachlan, A. J., Redfern, J., Bennett, A. A., Briffa, T., Bauman, A., Martinez, C., et al. (2014) · Thrombosis and Haemostasis · doi:10.1160/th14-03-0231 · Cited by 458 · see citing works
Smartwatch Algorithm for Automated Detection of Atrial Fibrillation
Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.003 · Cited by 446 · see citing works
Lau, J. K., Lowres, N., Neubeck, L., Brieger, D. B., Sy, R. W., Galloway, C. D., Albert, D. E., & Freedman, S. B. (2013) · International Journal of Cardiology · doi:10.1016/j.ijcard.2013.01.220 · Cited by 326 · see citing works
McManus, D. D., Lee, J., Maitas, O., Esa, N., Pidikiti, R., Carlucci, A., Harrington, J., Mick, E., & Chon, K. H. (2013) · Heart Rhythm · doi:10.1016/j.hrthm.2012.12.001 · Cited by 253 · see citing works
Han, M., & Lee, E. (2018) · Healthcare Informatics Research · doi:10.4258/hir.2018.24.3.207 · Cited by 247 · see citing works
The WATCH AF trial: SmartWATCHes for detection of atrial fibrillation
Dörr, M., Nohturfft, V., Brasier, N., Bosshard, E., Djurdjevic, A., Gross, S., Raichle, C. J., Rhinisperger, M., Stöckli, R., & Eckstein, J. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2018.10.006 · Cited by 245 · see citing works
Chan, P., Wong, C., Poh, Y. C., Pun, L., Leung, W. W., Wong, Y., Wong, M. M., Poh, M., Chu, D. W., & Siu, C. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.003428 · Cited by 240 · see citing works
Wireless smartphone ECG enables large-scale screening in diverse populations
HABERMAN, Z. C., JAHN, R. T., BOSE, R., TUN, H., SHINBANE, J. S., DOSHI, R. N., CHANG, P. M., & SAXON, L. A. (2015) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.12634 · Cited by 212 · see citing works
Sterne, J. A. C., Bodalia, P. N., Bryden, P. A., Davies, P. A., López-López, J. A., Okoli, G. N., Thom, H. H. Z., Caldwell, D. M., Dias, S., Eaton, D., et al. (2017) · Health Technology Assessment · doi:10.3310/hta21090 · Cited by 201 · see citing works
Electrocardiographic patch devices and contemporary wireless cardiac monitoring
Fung, E., Järvelin, M., Doshi, R. N., Shinbane, J. S., Carlson, S. K., Grazette, L. P., Chang, P. M., Sangha, R. S., Huikuri, H. V., & Peters, N. S. (2015) · Frontiers in Physiology · doi:10.3389/fphys.2015.00149 · Cited by 191 · see citing works
Portable out-of-hospital electrocardiography: A review of current technologies
Bansal, A., & Joshi, R. (2018) · Journal of Arrhythmia · doi:10.1002/joa3.12035 · Cited by 174 · cited 3× · see citing works
“The combination of these sensors and contact points yield the six classic frontal leads of a full 12-lead ECG.17 The third method is based on mechanocardiography principles.”
MethodInfluentialDesteghe, L., Raymaekers, Z., Lutin, M., Vijgen, J. M., Dilling-Boer, D. M., Koopman, P., Schurmans, J., Vanduynhoven, P., Dendale, P., & Heidbüchel, H. (2016) · EP Europace · doi:10.1093/europace/euw025 · Cited by 171 · see citing works
PULSE‐SMART: Pulse‐Based Arrhythmia Discrimination Using a Novel Smartphone Application
McMANUS, D. D., CHONG, J. W., SONI, A., SACZYNSKI, J. S., ESA, N., NAPOLITANO, C., DARLING, C. E., BOYER, E., ROSEN, R. K., FLOYD, K. C., et al. (2016) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.12842 · Cited by 156 · see citing works
Aronsson, M., Svennberg, E., Rosenqvist, M., Engdahl, J., Al-Khalili, F., Friberg, L., Frykman-Kull, V., & Levin, L.-Å. (2015) · Europace · doi:10.1093/europace/euv083 · Cited by 154 · see citing works
William, A. D., Kanbour, M., Callahan, T., Bhargava, M., Varma, N., Rickard, J., Saliba, W., Wolski, K., Hussein, A., Lindsay, B. D., et al. (2018) · Heart Rhythm · doi:10.1016/j.hrthm.2018.06.037 · Cited by 154 · cited 2× · see citing works
“In one study assessing Kardia Mobile, 28% of recordings were unclassified by the algorithm.(24) Of these unclassified recordings, only 8% were non-interpretable by the physician.”
BackgroundMethodAtrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone
Lahdenoja, O., Pänkäälä, M., Koivisto, T., Hurnanen, T., Iftikhar, Z., Nieminen, S., Knuutila, T., Saraste, A., Kiviniemi, T., Vasankari, T., et al. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2688473 · Cited by 146 · see citing works
Brasier, N., Raichle, C. J., Dörr, M., Becke, A., Nohturfft, V., Weber, S., Bulacher, F., Salomon, L., Noah, T., Birkemeyer, R., et al. (2018) · EP Europace · doi:10.1093/europace/euy176 · Cited by 146 · see citing works
Monitoring and detecting atrial fibrillation using wearable technology
Nemati, S., Ghassemi, M. M., Ambai, V., Isakadze, N., Levantsevych, O. M., Shah, A. J., & Clifford, G. D. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591456 · Cited by 144 · see citing works
Proesmans, T., Mortelmans, C., Haelst, R. V., Verbrugge, F. H., Vandervoort, P. M., & Vaes, B. (2018) · JMIR mhealth and uhealth · doi:10.2196/12284 · Cited by 135 · see citing works
Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis.
Welton, N., McAleenan, A., Thom, H., Davies, P., Hollingworth, W., Higgins, J., Okoli, G., Sterne, J., Feder, G., Eaton, D., et al. (2017) · Health Technology Assessment · doi:10.3310/hta21290 · Cited by 133 · see citing works
Gudmundsdottir, K. K., Fredriksson, T., Svennberg, E., Al‐Khalili, F., Friberg, L., Frykman, V., Hijazi, Z., Rosenqvist, M., & Engdahl, J. (2019) · EP Europace · doi:10.1093/europace/euz255 · Cited by 131 · see citing works
Atrial Fibrillation and Stroke: Epidemiology
Reiffel, J. A. (2014) · The American Journal of Medicine · doi:10.1016/j.amjmed.2013.06.002 · Cited by 128 · see citing works
Wei, S., Chen, M., Chen, N., et al. (2017) · World Journal of Surgical Oncology · doi:10.1186/s12957-017-1168-6 · Cited by 123 · see citing works
Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms
Poh, M., Poh, Y. C., Chan, P., Wong, C., Pun, L., Leung, W. W., Wong, Y., Wong, M. M., Chu, D. W., & Siu, C. (2018) · Heart · doi:10.1136/heartjnl-2018-313147 · Cited by 118 · see citing works
Screening for atrial fibrillation in 13 122 Hong Kong citizens with smartphone electrocardiogram
Chan, N.-Y., & Choy, C.-C. (2017) · Heart · doi:10.1136/heartjnl-2016-309993 · Cited by 116 · see citing works
Smart detection of atrial fibrillation
Krivoshei, L., Weber, S., Burkard, T., Maseli, A., Brasier, N., Kühne, M., Conen, D., Huebner, T. A., Seeck, A., & Eckstein, J. (2016) · EP Europace · doi:10.1093/europace/euw125 · Cited by 114 · see citing works
Yan, B. P., Lai, W. H., Chan, C. K. Y., Chan, S. C., Chan, L.-H., Lam, K., Lau, H., Ng, C., Tai, L., Yip, K., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008585 · Cited by 112 · see citing works
Shanbhag, D., Graham, I. D., Harlos, K., Haynes, R. B., Gabizon, I., Connolly, S. J., & Spall, H. G. C. V. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-017765 · Cited by 105 · see citing works
Orchard, J. J., Lowres, N., Freedman, B., Ladak, L. A., Lee, W., Zwar, N. A., Peiris, D., Kamaladasa, Y., Li, J., & Neubeck, L. (2016) · European Journal of Preventive Cardiology · doi:10.1177/2047487316670255 · Cited by 100 · see citing works
Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review
Giebel, G. D., & Gissel, C. (2019) · JMIR mHealth and uHealth · doi:10.2196/13641 · Cited by 99 · see citing works
Detection of atrial fibrillation episodes using a wristband device
Corino, V., Laureanti, R., Ferranti, L., Scarpini, G., Lombardi, F., & Mainardi, L. T. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa5dd7 · Cited by 95 · see citing works
High-Throughput, Contact-Free Detection of Atrial Fibrillation From Video With Deep Learning
Yan, B. P., Lai, W. H. S., Chan, C. K. Y., Au, A. C. K., Freedman, B., Poh, Y. C., & Poh, M.-Z. (2020) · JAMA Cardiology · doi:10.1001/jamacardio.2019.4004 · Cited by 94 · see citing works
Smart-watches: a potential challenger to the implantable loop recorder?
Carpenter, A., & Frontera, A. (2016) · Europace · doi:10.1093/europace/euv427 · Cited by 86 · see citing works
Bonomi, A., Schipper, F., Eerikäinen, L. M., Margarito, J., van Dinther, R., Muesch, G., de Morree, H. M., Aarts, R. M., Babaeizadeh, S., McManus, D. D., et al. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009351 · Cited by 86 · see citing works
The diagnostic accuracy of the MyDiagnostick to detect atrial fibrillation in primary care
Vaes, B., Stalpaert, S., Tavernier, K., Thaels, B., Lapeire, D., Mullens, W., & Degryse, J. (2014) · BMC Family Practice · doi:10.1186/1471-2296-15-113 · Cited by 81 · see citing works
Fan, Y.-Y., Li, Y.-G., Li, J., Cheng, W.-K., Shan, Z.-L., Wang, Y.-T., & Guo, Y.-T. (2019) · JMIR mHealth and uHealth · doi:10.2196/11437 · Cited by 81 · see citing works
Chan, P.-H., Wong, C.-K., Pun, L., Wong, Y.-F., Wong, M. M.-Y., Chu, D. W.-S., & Siu, C.-W. (2017) · Circulation · doi:10.1161/circulationaha.116.024439 · Cited by 79 · see citing works
Economic evaluations of eHealth technologies: A systematic review.
Sanyal, C., Stolee, P., Juzwishin, D., & Husereau, D. (2018) · PLOS ONE · doi:10.1371/journal.pone.0198112 · Cited by 77 · see citing works
Lowres, N., Mulcahy, G., Gallagher, R. D., Freedman, S. B., Marshman, D., Kirkness, A., Orchard, J. J., & Neubeck, L. (2016) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezv486 · Cited by 72 · see citing works
Mobile Phone Detection of Atrial Fibrillation With Mechanocardiography
Jaakkola, J., Jaakkola, S., Lahdenoja, O., Hurnanen, T., Koivisto, T., Pänkäälä, M., Knuutila, T., Kiviniemi, T., Vasankari, T., & Airaksinen, J. K. E. (2018) · Circulation · doi:10.1161/circulationaha.117.032804 · Cited by 66 · see citing works
Kwon, S., Hong, J., Choi, E.-K., Lee, B., Baik, C., Lee, E., Jeong, E.-R., Koo, B.-K., Oh, S., & Yi, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/16443 · Cited by 65 · see citing works
Assessing discriminative ability of risk models in clustered data
Klaveren, D. V., Steyerberg, E. W., Perel, P., & Vergouwe, Y. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-5 · Cited by 61 · see citing works
Chang, T. E., Lichtman, J. H., Goldstein, L. B., & George, M. G. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.003056 · Cited by 60 · see citing works
Hochstadt, A., Chorin, E., Viskin, S., Schwartz, A. L., Lubman, N., & Rosso, R. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.10.096 · Cited by 55 · see citing works
Evans, G. F., Shirk, A., Muturi, P., & Soliman, E. Z. (2017) · Global Heart · doi:10.1016/j.gheart.2016.12.003 · Cited by 54 · see citing works
Atrial fibrillation detection using a smart phone
Lee, J., Reyes, B. A., McManus, D. D., Mathias, O., & Chon, K. H. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346146 · Cited by 53 · see citing works
Rozen, G., Vaid, J., Hosseini, S. M., Kaadan, M. I., Rafael, A., Róka, A., Poh, Y. C., Poh, M., Heist, E. K., & Ruskin, J. N. (2018) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2018.01.035 · Cited by 50 · see citing works
Soni, A., Karna, S., Fahey, N., Sanghai, S., Patel, H. A., Raithatha, S. J., Thanvi, S., Nimbalkar, S. M., Freedman, B., Allison, J. J., et al. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.12.016 · Cited by 50 · see citing works
Atrial fibrillation screening with photo-plethysmography through a smartphone camera
Verbrugge, F. H., Proesmans, T., Vijgen, J. M., Müllens, W., Rivero‐Ayerza, M., Herendael, H. V., Vandervoort, P. M., & Nuyens, D. (2019) · EP Europace · doi:10.1093/europace/euz119 · Cited by 50 · see citing works
Detection of atrial fibrillation using an earlobe photoplethysmographic sensor
Conroy, T. B., Guzman, J. H., HALL, B. W., Tsouri, G. R., & Couderc, J. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa8830 · Cited by 48 · see citing works
Chan, N., Choy, C.-C., Chan, C. K., & Siu, C.-W. (2018) · Heart Rhythm · doi:10.1016/j.hrthm.2018.06.006 · Cited by 38 · see citing works
Smartphone accelerometers for the detection of heart rate
Landreani, F., & Caiani, E. G. (2017) · Expert Review of Medical Devices · doi:10.1080/17434440.2017.1407647 · Cited by 34 · see citing works
Jiang, Y., Sheng, H., Meng, L., et al. (2017) · World Journal of Surgical Oncology · doi:10.1186/s12957-016-1084-1 · Cited by 31 · see citing works
Schäck, T., Harb, Y. S., Muma, M., & Zoubir, A. M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036773 · Cited by 30 · see citing works
Aliarzadeh, B., Greiver, M., Moineddin, R., Meaney, C. A., White, D., Moazzam, A., Moore, K. M., & Bélanger, P. (2014) · BMC Family Practice · doi:10.1186/1471-2296-15-7 · Cited by 16 · see citing works
Lowres, N., Neubeck, L., Salkeld, G. P., et al. (2014) · Thrombosis and Haemostasis · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Quer G, Nima N, Shraddha L, Reinier , Steinhubl SR. Preliminary evaluation of a wrist wearable heart rate sensor for the detection of undiagnosed atrial fibrilla- tion in a realworld setting. Circulation 2018;136:A18610.
Cited by
Sorted by citations
Whitelaw, S., Pellegrini, D. M., Mamas, M. A., Cowie, M., & Van Spall, H. G. C. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab005 · Cited by 241 · see citing works
Self-citationHuman-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Lopez Perales, C. R., Van Spall, H. G. C., Maeda, S., Jimenez, A., Laţcu, D. G., Milman, A., Kirakoya-Samadoulougou, F., Mamas, M. A., Muser, D., & Casado Arroyo, R.
- Affiliations
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
- Division of Cardiology, Department of Medicine, Population Health Research Institute, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, Canada
- Tokyo Medical and Dental University (JP)
- Division of Cardiology, University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201, USA
- Department of Cardiology, Centre Hospitalier Princesse Grace, Avenue Pasteur, 98000, Monaco, Monaco (Principalty)
- Tel Aviv University (IL)
- Centre de Recherche en Epidémiologie, Biostatistiques et Recherche Clinique, Ecole de Santé Publique, Université librede Bruxelles, Avenue Franklin Roosevelt 50 - 1050, Brussels, Belgium
- Royal Stoke University Hospital (GB)
- Hospital of the University of Pennsylvania (US)
- Venue
- EP Europace
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-10-12
- Volume
- 23
- Issue
- 1
- Pages
- 11-28
- ISSN
- 1099-5129 (ISSN-L)
- 1532-2092
- PMC
- PMC7842109
- OpenAlex
- W3092170269
- Cited by
- 77
- References
- 73
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 12 · 2023: 16 · 2022: 23 · 2021: 9
- FWCI
- 1.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- EP Europace (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
- Funders
- Hamilton Health Sciences Career
- Canadian Institutes of Health Research
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Keywords
- atrial fibrillation, mobile health, photoplethysmography, acceleration sensors, smart band, AliveCor, arrhythmia screening, diagnostic accuracy
- MeSH terms
- Smartphone, Atrial Fibrillation, Electrocardiography, Humans, Telemedicine, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
