Jay Pandit

807 total citations
31 papers, 442 citations indexed

About

Jay Pandit is a scholar working on Cardiology and Cardiovascular Medicine, General Health Professions and Epidemiology. According to data from OpenAlex, Jay Pandit has authored 31 papers receiving a total of 442 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cardiology and Cardiovascular Medicine, 4 papers in General Health Professions and 4 papers in Epidemiology. Recurrent topics in Jay Pandit's work include Heart Rate Variability and Autonomic Control (7 papers), Data-Driven Disease Surveillance (3 papers) and COVID-19 epidemiological studies (3 papers). Jay Pandit is often cited by papers focused on Heart Rate Variability and Autonomic Control (7 papers), Data-Driven Disease Surveillance (3 papers) and COVID-19 epidemiological studies (3 papers). Jay Pandit collaborates with scholars based in United States, Israel and Germany. Jay Pandit's co-authors include Daniel Batlle, Eric J. Topol, Giorgio Quer, Jennifer M. Radin, Jeff Pawelek, Bruce Leff, Jan Wysocki, Edward Ramos, Katie Baca-Motes and Andreas Tzavelis and has published in prestigious journals such as JAMA, Circulation and Nature Medicine.

In The Last Decade

Jay Pandit

29 papers receiving 426 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jay Pandit United States 12 139 127 70 62 61 31 442
Ethan R. Harlow United States 8 135 1.0× 84 0.7× 34 0.5× 29 0.5× 137 2.2× 21 499
Vik Kheterpal United States 8 120 0.9× 102 0.8× 66 0.9× 88 1.4× 16 0.3× 20 520
Edward Ramos United States 6 126 0.9× 75 0.6× 88 1.3× 64 1.0× 15 0.2× 11 495
Lauren Ariniello United States 8 171 1.2× 367 2.9× 51 0.7× 111 1.8× 44 0.7× 15 780
Katie Baca-Motes United States 9 181 1.3× 363 2.9× 91 1.3× 104 1.7× 44 0.7× 20 882
Emily R. Capodilupo Australia 8 54 0.4× 53 0.4× 24 0.3× 35 0.6× 12 0.2× 19 293
Nino Isakadze United States 10 125 0.9× 332 2.6× 8 0.1× 48 0.8× 36 0.6× 33 509
Dariusz Świetlik Poland 14 45 0.3× 38 0.3× 28 0.4× 15 0.2× 46 0.8× 55 586
María López Spain 13 29 0.2× 47 0.4× 12 0.2× 60 1.0× 113 1.9× 51 486
Richa Sinha India 8 323 2.3× 22 0.2× 38 0.5× 27 0.4× 106 1.7× 34 751

Countries citing papers authored by Jay Pandit

Since Specialization
Citations

This map shows the geographic impact of Jay Pandit's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jay Pandit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Pandit more than expected).

Fields of papers citing papers by Jay Pandit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jay Pandit. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jay Pandit. The network helps show where Jay Pandit may publish in the future.

Co-authorship network of co-authors of Jay Pandit

This figure shows the co-authorship network connecting the top 25 collaborators of Jay Pandit. A scholar is included among the top collaborators of Jay Pandit based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jay Pandit. Jay Pandit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ariniello, Lauren, Katie Baca-Motes, Arij Faksh, et al.. (2025). Association between wearable sensor signals and expected hormonal changes in pregnancy. EBioMedicine. 119. 105888–105888. 1 indexed citations
2.
Pandit, Jay, Matteo Gadaleta, Juan A. Raygoza Garay, et al.. (2025). Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes. Nature Medicine. 31(9). 3121–3127. 5 indexed citations
3.
Kueper, Jacqueline K. & Jay Pandit. (2025). Artificial Intelligence for Healthcare in Canada: Contrasting Advances and Challenges. A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy. 22(4). 11–30. 1 indexed citations
4.
Kueper, Jacqueline K. & Jay Pandit. (2025). Artificial Intelligence in the Canadian Healthcare System: Scaling From Novelty to Utility. A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy. 22(4). 79–83. 1 indexed citations
5.
Jaiswal, Stuti J., et al.. (2024). Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies. Current Sleep Medicine Reports. 10(1). 82–92. 6 indexed citations
6.
Quer, Giorgio, Erin Coughlin, Jorge Villacian, et al.. (2024). Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial. The Lancet Digital Health. 6(8). e546–e554. 5 indexed citations
7.
Radin, Jennifer M., Julia Moore Vogel, Erin Coughlin, et al.. (2024). Long-term changes in wearable sensor data in people with and without Long Covid. npj Digital Medicine. 7(1). 246–246. 7 indexed citations
8.
Jaiswal, Stuti J., Matteo Gadaleta, Giorgio Quer, et al.. (2024). Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection. Scientific Reports. 14(1). 4655–4655. 2 indexed citations
9.
Pandit, Jay, Jeff Pawelek, Bruce Leff, & Eric J. Topol. (2024). The hospital at home in the USA: current status and future prospects. npj Digital Medicine. 7(1). 48–48. 32 indexed citations
10.
Gazit, Tomer, et al.. (2024). A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days. Frontiers in Digital Health. 6. 1485508–1485508.
11.
Beestrum, Molly, et al.. (2023). Heart Rate Variability in Psychiatric Disorders: A Systematic Review. Neuropsychiatric Disease and Treatment. Volume 19. 2217–2239. 16 indexed citations
12.
Franklin, Daniel, Andreas Tzavelis, Jong Yoon Lee, et al.. (2023). Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography. Nature Biomedical Engineering. 7(10). 1229–1241. 71 indexed citations
13.
Radin, Jennifer M., et al.. (2023). Prevalence of Positive COVID-19 Test Results Collected by Digital Self-report in the US and Germany. JAMA Network Open. 6(1). e2253800–e2253800. 8 indexed citations
14.
Huber, M, Jay Pandit, Paul N. Jensen, et al.. (2022). Left Atrial Strain and the Risk of Atrial Arrhythmias From Extended Ambulatory Cardiac Monitoring: MESA. Journal of the American Heart Association. 11(21). e026875–e026875. 18 indexed citations
15.
Ramos, Edward, et al.. (2022). Improving participant representation in the era of digital clinical studies. Trends in Molecular Medicine. 28(12). 1019–1021. 1 indexed citations
16.
Pawelek, Jeff, et al.. (2022). The Power of Patient Engagement With Electronic Health Records as Research Participants. JMIR Medical Informatics. 10(7). e39145–e39145. 14 indexed citations
17.
Radin, Jennifer M., Giorgio Quer, Jay Pandit, et al.. (2022). Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study. The Lancet Digital Health. 4(11). e777–e786. 14 indexed citations
18.
Wysocki, Jan, et al.. (2020). An update on ACE2 amplification and its therapeutic potential. Acta Physiologica. 231(1). e13513–e13513. 28 indexed citations
19.
Pandit, Jay, et al.. (2016). Abstract 12960: Differential Pulse Arrival Time: A Novel Approach to Continuous Cuff-less Blood Pressure Monitoring. Circulation. 134(suppl_1). 1 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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