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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Physiol.</journal-id>
<journal-title>Frontiers in Physiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Physiol.</abbrev-journal-title>
<issn pub-type="epub">1664-042X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1636405</article-id>
<article-id pub-id-type="doi">10.3389/fphys.2025.1636405</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Physiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Influence of upper-body high-intensity intermittent training on energy metabolism and maximal oxygen uptake in elite swimmers</article-title>
<alt-title alt-title-type="left-running-head">Zhang et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphys.2025.1636405">10.3389/fphys.2025.1636405</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Lei</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Hanyi</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3080976/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/resources/"/>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
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<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Tongling</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/Writing - review &#x26; editing/"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Chen</surname>
<given-names>Chao</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3053886/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
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<aff id="aff1">
<sup>1</sup>
<institution>College of General Education</institution>, <institution>Wenzhou Business College</institution>, <addr-line>Wenzhou</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>College of Physical Education, Dalian University</institution>, <addr-line>Dalian</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Institute of Physical Education, Huzhou University</institution>, <addr-line>Huzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/402620/overview">Giuseppe D&#x2019;Antona</ext-link>, University of Pavia, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/760253/overview">Athanasios A. Dalamitros</ext-link>, Aristotle University, Greece</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1689649/overview">Ratko Peric</ext-link>, OrthoSport Banja Luka, Bosnia and Herzegovina</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Chao Chen, <email>chenchao@dlu.edu.cn</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1636405</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Zhang, Li, Wang and Chen.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Zhang, Li, Wang and Chen</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Purpose</title>
<p>This paper aimed to investigate the effects of upper-body high-intensity interval training (HIIT) on energy metabolism and maximal oxygen uptake (<inline-formula id="inf1">
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</inline-formula>max) in elite swimmers.</p>
</sec>
<sec>
<title>Methods</title>
<p>A randomized controlled trial was conducted, in which elite swimmers were stratified and randomly assigned to either an upper-body HIIT group or an upper-body moderate-intensity continuous training (MICT) group. The HIIT group performed upper-body HIIT sessions lasting 60 min, including a warm-up, main workout, and cool-down at a 2:3:1 time ratio. The main workout consisted of circuit-based HIIT involving eight exercises, each performed for 20 s with 10 s of rest, totaling 230 s per circuit, with 3-min interset intervals, repeated for three sets. The MICT group followed a similar session structure except that the main workout involved eight continuous exercises performed for 60 s each with 20-s rest intervals and 20-s interset intervals and also repeated for three sets. Pre- and post-intervention assessments included upper-body cycle ergometry to evaluate the <inline-formula id="inf2">
<mml:math id="m2">
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</mml:mrow>
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</inline-formula>max and indices of energy metabolism. Repeated-measure ANOVA was used to analyze changes in <inline-formula id="inf3">
<mml:math id="m3">
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</inline-formula>max and energy metabolism indicators.</p>
</sec>
<sec>
<title>Results</title>
<p>Prior to the intervention, no significant differences in <inline-formula id="inf4">
<mml:math id="m4">
<mml:mrow>
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<mml:mtext>VO</mml:mtext>
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<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max or energy metabolism indices between the two groups were noted. After 4 weeks of training, the HIIT group exhibited significant improvements in <inline-formula id="inf5">
<mml:math id="m5">
<mml:mrow>
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<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
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</inline-formula>max and energy metabolism parameters as assessed by upper-body ergometry (p <inline-formula id="inf6">
<mml:math id="m6">
<mml:mrow>
<mml:mo>&#x3c;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 0.05). By contrast, the MICT group showed no significant changes in these indicators (p <inline-formula id="inf7">
<mml:math id="m7">
<mml:mrow>
<mml:mo>&#x3e;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 0.05). A significant interaction effect was observed between time and group (p <inline-formula id="inf8">
<mml:math id="m8">
<mml:mrow>
<mml:mo>&#x3c;</mml:mo>
</mml:mrow>
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</inline-formula> 0.05).</p>
</sec>
<sec>
<title>Conclusion</title>
<p>A 4-week program of upper-body HIIT significantly enhances energy metabolism and <inline-formula id="inf9">
<mml:math id="m9">
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</inline-formula>max in elite swimmers. These findings provide a theoretical basis for incorporating upper-body HIIT into the training regimens of competitive swimmers to optimize aerobic capacity and metabolic efficiency.</p>
</sec>
</abstract>
<kwd-group>
<kwd>athletic performance</kwd>
<kwd>upper-body training</kwd>
<kwd>cardiopulmonary function</kwd>
<kwd>metabolic efficiency</kwd>
<kwd>high-intensity interval training (HIIT)</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Exercise Physiology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>High-intensity interval training (HIIT) is a modern exercise regimen characterized by alternating bouts of high-intensity activity and periods of passive or active recovery at moderate or low intensity <xref ref-type="bibr" rid="B50">Zhao et al. (2020)</xref>; <xref ref-type="bibr" rid="B11">Coates et al. (2023)</xref>. Compared with moderate-intensity continuous training (MICT), HIIT provides superior physiological stimuli due to its repeated bouts of high-intensity effort, which generate greater cardiovascular and metabolic stress, leading to improved <inline-formula id="inf10">
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</inline-formula>max, enhanced mitochondrial biogenesis, and increased excess postexercise oxygen consumption (EPOC), all contributing to better cardiopulmonary and metabolic adaptations <xref ref-type="bibr" rid="B46">Taylor et al. (2019)</xref>. Moreover, HIIT reduces total training volume and time demands while mitigating the risks associated with early sport specialization <xref ref-type="bibr" rid="B27">Jayanthi et al. (2013)</xref>; <xref ref-type="bibr" rid="B4">Atakan et al. (2021)</xref>. In addition, HIIT improves cardiovascular fitness <xref ref-type="bibr" rid="B6">Bang-Kittilsen et al. (2022)</xref>, muscular strength <xref ref-type="bibr" rid="B33">Merchant et al. (2021)</xref>, athletic performance, and skeletal muscle energy metabolism <xref ref-type="bibr" rid="B29">Kistner et al. (2019)</xref>.</p>
<p>Considering the unique demands of specific sports such as swimming is important. Swimming is a complex full-body activity that requires coordinated cyclic movements of the upper and lower limbs to overcome water resistance and to generate propulsion. The strength, endurance, and efficiency of the upper limbs play a critical role in determining swimming performance <xref ref-type="bibr" rid="B8">Barbosa et al. (2013)</xref>; <xref ref-type="bibr" rid="B20">Guignard et al. (2019)</xref>. Muscle strength is particularly important for sprint swimming <xref ref-type="bibr" rid="B41">Sharp et al. (1982)</xref>, and upper-body strength and power output have been linked to maximal swim velocity over distances ranging from 25 m to 400 m <xref ref-type="bibr" rid="B21">Hawley and Williams (1991)</xref>; <xref ref-type="bibr" rid="B22">Hawley et al. (1992)</xref>. Therefore, upper-body training is a vital component of swimmers&#x2019; overall training regimens and directly influences speed and endurance. Emphasis on upper-limb conditioning is essential for optimizing swimming performance and technique <xref ref-type="bibr" rid="B52">Zwierzchowska et al. (2023)</xref>.</p>
<p>Given the critical role of muscle function in swimming, understanding the underlying energy metabolism becomes essential. Energy metabolism is a fundamental physiological process required to sustain basic life functions <xref ref-type="bibr" rid="B51">Zhu et al. (2022)</xref>, encompassing aerobic and anaerobic systems <xref ref-type="bibr" rid="B30">Latham et al. (2022)</xref>. For endurance events and short, high-intensity efforts, understanding an athlete&#x2019;s metabolic profile is essential for designing effective training programs <xref ref-type="bibr" rid="B10">Brooks and Mercier (1994)</xref>. In swimming, events typically range from 22 s to 15 min (50&#x2013;1500 m), and energy demands are primarily met through anaerobic and aerobic glycolytic pathways <xref ref-type="bibr" rid="B25">Hollander et al. (2005)</xref>. Beyond performance enhancement, metabolic training influences body composition, energy efficiency, recovery, and injury prevention, which makes it a cornerstone of athletic conditioning across disciplines <xref ref-type="bibr" rid="B7">Bangsbo (1994)</xref>; <xref ref-type="bibr" rid="B36">Mujika and Padilla (2001)</xref>. Repeated HIIT sessions promote mitochondrial biogenesis in muscle cells and enhance ATP production via aerobic pathways <xref ref-type="bibr" rid="B40">Ryan et al. (2020)</xref>; <xref ref-type="bibr" rid="B13">Cordeiro et al. (2021)</xref>. HIIT also increases the activity of key enzymes involved in anaerobic glycolysis and improves the muscle&#x2019;s capacity to generate energy through lactate metabolism during brief, high-intensity exertion <xref ref-type="bibr" rid="B49">Yt et al. (2019)</xref>.</p>
<p>Closely related to energy metabolism is the concept of maximum oxygen uptake (<inline-formula id="inf11">
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</inline-formula>max), which is another key factor for swimmers. <inline-formula id="inf12">
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</inline-formula>max is widely regarded as the gold standard for evaluating cardiorespiratory fitness and serves as a core indicator of athletic performance <xref ref-type="bibr" rid="B9">Bassett and Howley (2000)</xref>; <xref ref-type="bibr" rid="B5">Kurl et al. (2022)</xref>. For swimmers, an increase in <inline-formula id="inf13">
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</inline-formula>max reflects improved cardiovascular health and is linked to performance, particularly in middle- and long-distance events. This association arises from the enhanced capacity to sustain higher exercise intensity through more efficient oxygen utilization <xref ref-type="bibr" rid="B47">Truijens et al. (2003)</xref>; <xref ref-type="bibr" rid="B3">Aspenes et al. (2009)</xref>. Previous research confirmed that HIIT is a time-efficient, effective method for improving <inline-formula id="inf14">
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</inline-formula>max <xref ref-type="bibr" rid="B48">Wen et al. (2019)</xref>; <xref ref-type="bibr" rid="B19">Gibala (2021)</xref>. Therefore, incorporating HIIT into training may be beneficial for swimmers aiming to enhance <inline-formula id="inf15">
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</inline-formula>max and overall performance.</p>
<p>While evidence for HIIT&#x2019;s benefits is extensive in various sports disciplines, its specific application to swimming warrants further exploration. Numerous studies have confirmed the effectiveness of HIIT in enhancing sport-specific performance, such as increasing punching power and endurance in boxing <xref ref-type="bibr" rid="B14">Davis et al. (2015)</xref>, sprint capacity in kayaking <xref ref-type="bibr" rid="B16">Du and Tao (2022)</xref>, and serve velocity in volleyball <xref ref-type="bibr" rid="B42">Sheppard et al. (2007)</xref>. However, research specifically targeting swimmers remains limited. This gap gives rise to the current paper&#x2019;s aim to investigate the influence of upper-body HIIT on energy metabolism and <inline-formula id="inf16">
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</inline-formula>max in elite swimmers. Existing literature on HIIT in swimming has primarily focused on whole-body training and its effects on cardiorespiratory endurance, and limited attention has been given to how upper-body HIIT alone may optimize energy utilization and performance outcomes. Therefore, this paper aims to address this research gap by providing empirical evidence on the effectiveness of upper-body HIIT in competitive swimming. Using a quantitative research design, <inline-formula id="inf17">
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</inline-formula>max and energy metabolism data are collected and analyzed before and after a targeted HIIT intervention to evaluate, with scientific rigor, the specific effects of upper-body HIIT on elite swimmers, extend the application of HIIT in aquatic sports, and offer athletes and coaches a potentially more time-efficient, performance-enhancing training strategy. Notably, this paper focuses on metabolic and cardiorespiratory adaptations to upper-body HIIT rather than direct measurements of in-water performance. While kinematic variables (e.g., stroke rate) are critical for swimming performance, this paper&#x2019;s primary goal is to establish the physiological mechanisms underlying upper-body metabolic responses to targeted HIIT and to provide a foundation for future sport-specific investigations.</p>
</sec>
<sec id="s2">
<title>2 Research subjects and methods</title>
<sec id="s2-1">
<title>2.1 Research subjects</title>
<p>
<italic>A priori</italic> power analysis was conducted using G&#x2a;Power 3.1.9.7 <xref ref-type="bibr" rid="B17">Faul et al. (2007)</xref> to determine the required sample size. For a 2<inline-formula id="inf18">
<mml:math id="m18">
<mml:mrow>
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</inline-formula> two repeated-measure ANOVA with an alpha level of 0.05, an effect size of f &#x3d; 0.4, and a desired power of 0.80, the minimum total sample size was estimated as 16 participants (8 per group). To account for potential attrition or data loss, the final sample size was set at 24 participants.</p>
<p>Twenty-four swimmers (16 males, 8 females) from local sports faculty were voluntarily recruited (see <xref ref-type="table" rid="T1">Table 1</xref> for basic participant information) through official university announcements, coach recommendations, and on-campus information sessions to ensure they met the required training background and could consistently participate in the study. Inclusion and exclusion criteria were as follows: (1) Athletes must meet or exceed the national first-class athlete standard (Note: According to China&#x2019;s 2025 Swimming Athlete Technical Grade Standards, first-class athletes must achieve benchmark times in official competitions, for example, 55.50 s for men&#x2019;s 100 m freestyle [50 m pool] or 1:02.50 for women&#x2019;s 100 m freestyle, verified via electronic timing). (2) Participants must be aged 18&#x2013;24 years and free from chronic pain or cardiovascular disease. (3) Individuals with medical conditions contraindicating high-intensity exercise were excluded. (4) Those unable to train due to sports injuries were excluded. (5) Athletes below the required competitive level were excluded. Regarding sport specialization, all participants specialized in Olympic swimming events, and primary disciplines included freestyle (50, 100, or 400 m), backstroke (100 or 200 m), and butterfly (100 or 200 m), as confirmed via coach verification and competition records.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Basic information of swimmers.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Indicator</th>
<th align="center">Mean <inline-formula id="inf61">
<mml:math id="m61">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
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</mml:math>
</inline-formula> SD</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Age (years)</td>
<td align="center">20.71 <inline-formula id="inf62">
<mml:math id="m62">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 1.38</td>
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<td align="center">Height (cm)</td>
<td align="center">178.16 <inline-formula id="inf63">
<mml:math id="m63">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
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<td align="center">Weight (kg)</td>
<td align="center">72.09 <inline-formula id="inf64">
<mml:math id="m64">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 13.01</td>
</tr>
<tr>
<td align="center">Training Years</td>
<td align="center">10.59 <inline-formula id="inf65">
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</tr>
</tbody>
</table>
</table-wrap>
<p>The study was approved by the Local Ethics Committee (Approval Number 102772021RT031), and all participants provided written informed consent. To minimize external variables, participants avoided high-intensity training 24 h before testing, maintained regular dietary and sleep routines throughout the testing period, and consumed meals at least 2 h pretest (with moderate water intake permitted) to avoid fasting or postprandial states.</p>
</sec>
<sec id="s2-2">
<title>2.2 Study design</title>
<p>This study was a 4-week experimental longitudinal investigation designed to examine the specific effects of upper-body high-intensity interval training (HIIT) on energy metabolism and <inline-formula id="inf19">
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</sec>
<sec id="s2-3">
<title>2.3 Testing protocol and intensity monitoring</title>
<sec id="s2-3-1">
<title>2.3.1 Equipment</title>
<p>The following equipment and materials were utilized throughout the study. An upper-body cycle ergometer was used to conduct incremental load testing. A portable metabolic analyzer (COSMED K5, Rome, Italy) was employed to measure respiratory gas exchange parameters during and after exercise. Heart rate was monitored in real time using a Polar heart rate strap (Polar Accurex Plus, Polar Electro Oy, Kempele, Finland). Blood lactate concentrations were assessed using a benchtop blood lactate analyzer (BIOSEN S Line, EKF Diagnostic, Barleben, Germany). Additional materials included the Borg Rating of Perceived Exertion (RPE) scale, sterile lancets, EKF blood sampling tubes, alcohol swabs, medical-grade rubber gloves, a stopwatch, and marker pens.</p>
</sec>
<sec id="s2-3-2">
<title>2.3.2 Testing methods and indicators</title>
<sec id="s2-3-2-1">
<title>2.3.2.1 Gas exchange data collection</title>
<p>Respiratory gas exchange was continuously measured during all exercise tests using a portable metabolic cart (Cosmed Quark RMR, Rome, Italy), which was calibrated before each testing session using standard gases (16% <inline-formula id="inf20">
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</inline-formula>). These data were used to calculate total aerobic energy contribution (via accumulated <inline-formula id="inf35">
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</inline-formula>). Data were sampled at 10-s intervals and averaged over 30-s epochs to ensure stability.</p>
</sec>
<sec id="s2-3-2-2">
<title>2.3.2.2 Validation and quality control</title>
<p>Equipment Accuracy: The metabolic cart was calibrated before each testing session for gas concentration and flowmeter precision.</p>
<p>Steady-State Requirement: Aerobic energy contribution was only calculated during exercise stages with stable <inline-formula id="inf36">
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</inline-formula> (coefficient of variation <inline-formula id="inf38">
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</mml:math>
</inline-formula> 5% over 2 min) to ensure reliable measurement of oxygen uptake.</p>
<p>EPOC was not used in energy system contribution calculations because its primary role is quantifying postexercise recovery energy expenditure.</p>
</sec>
</sec>
<sec id="s2-3-3">
<title>2.3.3 Ergometer setup</title>
<p>The height of the upper-body ergometer was adjusted individually to ensure standardized positioning for each participant. Specifically, when the elbow was fully extended, the crank axis was aligned with the midpoint of the forearm; when the elbow was flexed, the elbow joint remained at the same horizontal level as the axis. This positioning guaranteed consistency across all tests. The training load on the Lode upper-body ergometer was 3% of the participant&#x2019;s body mass, following the protocol of Franchini et al. <xref ref-type="bibr" rid="B18">Franchini et al. (2016)</xref>. Torque (T) was calculated using the formula:</p>
<p>T &#x3d; body mass <inline-formula id="inf39">
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</inline-formula> crank radius, resulting in a load expressed in Newton-meters (N<inline-formula id="inf42">
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</sec>
<sec id="s2-3-4">
<title>2.3.4 Testing procedure</title>
<p>Prior to testing, the K5 gas analysis system underwent a 30-min warm-up, followed by calibration procedures in accordance with the manufacturer&#x2019;s specifications. Calibration included barometric pressure, gas concentration using standard calibration gas (comprising 15.00% <inline-formula id="inf43">
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</inline-formula>, and 80.00% <inline-formula id="inf45">
<mml:math id="m45">
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</mml:math>
</inline-formula>), and volume calibration using a 3-L calibration syringe. Gas collection, storage, and analysis were conducted using the manufacturer&#x2019;s proprietary software (Mate Soft, COSMED, Italy). The overall testing procedure is illustrated in <xref ref-type="fig" rid="F1">Figure 1</xref>. Upon arrival at the testing site, each athlete was instructed to remain seated quietly for 5 min to allow measurement of baseline (resting) blood lactate levels. This step was followed by a 5-min warm-up on a treadmill at a constant speed of 10 km/h. After the warm-up, participants rested for an additional 5 min, during which the previously calibrated K5 gas analyzer was fitted to the subject. The testing protocol consisted of six sets of maximal arm cranking for 20 s, each separated by 10-s rest intervals, for a total of 180 s of exercise. Blood lactate samples (20 &#xb5;L) were collected from the earlobe at 3, 5, 7, and 10 min postexercise and examined using a lactate analyzer. Following the final sample collection at 10 min postexercise, the K5 device was removed. During this recovery period, athletes remained seated and were instructed to rest passively. Subjects were also advised to minimize verbal communication while wearing the K5 system to avoid data interference.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Schematic diagram of the test procedure.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g001.tif">
<alt-text content-type="machine-generated">Flowchart illustrating a workout routine. Symbols represent rest, warm-up, blood lactate sampling and analysis, twenty-second all-out arm cranking, and ten-second rest intervals. It starts with thirty-minute preheating calibration, followed by cycles of exercise spanning one hundred seventy seconds. Blood lactate is sampled at three, five, seven, and ten minutes post-exercise.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s2-3-5">
<title>2.3.5 Quality control</title>
<p>During each testing session, standardized movement techniques were strictly monitored to ensure performance consistency and data reliability. Technical deviations were corrected promptly to prevent injury, and verbal encouragement was provided to participants struggling with protocol completion. All tests followed the designated sequence and standardized rest intervals as per the experimental design.</p>
</sec>
</sec>
<sec id="s2-4">
<title>2.4 Training protocol and intensity monitoring</title>
<sec id="s2-4-1">
<title>2.4.1 Training protocol</title>
<p>The training program lasted 4 weeks, with three sessions per week, totaling 12 sessions. Each session lasted 60 min and consisted of three parts: 20 min of warm-up, 30 min of upper-body HIIT or MICT, and 10 min of stretching and relaxation at the end.</p>
<p>The upper-body HIIT protocol was based on the Tabata model, which is widely recognized for its efficiency in stimulating aerobic and anaerobic energy systems through repeated short bursts of maximal effort followed by brief rest periods <xref ref-type="bibr" rid="B44">Tabata (2019)</xref>; <xref ref-type="bibr" rid="B45">Tabata et al. (1996)</xref>. Specifically, the HIIT sessions included eight resistance exercises targeting key upper-body muscle groups involved in swimming propulsion: resistance band incline pull-down, resistance band bent-over lateral pull, resistance band push-up, resistance band rear pull, resistance band front pull-down, resistance band prone pull-down, resistance band shoulder press, and decline push-up. Each exercise was performed for 20 s at maximum intensity, followed by a 10-s rest. This circuit lasted approximately 230 s and was repeated for three sets with a 3-min rest interval between sets, in line with protocols shown to improve <inline-formula id="inf46">
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<p>Resistance bands were used as the main training equipment to provide progressive resistance across the full range of motion. This choice is supported by previous studies demonstrating that elastic resistance is a practical and effective method for improving muscle strength and endurance in both general fitness and sport-specific dry-land training for swimmers <xref ref-type="bibr" rid="B3">Aspenes et al. (2009)</xref>; <xref ref-type="bibr" rid="B24">Hibbs et al. (2008)</xref>. Moreover, studies comparing resistance bands with free weights and machines have shown comparable benefits for strength gains and functional performance (<xref ref-type="bibr" rid="B12">Colado et al., 2009</xref>; <xref ref-type="bibr" rid="B2">Andersen et al., 2010</xref>). A 25 kg yellow resistance band was used in all sessions to ensure adequate training load.</p>
<p>The MICT group followed the same session duration, warm-up, and cool-down as the HIIT group but performed the main workout with moderate-intensity continuous resistance band exercises. Each exercise was performed continuously for 60 s with a 20-s rest, emphasizing endurance and aerobic metabolism. This design allows a direct comparison between upper-body HIIT and MICT on energy metabolism and <inline-formula id="inf47">
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<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Schematic diagram of the training protocol.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g002.tif">
<alt-text content-type="machine-generated">Exercise regimen diagram showing a 4-week duration with 3 sessions per week. It includes warm-up, stretching, and 8 resistance band exercises. The HIIT group performs exercises with 20-second activity and 10-second rest over 3 minutes. The MICT group performs 60 seconds of exercise with 20-second rest. The session consists of 20 minutes warm-up, 30 minutes of exercises, and 10 minutes of stretching. Exercise types include various pulls and pushes with resistance bands.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s2-4-2">
<title>2.4.2 Training monitoring</title>
<p>The HRmax estimation using the formula &#x201c;208 - (0.7 <inline-formula id="inf48">
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<p>To enhance precision, training intensity was simultaneously monitored using Polar heart rate straps and the Borg RPE scale, per recommendations to combine objective and subjective measures when relying on estimated HRmax. This dual approach minimized errors from HRmax estimation variability (<inline-formula id="inf51">
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</sec>
</sec>
<sec id="s2-5">
<title>2.5 Statistical analysis</title>
<p>Data were tabulated and analyzed using the Statistical Package for the Social Sciences (SPSS, Version 25). Statistical significance was set at <inline-formula id="inf52">
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<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x2248;</mml:mo>
<mml:mn>0.06</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula>, and large effect <inline-formula id="inf59">
<mml:math id="m59">
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x2248;</mml:mo>
<mml:mn>0.14</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula>, providing a framework for evaluating the magnitude of observed effects. Graphical representations of results and trends were generated using Prism (Viewer Mode, Version 10) to facilitate visual interpretation.</p>
</sec>
</sec>
<sec id="s3">
<title>3 Results and analysis</title>
<sec id="s3-1">
<title>3.1 Participant characteristics</title>
<p>All participants completed the full testing and training intervention. The baseline characteristics of the athletes are presented in <xref ref-type="table" rid="T2">Table 2</xref>. There were no significant differences between groups in any of the measured variables, including age, height, body mass, and training years (p <inline-formula id="inf60">
<mml:math id="m60">
<mml:mrow>
<mml:mo>&#x3e;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 0.05).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Basic information of swimmers.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Characteristics</th>
<th align="center">Upper - body HIIT group</th>
<th align="center">Upper - body MICT group</th>
<th align="center">t</th>
<th align="center">p</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Age (years)</td>
<td align="center">20.25 <inline-formula id="inf66">
<mml:math id="m66">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 1.23</td>
<td align="center">21.17 <inline-formula id="inf67">
<mml:math id="m67">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 1.46</td>
<td align="center">&#x2212;1.67</td>
<td align="center">0.11</td>
</tr>
<tr>
<td align="center">Height (cm)</td>
<td align="center">178.78 <inline-formula id="inf68">
<mml:math id="m68">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 9.22</td>
<td align="center">177.54 <inline-formula id="inf69">
<mml:math id="m69">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.27</td>
<td align="center">0.35</td>
<td align="center">0.73</td>
</tr>
<tr>
<td align="center">Weight (kg)</td>
<td align="center">70.22 <inline-formula id="inf70">
<mml:math id="m70">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 13.54</td>
<td align="center">73.95 <inline-formula id="inf71">
<mml:math id="m71">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 12.47</td>
<td align="center">&#x2212;0.70</td>
<td align="center">0.49</td>
</tr>
<tr>
<td align="center">Training experience</td>
<td align="center">9.50 <inline-formula id="inf72">
<mml:math id="m72">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2.47</td>
<td align="center">11.67 <inline-formula id="inf73">
<mml:math id="m73">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2.66</td>
<td align="center">&#x2212;2.07</td>
<td align="center">0.5</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-2">
<title>3.2 The impact of upper-body high-intensity intermittent training on energy metabolism</title>
<p>A 2 (Group: Upper-Body HIIT vs. Upper-Body MICT) <inline-formula id="inf74">
<mml:math id="m74">
<mml:mrow>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2 (Time: Pre-vs. Post-) repeated measures ANOVA was used to analyze glycolysis within energy metabolism, with results presented in <xref ref-type="table" rid="T3">Table 3</xref>. The analysis revealed a significant main effect of group, with the Upper-Body HIIT group exhibiting higher glycolysis levels than the Upper-Body MICT group (<inline-formula id="inf75">
<mml:math id="m75">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>11.133</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf76">
<mml:math id="m76">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.01</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf77">
<mml:math id="m77">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.336</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). A significant main effect of time was also found, with higher glycolysis levels post-intervention than pre-intervention (<inline-formula id="inf78">
<mml:math id="m78">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>70.383</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf79">
<mml:math id="m79">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf80">
<mml:math id="m80">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.762</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). Moreover, the interaction between groups and timing proved significant (<inline-formula id="inf81">
<mml:math id="m81">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>87.882</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf82">
<mml:math id="m82">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf83">
<mml:math id="m83">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.800</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Repeated measures ANOVA tests for glycolysis of different groups and test timing.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Group</th>
<th rowspan="2" align="center">Pre</th>
<th rowspan="2" align="center">Post</th>
<th colspan="3" align="center">Group effect</th>
<th colspan="3" align="center">Time effect</th>
<th colspan="3" align="center">Interaction</th>
</tr>
<tr>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf84">
<mml:math id="m84">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf85">
<mml:math id="m85">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf86">
<mml:math id="m86">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="center">MICT</td>
<td rowspan="2" align="center">35.51 <inline-formula id="inf87">
<mml:math id="m87">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 9.19</td>
<td rowspan="2" align="center">34.66 <inline-formula id="inf88">
<mml:math id="m88">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.96</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">11.133</td>
<td align="center">0.01</td>
<td align="center">0.336</td>
<td align="center">70.383</td>
<td align="center">0.001</td>
<td align="center">0.762</td>
<td align="center">87.882</td>
<td align="center">0.001</td>
<td align="center">0.8</td>
</tr>
<tr>
<td align="center">HIIT</td>
<td align="center">39.12 <inline-formula id="inf89">
<mml:math id="m89">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.69</td>
<td align="center">52.42 <inline-formula id="inf90">
<mml:math id="m90">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.47</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
<p>To delve deeper into the interaction between the group and timing, a simple effects analysis was conducted. For the Upper-Body HIIT group, pre-intervention glycolysis levels were significantly lower than post-intervention levels (<inline-formula id="inf91">
<mml:math id="m91">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>157.780</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf92">
<mml:math id="m92">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf93">
<mml:math id="m93">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.878</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). Conversely, no significant difference between pre- and post-intervention glycolysis levels was observed in the Upper-Body MICT group (<inline-formula id="inf94">
<mml:math id="m94">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.485</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf95">
<mml:math id="m95">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf96">
<mml:math id="m96">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.022</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). At the pre-intervention stage, glycolysis levels did not significantly differ between the two groups (<inline-formula id="inf97">
<mml:math id="m97">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.976</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf98">
<mml:math id="m98">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf99">
<mml:math id="m99">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.042</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). However, at the post-intervention stage, glycolysis levels were significantly higher in the Upper-Body HIIT group than in the Upper-Body MICT group (<inline-formula id="inf100">
<mml:math id="m100">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>30.807</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf101">
<mml:math id="m101">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf102">
<mml:math id="m102">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.583</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<p>A 2 (Group: Upper-Body HIIT vs. Upper-Body MICT) <inline-formula id="inf103">
<mml:math id="m103">
<mml:mrow>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2 (Time: Pre-vs. Post-) repeated-measures ANOVA was used to analyze the aerobic oxidation within energy metabolism, with results presented in <xref ref-type="table" rid="T4">Table 4</xref>. The analysis revealed a significant main effect of group (<inline-formula id="inf104">
<mml:math id="m104">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>5.189</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf105">
<mml:math id="m105">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf106">
<mml:math id="m106">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.191</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), with the Upper-Body HIIT group exhibiting higher aerobic oxidation levels than the Upper-Body MICT group. A significant main effect of timing was also found (<inline-formula id="inf107">
<mml:math id="m107">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>69.359</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf108">
<mml:math id="m108">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf109">
<mml:math id="m109">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.759</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), with higher aerobic oxidation levels post-intervention than pre-intervention. Moreover, the interaction between group and timing was significant (<inline-formula id="inf110">
<mml:math id="m110">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>63.809</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf111">
<mml:math id="m111">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf112">
<mml:math id="m112">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.744</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Repeated-measure ANOVA tests for aerobic oxidation of different groups and test timing.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Group</th>
<th rowspan="2" align="center">Pre</th>
<th rowspan="2" align="center">Post</th>
<th colspan="3" align="center">Group effect</th>
<th colspan="3" align="center">Time effect</th>
<th colspan="3" align="center">Interaction</th>
</tr>
<tr>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf113">
<mml:math id="m113">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf114">
<mml:math id="m114">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf115">
<mml:math id="m115">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="center">MICT</td>
<td rowspan="2" align="center">102.45 <inline-formula id="inf116">
<mml:math id="m116">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 21.63</td>
<td rowspan="2" align="center">103.35 <inline-formula id="inf117">
<mml:math id="m117">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 19.83</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">5.189</td>
<td align="center">0.05</td>
<td align="center">0.191</td>
<td align="center">69.359</td>
<td align="center">0.001</td>
<td align="center">0.759</td>
<td align="center">63.809</td>
<td align="center">0.001</td>
<td align="center">0.744</td>
</tr>
<tr>
<td align="center">HIIT</td>
<td align="center">104.94 <inline-formula id="inf118">
<mml:math id="m118">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 30.83</td>
<td align="center">148.17 <inline-formula id="inf119">
<mml:math id="m119">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 30.73</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
<p>To further explore the interaction effect between group and timing, simple effects analyses were conducted. For the Upper-Body HIIT group, pre-intervention aerobic oxidation levels were significantly lower than post-intervention levels (<inline-formula id="inf120">
<mml:math id="m120">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>133.111</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf121">
<mml:math id="m121">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf122">
<mml:math id="m122">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.858</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). In contrast, no significant difference between pre- and post-intervention aerobic oxidation levels was observed in the Upper-Body MICT group (<inline-formula id="inf123">
<mml:math id="m123">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.058</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf124">
<mml:math id="m124">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf125">
<mml:math id="m125">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.003</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). At the pre-intervention stage, aerobic oxidation levels did not significantly differ between the two groups (<inline-formula id="inf126">
<mml:math id="m126">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.053</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf127">
<mml:math id="m127">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf128">
<mml:math id="m128">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.002</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). However, at the post-intervention stage, aerobic oxidation levels were significantly higher in the Upper-Body HIIT group than in the Upper-Body MICT group (<inline-formula id="inf129">
<mml:math id="m129">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>18.017</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf130">
<mml:math id="m130">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf131">
<mml:math id="m131">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.450</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<p>A 2 (Group: Upper-Body HIIT vs. Upper-Body MICT) <inline-formula id="inf132">
<mml:math id="m132">
<mml:mrow>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2 (Time: Pre-vs. Post-) repeated measures ANOVA was conducted to analyze the phosphagen content in energy metabolism, with results presented in <xref ref-type="table" rid="T5">Table 5</xref>. The analysis revealed a significant main effect of group (<inline-formula id="inf133">
<mml:math id="m133">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>6.376</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf134">
<mml:math id="m134">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf135">
<mml:math id="m135">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.225</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), with higher phosphagen content in the Upper-Body HIIT group than in the Upper-Body MICT group. A significant main effect of time was also found (<inline-formula id="inf136">
<mml:math id="m136">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>25.342</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf137">
<mml:math id="m137">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf138">
<mml:math id="m138">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.535</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), with higher phosphagen content post-intervention than pre-intervention. Furthermore, the interaction between group and time proved significant (<inline-formula id="inf139">
<mml:math id="m139">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>29.338</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf140">
<mml:math id="m140">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf141">
<mml:math id="m141">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.571</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Repeated measures ANOVA tests for phosphagen of different groups and test timing.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Group</th>
<th rowspan="2" align="center">Pre</th>
<th rowspan="2" align="center">Post</th>
<th colspan="3" align="center">Group effect</th>
<th colspan="3" align="center">Time effect</th>
<th colspan="3" align="center">Interaction</th>
</tr>
<tr>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf142">
<mml:math id="m142">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf143">
<mml:math id="m143">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf144">
<mml:math id="m144">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="center">MICT</td>
<td rowspan="2" align="center">37.19 <inline-formula id="inf145">
<mml:math id="m145">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.77</td>
<td rowspan="2" align="center">36.63 <inline-formula id="inf146">
<mml:math id="m146">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 8.12</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">6.376</td>
<td align="center">0.05</td>
<td align="center">0.225</td>
<td align="center">25.342</td>
<td align="center">0.001</td>
<td align="center">0.535</td>
<td align="center">29.338</td>
<td align="center">0.001</td>
<td align="center">0.571</td>
</tr>
<tr>
<td align="center">HIIT</td>
<td align="center">39.72 <inline-formula id="inf147">
<mml:math id="m147">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 9.54</td>
<td align="center">55.17 <inline-formula id="inf148">
<mml:math id="m148">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 15.37</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
<p>To further explore the interaction between group and time, simple effects analyses were conducted. For the Upper-Body HIIT group, pre-intervention phosphagen content was significantly lower than post-intervention content (<inline-formula id="inf149">
<mml:math id="m149">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>54.607</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf150">
<mml:math id="m150">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf151">
<mml:math id="m151">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.713</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). In contrast, no significant difference in phosphagen content between pre- and post-intervention was observed in the Upper-Body MICT group (<inline-formula id="inf152">
<mml:math id="m152">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.073</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf153">
<mml:math id="m153">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf154">
<mml:math id="m154">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.003</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). At the pre-intervention stage, phosphagen content did not significantly differ between the two groups (<inline-formula id="inf155">
<mml:math id="m155">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.455</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf156">
<mml:math id="m156">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf157">
<mml:math id="m157">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.020</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). However, at the post-intervention stage, phosphagen content was significantly higher in the Upper-Body HIIT group than in the Upper-Body MICT group (<inline-formula id="inf158">
<mml:math id="m158">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>13.657</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf159">
<mml:math id="m159">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.01</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf160">
<mml:math id="m160">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.383</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
</sec>
<sec id="s3-3">
<title>3.3 The impact of upper-body high-intensity intermittent training on maximal oxygen uptake</title>
<p>A 2 (Group: Upper-Body HIIT vs. Upper-Body MICT) <inline-formula id="inf161">
<mml:math id="m161">
<mml:mrow>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2 (Time: Pre-vs. Post-) repeated measures ANOVA was conducted to analyze maximal oxygen uptake (<inline-formula id="inf162">
<mml:math id="m162">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max), and the results are presented in <xref ref-type="table" rid="T6">Table 6</xref>. The analysis revealed a significant main effect of group (<inline-formula id="inf163">
<mml:math id="m163">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>7.359</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf164">
<mml:math id="m164">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf165">
<mml:math id="m165">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.251</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), with the Upper-Body HIIT group showing higher <inline-formula id="inf166">
<mml:math id="m166">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max levels than the Upper-Body MICT group. The main effect of timing was not significant (<inline-formula id="inf167">
<mml:math id="m167">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>2.617</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf168">
<mml:math id="m168">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf169">
<mml:math id="m169">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.106</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>), indicating no significant difference in <inline-formula id="inf170">
<mml:math id="m170">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max between the pre- and post-tests overall. However, the correlation between group and timing was significant (<inline-formula id="inf171">
<mml:math id="m171">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>18.904</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf172">
<mml:math id="m172">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf173">
<mml:math id="m173">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.462</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Repeated-measures ANOVA tests of maximum oxygen uptake in different groups and test times.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Group</th>
<th rowspan="2" align="center">Pre</th>
<th rowspan="2" align="center">Post</th>
<th colspan="3" align="center">Group effect</th>
<th colspan="3" align="center">Time effect</th>
<th colspan="3" align="center">Interaction</th>
</tr>
<tr>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf174">
<mml:math id="m174">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf175">
<mml:math id="m175">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
<th align="center">F</th>
<th align="center">P</th>
<th align="center">
<inline-formula id="inf176">
<mml:math id="m176">
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>
<sup>2</sup>p</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="2" align="center">Control</td>
<td rowspan="2" align="center">49.40 <inline-formula id="inf177">
<mml:math id="m177">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 3.75</td>
<td rowspan="2" align="center">48.19 <inline-formula id="inf178">
<mml:math id="m178">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 3.54</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="center">7.359</td>
<td align="center">0.05</td>
<td align="center">0.251</td>
<td align="center">2.617</td>
<td align="center">0.05</td>
<td align="center">0.106</td>
<td align="center">18.904</td>
<td align="center">0.001</td>
<td align="center">0.462</td>
</tr>
<tr>
<td align="center">Upper</td>
<td align="center">51.00 <inline-formula id="inf179">
<mml:math id="m179">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 2.92</td>
<td align="center">53.63 <inline-formula id="inf180">
<mml:math id="m180">
<mml:mrow>
<mml:mo>&#xb1;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> 3.15</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
<p>To further explore the interaction between group and time, simple effects analyses were conducted. For the Upper-Body HIIT group, pre-test <inline-formula id="inf181">
<mml:math id="m181">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max levels were significantly lower than post-test levels (<inline-formula id="inf182">
<mml:math id="m182">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>17.794</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf183">
<mml:math id="m183">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf184">
<mml:math id="m184">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.447</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). In contrast, no significant difference between pre- and post-test <inline-formula id="inf185">
<mml:math id="m185">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max levels was observed in the Upper-Body MICT group (<inline-formula id="inf186">
<mml:math id="m186">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>3.727</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf187">
<mml:math id="m187">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf188">
<mml:math id="m188">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.145</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). At the pre-test stage, <inline-formula id="inf189">
<mml:math id="m189">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max levels did not significantly differ between the two groups (<inline-formula id="inf190">
<mml:math id="m190">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>1.353</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf191">
<mml:math id="m191">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3e;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf192">
<mml:math id="m192">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.058</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>). However, at the post-test stage, <inline-formula id="inf193">
<mml:math id="m193">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max levels were significantly higher in the Upper-Body HIIT group than in the Upper-Body MICT group (<inline-formula id="inf194">
<mml:math id="m194">
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:mn>1,22</mml:mn>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>15.823</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf195">
<mml:math id="m195">
<mml:mrow>
<mml:mi>p</mml:mi>
<mml:mo>&#x3c;</mml:mo>
<mml:mn>0.01</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>, <inline-formula id="inf196">
<mml:math id="m196">
<mml:mrow>
<mml:msubsup>
<mml:mrow>
<mml:mi>&#x3b7;</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mi>p</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>0.418</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>).</p>
<p>These findings indicate that 4 weeks of upper-body HIIT significantly enhances upper-body energy metabolism and <inline-formula id="inf197">
<mml:math id="m197">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max. The 4-week upper-body HIIT intervention proved effective for improving these parameters. Analysis via upper-body cycle ergometry (see <xref ref-type="fig" rid="F3">Figures 3</xref>&#x2013;<xref ref-type="fig" rid="F6">6</xref>) revealed no significant pre-intervention differences between the Upper-Body HIIT and MICT groups across the three energy systems and <inline-formula id="inf198">
<mml:math id="m198">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max. However, post-intervention, the Upper-Body HIIT group demonstrated more efficient energy regulation and higher <inline-formula id="inf199">
<mml:math id="m199">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mtext>VO</mml:mtext>
</mml:mrow>
<mml:mrow>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>max than the MICT group.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Upper-body cycle ergometry glycolysis energy supply characteristics.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g003.tif">
<alt-text content-type="machine-generated">Bar graph comparing glycogen energy use in upper limbs between HIIT and MICT, pre- and post-exercise. HIIT is in blue, MICT in red. Post values show significant differences, indicated by &#x22;&#x2a;&#x2a;&#x2a;&#x22;. Pre-values are marked with &#x22;ns&#x22; for not significant.</alt-text>
</graphic>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Upper-body cycle ergometry aerobic oxidation energy supply characteristics.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g004.tif">
<alt-text content-type="machine-generated">Bar chart titled &#x22;Upper Limbs HIIT Emergy Metabolic: Aerobic&#x22; compares energy (in KJ) for HIIT and MICT, pre and post intervention. Blue bars represent HIIT, red bars represent MICT. Key results show significant improvement in HIIT post, marked by asterisks indicating statistical significance (&#x2a;&#x2a;&#x2a;), while others are not significant (ns).</alt-text>
</graphic>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Upper-body cycle ergometry phosphagen energy supply characteristics.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g005.tif">
<alt-text content-type="machine-generated">Bar chart titled &#x22;Upper Limbs HIIT Energy Metabolic: phosphoric.&#x22; It compares the energy in kilojoules between HIIT (blue) and MICT (red) before (pre) and after (post) exercise. The pre-exercise bars are similar, with no significant difference (&#x22;ns&#x22;). The post-exercise HIIT bar is significantly higher than MICT (&#x2a;&#x2a;&#x2a;), while post-exercise difference between pre and post is not significant for MICT. Error bars indicate variability.</alt-text>
</graphic>
</fig>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Upper-body cycle ergometry maximal oxygen uptake characteristics.</p>
</caption>
<graphic xlink:href="fphys-16-1636405-g006.tif">
<alt-text content-type="machine-generated">Bar chart comparing upper limb maximum oxygen intake before and after high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT). HIIT bars are in blue and MICT bars in red. Pre-training levels show no significant difference, while post-training HIIT shows a significant increase (&#x2a;&#x2a;), with no significant difference between post-training HIIT and MICT.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>This study demonstrated the significant impact of upper-body high-intensity interval training (HIIT) on enhancing energy metabolism and <inline-formula id="inf200">
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</inline-formula>max in swimmers. After 4 weeks of upper-body-focused HIIT, participants showed marked improvements in phosphagen, aerobic, and glycolytic energy systems, as well as <inline-formula id="inf201">
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</inline-formula>max. These results suggest that upper-body HIIT can be an effective method for improving swimming performance. The findings complement existing sports training literature and offer swim-specific insights for upper-limb strength and endurance development.</p>
<p>We speculate that these improvements stem from HIIT&#x2019;s dual stimulation of the cardiovascular and muscular systems. According to <xref ref-type="bibr" rid="B35">Mitropoulos et al. (2018)</xref>, short bursts of high-intensity effort in HIIT can enhance cardiac output and vascular function. Laursen and Jenkins <xref ref-type="bibr" rid="B31">Laursen and Jenkins (2002)</xref> proposed that HIIT improves <inline-formula id="inf202">
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</inline-formula>max by increasing stroke volume and blood volume, as well as by enhancing the muscles&#x2019; ability to extract oxygen. <xref ref-type="bibr" rid="B15">Dohlmann et al. (2018)</xref> also found that HIIT can promote mitochondrial density in muscle, thereby improving energy metabolic pathways and muscular energy efficiency. These adaptations are particularly crucial for swimmers, whose performance largely relies on upper-body strength and endurance to sustain repetitive movement patterns.</p>
<p>The training protocol employed in this study was based on a 20-s work and 10-s rest interval format, closely aligned with the Tabata training model. Tabata is widely recognized as one of the most effective forms of HIIT owing to its capacity to enhance aerobic and anaerobic energy systems simultaneously <xref ref-type="bibr" rid="B44">Tabata (2019)</xref>. Previous studies have consistently reported improvements in cardiorespiratory fitness following HIIT, as assessed by <inline-formula id="inf203">
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</inline-formula>max <xref ref-type="bibr" rid="B28">Kessler et al. (2012)</xref>; <xref ref-type="bibr" rid="B32">Lu et al. (2023)</xref>. Specifically, these investigations observed an average increase of 12.87% <inline-formula id="inf204">
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</inline-formula>max among participants in the Tabata training groups postintervention. However, the findings from Islam et al. <xref ref-type="bibr" rid="B26">Islam et al. (2020)</xref> presented a contrasting result. In their study, participants engaged in a 4-week intervention consisting of four weekly Tabata sessions, each comprising full-body functional exercises such as burpees, mountain climbers, jump squats, deep squats, and bench presses. Despite the adherence to the Tabata structure, no statistically significant improvements in <inline-formula id="inf206">
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</inline-formula>max were observed following the intervention. This discrepancy may be attributed to the short intervention period or to the participants&#x2019; superior baseline cardiorespiratory fitness levels. Indeed, prior research has indicated that baseline fitness status and initial training level can significantly influence training outcomes <xref ref-type="bibr" rid="B34">Milanovi&#x107; et al. (2015)</xref>. Nevertheless, other studies have confirmed that Tabata training can effectively enhance aerobic and anaerobic capacity, which is consistent with the energy metabolism improvements observed in the present study <xref ref-type="bibr" rid="B45">Tabata et al. (1996)</xref>; <xref ref-type="bibr" rid="B43">Sumpena and Sidik (2017)</xref>.</p>
<p>In current sports training research, the benefits of high-intensity interval training (HIIT) have been widely recognized. Jim&#xe9;nez-Maldonado et al. (2018) demonstrated that HIIT can significantly enhance athletes&#x2019; energy metabolism and maximal oxygen uptake (<inline-formula id="inf207">
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</inline-formula>max). Similarly, <xref ref-type="bibr" rid="B23">Helgerud et al. (2007)</xref> reported that HIIT improved metabolic function and reduced resting uric acid concentrations. Furthermore, <xref ref-type="bibr" rid="B29">Kistner et al. (2019)</xref> provided evidence that HIIT can enhance athletic performance by improving metabolic pathways. These findings align with the results of the present study, highlighting the broad impact of HIIT on cardiorespiratory function and energy metabolism. Although prior research has established the effectiveness of HIIT, most studies have primarily focused on lower-limb or whole-body exercises. In contrast, the present study, consistent with emerging evidence from other sport-specific contexts, emphasizes the unique benefits of HIIT for swimmers. Specifically, our investigation explores the impact of upper-limb-focused HIIT on the cardiorespiratory fitness and energy metabolism of competitive swimmers. This focus reveals the distinct value and potential application of targeted HIIT protocols in enhancing performance through sport-specific muscle group training.</p>
<p>These findings carry practical significance for competitive swim training. Incorporating upper-body HIIT can optimize training time and improve muscular strength and endurance, especially in muscle groups critical to performance. Coaches may consider adding upper-body HIIT to maximize both aerobic and anaerobic capacity, particularly in long-distance events where energy conservation is vital. This research focuses on upper-limb HIIT, a domain less explored in prior literature that has primarily emphasized cardiorespiratory endurance and lower-limb performance improvements <xref ref-type="bibr" rid="B37">Nugent et al. (2017)</xref>; <xref ref-type="bibr" rid="B38">Pang (2022)</xref>; <xref ref-type="bibr" rid="B1">Amara et al. (2023)</xref>. By examining upper-body-specific adaptations, our findings contribute to a more nuanced understanding of HIIT&#x2019;s modality-dependent effects. Furthermore, our results align with <xref ref-type="bibr" rid="B24">Hibbs et al. (2008)</xref>, who highlighted the importance of muscle-specific training for sport-specific skill development&#x2014;a principle we extend to upper-body resistance protocols.</p>
<p>Despite these promising results, limitations must be acknowledged. The relatively small sample size may limit the generalizability of the findings, and future research should involve larger and more diverse populations. The short intervention period also restricts long-term effect evaluation; hence, longer follow-up studies are warranted. Additionally, while this study focused on <inline-formula id="inf208">
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</inline-formula>max and energy metabolism, future investigations should explore other dimensions, such as muscular strength, endurance, technical improvements, and psychological factors like confidence and stress coping. Examining mental impact of upper-body HIIT may provide valuable perspectives for sports training science.</p>
<p>Future research should consider broader participant groups, diverse training models, and long-term tracking. Including swimmers of varying skill levels would yield more comprehensive insights into the effects of upper-body HIIT. Exploring variation in training modes, intensity, duration, and frequency may help determine optimal training protocols. Ultimately, long-term studies are essential for evaluating the sustained benefits of upper-body HIIT and for designing enduring, high-performance training plans.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>This randomized controlled trial explored the effects of upper-body HIIT on energy metabolism and <inline-formula id="inf209">
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</inline-formula>max in swimmers. After a 4-week intervention, the HIIT group showed significantly greater improvements in energy system indicators and <inline-formula id="inf210">
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</inline-formula>max compared with the MICT group. These findings suggest that upper-body HIIT is an effective, time-efficient method for enhancing cardiopulmonary function and energy utilization in swimmers. For athletes seeking performance enhancement, upper-body HIIT can serve as a valuable addition to swim training programs. These findings demonstrate that upper-body HIIT enhances metabolic adaptations relevant to swimming physiology, including <inline-formula id="inf211">
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</inline-formula>max and substrate utilization efficiency. However, confirmation of practical application requires integration with kinematic analyses and performance trials because physiological improvements alone do not guarantee enhanced swimming outcomes. However, these results cannot be generalized to total-body HIIT because the distribution of workload, muscle mass recruitment, and systemic physiological responses likely differ substantially between upper-body-focused and total-body interventions. Future studies directly comparing upper-body, lower-body, and total-body HIIT designs would be valuable to clarify whether the observed effects are modality specific.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Scientific research project of Shanghai University of Sport (Ethical number: 102772021RT031). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants&#x2019; legal guardians/next of kin.</p>
</sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>LZ: Writing &#x2013; original draft, Validation, Conceptualization, Investigation, Writing &#x2013; review and editing. HL: Resources, Writing &#x2013; review and editing, Project administration, Formal Analysis, Data curation, Methodology. TW: Writing &#x2013; review and editing, Supervision, Software, Funding acquisition, Visualization. CC: Conceptualization, Writing &#x2013; review and editing, Supervision, Visualization.</p>
</sec>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research and/or publication of this article.</p>
</sec>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s11">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s12">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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