Giacomo Cappon

1.6k total citations · 2 hit papers
50 papers, 1.1k citations indexed

About

Giacomo Cappon is a scholar working on Endocrinology, Diabetes and Metabolism, Surgery and Genetics. According to data from OpenAlex, Giacomo Cappon has authored 50 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Endocrinology, Diabetes and Metabolism, 29 papers in Surgery and 25 papers in Genetics. Recurrent topics in Giacomo Cappon's work include Diabetes Management and Research (41 papers), Diabetes and associated disorders (25 papers) and Pancreatic function and diabetes (25 papers). Giacomo Cappon is often cited by papers focused on Diabetes Management and Research (41 papers), Diabetes and associated disorders (25 papers) and Pancreatic function and diabetes (25 papers). Giacomo Cappon collaborates with scholars based in Italy, Switzerland and United Kingdom. Giacomo Cappon's co-authors include Andrea Facchinetti, Giovanni Sparacino, Martina Vettoretti, Giada Acciaroli, Simone Del Favero, Morten Gram Pedersen, Francesca Marturano, Giulia Noaro, Pantelis Georgiou and Pau Herrero and has published in prestigious journals such as SHILAP Revista de lepidopterología, Diabetes Care and Diabetes.

In The Last Decade

Giacomo Cappon

41 papers receiving 1.1k citations

Hit Papers

Continuous Glucose Monitoring Sensors for Diabetes Manage... 2019 2026 2021 2023 2019 2023 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications
    2019 289 citations Giacomo Cappon, Martina Vettoretti et al. profile →
  2. The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP
    2023 76 citations Giacomo Cappon, Simone Del Favero et al. Scientific Reports profile →

Peers

Giacomo Cappon
Martina Vettoretti Italy
Monika Reddy United Kingdom
Sediqeh Samadi United States
Kamuran Turksoy United States
Taiyu Zhu United Kingdom
Scott A. Pardo United States
Elizabeth Littlejohn United States
Giada Acciaroli Italy
Joseph El Youssef United States
Mert Sevil United States
Martina Vettoretti Italy
Giacomo Cappon
Citations per year, relative to Giacomo Cappon Giacomo Cappon (= 1×) peers Martina Vettoretti

Countries citing papers authored by Giacomo Cappon

Since Specialization
Citations

This map shows the geographic impact of Giacomo Cappon'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 Giacomo Cappon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giacomo Cappon more than expected).

Fields of papers citing papers by Giacomo Cappon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Giacomo Cappon. 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 Giacomo Cappon. The network helps show where Giacomo Cappon may publish in the future.

Co-authorship network of co-authors of Giacomo Cappon

This figure shows the co-authorship network connecting the top 25 collaborators of Giacomo Cappon. A scholar is included among the top collaborators of Giacomo Cappon 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 Giacomo Cappon. Giacomo Cappon 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.
Elkind‐Hirsch, Karen, et al.. (2025). Continuous glucose monitoring in early gestational diabetes improves maternal and neonatal outcomes—The Steady Sugar trial. Diabetes Obesity and Metabolism. 28(1). 691–700. 1 indexed citations
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Cappon, Giacomo, et al.. (2025). AirPredict: an eHealth platform for asthma management leveraging wearable sensors, digital diaries, and air quality monitoring to optimize patient outcomes. Frontiers in Digital Health. 7. 1573342–1573342. 1 indexed citations
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Atzeni, Mattia, Giacomo Cappon, Jennifer K Quint, et al.. (2025). A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data. Scientific Reports. 15(1). 2385–2385. 2 indexed citations
6.
Cappon, Giacomo, et al.. (2025). Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions. BMC Medical Informatics and Decision Making. 25(1). 33–33.
7.
Cappon, Giacomo, et al.. (2024). Continuous Glucose Monitoring Among Patients With Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Prospective Observational Feasibility Study. JMIR Formative Research. 9. e64585–e64585. 1 indexed citations
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McNeilly, Alison D., Simona Hapca, Paul Fournier, et al.. (2023). High intensity interval training as a novel treatment for impaired awareness of hypoglycaemia in people with type 1 diabetes (HIT4HYPOS): a randomised parallel-group study. Diabetologia. 67(2). 392–402. 2 indexed citations
11.
Cappon, Giacomo, et al.. (2023). drCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management. Journal of Diabetes Science and Technology. 19(3). 711–721. 6 indexed citations
12.
Cappon, Giacomo, et al.. (2023). The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP. Scientific Reports. 13(1). 16865–16865. 76 indexed citations breakdown →
13.
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Cappon, Giacomo, et al.. (2023). A Deep-Learning Based Algorithm for the Management of Hyperglycemia in Type 1 Diabetes Therapy. Research Padua Archive (University of Padua). 1–4.
15.
Schönenberger, Katja A., Giacomo Cappon, Simon Mayer, et al.. (2022). Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia. Frontiers in Nutrition. 9. 855223–855223. 10 indexed citations
16.
Bruttomesso, Daniela, Federico Boscari, Giulia Noaro, et al.. (2021). A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life. Diabetes Therapy. 12(5). 1313–1324. 7 indexed citations
17.
Noaro, Giulia, Giacomo Cappon, Martina Vettoretti, et al.. (2020). Machine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy. IEEE Transactions on Biomedical Engineering. 68(1). 247–255. 35 indexed citations
18.
Vettoretti, Martina, et al.. (2019). A Real-Time Continuous Glucose Monitoring–Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events. Diabetes Technology & Therapeutics. 21(11). 644–655. 17 indexed citations
19.
Cappon, Giacomo, Andrea Facchinetti, Giovanni Sparacino, Pantelis Georgiou, & Pau Herrero. (2019). Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept. Sensors. 19(14). 3168–3168. 20 indexed citations
20.
Marturano, Francesca, et al.. (2017). In Silico Assessment of Literature Methods to Adjust Insulin Bolus Dose according to CGM Trend. Padua Research Archive (University of Padova). 1–1. 1 indexed citations

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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