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Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial


Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial (NCT04250220), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19–3.76; P= 0.010) and 2.75 (95% CI, 1.42–5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.

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Fig. 1: CONSORT diagram.
Fig. 2: Cumulative event rates for the primary and secondary endpoints.
Fig. 3: Time-dependent association of AF and abnormal PPG measurements with MACCE.

Data availability

The data are not publicly available because they contain information of electronic health records and health insurance data, consented for research use by the eBRAVE-AF investigators. Making the data publicly available without additional consent or ethical approval might compromise patient privacy and the original ethical approval. If other investigators are interested in performing additional analyses, requests can be made to the corresponding authors (A.B. and S.M.), and analyses will be performed in collaboration with LMU Munich.

Code availability

The source code of the PPG analysis algorithm cannot be made publicly available because it is a certified medical product and the intellectual property of a medical company. Analysis of raw PPGs (for example, PPGs recorded by other devices) with the software used in this study is possible. Requests can be made to Preventicus GmbH. For data analysis, CRAN R version 4.2.1 was used with the base packages and the following additional CRAN packages: survival, survminer, rms, dplyr, ggplot2, svglite, stringr and timereg. Custom scripts can be accessed upon request for invitation via


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The authors thank Versicherungskammer Bayern (VKB) for the possibility to conduct the study. In particular, the authors thank B. Daschner of VKB. Without her continuous support, the completion of the study would not have been possible.

Author information

Authors and Affiliations



A.B., S.M. and K.D.R. designed the study and analyzed and interpreted the data. A.B. and K.D.R. wrote the protocol. L.v.S. did the technical implementation and served as project manager. L.F. coordinated the clinical conduct of the study. M.Z. and L.v.S. were responsible for customizing the study app (eBRAVE-AF app). Data acquisition was done by all authors. U.M. and K.D.R. did the statistical analyses. A.B. wrote the first draft, with input from K.D.R., S.M. and U.M., and submitted the final version for publication. A.B., K.D.R., U.M. and S.M. had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors reviewed the final draft and agree with its content and conclusions. A.B. and S.M. contributed equally.

Corresponding authors

Correspondence to Steffen Massberg or Axel Bauer.

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Competing interests

All authors declare no competing interests with respect to the study or the products investigated in the study. None of the authors has a financial, academic or intellectual property conflict of interest with the mobile app company Preventicus.

Peer review

Peer review information

Nature Medicine thanks Mohamed Elshazly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Michael Basson, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study app and PPG app.

(a) shows a screenshot from the study app, (b-c) show app-based PPG self-measurements. (b) shows regular pulse wave sequence. (c) shows a rapid and irregular pulse wave sequence indicative of atrial fibrillation.

Extended Data Fig. 2 AF detection during digital screening.

Quantity and quality of PPG measurements in participants with usually detected AF during digital screening.

Extended Data Fig. 3 AF diagnosis following abnormal PPGs.

AF diagnosis (or lack thereof) following abnormal PPGs.

Extended Data Fig. 4 Details of PPG measurements.

(a-b): Plethysmographic (PPG) measurements during both phases of the trial (panel A phase 1; panel B phase 2). Solid blue line shows proportion of participants who performed ≥1 measurement per week, dotted blue line shows proportion of participants who performed ≥2 measurements per week. The percentage refers to the total number of subjects assigned to digital screening according to the modified intention-to-treat principle. The drop in PPG measurements at the end of the second study phase is due to the shorter average measurement duration in this phase, as app usage was limited to a total of 12 months and participants took a median of 10 days (IQR 22 days) to cross-over. (c-d): Number of total measurements performed by participants during the study stratified by age groups (< 59 yeas; 59 – 64 years; 65 – 71 years and > 71 years) for group 1 (c) and group 2 (d). For this analysis only active participants performing at least one PPG-measurement during the entire study duration were considered (Group 1 N = 2,484; age < 59 N = 549; age 59 – 64 N = 616; age 65 – 71 N = 787; age > 71 N = 532; Group 2 N = 2,140; age < 59 N = 452; age 59 – 64 N = 502; age 65 – 71 N = 739; age > 71 N = 447). Two-sided p-values were calculated by means of Kruskal-Wallis one-way analysis of variance; the exact p-value for (c) is 9.62 * 10−9; differences were considered statistically significant when the two-sided p-value was less than 0.05. There was no adjustment for multiple comparisons. (e-f): Number of total measurements performed by males and females during the study for group 1 (e) and group 2 (f). For this analysis only active participants performing at least one PPG-measurement during the entire study duration were considered (Group 1 N = 2,484; females N = 767; males = 1,717; Group 2 N = 2,140; females N = 658; males N = 1,482). Two-sided p-values were calculated by means of Mann-Whitney U test; the exact p-value for (e) is 1.76 * 10−5; differences were considered statistically significant when the two-sided p-value was less than 0.05. There was no adjustment for multiple comparisons. Boxplots in (c-f) show median and IQR. The whisker boundaries define the 10th and 90th percentiles.

Extended Data Fig. 5 Detection of treatment-relevant AF as function of PPG measurement frequency and CHA2DS2-VASc-Score.

Odds ratio for reaching the primary endpoint as function of number of photoplethysmographic (PPG) measurements and CHA2DS2-VASc-Score. Darker colors represent higher odds ratios. The red vertical line marks the number of the 76 scheduled PPG measurements per participant over the six months digital screening phase, the blue line marks the median of the 53 PPG measurements per participant observed in the study.

Extended Data Fig. 6 Sensitivity of AF detection as function of PPG measurement frequency and AF burden.

Model describing the weekly sensitivity for detecting atrial fibrillation (AF) as a function of the number of measurements per week and the AF-burden. We used the timestamps of the available 300,509 PPG-measurements in order to calculate the frequency of measurements for the various parts of the day. Based on this frequency pattern we subsequently simulated PPG-measurements for increasing AF-burden (we consequently increased AF-burden from 1 h/week, [0.69% AF-burden] to 168 h / week [100% AF-burden] by 1 h steps and performed 10.000 simulations for each AF-burden step), which was defined as and finally calculated the sensitivity of the AF-detection. For this model, 89% sensitivity of the PPG-algorithm was assumed. With 2 measurements / day (according to the study protocol for the first 2 weeks) an AF-burden > 12.5% would be required to detect AF with > 80% sensitivity in one week. With 2 measurements / week (according to the study protocol from week 3 to month 6) an AF-burden > 62.5% would be required to detect AF with > 80% sensitivity in one week.

Extended Data Table 1 Modes of AF diagnoses
Extended Data Table 2 Causes of death
Extended Data Table 3 Components of MACCE
Extended Data Table 4 Per-protocol analysis for the primary endpoint

Supplementary information

Supplementary Information

Supplementary Tables 1–3

Reporting Summary

Supplementary Data

Study protocol and statistical analysis plan

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Rizas, K.D., Freyer, L., Sappler, N. et al. Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat Med 28, 1823–1830 (2022).

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