{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T05:59:15Z","timestamp":1793426355570,"version":"4.5.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Red Bull Athlete Performance Center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sports Med - Open"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature\u00a0has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the \u201cred zone\u201d, during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TID<jats:sub>V<\/jats:sub>), running power (TID<jats:sub>P<\/jats:sub>) and heart rate (TID<jats:sub>HR<\/jats:sub>) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n\u2009=\u20099) or with (HSM\u2009+\u2009LIT, n\u2009=\u20099) additional low-intensity training or a control group (n\u2009=\u200911). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3<jats:sub>V<\/jats:sub>), running power (Z3<jats:sub>P<\/jats:sub>), heart rate (Z3<jats:sub>HR<\/jats:sub>), oxygen uptake (<jats:inline-formula><jats:alternatives><jats:tex-math>$${\\text{Z}}{3_{\\dot{\\text{V}}\\text{O}_2}}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mtext>Z<\/mml:mtext>\n                      <mml:msub>\n                        <mml:mn>3<\/mml:mn>\n                        <mml:mrow>\n                          <mml:mover>\n                            <mml:mtext>V<\/mml:mtext>\n                            <mml:mo>\u02d9<\/mml:mo>\n                          <\/mml:mover>\n                          <mml:msub>\n                            <mml:mtext>O<\/mml:mtext>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                      <\/mml:msub>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) and changes in endurance performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Both intervention groups revealed a polarized pattern for TID<jats:sub>V<\/jats:sub> (HSM: Z1:\u00a038\u2009\u00b1\u200917, Z2:\u00a016\u2009\u00b1\u200917, Z3:\u00a046\u2009\u00b1\u20092%; HSM\u2009+\u2009LIT:\u00a0Z1:\u00a059\u2009\u00b1\u200918, Z2:\u00a014\u2009\u00b1\u200918, Z3:\u00a027\u2009\u00b1\u20092%) and TID<jats:sub>P<\/jats:sub> (Z1:\u00a050\u2009\u00b1\u20098, Z2:\u00a014\u2009\u00b1\u200911, Z3:\u00a036\u2009\u00b1\u20097%; Z1:\u00a062\u2009\u00b1\u200915, Z2:\u00a012\u2009\u00b1\u200916, Z3:\u00a026\u2009\u00b1\u20092%), while TID<jats:sub>HR<\/jats:sub> (Z1:\u00a048\u2009\u00b1\u200913, Z2:\u00a026\u2009\u00b1\u200911, Z3:\u00a026\u2009\u00b1\u20097%; Z1:\u00a065\u2009\u00b1\u200917, Z2:\u00a022\u2009\u00b1\u200918, Z3:\u00a013\u2009\u00b1\u20094%) showed a pyramidal pattern. Time in Z3<jats:sub>HR<\/jats:sub> was significantly less compared to Z3<jats:sub>V<\/jats:sub> and Z3<jats:sub>P<\/jats:sub> in both intervention groups (all p\u2009&lt;\u20090.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM\u2009+\u2009LIT (p\u2009&lt;\u20090.001, <jats:sub>p<\/jats:sub>\u03b7<jats:sup>2<\/jats:sup>\u2009=\u20090.30). Time in Z3<jats:sub>V<\/jats:sub> and Z3<jats:sub>P<\/jats:sub> within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3<jats:sub>HR<\/jats:sub> declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM\u2009+\u2009LIT (45% to 16%). A moderate dose\u2013response relationship was found for time in Z3<jats:sub>V<\/jats:sub> and changes in peak power output (r<jats:sub>s<\/jats:sub>\u2009=\u20090.52, p\u2009=\u20090.028) as well as time trial performance (r<jats:sub>s<\/jats:sub>\u2009=\u2009\u2212\u00a00.47, p\u2009=\u20090.049) with no such associations regarding time in Z3<jats:sub>P<\/jats:sub>, Z3<jats:sub>HR<\/jats:sub>, and <jats:inline-formula><jats:alternatives><jats:tex-math>$${\\text{Z}}{3_{\\dot{\\text{V}}\\text{O}_2}}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mtext>Z<\/mml:mtext>\n                      <mml:msub>\n                        <mml:mn>3<\/mml:mn>\n                        <mml:mrow>\n                          <mml:mover>\n                            <mml:mtext>V<\/mml:mtext>\n                            <mml:mo>\u02d9<\/mml:mo>\n                          <\/mml:mover>\n                          <mml:msub>\n                            <mml:mtext>O<\/mml:mtext>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                      <\/mml:msub>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3<jats:sub>V<\/jats:sub> was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures.<\/jats:p>\n                <jats:p><jats:italic>Trial Registration<\/jats:italic> Trial register: Clinicaltrials.gov, registration number: NCT05067426.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40798-024-00761-1","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T07:02:41Z","timestamp":1725519761000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Training Intensity Distribution of a 7-Day HIIT Shock Microcycle: Is Time in the \u201cRed Zone\u201d Crucial for Maximizing Endurance Performance? 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