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.
Progression Models in Resistance Training for Healthy Adults
20022.5k citationsJay R. Hoffman, Nicholas A. Ratamess et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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Countries citing papers authored by Jay R. Hoffman
Since
Specialization
Citations
This map shows the geographic impact of Jay R. Hoffman'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 R. Hoffman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay R. Hoffman more than expected).
This network shows the impact of papers produced by Jay R. Hoffman. 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 R. Hoffman. The network helps show where Jay R. Hoffman may publish in the future.
Co-authorship network of co-authors of Jay R. Hoffman
This figure shows the co-authorship network connecting the top 25 collaborators of Jay R. Hoffman.
A scholar is included among the top collaborators of Jay R. Hoffman 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 R. Hoffman. Jay R. Hoffman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ratamess, Nicholas A., Adam M. Gonzalez, Jay R. Hoffman, et al.. (2016). The Effects of Multiple-Joint Isokinetic Resistance Training on Maximal Isokinetic and Dynamic Muscle Strength and Local Muscular Endurance.. PubMed Central. 15(1). 34–40.16 indexed citations
10.
Mangine, Gerald T., Jay R. Hoffman, David H. Fukuda, Jeffrey R. Stout, & Nicholas A. Ratamess. (2015). IMPROVING MUSCLE STRENGTH AND SIZE: THE IMPORTANCE OF TRAINING VOLUME, INTENSITY, AND STATUS. Journal of International Crisis and Risk Communication Research. 47(2). 131–138.8 indexed citations
Hoffman, Jay R.. (2012). NSCA's guide to program design. Human Kinetics eBooks.27 indexed citations
13.
Hoffman, Jay R., Nicholas A. Ratamess, Jie Kang, et al.. (2011). ACUTE L-ALANYL-L-GLUTAMINE INGESTION DURING SHORT DURATION, HIGH INTENSITY EXERCISE AND A MILD HYDRATION STRESS. Journal of International Crisis and Risk Communication Research. 43(2). 125–136.2 indexed citations
14.
Hoffman, Jay R.. (2010). Creatine and β-alanine supplementation in strength/power athletes.. Current Topics in Nutraceutical Research. 8(1). 19–32.1 indexed citations
15.
Faigenbaum, Avery D., Anne Farrell, Donald A. Chu, et al.. (2009). “PLYO PLAY”: A NOVEL PROGRAM OF SHORT BOUTS OF MODERATE AND HIGH INTENSITY EXERCISE IMPROVES PHYSICAL FITNESS IN ELEMENTARY SCHOOL CHILDREN. The Physical Educator. 66(1).37 indexed citations
Hoffman, Jay R., Jie Kang, Nicholas A. Ratamess, Stefanie L. Rashti, & Avery D. Faigenbaum. (2008). THERMOGENIC EFFECT OF A HIGH ENERGY, PRE-EXERCISE SUPPLEMENT. Kinesiology. 40. 200–206.2 indexed citations
18.
Faigenbaum, Avery D., et al.. (2007). EFFECTS OF A SHORT-TERM PLYOMETRIC AND RESISTANCE TRAINING PROGRAM ON FITNESS PERFORMANCE IN BOYS AGE 12 TO 15 YEARS. SHILAP Revista de lepidopterología.79 indexed citations
19.
Hoffman, Jay R.. (1997). The relationship between aerobic fitness and recovery from high-intensity exercise in infantry soldiers.. PubMed. 162(7). 484–8.17 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.