Title: The effects of COVID-19 infection on athletic performance: A systematic review



Authors: 1Marisella Villano, MS, CFT, CES and 2Frank Spaniol, PhD

Department of Kinesiology, Texas A & M University – Corpus Christi

Corresponding Author:

Marisella Villano, MS, CFT, CCES
111 Lynn Avenue
Hampton Bays, NY 11946
villanosven@optonline.net
1-631-697-2823

Marisella Villano recently graduated with a Master’s degree in Kinesiology from Texas A & M – Corpus Christi and has previously earned a Master’s degree in Gerontology from Long Island University. Additionally, she is a certified fitness trainer and corrective exercise specialist and is the founder and owner of MARVIL FIT, an indoor cycling, fitness and personal training studio in the Hamptons.

Frank Spaniol, PhD is a professor of sport and exercise science and also the program coordinator in the Kinesiology department at Texas A & M University – Corpus Christi. His
research interests include: sport performance, strength and conditioning, visual skills training, and sport technology.

Abstract
Purpose: This systematic review investigated the effects of COVID-19 infection on athletic performance. Methods: Using guidelines for a systematic search review, a comprehensive literature review was conducted utilizing the computer databases Google Scholar, PubMed and the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi. Results: Incidence of cardiac abnormalities is low among athletes with COVID-19 infection, but cardiopulmonary deficiencies like shortness of breath have been shown to affect aerobic capacity which can impair performance. A premature switch to anaerobic metabolism at higher intensities was observed during cardiopulmonary exercise testing (CPET). Increased exercise heart rate (HR) and blood pressure (BP) were also observed in some athletes during CPET. Finally, the effects of COVID-19 appear to be multisystemic as decrements were also observed in balance, sleep and high intensity performance. Conclusion: COVID-19 infection primarily affects the cardiorespiratory system, but other multisystemic disturbances to athletic performance may occur which can negatively affect performance. Applications to Sport: Athletes recovered from COVID-19 illness continue to experience shortness of breath which may decrease recoverability after high intensity exertions and increase fatigability during competition. Proper screening beginning with CPET and planned RTP protocols based on the individual needs of athletes are necessary for seamless return to sport and attainment of performance levels prior to infection.

Keywords
Return to play, cardiorespiratory, cardiopulmonary exercise test, cardiac magnetic resonance, heart rate, VO2max, aerobic threshold, anaerobic threshold, ventilation, ventilatory efficiency, ventilatory inefficiency, long covid

Introduction
The COVID-19 pandemic spared no one, including athletes, and became a significant worldwide problem that appeared to primarily cause respiratory and cardiovascular illness (29). While clinical manifestations of COVID-19 in athletes are generally mild, persisting symptoms like cough, fatigue and tachycardia are similar to individuals in the general public (25). Individuals affected with mild or moderate COVID-19 illness also have the possibility of experiencing persistent symptoms post infection called Long-Covid (LC) and asymptomatic infection can introduce symptoms once a person has recovered from the primary infection (32). Persistent symptoms lasting more than 28 days are defined as LC and generally include fatigue and shortness of breath (6, 34).

Aside from the common symptoms of COVID-19 which include cardiorespiratory and cardiovascular disturbances, multisystemic disturbances have been observed in the central and peripheral nervous systems, gastrointestinal system, hematological system, liver, skeletal muscle, and kidneys (11, 16, 29). Furthermore, post infection sequelae causing imbalances of the autonomic system have also been observed (15). To sum up, the virus responsible for COVID-19 attacks the immune system of its host and creates a systemic inflammatory response by activating a large number of cytokines, which induces inflammation and can affect multiple organ systems that could potentially contribute to their failure in severe cases (1).
Multiorgan damage by COVID-19 infection is caused by penetration of the virus through angiotensin-converting enzyme-2 (ACE2) receptors found on the surface of the cell (21). Further, large concentrations of ACE2 receptors are found in pulmonary and cardiac tissue, which may explain symptoms of shortness of breath and cardiac complications in recovered individuals (21). Additionally, COVID-19 complications have been observed to last longer than 30 days and up to 6 months (28). Even though the athletic population appears to develop mild to moderate COVID-19 infection, are not at high risk for severe illness and are quick to recover, they may experience lingering post infection sequelae from COVID-19 like myocarditis, exertional dyspnea, tachycardia, muscle pain, joint pain and fatigue (even with asymptomatic and mild infection) (9, 15, 25). Lastly, estimations of LC in athletes are between 3 and 17% (34).
First time symptoms of COVID-19 illness can occur once the primary infection has subsided (32). Because athletes exert demanding loads compared to the average population, understanding the long-term effects of COVID-19 is not only important to help maintain maximal performance levels, but should also be a concern for their safety (2, 6, 32). While athletes appear to fully recover after COVID-19 infection, cardiopulmonary exercise testing (CPET) post infection has aided medical professionals to uncover potentially detrimental symptomatology during exertional activities (5).

Fortunately, current research has demonstrated that the chance for cardiac abnormalities among athletes recovering from asymptomatic to moderate COVID-19 illness is very rare (3, 14, 16, 19). In a study by Maestrini et. al. (2023), 6% of the participants exhibited cardiac abnormalities post COVID-19 cardiovascular evaluations. Also, of 1597 athletes in Big Ten American Football Conference, 37 athletes (2.3%) exhibited clinical or subclinical signs of myocarditis (10). Of interest, some cardiac issues uncovered during CPET and cardiovascular testing while undergoing return to play (RTP) protocols after infection had no relationship to COVID-19 and appeared to result from preexisting conditions (3, 24, 32). This emphasizes the need for regular CPET (which has been used as a standard test to determine the cardiorespiratory and pulmonary health of individuals post infection) and cardiovascular screening for all athletes (3, 24, 32).

While most athletes will have mild or no symptoms during acute COVID-19 infection, 3-17% will be affected by continuing symptoms, like fatigue, that can have negative effects, to optimal performance (33). Unfortunately, the recommended forced rest of 14 days for elite and competitive athletes can be detrimental to power and maximal oxygen consumption (VO2 max), cardiac output and stroke volume (21). Information regarding the long-term effects of COVID-19 continues to evolve and only necessitates the importance of research and investigation, especially in athletes because their success relies on their physical capabilities (31). Additionally, little research is available on the consequences of any potential musculoskeletal cellular interruptions through the ACE2 receptors primarily occurring in pulmonary and cardiac tissue (31).
Although many athletes have a significantly reduced risk of severe COVID-19 illness, they are not immune to contracting the disease and its lingering effects (9,33). Further, compared to other acute respiratory viruses, the proportion of athletes who have not fully recovered from COVID-19 is significantly higher (34). The purpose of this review is to evaluate the effects of COVID-19 infection on the performance of athletes.

List of Abbreviations
Cardiopulmonary Exercise Test (CPET), Cardiovascular Magnetic Resonance (CMR), Heart Rate (HR), Maximal Heart Rate (MHR), Ventilation (VE), Ventilatory Efficiency (VEf), Ventilatory Inefficiency (ViE), Long Covid (LC), Maximal Oxygen Consumption (VO2max), peak oxygen uptake (VO2 peak), Beats Per Minute (bpm), Blood Lactate (BL), Oxygen (O2), Carbon Dioxide (CO2), Repetition Maximum (RM), Respiratory Compensation Point (RC), Ventilatory Aerobic Threshold (VAT), Beat per Minute (BPM), VE/CO2 Slope (pulmonary ventilation to CO2 production), Partial Pressure of CO2 (PETCO2), Forced Expiratory Volume in the First Second (FEV1), Second Forced Expiratory Volume (FEV2)

Methods
Using guidelines for a systematic search review, a comprehensive literature review was conducted from January 2020 to November 2023 using the computer databases Google Scholar, PubMed and the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi. Several search terms were used and include; covid and athlete; covid infection and athlete and performance and CPET; covid infection and athletes and power and performance and VO2max and cardiorespiratory; covid infection and CPET and anaerobic and athlete; covid infection and athletes and power and performance and VO2max and cardiorespiratory; covid infection and athletes and power and performance and VO2max and cardiopulmonary and sport. Larger search terms to narrow results were necessary when using Google Scholar as the use of two search terms like covid infection and athlete resulted in over 22,000 results. All search titles were carefully filtered to include athletic performance inferences and COVID-19 infection.

Once searches were filtered, article content was reviewed to determine relevance of the investigation as mentioned above. Research journal articles were selected along with 2 case studies due to lack of information in this newly emerged topic. 947 articles were retrieved using Google Scholar with search terms covid infection and athletes and power and performance and VO2max and cardiopulmonary and sport. Of the 947 articles, 10 were relevant to the research parameters. A second search on Google Scholar was conducted using the search terms covid infection and CPET and anaerobic and athlete with 899 results. Of the 889 resulting articles, 17 were relevant to the research parameters. Two separate searches were conducted in PubMed for the terms (1) covid infection and athlete and performance and CPET and (2) COVID infection and athlete and performance and CPET and anaerobic. The first search resulted in 122 outcomes with 18 relevant articles and the second search resulted in 7 outcomes with 5 relevant items. Larger search terms were used because using only the terms covid infection and athlete together resulted in almost 3,000 results. Lastly, the Mary and Jeff Bell Library was used in the review search using fewer search terms since using the larger terms resulted in an extreme narrowing of results. The search terms COVID and athlete were used and resulted in 237 articles. Using the option to include the search terms in the subject heading, the search was further narrowed to 88 where 7 of these search outcomes were selected based on the criteria. 6 articles were extracted from the final selection of articles that did not meet the search requirements and all results were compared for duplicates. In total, 32 articles were retrieved from the search. Additionally, a few articles were extracted from the articles obtained in the search for further investigation of research evidence.


Babity et al. (2022) observed a 10% decrease in VO2max in athletes recovered from COVID-19 infection when comparing their CPET values before illness. Also, post infection CPET times were longer among athletes recovered from COVID-19 infection (p=.003). Further, increased heart rate (HR) was observed in athletes previously infected with COVID-19 during testing. However, once adjustments for age were calculated, no statistically significant changes were evident. Additionally, 13% of elite athletes who participated in the study had asymptomatic infections and a small group appeared to have cardiac irregularities. Despite these differences, no difference was observed between COVID-19 athletes and the control group in ventilation (VE,) carbon dioxide (CO2) removal, blood lactate (BL) levels and percentage of time spent during the anaerobic phase. Vollrath et al. (2022) observed that athletes recovered from COVID-19 infection with persistent symptoms had lower ventilatory efficiency (VEf) than athletes who were symptom free and may indicate a slow recuperation of VEf for symptomatic individuals. Three months later, these persistent symptoms experienced by the athletes were reduced but still present in about 60% of the subjects.

Ventilatory inefficiency (ViE) was observed in competitive athletes that tested positive for COVID-19 by Komici et al. (2023) but was not observed to limit their exercise capacity. These athletes were tested after an RTP program of about 2 weeks. When comparing post infection sequelae, a study by Rinaldo et al. (2021), observed that nonathletic individuals exhibited similar symptoms at rest and at work whereas athletes did not appear to express symptomatology at rest. The exercise decrements observed between both groups in CPET included early AT, early termination of testing, lower peak oxygen (O2) pulse, lower work and a decreased slope relationship between O2 uptake and rate of work. Decreased capacity of exercise was not observed in athletes by Komici et al. 2021, however a trend was observed in the decrease of forced expiratory volume in the first second (FEV1) among the recovered athletes.
Similarly, Keller et al. also observed 4% lower peak values of VO2max in athletes recuperated from COVID-19 (p=.01) when compared to athletes who did not contract the virus. Along with reduced VO2max, Keller et al. (2023) observed an increased chance for exercise hypertension during CPET testing within this group which can be indicative of intolerance to exercise. Additionally, the authors noted that incidences of shortness of breath and chest pain were more prevalent with older athletes in the study group. Reduced peak oxygen uptake (VO2 peak) and increased BP among athletes recovered from COVID-19 illness were observed during CPET, but not at rest.

CPET values among COVID-19 recovered athletes with mild to moderate illness reached AT faster (p=.05) and also had lower measurements for minute ventilation (Ve) than the control group in an investigation by Anastasio et al. (2022). However, differences in maintaining CPET parameters between the two groups were not significant. Additionally, differences at maximal effort only differed by HR, with the COVID-19 group demonstrating higher HR values, but performance during testing was not altered. Finally, one month post COVID-19 infection athletes demonstrate a premature shift to anaerobic metabolism when compared to the control group.

Significant differences were not observed by Babity et al. (2022) when analyzing CPET values of elite athletes before and 3 months after COVID-19 infection. It is important to note that athletes in this study also underwent post COVID-19 retraining protocols where significant increases were observed in average exercise times (p=.003), time to achieve VO2max, respiration rate (p=.008), and HR achieved at AT (p=.004). Also, findings during examination uncovered arrhythmias or hypertension in asymptomatic athletes, and additional non-COVID-19 related to cardiac abnormalities. Moreover, Parpa & Michaelides (2022) observed significantly lower VO2max (p=.01) and decreased VO2max (p=.05) in 21 soccer players recovered from COVID-19. Significantly higher HR at ventilatory threshold (VT) (p=.01) and respiratory compensation point (RC) (p=.01) were also detected. Lastly, decreases in running speed during testing were only observed at VO2max (p=.05) and lower running times (p=.01) were observed.

In a study by Milovancev et al. (2021) of professional volleyball players recovered from COVID-19 infection with about 20 days of retraining, CPET values appeared to show fairly normal pulmonary function. After analyzing data from other studies of healthy athletes, the authors observed lower VO2max and second ventilatory threshold (VT2) in the participants of their study but contributed the deficits to detraining. Lastly, no cardiac disturbances were detected during testing. Similarly, testing results of athletes recovered from COVID-19 showed no statistically significant difference before and after COVID-19 in a study by Taralov et al. (2021) regardless of continued fatigue symptomatology. Due to the study’s small sample size, the authors looked at individual results and were able to see that one participant’s total CPET time was 30 seconds shorter post infection from 18 minutes to 17:30 minutes. Further, AT was reached earlier after acute infection. Additionally, maximal heart rates (MHR) were similar during testing before and after infection which suggests that the similar effort post infection resulted in decreased testing capacity. Another test subject had differing recovery HR 2 minutes into recovery from 141 beats per minute (bpm) before COVID-19 infection to 156 bpm after COVID-19 infection, which is an indication of diminished recovery capacity.

Wezenbeek et al. (2023) showed decreased aerobic performance after COVID-19 infection in elite soccer players about 2 months post infection. Statistically significant higher (MHR) percentages were observed 6 minutes into a Yo-Yo Intermittent Recovery Test (YYIR) (p=.006). When compared to non-infected team members, the MHR percentages were 6-11% greater in the players recovered from COVID-19. After a retest 4-5 months post recovery, these decreases dissipated to normal values. Lastly, the authors also investigated the effects of the viral infection on jumping, strength and sprinting capabilities and no significant differences were observed before and after infection.

A comparative study by Stavrou et al. (2023) between athletes that tested positive for COVID-19 and healthy athletes which never contracted COVID-19 demonstrated statistically significant differences during CPET even with non-significant differences in testing performance. First, the post-COVID-19 group had lower HR at maximal exertions than their healthy counterparts, 191.6 ±7.8 bpm and 196.6 ± 8.6 bpm respectively (p=.041). Mean arterial pressures were similar between both groups. Also, O2 consumption showed no significant difference between the groups. Second, BL levels in the post- COVID -19 group were significantly higher at rest (p=.001), during CPET and during recovery than the healthy group. Third, both groups achieved similar VO2max values, but the post-COVID-19 group did have greater exertional symptoms like increased VE. Fourth, increases in VE were observed in post-COVID-19 group even with non-significant performance differences in CPET between the two groups. Lastly, the post-COVID-19 group also had greater sleep disturbances based on study questionnaires (p=.001). Interestingly, no significant differences in O2 consumption were present between the groups, yet VE was higher at greater workloads in the post-COVID-19 group.
Another comparative study by Śliż et al. (2021) with endurance athletes before and after COVID-19 noted significant changes in CPET parameters after illness. These changes include aggravations to VO2 at AT (p=.00001), VO2 at RC (p=.00001), HR to RC (p=.00011) and VO2max (p=.00011). Additionally, lowered VO2max and early accumulation of lactate were observed during CPET.

Similar findings to ViE and decreased aerobic capacity in elite and highly trained recovered athletes were observed by Brito et al. (2023) with CPET 6-22 weeks after onset of illness. Further, statistically significant decrements were observed in both symptomatic and asymptomatic participants recovered from COVID-19 illness. Additionally, over 50% of all test subjects exhibited significant dysfunctional breathing (p=.023) and over 60% presented significant evidence of ViE (p=.001). Also, a statistically significant percentage of abnormalities were more prevalent among symptomatic individuals, specifically VE/CO2 slope (p<.001), PETCO2 rest (p=.007) and PETCO2 max (p=.008). Statistically significantly higher abnormalities of expiratory air flow/tidal volume were apparent with asymptomatic individuals (p=.012). Lastly, no changes in running economy were apparent in either group. Bruzzese et al. (2021) also noted statistically significant changes to oxygen uptake at second ventilatory threshold (VO2VT2) (p=.28), MHR (p=.04) and respiratory exchange ratio (RER) (p=.02).
In a case study by Barker-Davies et al. (2023) an elite runner recovering from COVID-19 experienced reduced work capacity and O2 uptake at AT 5 months after infection and occurred more rapidly than a previous CPET conducted 15 months earlier. Also, a decrease in workload by 27 watts (W) and a reduction of O2 uptake by 13% was also observed. When reviewing calorimetry, a 21% decrease in fat metabolism was observed and may explain the early onset to AT. Despite the decrements in performance, the absolute values of the CPET fell within normal range but the athlete complained of fatigue and difficulty generating power.

An investigation by Rajpal et al. (2021) which focused on cardiovascular magnetic resonance imaging (CMR) found incidence of current myocarditis or prior injury to the myocardium in almost 50% of 26 athletes recovered from COVID-19 (22). In another investigation by Maestrini et al. (2023), 2% of cardiac abnormalities were observed in 219 asymptomatic or mildly symptomatic athletes by Maestrini et al. (2023). Moreover, 3.3% of study participants demonstrated cardiac disturbances that included pericarditis and myopericarditis by Cavigli and colleagues. Juhász et al. (2023) also provided evidence that about 3% of recovered athletes had evidence of myocarditis or pericardial effusion. The authors also mentioned that persistent symptoms of COVID-19, like fatigue and chest pain, were factors that restricted players from RTP. Further, the disturbances seemed to be prevalent only among female athletes who had mild symptomatic COVID-19 infection. Additionally, these cardiac disturbances were determined during CPET testing and ECG monitoring. Biomarkers for cardiac disturbances, arrhythmias and structural abnormalities in the heart were also very low in the study by Sridi-Cheniti et al. (2022). Lastly, Cavigli et al. (2021) also observed that no athletes with asymptomatic COVID-19 infection demonstrated any cardiac complications.

Conversely, all athletes participating in an investigation by Fikenzer et al., (2021) had fluid accumulation in the pericardium (pericardial effusion) and magnetic resonance imaging (MRI) with high T1- and T2- values had a reduced maximal load, maximal O2 uptake, a higher HR at comparable exertion, and a significantly reduced O2 pulse when compared to previous testing. The changes to cardiac muscle in HR and O2 pulse were visible at moderate intensities, while the cardiopulmonary effects became apparent during higher intensities. Additionally, the respiratory minute volume which is used as a constraint of pulmonary function was considerably reduced. Malek and colleagues noted that 28 Olympic athletes recovered from COVID-19 infection did not appear to have any acute myocarditis findings after MRI testing. However, 5 of the subjects did show cardiac abnormalities. These individuals were all able to fully recover and RTP safely. Lastly, a case study by Nedeljkovic et al. (2021) observing native CMR images of an athlete recovered from asymptomatic COVID-19 infection demonstrated no signs of inflammation to the cardiac tissue. However, after contrast application, the indication of focal myocarditis became apparent where the athlete was advised to cease training for 3 months. Further, this individual continued to present with signs of myocarditis and decreased functional ability at a 3 month follow up visit.

Individuals like National Football League player Myles Garrett, National Basketball Association player Jayson Tatum and Major League Baseball player Yoan Moncada all experienced symptoms of fatigability (33). Because of this, Walker et al. (2023) compared the mean Pro Football Focus (PFF) game scores before and after a COVID-19 infection in players to examine performance. When analyzed by position before and after infection, statistically significant decreases in the numbers of snaps per game were observed in Defensive Backs (p=.03) and statistically significant decreases were further observed for mean scores in Defensive Linemen (p=.03). Additionally, similar findings were observed by Savicevic et al. (2021) in professional soccer players that were recovered from COVID-19 infection and completed RTP protocols where players demonstrated a decrease occurrence of high intensity accelerations and decelerations in game performance (p=.04).

Neuromuscular disturbances affecting balance may be another complication arising from COVID-19 as observed by Fernández-Rodríguez et al. (2023) which evaluated six handball players 1 month post infection and demonstrated degradation to static balance. Mild sleep disturbances were observed to affect 31% of individuals testing positive for COVID-19 by Śliż et al. (2023) and the sleep disturbances appear to influenced endurance athletes while performing CPET. Endurance athletes that experienced decreased sleep times experienced significant parameter changes in breath rate, pulmonary VE and BL concentration at AT. The study further observed several CPET correlations in athletes with sleep disturbances and performance and include (1) disturbances in HR and RC, (2) higher pulmonary VE at AT, (3) maximum power output and maximal HR and (4) individual habit which including methods to cope with sleep disturbances. Of interest, Vollrath et al. (2022) observed that sleep disturbances increased during the course of their investigation. Lastly, the authors further described that the most persistent symptoms observed in athletes included insomnia, fatigue and neurocognitive disorders, which can cause impairments to memory, learning and decision making.
Probing the influence of COVID-19 strains on athletic performance, Stojmenovic et. al. (2023) demonstrated that athletes infected with the Omicron variant, the latest virus strain, had higher VO2 max when compared to athletes infected with the older variants, Wuhan and Delta. Athletes affected by the Omicron variant had better VE and higher O2/HR values when compared to the two previous strains, Wuhan and Delta. Further, O2 transport to skeletal muscle was also greater with the Omicron variant. No statistical difference was observed with MHR at the completion of CPET and during the 3-minute recovery. Of further interest, the early transition of aerobic to anaerobic metabolism, which has been observed in several studies with the Wuhan and Delta variants, was not present for the Omicron variant (29).

Stojmenovic et. al. and colleagues further observed values of HR at ventilatory anaerobic threshold (VAT) and RC that were much higher in the athletes who contracted the Omicron strain versus the groups of athletes which contracted the Wuhan or Delta strain (p=.01). Additionally, higher HR values at the VAT were observed with the Wuhan and Delta variants when compared to the Omicron variant (p=.001). The RER at lower intensities was greater among the Wuhan and Delta group (p=.001) which demonstrates a greater dependence toward carbohydrate as a fuel rather than fat and further indicating an inability to utilize O2 for energy production. The efficiency of O2 delivery was the greatest for athletes with the Omicron variant. Moreover, VEf, although within normal limits for all three strains, was the best for individuals recovered from Omicron which further highlights more effective O2 transport to the skeletal muscle. Also, this study demonstrated meaningful decreases in aerobic capacity for all COVID-19 strains. Deng et al. (2023) investigated the neuromuscular performance of the upper body and mental health in a group of vaccinated kayakers recovered from the Omicron variant. No decrements were evident in 1RM bench press about 22 days post infection. Mental health appeared to be intact.

In an investigation by Jafarnezhadgero et al. (2022) recreational female runners that were hospitalized for COVID-19 were able to maintain steady state running with similar HR as the control group but ran at slower paces than the control group (p=.0001). Further, running test in COVID-19 recovered female runners terminated early (p=.0001). Also, these individuals had longer foot contact time (p=.002), peak propulsion forces (p=.0004) and reductions in loading rate (p=.04). Another study by Toresdahl and colleagues explored a potential link to COVID-19 infection and increased chances for injury in recovered runners due to systemic inflammation. While the investigation relied on self-reported questionaries, the outcome presented finding that about 20% of 1947 study participants, which included both males and females, experienced injury after a positive COVID-19 infection that prevented them from running for at least one week.

Juhász, et al. (2023) also noted that females, when compared to men, were more likely to suffer from short term prolonged symptoms of COVID-19 infection (34% vs. 19%, p = 0.005). However, females conveyed information through study surveys which indicated that they were able to regain peak form and maximal training strength faster than their male counterparts (3 vs. 4 weeks, p = 0.01). Further, LC was statistically significant with age groups in the study, with older age groups experiencing LC and severe symptoms more than their younger counterparts (p= .02%).

Gattoni et al. (2022) noted significantly lower performance outcomes among soccer players recovered from COVID-19 infection (p<.01). Additionally, no cardiopulmonary or cardiovascular abnormalities were present among test subjects. Also, while no statistical significance was observed for cardiopulmonary abnormalities, individual impairments were noted.
Of 26 elite athletes and 20 physically trained individuals (average age 30) participating in a study by Brito and authors, 65% of them continued to have persisting symptoms approximately 2-3 weeks after COVID-19 diagnosis, with the most frequent symptom being dyspnea (or shortness of breath). Additionally, participants with symptomatic illness showed statistically significant impairment to minute ventilation/CO2 production (VE/VCO2) slope (p < 0.001), partial pressure of CO2 (PETCO2) rest (p = 0.007), and PETCO2 max (p = 0.009) when compared to asymptomatic individuals. However, expiratory air flow/tidal volume occurred more often among asymptomatic individuals (p = 0.012). Lastly, impairments during CPET did not differ between symptomatic and asymptomatic individuals.

Discussion
With increased research regarding the influence of COVID-19 infection to athletic performance, new information is emerging, and prior implications of significant cardiac involvement have been quelled. The concern for myocarditis and sport related cardiac complications lies in fears of sudden cardiac death due to high intensity workloads, but these complications in athletes who are typically healthy and young with asymptomatic or mild symptoms COVID-19 infection are low, yet the risk does exist (7, 23). Occurrences of myocarditis, pericarditis, intolerance to exercise, fatigue and shortness of breath in athletes presented the need for more regular medical examinations and screening post infection not only to conserve athletic capabilities, but to also prevent the possibility of sudden cardiac death (SCD) (29). Further, some of the cardiac issues uncovered with CPET and cardiovascular testing with RTP protocol may have been preexisting conditions in athletes which had no relationship to COVID-19 (3). For this reason alone, CPET testing and cardiovascular screening is recommended for all athletes (3). To complicate matters, changes to the heart muscle can occur in athletes due to adaptations resulting from exercise quantity and intensities that are necessary to maintain athletic performance and may make testing athletes at resting conditions to be counterproductive (22, 32).


Cardiac abnormality involvement in athletes recovered from COVID-19 is inconsistent (18). Clinical cardiac events in elite and high-level athletes after mild or asymptomatic infection are very low even after resuming high-level training (27). Because of the low prevalence of cardiac complications associated with COVID-19 infection, the use of resonance CMR has been suggested to be reserved as a screening tool for athletes that may be at risk for cardiovascular abnormalities, although cardiac screening in athletes was suggested to be performed at least once to help detect underlying heart abnormalities (3, 17).


COVID-19 seems to affect the cardiorespiratory system more than the cardiovascular system (19). Several studies have observed an early switch to anaerobic metabolism during CPET. The greater recruitment of anaerobic metabolism at a specific workload can help to explain the inability of athletes to develop a significant power output during exertion, most probably due to fatiguability (4). Further, a study by Ajaz et al., observed decreased cellular respiration in hospitalized COVID-19 patients when the glucose pathway for energy was blocked. Additionally, Stavrou et al. (2023) also emphasized this to be a deficiency in the aerobic pathway for energy production as ventilation increased during physical exertion. Keller and colleagues suggested that the limitations to performance are directly related to delivery of O2 to muscles tissue rather than occurring from cardiac complications. Also, Jafarnezhadgero et al. (2022) determined that decreased performance during running tests were caused by deficits in O2 transport rather than fatigue, and they did not affect running mechanics in the study participants which recovered from COVID-19. Finally, these ideas are further supported by Wezenbeek and colleagues, who believe that COVID-19 infection can cause disruption to capillary blood flow, thus limiting the uptake of O2.


Of interest, a study by Ajaz et al. (2021) observed decreased cellular respiration in hospitalized COVID-19 patients when the glucose pathway for energy was blocked. Additionally, cellular respiration in the healthy control group and a separate group with various chest infections not related to COVID-19 did not exhibit any augmentation to cellular respiration. Also, in all three groups, no changes were present when other energy pathways with glutamine and long chain fatty acids were blocked. Further, Ajaz and authors believe the dependency on glucose may explain the early shift from aerobic to anaerobic metabolism observed in some athletes post COVID-19 infection. Moreover, the authors consider that mitochondrial dysfunction from COVID-19 infection is responsible for the preference of cells to utilize the process of glycolysis for cellular respiration and energy production. Lastly, greater metabolism of carbohydrates may have more negative implications in female athletes since females are more reliant on fat metabolism than men (4).


Although testing parameters in a study by Taralov et al. (2021) demonstrated no statistical significance in CPET and blood testing before and after COVID-19 infection in athletes, many continued to complain of persisting fatigue for several months. By examining the individual differences among the small sample group, the authors were able to detect small changes in performance. For example, a 30 second shorter CPET time post infection with time with similar MHR and intensity values before infection may be indicative of fatigue. Additionally, another test subject exhibited higher recovery HR parameters post infection which can be indicative of a reduced capacity for recovery. Further, AT was reached earlier after acute infection. Taralov and authors further emphasized that these findings can be significant during competition. While these changes seem small when comparing test results, these small differences can make large differences during competition by increasing fatigability and decreased recovery capacity that can have a negative impact to performance.


Recovery from persistent COVID-19 infection sequelae can take several months. Parpa & Michaelides (2022) concluded that 2 months of recovery post infection may not be sufficient for athletes, especially since some symptoms are not detectable at rest. Subsequently, post COVID-19 infection can cause reduced VO2 peak during exercise testing and increases in blood pressure during exercise despite presenting normal findings at rest reinforces the need for return to play testing in athletes (14). Additionally, even mild illness in athletes who have non-significant differences in VO2max when compared to non-infected individuals will experience aerobic burdens, which will display strains in performance and respiration (28). Lastly, the authors recommended that factors like VT, RC and HR and running speed should be observed during VO2max and respiratory threshold (RT).


One of the primary reasons for performance decrement may be due to detraining from COVID-19 infection and the necessary forced rest (19). Declines in VO2max with detraining have been observed in as little as 12 days and are caused by decreased stroke volume and arteriovenous gas exchange due to decreased volume of plasma from decreased exercise exertion (19). In addition, decreases in mitochondrial density have been observed after three weeks of exercise with no changes in muscle capillarization (19). Finally, cessation of exercise in 42-85 days has been noted to change the oxidative capacity of intermediate type IIa muscle fibers toward type IIb muscle fibers (19).


Moreover, an increase in VE/VCO2 slope is suggestive of intolerance to exercise along with cardiovascular or cardiopulmonary disease (15). Komici et al., (2023) did not believe that deconditioning was associated with ViE from their ventilatory parameters because the slope of VE/VCO2 appeared similar within all groups of athletes recovered from COVID-19 in their study. However, a perceived inverse correlation among ventilatory efficiency slope (VCO2/VE) maximum and ventilatory equivalents for O2 (VO2/VE) maximum among test subjects was suggestive of a perfusion mismatching in ventilation which is indicative to ViE (15). While the mechanisms involved in their ViE were not clearly understood, an inverse relation was observed between maximum volume of CO2 during ventilatory exchange and the volume of max O2 during ventilatory exchange and may have implications to a mismatching of O2 (24). Moreover, cardiorespiratory deficits have been attributed to muscle deconditioning in patients, not athletes, when decreased ventilatory response and early AT was observed in post COVID-19 patients (24). Athletes recovered from COVID-19 infection may demonstrate shortness of breath but can also have reduced pulmonary capacity and cardiac symptoms only detectable during sub-maximal conditions which can result in reduced physical capacity (10,15, 25). Moreover, increases in HR at VT and RC may be a response in the cardiovascular system resulting from hypoxemia, which has been observed as a mismatching of gas exchange in several studies (21). To conclude, understanding how to assist athletes with regaining pre-COVID-19 infection performance is not only important for a safe return to play, but for performance too.


Sleep is important for the body to function properly, and can affect attitude, breathing, pulmonary VE, memory impairment, stress tolerance, BL concentrations, glycogen recovery, metabolic processes and immune function (26). In addition, reaction times, accuracy, perceptual abilities, skill performance, strength, power, endurance and overall athletic performance can be affected by sleep disturbances and may not allow adequate recovery from physical exertion (26, 28). Finally, lack of sleep and decreased ability for recovery may increase injury risk because of slower reaction times and decreased perceptual abilities (28).


Also, Jafarnezhadgero et al. (2022) suggested that COVID-19 infection may also alter rates of perceived exertion which can possibly affect running biomechanics. Further, Toresdahl et al. (2022) observed a potential cellular musculoskeletal deterioration from systemic inflammation of COVID-19 in a group of endurance runners. Since this investigation used only questionnaires, more research is necessary to confirm if the outcomes were a result of cellular musculoskeletal deterioration or if they were a result of deconditioning due to forced rest associated with illness which could be responsible for developing muscle weakness or neuromuscular control.


With the emergence of COVID-19 strains, understanding the symptomatology before and after disease is important for the determination of athletic integrity among individuals in sports (28). Fortunately, the emergence of new COVID-19 variants appears to have diluted pathologies or symptomatologies (8, 29). However, the authors emphasized that all athletes affected with the COVID-19 variants exhibited decreased collected values in VO2max. Stojmenovic and authors examined the effects of COVID-19 virus variants when compared to healthy athletes which never tested positive for COVID-19. Furthermore, testing was conducted during the athletic season where athletic capacity should be optimal. Additionally, Stojmenovic et. al. (2023) observed an adequate supply of O2 to the muscle in all three groups during testing and speculate an inefficiency with mitochondrial or cellular respiration caused by COVID-19 infection. Finally, Bruzzese and colleagues noted that although significant performance differences during CPET were observed in athletes pre and post COVID-19 infection, significant work intensities were attained.


Static balance is a skill for all sports and may help increase strength, power and speed (9). Fernández-Rodríguez and colleagues suggested that decrements to static balance may be due to the neurological impairment of sensory processing that may occur with COVID-19 infection. Moreover, sensory processing in sports is important for the cognitive control of decision making, planning of movement, organization of movement, thought planning and actual execution of performance (9). Lastly, other reasons for balance decrement can include mental health issues like depression, anxiety, inability to make decisions, fatigue and lack of sleep, cardiorespiratory impairments, or simply the forced break after infection with limited physical activity (9).


Bruzzese et al. (2023) suggested that a decrease in volume of O2 at second forced expiratory volume (FEV2) in evaluated athletes was a result of detraining from forced rest and isolation due to a positive COVID-19 diagnosis. Additionally, a case study by Barker-Davies et al. (2023) suggested that deconditioning due to imposed rest was a potential reason that might explain performance decrements. However, the individuals observed presented with normal stroke volume and cardiac output and values did not decrease as they would in a deconditioned individual. The authors further hypothesized that decreases in performance may also be a result of mitochondrial dysfunction, which has been observed with COVID-19 infection. Mitochondrial dysfunction is the result of the cells possessing an increased dependence of glucose rather than fat metabolism (4). Further, the authors explain that after calorimetry data review, the larger ratio of the metabolism of anaerobic to aerobic pathways may be another possible explanation for perceived decreases in power output. Finally, women appear to be more dependent on fat metabolism than men, thus reductions in aerobic pathways will probably have a greater impact on women (4).


Unfortunately, RTP for some athletes may not be an option because of persistent sequelae due to COVID-19 illness (12). Organizations like the National Strength and Conditioning Association and the Collegiate Strength and Conditioning Coaches Association Joint committee have recommended a gradual RTP which involves low intensity exercise once symptoms have subsided (12). From current studies, persisting sequalae among athletes post COVID-19 infection appear to resolve in 3-4 months and incidences of LC lasting more than three months was very low (3, 17, 23). Because the physical long-term health implications of COVID-19 to athletes are not fully understood and research is limited, an ambivalence for RTS protocols exists (11).
Currently, RTP protocols, which assist athletes to fully recover from illness, ranges from 1-4 weeks depending on severity of COVID-19 and generally have not included exercise stress (17, 25). Exercise should not be continued among symptomatic players that continue to experience persistent fever, dyspnea at rest, cough, chest pain, or palpitations, since high intensity exercise may increase inflammation and advance the rate of viral replication therefore negatively impacting immunity to exacerbate or even lengthen duration of illness (7, 19). Conversely, moderate exercise intensity has been noted to have positive effects on immunity (19).


Performance can be limiting as some athletes, specifically those with cardiac symptomatology, will require several months to clear symptoms and can lead to deconditioning, specifically to power and VO2max(5). Savicevic et al. (2021) noted absences resulting from COVID-19 infection ranged from 7-91 days and can have implications to detraining. Unfortunately, forced breaks in training due to COVID-19 illness may be the reason for decreases in mitochondrial functioning, which will decrease the oxidative capacity of the muscle and capabilities (12). Further, the lack of energy supply, coupled with possible decreases in oxygen transport, suggested to be a common consequence of COVID-19 infection, may contribute to fatigue during performances in sports (12). Additionally, voluntary skeletal muscle function and activation can also be compromised under circumstances of fatigue and can further precipitate early onset of fatigue and alter biomechanics of movement (12). However, the effects of detraining among elite athletes lasting less than 28 days have been observed to have non-significant effects on neuromuscular functioning (8).


Even with decrements in VO2max from COVID-19 infection, different sports have varying uses for aerobic capacity (21). For instance, sports like basketball and tennis rely primarily on anaerobic energy pathways, but rely on aerobic fitness for recovery, and resynthesis of phosphocreatine for the ATP-PC (21). Conversely, a sport like soccer will rely heavily on aerobic fitness with total distances covered by players in a game can range between 9-14 kilometers (21). Further, distance covered in a 90-minute soccer game is dependent on VO2max and lactate threshold and metabolite removal/recoverability (21). Additionally, with reference to team sports, Savicevic et al. stressed the fact that a decline in the performance of one team player could affect the performance of the entire team. Lastly, using CPET can be beneficial to observe athlete responses to high intensity demands and help distinguish between the effects of detraining or cardiorespiratory inefficiency from illness (21).

Conclusion
To conclude, COVID-19 infection does appear to affect athletes adversely and may last for several months. Although small, these differences could affect team success or individual success in sports. Additionally, some athletes recovered from acute COVID-19 infection continue to feel fatigued under physical exertions even when medical screening, physical fitness tests and power output results were within normal limits and may cause limitations during athletic performance. Individuals experiencing these symptoms of fatigue after a short-forced rest may be a result of viral infiltration resulting in mitochondrial dysfunction, while longer forced rest times may be contributed to deconditioning along with metabolic deficiencies. Fortunately, these issues appear to be reversible as observed with Babity et. al. (2022), where athletes were observes to have better CPET values post infection with a rigorous retraining protocol. Lastly, further research on decrements during competitive performance is necessary to fully understand the true effects of the virus infiltration among athletes since laboratory conditions cannot replicate the actual competitive environment.

Applications to Sport
Due to the complicated nature of COVID-19 and slow recovery associated with persistent fatigue which may be a result from a possible disconnect to pulmonary efficiency, capillary perfusion or mitochondrial function, screening for exertional stressors during athletic performance is highly recommended with CPET and spirometry. Further, the problematic physical circumstances of COVID-19 illness can prevent athletes from returning to sport at physically competitive levels. Individualized gradual RTP is recommended to acclimatize athletes to the high intensity demands of sports since small decrements to performance can produce negative consequential outcomes during play in competitive sports.

Limitations
There were several limitations to this review. First, many of the studies conducted had small sample sizes. Second, most of the testing was conducted with male athletes. Third, limited data was available from CPET and cardiac screening before infection among test subjects which did not allow for comparative investigations. Also, since COVID-19 is a relatively new epidemic and disease, limited data is available, especially among the athletic population and vaccinated individuals. Additionally, data varies with respect to recovery times and physical conditioning as some testing was conducted after RTP or during the competitive season. Lastly, very limited data investigating strength and power was available and is of interest since many decrements to performance were observed during high intensity exercises in a few investigations.

Acknowledgements
Special thanks to Drs. Frank Spaniol and Dr. Donald Melrose for all their support and advice.

  1. Ajaz S., McPhail M. J., Singh K. K., Mujib, S., Trovato, F. M., Napoli, S., and Agerwal, K.
    (2021). Mitochondrial metabolic manipulation by SARScov-2 in peripheral blood
    mononuclear cells of patients with COVID-19. Am J Physiol Cell Physiol, 320:C57–65
    crook
  2. Anastasio F, La Macchia T, Rossi G, D’Abbondanza M, Curcio R, Vaudo G and Pucci, G.et
    al. (2022). Mid-term impact of mild-moderate COVID-19 on cardiorespiratory fitness in élite athletes. J Sports Med Phys Fitness, 62:1383-90. DOI: 10.23736/S0022-4707.21.13226-8
  3. Babity, M., Zamodics, M., Konig, A., Kiss, A. R., Horvath, M., Gregor, Z., Rakoczi, R.,
    Kovacs, E., Fabian, A., Tokodi, M., Sydo, N., Csulak, E., Juhasz, V., Lakatos, B. K., Vago, H., Kovacs, A., Merkely, B., & Kiss, O. (2022). Cardiopulmonary examinations of athletes returning to high-intensity sport activity following SARS-CoV-2 infection. Scientific Reports, 12(1), 21686-21686. https://doi.org/10.1038/s41598-022-24486-x
  4. Barker-Davies, R. M., Ladlow, P., Chamley, R., Nicol, E., & Holdsworth, D. A. (2023).
    Reduced athletic performance post-COVID-19 is associated with reduced anaerobic threshold. BMJ Case Reports CP, 16(2), e250191.
  5. Brito, G.M., Prado, D.M.L.D., Rezende, D.A., de Matos, L.D.N.J., Loturco, I., Vieira,
    M.L.C., de Sá Pinto, A.L., Alô, R.O.B., de Albuquerque, L.C.A., Bianchini, F.R. and Pinto, A.J. (2023). The utility of cardiopulmonary exercise testing in athletes and physically active individuals with or without persistent symptoms after COVID-19. Frontiers in medicine, 10, 1128414
  6. Bruzzese, M.F., Bazán, N.E., Echandía, N.A. and Garcia, G.C. (2023). Evaluation of
    maximal oxygen uptake pre-and post-COVID-19 in elite footballers in Argentina. doi: 10.18176/archmeddeporte.00138
  7. Cavigli, L., Frascaro, F., Turchini, F., Mochi, N., Sarto, P., Bianchi, S., Parri, A., Carraro, N.,
    Valente, S., Focardi, M. and Cameli, M. (2021). A prospective study on the consequences of SARS-CoV-2 infection on the heart of young adult competitive athletes: implications for a safe return-to-play. International journal of cardiology, 336, 130-136.
  8. Deng, S., Deng, J., Yin, M., Li, Y., Chen, Z., Nassis, G.P., Zhu, S., Hu, S., Zhang, B. and Li,
    Y. (2023). Short-term effects of SARS-CoV-2 infection and return to sport on neuromuscular performance, body composition, and mental health—A case series of well-trained young kayakers. Journal of Exercise Science & Fitness, 21(4), 345-353.
  9. Fernández-Rodríguez, E., Niźnikowski, T., Ramos, O. R., & Markwell, L. (2023). Effect of
    COVID-19 on maintaining balance in highly skilled handball players. Polish Journal of Sport and Tourism, 30(3), 18-22. https://doi Śliż.org/10.2478/pjst-2023-0015
  10. Fikenzer, S., Kogel, A., Pietsch, C., Lavall, D., Stöbe, S., Rudolph, U., Laufs, U., Hepp, P.,
    & Hagendorff, A. (2021). SARS-CoV2 infection: functional and morphological cardiopulmonary changes in elite handball players. Scientific reports, 11(1), 17798. https://doi.org/10.1038/s41598-021-97120-x
  11. Gattoni, C., Conti, E., Casolo, A., Nuccio, S., Baglieri, C., Capelli, C. and Girardi, M.
    (2022). COVID‐19 disease in professional football players: symptoms and impact on pulmonary function and metabolic power during matches. Physiological Reports, 10(11), e15337.
  12. Jafarnezhadgero, A. A., Noroozi, R., Fakhri, E., Granacher, U., & Oliveira, A. S. (2022).
    The Impact of COVID-19 and Muscle Fatigue on Cardiorespiratory Fitness and Running Kinetics in Female Recreational Runners. Frontiers in physiology, 13, 942589.
  13. Juhász, V., Szabó, L., Pavlik, A., Tállay, A., Balla, D., Kiss, O., … & Vágó, H. (2023). Short
    and mid‐term characteristics of COVID‐19 disease course in athletes: A high‐volume, single‐center study. Scandinavian Journal of Medicine & Science in Sports, 33(3), 341-352.
  14. Keller, K., Friedrich, O., Treiber, J., Quermann, A., & Friedmann-Bette, B. (2023). Former
    SARS-CoV-2 infection was related to decreased VO2 peak and exercise hypertension in athletes. Diagnostics (Basel), 13(10)
  15. Komici, K., Bianco, A., Perrotta, F., Dello Iacono, A., Bencivenga, L., D’Agnano, V., Rocca,
    A., et al. (2021). Clinical Characteristics, Exercise Capacity and Pulmonary Function in Post-COVID-19 Competitive Athletes. Journal of Clinical Medicine, 10(14), 3053. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jcm10143053
  16. Komici, K., Bencivenga, L., Rengo, G., Bianco, A., & Guerra, G. (2023). Ventilatory
    efficiency in post‐COVID‐19 athletes. Physiological Reports, 11(18), e15795-e15795. https://doi.org/10.14814/phy2.15795
  17. Maestrini, V., Penza, M., Filomena, D., Birtolo, L.I., Monosilio, S., Lemme, E., Squeo,
    M.R., Mango, R., Di Gioia, G., Serdoz, A. and Fiore, R. (2023). Low prevalence of cardiac abnormalities in competitive athletes at return-to-play after COVID-19. Journal of Science and Medicine in Sport, 26(1), 8-13.
  18. Małek, Ł. A., Marczak, M., Miłosz‐Wieczorek, B., Konopka, M., Braksator, W., Drygas, W.,
    & Krzywański, J. (2021). Cardiac involvement in consecutive elite athletes recovered from Covid‐19: A magnetic resonance study. Journal of Magnetic Resonance Imaging, 53(6), 1723-1729. doi:10.1002/jmri.27513
  19. Milovancev, A., Avakumovic, J., Lakicevic, N., Stajer, V., Korovljev, D., Todorovic, N.,
    Bianco, A., Maksimovic, N., Ostojic, S., Drid, P. (2021). Cardiorespiratory Fitness in Volleyball Athletes Following a COVID-19 Infection: A Cross-Sectional Study. International Journal of Environmental Research and Public Health. 18, 4059. doi 10.3390/ijerph18084059.
  20. Nedeljkovi, I. P., Giga, V., Ostojic, M., Djordjevic-Dikic, A., Stojmenovic, T., Nikolic, I.,
    Dikic, N., Nedeljkovic-Arsenovic, O., Maksimovic, R, Dobric, M, Mujovic, M., & Beleslin, B. (2021). Focal Myocarditis after Mild COVID-19 Infection in Athletes (Case Report). Diagnostics, 11, 1519. doi 10.3390/diagnostics11081519
  21. Parpa, K., & Michaelides, M. (2022). Aerobic capacity of professional soccer players before
    and after COVID-19 infection. Scientific Reports, 12(1), 11850-1850. https://doi.org/10.1038/s41598-022-16031-7
  22. Rajpal, S., Tong, M. S., Borchers, J., Zareba, K. M., Obarski, T. P., Simonetti, O. P., &
    Daniels, C. J. (2021). Cardiovascular Magnetic Resonance Findings in Competitive Athletes Recovering From COVID-19 Infection. JAMA cardiology, 6(1), 116–118.
  23. Rasmusen, H.K., Aarøe, M., Madsen, C.V., Gudmundsdottir, H.L., Mertz, K.H., Mikkelsen,
    A.D., Dall, C.H., Brushøj, C., Andersen, J.L., Vall-Lamora, M.H.D. and Bovin, A. (2023). The COVID-19 in athletes (COVA) study: a national study on cardio-pulmonary involvement of SARS-CoV-2 infection among elite athletes. European Clinical Respiratory Journal, 10(1), 2149919.
  24. Rinaldo, R. F., Mondoni, M., Parazzini, E. M., Pitari, F., Brambilla, E., Luraschi, S., Balbi,
    M., Sferrazza Papa, G. F., Sotgiu, G., Guazzi, M., Di Marco, F., & Centanni, S. (2021). Deconditioning as main mechanism of impaired exercise response in COVID-19 survivors. The European Respiratory Journal, 58(2), 2100870. https://doi.org/10.1183/13993003.00870-2021
  25. Savicevic, A. J., Nincevic, J., Versic, S., Cuschieri, S., Bandalovic, A., Turic, A., Becir, B.,
    Modric, T., & Sekulic, D. (2021). Performance of professional soccer players before and after COVID-19 infection; observational study with an emphasis on graduated return to play. International Journal of Environmental Research and Public Health, 18(21), 11688.
  26. Śliż, D., Wiecha, S., Gąsior, J.S., Kasiak, P.S., Ulaszewska, K., Lewandowski, M., Barylski,
    M. and Mamcarz, A. (2023). Impact of COVID-19 Infection on Cardiorespiratory Fitness, Sleep, and Psychology of Endurance Athletes—CAESAR Study. Journal of Clinical Medicine, 12(8), 3002.
  27. Sridi Cheniti, S., Benhenda, S., Doutreleau, S., Cade, S., Guerard10, S., Guy11, J.M.,
    Trimoulet12, P., Picard13, S., Dusfour, B., Pouzet14, A. and Roseng, S. (2022). Resuming Training in High-Level Athletes After Mild COVID-19 Infection: A Multicenter Prospective Study (ASCCOVID-19).
  28. Stavrou, V.T., Kyriaki, A., Vavougios, G.D., Fatouros, I.G., Metsios, G.S., Kalabakas, K.,
    Karagiannis, D., Daniil, Z., Gourgoulianis, K.I. and Βasdekis, G. (2023). Athletes with mild post-COVID-19 symptoms experience increased respiratory and metabolic demands: Α cross-sectional study. Sports Medicine and Health Science, 5(2), 106-111.
  29. Stojmenovic, D., Stojmenovic, T., Andjelkovic, M., Trunic, N., Dikic, N., Kilibarda, N.,
    Nikolic, I., Nedeljkovic, I., Ostojic, M., Purkovic, M. and Radovanovic, J. (2023). The Influence of Different SARS-CoV-2 Strains on Changes in Maximal Oxygen Consumption, Ventilatory Efficiency and Oxygen Pulse of Elite Athletes. Diagnostics, 13(9), 1574. Retrieved from http://dx.doi.org/10.3390/diagnostics13091574
  30. Taralov, Z., Dimov, P., Gruev, I., Marinov, B., & Kostianev, S. (2021). Mild Case of
    COVID-19 Do Not Affect the Cardiorespiratory Fitness of Elite Bulgarian Football Players. Science and Research.
  31. Toresdahl, B. G., Robinson, J. N., Kliethermes, S. A., Metzl, J. D., Dixit, S., Quijano, B., &
    Fontana, M. A. (2022). Increased incidence of injury among runners with COVID-19. Sports Health, 14(3), 372-376.doi: 10.1177/19417381211061144. PMID: 34906009; PMCID: PMC9112708.
  32. Vollrath, S., Bizjak, D.A., Zorn, J., Matits, L., Jerg, A., Munk, M., Schulz, S.V.W., Kirsten,
    J., Schellenberg, J. and Steinacker, J.M. (2022). Recovery of performance and persistent symptoms in athletes after COVID-19. Plos one, 17(12), e0277984.
  33. Walker, C. R., Belisario, J. C., & Abramoff, B. (2023). The effect of probable COVID-19
    infection on the national football league players’ performance and endurance during the 2020 season. Curēus (Palo Alto, CA), 15(3), e35821-e35821. https://doi.org/10.7759/cureus.35821
  34. Wezenbeek E, Denolf S, Bourgois JG, Philippaerts RM, De Winne B, Willems TM,
    Witvrouw E, Verstockt S, Schuermans J. Impact of (long) COVID on athletes’ performance: a prospective study in elite football players. Ann Med. 2023 Dec;55(1):2198776. doi: 10.1080/07853890.2023.2198776. PMID: 37126052; PMCID: PMC10134946.
2024-11-14T07:40:15-06:00November 15th, 2024|COVID-19, Research, Sports Exercise Science, Sports Health & Fitness|Comments Off on Title: The effects of COVID-19 infection on athletic performance: A systematic review

Prevalence of Normal Weight Obesity Amongst Young Adults in the Southeastern United States

Authors: 1Helena Pavlovic, 2Tristen Dolesh, 3Christian Barnes, 4Angila Berni, 5Nicholas Castro, 6Michel Heijnen, 7Alexander McDaniel, 8Sarah Noland, 9Lindsey Schroeder, 10Tamlyn Shields, 11Jessica Van Meter, and 12Wayland Tseh*

1Northern Kentucky University, Highland Heights, Kentucky, USA

AUTHORS INSTITUATIONAL AFFILIATION:

School of Health and Applied Human Sciences, University of North Carolina Wilmington, Wilmington, North Carolina, United States of America

Corresponding Author:

*CORRESPONDING AUTHOR:  

Wayland Tseh, Ph.D. 

University of North Carolina Wilmington 

School of Health and Applied Human Sciences 

601 South College Road 

Wilmington, North Carolina, 28403-5956 

Phone Number: 910.962.2484 

E-Mail: tsehw@uncw.edu 

ABSTRACT

‘Normal weight obesity (NWO) is characterized by a normal or low body mass index (BMI) alongside a high percentage of body fat, which increases the risk for hypokinetic diseases. This study aims to investigate the prevalence of NWO among a sample of young, non-sedentary adults. Two hundred and fifty-four apparently healthy volunteers (Age = 22.2 ± 7.2 yrs; Height = 171.5 ± 9.6 cm; Body Mass = 69.9 ± 13.4 kg) provided informed consent prior to participation. Body mass index was calculated by dividing body mass (kg) by height squared (m2). Body fat percentage was measured using the BODPOD® G/S, which utilizes air displacement plethysmography to accurately estimate body composition. Class I Obesity and Low/Normal BMI categorizations were defined by the American College of Sports Medicine. Data revealed that 12.2% of the overall sample exhibited NWO, with a higher prevalence among males (17.2%) compared to females (9.8%). The study also seeks to evaluate whether individuals with NWO face greater health risks than those with similar BMI but lower body fat percentages. From a practical perspective, identifying individuals with NWO is an opportunity for clinicians to proactively educate their clients regarding the health risks associated with hypokinetic disease(s).

KEYWORDS: Body Mass Index, BODPOD, Percent Body Fat, Normal Weight Obesity

INTRODUCTION

Within the United States, the prevalence of obesity has dramatically increased over the past 50 years given the ubiquitous obesogenic environment (31). In 2019, Ward and colleagues yielded compelling predictive insights indicating a trajectory wherein, by the year 2030, nearly 50% of adults will be afflicted by obesity (48.9%) with heightened prevalence exceeding 50% in 29 states, demonstrating a pervasive nationwide trend (50). Moreover, no state is anticipated to exhibit a prevalence below 35% (50). Projections also indicate that a substantial proportion of the adult population is anticipated to experience severe obesity, with an estimated 24.2% affected by 2030 (50). Against this backdrop, the predictive analyses conducted by Ward and associates (50) underscored the widespread and escalating severity of the obesity epidemic across the United States. These findings are indicative of an impending public health challenge, necessitating strategic interventions and policy considerations to mitigate the escalating burden of obesity and its associated health implications. When delineating the magnitude of obesity, clinicians and practitioners must employ precise instrumentation capable of quantifying a client’s body composition in terms of percentage body fat. Numerous methodologies exist for this purpose, encompassing hydrostatic weighing, bioelectrical impedance analysis, air displacement plethysmography, skinfold assessment, and dual-energy x-ray absorptiometry scan.

Drawing from antecedent research studies, dual-energy X-ray absorptiometry (DXA) is acknowledged as the clinical gold standard for appraising body composition (9, 10, 12, 21, 25, 26, 42, 47). However, a notable drawback of DXA lies in its emission of low-level radiation (6, 9, 32, 45, 47), thereby subjecting clients to unnecessary radiation exposure (1, 33). An alternative method is utilizing the BOD POD® Gold Standard (GS), which employs air displacement plethysmography to estimate body composition. Previous literature has heralded the BOD POD® GS as the applied, pragmatic gold standard for assessing body composition due to its validity (2, 7, 38), as well as its within- and between-day reliability (48). Additionally, owing to the BOD POD® GS’s facile and non-invasive procedures, most individuals can attain accurate measures of body composition values, specifically pertaining to percent body fat, enabling the discernment of pounds of fat-free mass and fat mass.

According to the American College of Sports Medicine (ACSM), males with a percent body fat ≥ 25% and females ≥ 32% (4) are predisposed to an elevated risk of developing a myriad of hypokinetic diseases, notably cardiovascular disease(s), metabolic syndrome, and cardiometabolic dysfunction (14, 27, 35, 37, 39, 40, 43, 44, 46, 51, 56). Another evaluative approach involves the calculation of Body Mass Index (BMI), derived from dividing body weight in kilograms by square of height in meters (4). Given the ease and efficiency of calculating BMI, the obesity-related classification in which it provides at the individual level is potentially flawed (3, 8, 22, 24, 41, 53, 56).

Presently, within the United States, a dearth of research exists on the prevalence of normal weight obesity (NWO) amongst apparently healthy young adults (11,52). Normal weight obesity is characterized by individuals exhibiting a low BMI (<18.5 kg∙m-2) or normal BMI (18.5 – 24.9 kg∙m-2) yet manifesting obesity-related percentage body fat values (male = ≥20%; female = ≥30%) (5, 14, 20, 36, 37, 40, 43, 44, 57). Individuals with low/normal BMI and high percentage body fat values face an augmented risk of hypokinetic diseases, as their seemingly normal exterior masks a deleteriously high amount of body fat beneath the surface layer. Previous research endeavors have revealed the prevalence of NWO amongst a population of South Americans (14, 34, 40, 44), Central Europeans (15), and Asians (28-30, 37, 54, 55, 57, 58). Given that most aforesaid research studies on NWO have been conducted internationally, it is of paramount interest to ascertain the prevalence of NWO domestically. Consequently, the primary objective of this research study is to investigate the prevalence of normal-weight obesity among a sample of ostensibly healthy males and females.

METHODS

Participants

All participants were required to report to the Body Composition Laboratory to complete a singular session. Before the participants arrived, volunteers were instructed to abstain from consuming caffeinated sustenance or beverages that may acutely influence body mass. Moreover, researchers advised participants to refrain from vigorous physical activity/exercise the night before and prior to their appointed session. Upon arrival, volunteers read and signed an informed consent form approved by the University’s Institutional Review Board for human subject use (IRB#: H23-0499). As displayed in Table 1, a cohort comprising 254 male and female volunteers were recruited to participate in this study.

Table 1. Descriptive characteristics (Mean ± SD) of all male and female participants (N = 254). 

Variables Overall (N = 254) Male (n = 101) Female (n = 153) 
Age (yrs) 22.2 ± 7.2 22.5 ± 7.7 22.0 ± 6.8 
Height (cm) 171.5 ± 9.6 179.5 ± 7.2 166.2 ± 7.0 
Body Mass (kg) 69.9 ± 13.4 79.9 ± 11.6 63.2 ± 10.0 

Below highlights the details of the singular Session required for each participant.

Body Mass Index (BMI)

Before each assessment, participants were asked to remove any unattached item(s) from their body, such as shoes, socks, rings, bracelets, and/or glasses. Height was measured to the nearest 0.5 cm as participants stood barefoot, with both legs together, with their back to a Seca 217 Mobile Stadiometer (Model Number 2171821009, USA). Body mass was measured on a Tanita Multi-Frequency Total Body Composition Analyzer with Column (Model DC-430U, Tanita Corporation, Japan) to the nearest 0.1 kg. Body mass index was calculated using body mass expressed in kilograms (kg) divided by height expressed in meters squared (m2). Body mass index categorizations, set forth via ACSM (4), for low BMI was (<18.5 kg∙m-2) and normal BMI was (18.5 – 24.9 kg∙m-2).

BOD POD® Gold Standard (GS)

BOD POD® Gold Standard (GS) (COSMED USA Inc., USA) was calibrated daily according to the manufacturer’s instructions with a 50.238 Liter cylindrical volume provided by COSMED USA Inc. Specific details illustrating the technicalities of the calibration mechanism are published elsewhere (16, 18). Because different clothing schemes have been shown to underestimate percentage body fat (%BF) results from the BOD POD® (19, 49), female participants were instructed to wear one- or two-piece bathing suit or sports bra and compression shorts, while male participants were instructed to wear form-fitted compression shorts. All participants wore a swim-like cap provided by COSMED USA Inc. After race, height, and age were inputted by a technician into the BOD POD® GS kiosk, participants were asked to step on an electronic scale to determine body mass to the nearest .045 kg. Once the BOD POD® GS system recorded body mass, participants were instructed to sit comfortably and breathe normally within the BOD POD® GS for two trials lasting 40 seconds per trial. A third trial was conducted if Trials 1 and 2 had high variability. Once both (or three) trials were conducted, body composition values, specifically, body mass, percent body fat, fat-free mass, and fat mass, were immediately displayed on the kiosk viewer and recorded by a technician. Once height, body mass, and body composition assessments were completed, participants dressed back into their original clothing and exited the Body Composition Lab.

Statistical Analyses

Descriptive statistics (mean ± SD) were derived to describe the sample population. A Chi-Square Goodness of Fit Test was used to determine the prevalence of low/normal BMI values with obesity-related percent body fat. For all analyses, statistical significance was established at p < 0.05.

RESULTS

At the conclusion of the study, 254 volunteers were recruited, and zero dropped out, therefore, all 254 participants’ results were included in the statistical analyses. Table 2 displays the descriptive measures of the study participants.

Table 2. Body Mass Index, Class I Obesity, and Percent Normal Weight Obesity Amongst Males and Females. 

 Total Male Female 
Participants 254 101 153 
Low BMI (≤ 18.4 kg∙m-2
Normal BMI (18.5 – 24.9 kg∙m-2181 58 123 
Class I Obesity (F ≥ 32%; M ≥ 25%) 22 10 12 
Masked Obesity 12.2% 17.2% 9.8% 
High BMI (≥ 25.0 kg∙m-271 43 28 

The chi-squared statistic was 1.886 (df = 1, p = 0.17) indicating no statistical difference in NWO between males (17.2%) and females (9.8%).

DISCUSSION

As stated previously, there is a dearth of data determining the prevalence of NWO domestically, more specifically, within the southeast region of the United States. Therefore, the primary objective of this research study was to investigate the frequency of NWO amongst a sample of apparently healthy individuals. Participants completed a singular data collection session whereby height, body mass, and percentage body fat were quantified via BOD POD® GS. Within this current study, low and normal BMI classifications were <18.5 kg∙m-2 and 18.5 – 24.9 kg∙m-2, respectively. Class I obesity for females and males were ≥ 32% and ≥ 25%, respectively. Given said thresholds, data revealed that 12.2% of the overall sample exhibited NWO, with a higher prevalence amongst males (17.2%) compared to females (9.8%). These findings are relatively comparable within other research investigating the prevalence of NWO amongst a sample of young adults (5, 35, 44, 57).

In 2017, Ramsaran and Maharaj investigated the prevalence of NWO within a cohort of 236 young adults (mean age = 21.3 ± 2.5 years). The quantification of %BF was accomplished using the Tanita Ironman body composition analyzer. Subsequent data analyses unveiled a heightened prevalence of NWO among the male participants (14.4%), surpassing their female counterparts (5.5%). The outcomes of the current study align with the findings reported by Ramsaran and Maharaj (44), wherein NWO manifested in 17.2% of males and 9.8% of females. A nuanced distinction between the two investigations lies in the designated thresholds for %BF. Ramsaran and Maharaj (44) set the elevated %BF thresholds at ≥ 23.1% for males and ≥ 33.3% for females. In contrast, the current study employed thresholds of ≥ 25.0% for males and ≥ 32.0% for females. Notwithstanding the marginal elevation (+1.9%) in the %BF threshold within the current study, males exhibited a greater prevalence (+2.8%) compared to Ramsaran and Maharaj’s (44) dataset. Conversely, the current study adopted a lower %BF threshold (–1.3%) for females and uncovered a higher prevalence of NWO (+4.4%). These subtle yet discernible variations in %BF thresholds may elucidate the divergent prevalence rates of NWO observed between the two scholarly investigations.

Akin to Ramsaran and Maharaj (44) and the present investigation, Anderson and colleagues (5) examined the incidence of NWO within a more modest cohort of 94 young adults (mean age = 19.6 ± 1.5 years). The quantification of %BF was assessed via DXA. The %BF thresholds were predicated on National Health and Nutrition Examination Survey standards, establishing obesity values of ≥ 30.0% for males and ≥ 35.0% for females. Findings elucidated an NWO prevalence in males (26.7%) and females (7.8%). Noteworthy is the marked elevation in male NWO rates (+9.5%) and marginal reduction (–2.0%) in female NWO rates compared to the current study. While discrepancies may be attributed to variances in sample size (254 in the present study vs. 94 in Anderson et al.), divergent methodologies for %BF assessment (utilizing BOD POD® GS presently as opposed to DXA in Anderson et al.), and distinct %BF thresholds (ACSM criteria in the current study versus NHANES in Anderson et al.), the overarching findings remain concordant. Specifically, data from all three research investigations underscore the consistent pattern wherein males manifest elevated NWO prevalence rates relative to their female counterparts.

In contradistinction to the two previous research investigations and the current study, Zhang et al. (57) explored the NWO prevalence amongst 383 young adults (mean age = 20.4 ± 1.6 years). Assessment of %BF was executed through bioelectrical impedance analyses (BIA) employing the InBody 720 device. Obesity classification was contingent upon threshold values of ≥20.0% for males and ≥30.0% for females, as established by Zhang and associates (57). Analyses unveiled an NWO prevalence of 13.2% in males and 27.5% in females, a prominent deviation from the present study’s findings. The contrasting NWO prevalence patterns observed between the two studies are notably discernible. Specifically, Zhang and colleagues (57) reported a higher prevalence in females than males, whereas the current investigation revealed the converse. This discordance is seemingly attributable to variances in the %BF thresholds implemented for obesity classification. Zhang et al. (57) utilized a considerably lower threshold for males at 20.0%, as opposed to the 25.0% threshold applied in the current study. Similarly, for females, Zhang et al. (57) employed a lower %BF threshold at 30.0%, whereas the present study utilized a more conservative threshold of 32.0%. Moreover, a salient methodological distinction lies in the apparatus employed for %BF quantification. The current study utilized the BOD POD® GS, acknowledged as the applied gold standard for assessing body composition, while Zhang et al. (57) employed the InBody 720 BIA. These methodological nuances likely contribute to the divergent findings between the present research and Zhang et al. (57), underscoring the importance of rigorously evaluating both threshold criteria and assessment modalities when interpreting and comparing NWO prevalence data.

In a recent investigation, Maitiniyazi et al. (35) endeavored to ascertain the prevalence of NWO within a cohort of 279 young adults (mean age = 21.7 ± 2.1 years). Percentage body fat was assessed utilizing the InBody 770 BIA method. Obesity classification thresholds were established at 20.0% for males and 30.0% for females. Parallel to the observed NWO patterns delineated by Zhang and colleagues (57), Maitiniyazi et al. also discerned a higher prevalence of NWO in females (40.1%) as opposed to males (25.5%). Notably, while these NWO trends align with the patterns identified by Zhang et al. (57), they markedly deviate from the outcomes of the current investigation. Such discordant findings may find elucidation in the nuanced disparities in the thresholds employed to categorize obesity and the instrumentation deployed for %BF quantification. Specifically, the divergence in %BF thresholds used for obesity classification emerges as a significant factor. Maitiniyazi et al. (35) employed thresholds different from those of Zhang et al. (57) and the current study, thereby contributing to the observed inconsistencies. Additionally, the equipment utilized to quantify %BF introduces another layer of methodological variation. While Zhang et al. (57) implemented InBody 720 BIA and the current study utilized BOD POD® GS, Maitiniyazi et al. deployed the InBody 770 BIA method. These divergent methodological approaches underscore the imperative of meticulous consideration when interpreting and comparing NWO prevalence data, highlighting the multifaceted nature of the interplay between obesity thresholds and assessment methodologies in elucidating NWO prevalence.

CONCLUSIONS

This comprehensive investigation contributes significantly to our understanding of NWO prevalence within a young adult population, particularly within the Southeast region of the United States. The study employed the BOD POD® GS for precise measurement of height, body mass, and percentage body fat, revealing a higher, but not statistically different, prevalence in NWO between males and females. These results align with similar studies collectively emphasizing the consistent pattern of elevated NWO prevalence in males relative to females. The study’s alignment with said research investigations further underscores the robustness of the findings, notwithstanding variations in sample size, methodology, and threshold criteria. Conversely, discrepancies with other research investigations highlight the sensitivity of NWO prevalence to %BF thresholds and assessment modalities. Despite the divergence in outcomes, these studies collectively reinforce the need for careful consideration of methodological nuances in interpreting and comparing NWO prevalence data.

APPLICATION IN SPORTS

From a practical perspective, the findings emphasize the importance of incorporating regional and demographic variations when assessing NWO prevalence. Furthermore, the study underscores the relevance of employing standardized methodologies in ensuring consistency and comparability across investigations. Future endeavors in this domain should continue to explore regional variations, refine %BF threshold criteria, and employ advanced methodologies for accurate NWO characterization. This knowledge is pivotal for tailoring preventive measures and interventions; more precisely, accurately identifying NWO individuals is an opportunity for clinicians to proactively educate their clients regarding the health risks associated with hypokinetic disease(s), particularly cardiovascular disease(s), metabolic syndrome, and cardiometabolic dysfunction.

ACKNOWLEDGMENTS

The author would like to personally thank the following research assistants that contributed to the success of this research investigation: Tristen, Brennan, Marisa, Maddie, Samantha, Caylin, and Ethan.

REFERENCES

1.Alawi, M., Begum, A., Harraz, M., Alawi, H., Bamagos, S., Yaghmour, A., & Hafiz, L. (2021). Dual-Energy X-Ray Absorptiometry (DEXA) Scan Versus Computed Tomography for Bone Density Assessment. Cureus, 13(2), e13261. https://doi.org/10.7759/cureus.13261

2.Alemán-Mateo, H., Huerta, R. H., Esparza-Romero, J., Méndez, R. O., Urquidez, R., & Valencia, M. E. (2007). Body composition by the four-compartment model: validity of the BOD POD for assessing body fat in Mexican elderly. European journal of clinical nutrition, 61(7), 830–836. https://doi.org/10.1038/sj.ejcn.1602597

3.Alqarni, A. M., Aljabr, A. S., Abdelwahab, M. M., Alhallafi, A. H., Alessa, M. T., Alreedy, A. H., Elmaki, S. A., Alamer, N. A., & Darwish, M. A. (2023). Accuracy of body mass index compared to whole-body dual energy X-ray absorptiometry in diagnosing obesity in adults in the Eastern Province of Saudi Arabia: A cross-sectional study. Journal of family & community medicine, 30(4), 259–266. https://doi.org/10.4103/jfcm.jfcm_85_23

4.American College of Sports Medicine. (2020). ACSM’s guidelines for exercise testing and prescription (11th ed.). Wolters Kluwer.

5.Anderson, K. C., Hirsch, K. R., Peterjohn, A. M., Blue, M. N. M., Pihoker, A. A., Ward, D. S., Ondrak, K. S., & Smith-Ryan, A. E. (2020). Characterization and prevalence of obesity among normal weight college students. International journal of adolescent medicine and health, 35(1), 81–88. https://doi.org/10.1515/ijamh-2020-0240

6.Bachrach L. K. (2000). Dual energy X-ray absorptiometry (DEXA) measurements of bone density and body composition: promise and pitfalls. Journal of pediatric endocrinology & metabolism: JPEM, 13 Suppl 2, 983–988.

7.Ballard, T. P., Fafara, L., & Vukovich, M. D. (2004). Comparison of Bod Pod and DXA in female collegiate athletes. Medicine and science in sports and exercise, 36(4), 731–735. https://doi.org/10.1249/01.mss.0000121943.02489.2b

8.Batsis, J. A., Sahakyan, K. R., Rodriguez-Escudero, J. P., Bartels, S. J., Somers, V. K., & Lopez-Jimenez, F. (2013). Normal weight obesity and mortality in United States subjects ≥60 years of age (from the Third National Health and Nutrition Examination Survey). The American journal of cardiology, 112(10), 1592–1598. https://doi.org/10.1016/j.amjcard.2013.07.014

9.Bazzocchi, A., Ponti, F., Albisinni, U., Battista, G., & Guglielmi, G. (2016). DXA: Technical aspects and application. European journal of radiology, 85(8), 1481–1492. https://doi.org/10.1016/j.ejrad.2016.04.004

10.Bilsborough, J. C., Greenway, K., Opar, D., Livingstone, S., Cordy, J., & Coutts, A. J. (2014). The accuracy and precision of DXA for assessing body composition in team sport athletes. Journal of sports sciences, 32(19), 1821–1828. https://doi.org/10.1080/02640414.2014.926380

11Brown, A. F., Alfiero, C. J., Brooks, S. J., Kviatkovsky, S. A., Smith-Ryan, A. E., & Ormsbee, M. J. (2021). Prevalence of Normal Weight Obesity and Health Risk Factors for the Female Collegiate Dancer. Journal of strength and conditioning research, 35(8), 2321–2326. https://doi.org/10.1519/JSC.0000000000004064

12.Clayton, P., Trak-Fellermeier, M. A., Macchi, A., Galván, R., Bursac, Z., Huffman-Ercanli, F., Liuzzi, J., & Palacios, C. (2023). The association between hydration status and body composition in healthy children and adolescents. Journal of pediatric endocrinology & metabolism : JPEM, 36(5), 470–477. https://doi.org/10.1515/jpem-2022-0462

13.Centers for Disease Control and Prevention. (Year). National Health and Nutrition Examination Survey (NHANES). Retrieved from [URL]

14.Cota, B. C., Priore, S. E., Ribeiro, S. A. V., Juvanhol, L. L., de Faria, E. R., de Faria, F. R., & Pereira, P. F. (2022). Cardiometabolic risk in adolescents with normal weight obesity. European journal of clinical nutrition, 76(6), 863–870. https://doi.org/10.1038/s41430-021-01037-7

15.Čuta, M., Bařicová, K., Černý, D., & Sochor, O. (2019). Normal-weight obesity frequency in the Central European urban adult female population of Brno, Czech Republic. Central European journal of public health, 27(2), 131–134. https://doi.org/10.21101/cejph.a5133

16.Collins, M. A., Millard-Stafford, M. L., Sparling, P. B., Snow, T. K., Rosskopf, L. B., Webb, S. A., & Omer, J. (1999). Evaluation of the BOD POD for assessing body fat in collegiate football players. Medicine and science in sports and exercise, 31(9), 1350–1356. https://doi.org/10.1097/00005768-199909000-00019

17.Dencker, M., Thorsson, O., Lindén, C., Wollmer, P., Andersen, L. B., & Karlsson, M. K. (2007). BMI and objectively measured body fat and body fat distribution in prepubertal children. Clinical physiology and functional imaging, 27(1), 12–16. https://doi.org/10.1111/j.1475-097X.2007.00709

18.Dempster, P., & Aitkens, S. (1995). A new air displacement method for the determination of human body composition. Medicine and science in sports and exercise, 27(12), 1692–1697.

19.Fields, D. A., Hunter, G. R., & Goran, M. I. (2000). Validation of the BOD POD with hydrostatic weighing: influence of body clothing. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity, 24(2), 200–205. https://doi.org/10.1038/sj.ijo.0801113

20.Franco, L. P., Morais, C. C., & Cominetti, C. (2016). Normal-weight obesity syndrome: diagnosis, prevalence, and clinical implications. Nutrition reviews, 74(9), 558–570. https://doi.org/10.1093/nutrit/nuw019

21.Frija-Masson, J., Mullaert, J., Vidal-Petiot, E., Pons-Kerjean, N., Flamant, M., & d’Ortho, M. P. (2021). Accuracy of Smart Scales on Weight and Body Composition: Observational Study. JMIR mHealth and uHealth, 9(4), e22487. https://doi.org/10.2196/22487

22.Gómez-Ambrosi, J., Silva, C., Galofré, J. C., Escalada, J., Santos, S., Gil, M. J., Valentí, V., Rotellar, F., Ramírez, B., Salvador, J., & Frühbeck, G. (2011). Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI. Obesity (Silver Spring, Md.), 19(7), 1439–1444. https://doi.org/10.1038/oby.2011.36

23.Gómez-Ambrosi, J., Silva, C., Galofré, J. C., Escalada, J., Santos, S., Millán, D., Vila, N., Ibañez, P., Gil, M. J., Valentí, V., Rotellar, F., Ramírez, B., Salvador, J., & Frühbeck, G. (2012). Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. International journal of obesity (2005), 36(2), 286–294. https://doi.org/10.1038/ijo.2011.100

24.Hung, C. H. (2011). The Association between Body Mass Index and Body Fat in College Students: Asian Journal of Physical Education &Amp; Recreation, 17(1), 18–24. https://doi.org/10.24112/ajper.171883

25.Murray-Hurtado, M., Martín Rivada, Á., Quintero Alemán, C., Ruiz Alcántara, M. P., & Ramallo Fariña, Y. (2023). Body composition and nutritional status changes in adolescents with anorexia nervosa. Anales de pediatria, 99(3), 162–169. https://doi.org/10.1016/j.anpede.2023.06.015

26.Hussain, Z., Jafar, T., Zaman, M. U., Parveen, R., & Saeed, F. (2014). Correlations of skin fold thickness and validation of prediction equations using DEXA as the gold standard for estimation of body fat composition in Pakistani children. BMJ open, 4(4), e004194. https://doi.org/10.1136/bmjopen-2013-004194

27.Jean, N., Somers, V. K., Sochor, O., Medina-Inojosa, J., Llano, E. M., & Lopez-Jimenez, F. (2014). Normal-weight obesity: implications for cardiovascular health. Current atherosclerosis reports, 16(12), 464. https://doi.org/10.1007/s11883-014-0464-7

28.Jia, A., Xu, S., Xing, Y., Zhang, W., Yu, X., Zhao, Y., Ming, J., & Ji, Q. (2018). Prevalence and cardiometabolic risks of normal weight obesity in Chinese population: A nationwide study. Nutrition, metabolism, and cardiovascular diseases: NMCD, 28(10), 1045–1053. https://doi.org/10.1016/j.numecd.2018.06.015

29.Kapoor, N., Furler, J., Paul, T. V., Thomas, N., & Oldenburg, B. (2019). Normal Weight Obesity: An Underrecognized Problem in Individuals of South Asian Descent. Clinical therapeutics, 41(8), 1638–1642. https://doi.org/10.1016/j.clinthera.2019.05.016

30.Kobayashi, M., Pattarathitwat, P., Pongprajakand, A., & Kongkaew, S. (2023). Association of normal weight obesity with lifestyle and dietary habits in young Thai women: A cross-sectional study. Obesity Pillars (Online), 5, 100055. https://doi.org/10.1016/j.obpill.2023.100055

31.Kranjac, A. W., & Kranjac, D. (2023). Explaining adult obesity, severe obesity, and BMI: Five decades of change. Heliyon, 9(5), e16210. https://doi.org/10.1016/j.heliyon.2023.e16210

32.Krugh, M., & Langaker, M. D. (2023). Dual-Energy X-Ray Absorptiometry. In StatPearls. StatPearls Publishing.

33.Kurmaev, D. P., Bulgakova, S. V., & Treneva, E. V. (2022). Advances in gerontology, 35(2), 294–301.

34.Madeira, F. B., Silva, A. A., Veloso, H. F., Goldani, M. Z., Kac, G., Cardoso, V. C., Bettiol, H., & Barbieri, M. A. (2013). Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PloS one, 8(3), e60673. https://doi.org/10.1371/journal.pone.0060673

35.Maitiniyazi, G., Chen, Y., Qiu, Y. Y., Xie, Z. X., He, J. Y., & Xia, S. F. (2021). Characteristics of Body Composition and Lifestyle in Chinese University Students with Normal-Weight Obesity: A Cross-Sectional Study. Diabetes, metabolic syndrome and obesity: targets and therapy, 14, 3427–3436. https://doi.org/10.2147/DMSO.S325115

36.Manapurath, R. M., Hadaye, R., & Gadapani, B. (2022). Normal Weight Obesity: Role of apoB and Insulin Sensitivity in Predicting Future Cardiovascular Risk. International journal of preventive medicine, 13, 31. https://doi.org/10.4103/ijpvm.IJPVM_139_20

37.Mohammadian Khonsari, N., Khashayar, P., Shahrestanaki, E., Kelishadi, R., Mohammadpoor Nami, S., Heidari-Beni, M., Esmaeili Abdar, Z., Tabatabaei-Malazy, O., & Qorbani, M. (2022). Normal Weight Obesity and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Frontiers in endocrinology, 13, 857930. https://doi.org/10.3389/fendo.2022.857930

38.Noreen, E. E., & Lemon, P. W. (2006). Reliability of air displacement plethysmography in a large, heterogeneous sample. Medicine and science in sports and exercise, 38(8), 1505–1509. https://doi.org/10.1249/01.mss.0000228950.60097.01

39.Oliveros, E., Somers, V. K., Sochor, O., Goel, K., & Lopez-Jimenez, F. (2014). The concept of normal weight obesity. Progress in cardiovascular diseases, 56(4), 426–433. https://doi.org/10.1016/j.pcad.2013.10.003

40.Passos, A. F. F., Santos, A. C., Coelho, A. S. G., & Cominetti, C. (2023). Associations between Normal-Weight Obesity and Disturbances in the Lipid Profile of Young Adults. Associações entre Obesidade Eutrófica e Alterações no Perfil Lipídico de Adultos Jovens. Arquivos brasileiros de cardiologia, 120(9), e20220914. https://doi.org/10.36660/abc.20220914

41.Phillips, C. M., Tierney, A. C., Perez-Martinez, P., Defoort, C., Blaak, E. E., Gjelstad, I. M., Lopez-Miranda, J., Kiec-Klimczak, M., Malczewska-Malec, M., Drevon, C. A., Hall, W., Lovegrove, J. A., 42.Karlstrom, B., Risérus, U., & Roche, H. M. (2013). Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype. Obesity (Silver Spring, Md.), 21(1), E154–E161. https://doi.org/10.1002/oby.20263

43.Pinheiro, A. C. D. B., Filho, N. S., França, A. K. T. D. C., Fontenele, A. M. M., & Santos, A. M. D. (2019). Sensitivity and specificity of the body mass index in the diagnosis of obesity in patients with non-dialysis chronic kidney disease: a comparison between gold standard methods and the cut-off value purpose. Nutricion hospitalaria, 36(1), 73–79. https://doi.org/10.20960/nh.1880

44.Rakhmat, I. I., Putra, I. C. S., Wibowo, A., Henrina, J., Nugraha, G. I., Ghozali, M., Syamsunarno, M. R. A. A., Pranata, R., Akbar, M. R., & Achmad, T. H. (2022). Cardiometabolic risk factors in adults with normal weight obesity: A systematic review and meta-analysis. Clinical obesity, 12(4), e12523. https://doi.org/10.1111/cob.12523

45.Ramsaran, C., & Maharaj, R. G. (2017). Normal weight obesity among young adults in Trinidad and Tobago: prevalence and associated factors. International journal of adolescent medicine and health, 29(2), /j/ijamh.2017.29.issue-2/ijamh-2015-0042/ijamh-2015-0042.xml. https://doi.org/10.1515/ijamh-2015-0042

46.Sabatier, J. P., & Guaydier-Souquieres, G. (1989). Noninvasive methods of bone-mass measurement. Clinical rheumatology, 8 Suppl 2, 41–45. https://doi.org/10.1007/BF02207232

47.Shea, J. L., King, M. T., Yi, Y., Gulliver, W., & Sun, G. (2012). Body fat percentage is associated with cardiometabolic dysregulation in BMI-defined normal weight subjects. Nutrition, metabolism, and cardiovascular diseases: NMCD, 22(9), 741–747. https://doi.org/10.1016/j.numecd.2010.11.009

48.Toombs, R. J., Ducher, G., Shepherd, J. A., & De Souza, M. J. (2012). The impact of recent technological advances on the trueness and precision of DXA to assess body composition. Obesity (Silver Spring, Md.), 20(1), 30–39. https://doi.org/10.1038/oby.2011.211

49.Tseh, W., Caputo, J. L., & Keefer, D. J. (2010). Validity and reliability of the BOD POD® S/T tracking system. International journal of sports medicine, 31(10), 704–708. https://doi.org/10.1055/s-0030-1255111

50.Vescovi, J. D., Zimmerman, S. L., Miller, W. C., & Fernhall, B. (2002). Effects of clothing on accuracy and reliability of air displacement plethysmography. Medicine and science in sports and exercise, 34(2), 282–285. https://doi.org/10.1097/00005768-200202000-00016

51.Ward, Z. J., Bleich, S. N., Cradock, A. L., Barrett, J. L., Giles, C. M., Flax, C., Long, M. W., & Gortmaker, S. L. (2019). Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity. The New England journal of medicine, 381(25), 2440–2450. https://doi.org/10.1056/NEJMsa1909301

52.Wijayatunga, N. N., & Dhurandhar, E. J. (2021). Normal weight obesity and unaddressed cardiometabolic health risk-a narrative review. International journal of obesity (2005), 45(10), 2141–2155. https://doi.org/10.1038/s41366-021-00858-7

53.Wijayatunga, N. N., Kim, H., Hays, H. M., & Kang, M. (2022). Objectively Measured Physical Activity Is Lower in Individuals with Normal Weight Obesity in the United States. International journal of environmental research and public health, 19(18), 11747. https://doi.org/10.3390/ijerph191811747

54.Wilson, O. W. A., Zou, Z. H., Bopp, M., & Bopp, C. M. (2019). Comparison of obesity classification methods among college students. Obesity research & clinical practice, 13(5), 430–434. https://doi.org/10.1016/j.orcp.2019.09.003

55.Yamashiro, K., Yamaguchi, N., Sagawa, K., Tanei, S., Ogata, F., Nakamura, T., & Kawasaki, N. (2023). Relationship of masked obesity to self-reported lifestyle habits, ideal body image, and anthropometric measures in Japanese university students: A cross-sectional study. PloS one, 18(2), e0281599. https://doi.org/10.1371/journal.pone.0281599

56.Yasuda T. (2019). Anthropometric, body composition, and somatotype characteristics of Japanese young women: Implications for normal-weight obesity syndrome and sarcopenia diagnosis criteria. Interventional medicine & applied science, 11(2), 117–121. https://doi.org/10.1556/1646.11.2019.14

57.Zapata, J. K., Azcona-Sanjulian, M. C., Catalán, V., Ramírez, B., Silva, C., Rodríguez, A., Escalada, J., Frühbeck, G., & Gómez-Ambrosi, J. (2023). BMI-based obesity classification misses children and adolescents with raised cardiometabolic risk due to increased adiposity. Cardiovascular diabetology, 22(1), 240. https://doi.org/10.1186/s12933-023-01972-8

58.Zhang, M., Schumann, M., Huang, T., Törmäkangas, T., & Cheng, S. (2018). Normal weight obesity and physical fitness in Chinese university students: an overlooked association. BMC public health, 18(1), 1334. https://doi.org/10.1186/s12889-018-6238-3

59.Zhu, Y., Maruyama, H., Onoda, K., Zhou, Y., Huang, Q., Hu, C., Ye, Z., Li, B., & Wang, Z. (2023). Body mass index combined with (waist + hip)/height accurately screened for normal-weight obesity in Chinese young adults. Nutrition, 108, 111939. https://doi.org/10.1016/j.nut.2022.111939

60.Zhu, Y., Wang, Z., Maruyama, H., Onoda, K., & Huang, Q. (2022). Body Fat Percentage and Normal-Weight Obesity in the Chinese Population: Development of a Simple Evaluation Indicator Using Anthropometric Measurements. International journal of environmental research and public health, 19(7), 4238. https://doi.org/10.3390/ijerph19074238

2024-10-24T15:50:50-05:00October 25th, 2024|Research, Sports Health & Fitness, Sports Nutrition|Comments Off on Prevalence of Normal Weight Obesity Amongst Young Adults in the Southeastern United States

Prevalence of Normal Weight Obesity Amongst Young Adults in the Southeastern United States

Authors: Helena Pavlovic, Tristen Dolesh, Christian Barnes, Angila Berni, Nicholas Castro, Michel Heijnen, Alexander McDaniel, Sarah Noland, Lindsey Schroeder, Tamlyn Shields, Jessica Van Meter, and Wayland Tseh*

AUTHORS INSTITUATIONAL AFFILIATION: School of Health and Applied Human Sciences, University of North Carolina Wilmington, Wilmington, North Carolina, United States of America

Corresponding Author:

Wayland Tseh, Ph.D.

University of North Carolina Wilmington

School of Health and Applied Human Sciences

601 South College Road

Wilmington, North Carolina, 28403-5956

Phone Number: 910.962.2484

ABSTRACT

E-Mail:  tsehw@uncw.edu

‘Normal weight obesity (NWO) is characterized by a normal or low body mass index (BMI) alongside a high percentage of body fat, which increases the risk for hypokinetic diseases.  This study aims to investigate the prevalence of NWO among a sample of young, non-sedentary adults.  Two hundred and fifty-four apparently healthy volunteers (Age = 22.2 ± 7.2 yrs; Height = 171.5 ± 9.6 cm; Body Mass = 69.9 ± 13.4 kg) provided informed consent prior to participation. Body mass index was calculated by dividing body mass (kg) by height squared (m2).  Body fat percentage was measured using the BODPOD® G/S, which utilizes air displacement plethysmography to accurately estimate body composition.  Class I Obesity and Low/Normal BMI categorizations were defined by the American College of Sports Medicine. Data revealed that 12.2% of the overall sample exhibited NWO, with a higher prevalence among males (17.2%) compared to females (9.8%).  The study also seeks to evaluate whether individuals with NWO face greater health risks than those with similar BMI but lower body fat percentages.  From a practical perspective, identifying individuals with NWO is an opportunity for clinicians to proactively educate their clients regarding the health risks associated with hypokinetic disease(s).

Keywords: Body Mass Index, BODPOD, Percent Body Fat, Normal Weight Obesity

INTRODUCTION

Within the United States, the prevalence of obesity has dramatically increased over the past 50 years given the ubiquitous obesogenic environment (31). In 2019, Ward and colleagues yielded compelling predictive insights indicating a trajectory wherein, by the year 2030, nearly 50% of adults will be afflicted by obesity (48.9%) with heightened prevalence exceeding 50% in 29 states, demonstrating a pervasive nationwide trend (50). Moreover, no state is anticipated to exhibit a prevalence below 35% (50). Projections also indicate that a substantial proportion of the adult population is anticipated to experience severe obesity, with an estimated 24.2% affected by 2030 (50). Against this backdrop, the predictive analyses conducted by Ward and associates (50) underscored the widespread and escalating severity of the obesity epidemic across the United States. These findings are indicative of an impending public health challenge, necessitating strategic interventions and policy considerations to mitigate the escalating burden of obesity and its associated health implications. When delineating the magnitude of obesity, clinicians and practitioners must employ precise instrumentation capable of quantifying a client’s body composition in terms of percentage body fat. Numerous methodologies exist for this purpose, encompassing hydrostatic weighing, bioelectrical impedance analysis, air displacement plethysmography, skinfold assessment, and dual-energy x-ray absorptiometry scan.

Drawing from antecedent research studies, dual-energy X-ray absorptiometry (DXA) is acknowledged as the clinical gold standard for appraising body composition (9, 10, 12, 21, 25, 26, 42, 47). However, a notable drawback of DXA lies in its emission of low-level radiation (6, 9, 32, 45, 47), thereby subjecting clients to unnecessary radiation exposure (1, 33). An alternative method is utilizing the BOD POD® Gold Standard (GS), which employs air displacement plethysmography to estimate body composition. Previous literature has heralded the BOD POD® GS as the applied, pragmatic gold standard for assessing body composition due to its validity (2, 7, 38), as well as its within- and between-day reliability (48). Additionally, owing to the BOD POD® GS’s facile and non-invasive procedures, most individuals can attain accurate measures of body composition values, specifically pertaining to percent body fat, enabling the discernment of pounds of fat-free mass and fat mass.

According to the American College of Sports Medicine (ACSM), males with a percent body fat ≥ 25% and females ≥ 32% (4) are predisposed to an elevated risk of developing a myriad of hypokinetic diseases, notably cardiovascular disease(s), metabolic syndrome, and cardiometabolic dysfunction (14, 27, 35, 37, 39, 40, 43, 44, 46, 51, 56). Another evaluative approach involves the calculation of Body Mass Index (BMI), derived from dividing body weight in kilograms by square of height in meters (4). Given the ease and efficiency of calculating BMI, the obesity-related classification in which it provides at the individual level is potentially flawed (3, 8, 22, 24, 41, 53, 56).

Presently, within the United States, a dearth of research exists on the prevalence of normal weight obesity (NWO) amongst apparently healthy young adults (11,52). Normal weight obesity is characterized by individuals exhibiting a low BMI (<18.5 kg∙m-2) or normal BMI (18.5 – 24.9 kg∙m-2) yet manifesting obesity-related percentage body fat values (male = ≥20%; female = ≥30%) (5, 14, 20, 36, 37, 40, 43, 44, 57). Individuals with low/normal BMI and high percentage body fat values face an augmented risk of hypokinetic diseases, as their seemingly normal exterior masks a deleteriously high amount of body fat beneath the surface layer. Previous research endeavors have revealed the prevalence of NWO amongst a population of South Americans (14, 34, 40, 44), Central Europeans (15), and Asians (28-30, 37, 54, 55, 57, 58). Given that most aforesaid research studies on NWO have been conducted internationally, it is of paramount interest to ascertain the prevalence of NWO domestically. Consequently, the primary objective of this research study is to investigate the prevalence of normal-weight obesity among a sample of ostensibly healthy males and females.

METHODS

Participants

All participants were required to report to the Body Composition Laboratory to complete a singular session. Before the participants arrived, volunteers were instructed to abstain from consuming caffeinated sustenance or beverages that may acutely influence body mass. Moreover, researchers advised participants to refrain from vigorous physical activity/exercise the night before and prior to their appointed session. Upon arrival, volunteers read and signed an informed consent form approved by the University’s Institutional Review Board for human subject use (IRB#: H23-0499). As displayed in Table 1, a cohort comprising 254 male and female volunteers were recruited to participate in this study.

Below highlights the details of the singular Session required for each participant.

Body Mass Index (BMI)

Before each assessment, participants were asked to remove any unattached item(s) from their body, such as shoes, socks, rings, bracelets, and/or glasses.  Height was measured to the nearest 0.5 cm as participants stood barefoot, with both legs together, with their back to a Seca 217 Mobile Stadiometer (Model Number 2171821009, USA).  Body mass was measured on a Tanita Multi-Frequency Total Body Composition Analyzer with Column (Model DC-430U, Tanita Corporation, Japan) to the nearest 0.1 kg.  Body mass index was calculated using body mass expressed in kilograms (kg) divided by height expressed in meters squared (m2).  Body mass index categorizations, set forth via ACSM (4), for low BMI was (<18.5 kg∙m-2) and normal BMI was (18.5 – 24.9 kg∙m-2).

BOD POD® Gold Standard (GS)

BOD POD® Gold Standard (GS) (COSMED USA Inc., USA) was calibrated daily according to the manufacturer’s instructions with a 50.238 Liter cylindrical volume provided by COSMED USA Inc.  Specific details illustrating the technicalities of the calibration mechanism are published elsewhere (16, 18).  Because different clothing schemes have been shown to underestimate percentage body fat (%BF) results from the BOD POD® (19, 49), female participants were instructed to wear one- or two-piece bathing suit or sports bra and compression shorts, while male participants were instructed to wear form-fitted compression shorts.  All participants wore a swim-like cap provided by COSMED USA Inc.  After race, height, and age were inputted by a technician into the BOD POD® GS kiosk, participants were asked to step on an electronic scale to determine body mass to the nearest .045 kg.  Once the BOD POD® GS system recorded body mass, participants were instructed to sit comfortably and breathe normally within the BOD POD® GS for two trials lasting 40 seconds per trial.  A third trial was conducted if Trials 1 and 2 had high variability.  Once both (or three) trials were conducted, body composition values, specifically, body mass, percent body fat, fat-free mass, and fat mass, were immediately displayed on the kiosk viewer and recorded by a technician.  Once height, body mass, and body composition assessments were completed, participants dressed back into their original clothing and exited the Body Composition Lab.

Statistical Analyses

Descriptive statistics (mean ± SD) were derived to describe the sample population.  A Chi-Square Goodness of Fit Test was used to determine the prevalence of low/normal BMI values with obesity-related percent body fat.  For all analyses, statistical significance was established at p < 0.05.

RESULTS

At the conclusion of the study, 254 volunteers were recruited, and zero dropped out, therefore, all 254 participants’ results were included in the statistical analyses. Table 2 displays the descriptive measures of the study participants.

The chi-squared statistic was 1.886 (df = 1, p = 0.17) indicating no statistical difference in NWO between males (17.2%) and females (9.8%).

DISCUSSION

As stated previously, there is a dearth of data determining the prevalence of NWO domestically, more specifically, within the southeast region of the United States.  Therefore, the primary objective of this research study was to investigate the frequency of NWO amongst a sample of apparently healthy individuals.  Participants completed a singular data collection session whereby height, body mass, and percentage body fat were quantified via BOD POD® GS.  Within this current study, low and normal BMI classifications were <18.5 kg∙m-2 and 18.5 – 24.9 kg∙m-2, respectively.  Class I obesity for females and males were ≥ 32% and ≥ 25%, respectively.  Given said thresholds, data revealed that 12.2% of the overall sample exhibited NWO, with a higher prevalence amongst males (17.2%) compared to females (9.8%).  These findings are relatively comparable within other research investigating the prevalence of NWO amongst a sample of young adults (5, 35, 44, 57).

In 2017, Ramsaran and Maharaj investigated the prevalence of NWO within a cohort of 236 young adults (mean age = 21.3 ± 2.5 years).  The quantification of %BF was accomplished using the Tanita Ironman body composition analyzer.  Subsequent data analyses unveiled a heightened prevalence of NWO among the male participants (14.4%), surpassing their female counterparts (5.5%).  The outcomes of the current study align with the findings reported by Ramsaran and Maharaj (44), wherein NWO manifested in 17.2% of males and 9.8% of females. A nuanced distinction between the two investigations lies in the designated thresholds for %BF. Ramsaran and Maharaj (44) set the elevated %BF thresholds at ≥ 23.1% for males and ≥ 33.3% for females. In contrast, the current study employed thresholds of ≥ 25.0% for males and ≥ 32.0% for females.  Notwithstanding the marginal elevation (+1.9%) in the %BF threshold within the current study, males exhibited a greater prevalence (+2.8%) compared to Ramsaran and Maharaj’s (44) dataset.  Conversely, the current study adopted a lower %BF threshold (–1.3%) for females and uncovered a higher prevalence of NWO (+4.4%).  These subtle yet discernible variations in %BF thresholds may elucidate the divergent prevalence rates of NWO observed between the two scholarly investigations.

Akin to Ramsaran and Maharaj (44) and the present investigation, Anderson and colleagues (5) examined the incidence of NWO within a more modest cohort of 94 young adults (mean age = 19.6 ± 1.5 years).  The quantification of %BF was assessed via DXA. The %BF thresholds were predicated on National Health and Nutrition Examination Survey standards, establishing obesity values of ≥ 30.0% for males and ≥ 35.0% for females.  Findings elucidated an NWO prevalence in males (26.7%) and females (7.8%). Noteworthy is the marked elevation in male NWO rates (+9.5%) and marginal reduction (–2.0%) in female NWO rates compared to the current study.  While discrepancies may be attributed to variances in sample size (254 in the present study vs. 94 in Anderson et al.), divergent methodologies for %BF assessment (utilizing BOD POD® GS presently as opposed to DXA in Anderson et al.), and distinct %BF thresholds (ACSM criteria in the current study versus NHANES in Anderson et al.), the overarching findings remain concordant. Specifically, data from all three research investigations underscore the consistent pattern wherein males manifest elevated NWO prevalence rates relative to their female counterparts.

In contradistinction to the two previous research investigations and the current study, Zhang et al. (57) explored the NWO prevalence amongst 383 young adults (mean age = 20.4 ± 1.6 years). Assessment of %BF was executed through bioelectrical impedance analyses (BIA) employing the InBody 720 device. Obesity classification was contingent upon threshold values of ≥20.0% for males and ≥30.0% for females, as established by Zhang and associates (57).  Analyses unveiled an NWO prevalence of 13.2% in males and 27.5% in females, a prominent deviation from the present study’s findings. The contrasting NWO prevalence patterns observed between the two studies are notably discernible. Specifically, Zhang and colleagues (57) reported a higher prevalence in females than males, whereas the current investigation revealed the converse. This discordance is seemingly attributable to variances in the %BF thresholds implemented for obesity classification. Zhang et al. (57) utilized a considerably lower threshold for males at 20.0%, as opposed to the 25.0% threshold applied in the current study. Similarly, for females, Zhang et al. (57) employed a lower %BF threshold at 30.0%, whereas the present study utilized a more conservative threshold of 32.0%. Moreover, a salient methodological distinction lies in the apparatus employed for %BF quantification. The current study utilized the BOD POD® GS, acknowledged as the applied gold standard for assessing body composition, while Zhang et al. (57) employed the InBody 720 BIA. These methodological nuances likely contribute to the divergent findings between the present research and Zhang et al. (57), underscoring the importance of rigorously evaluating both threshold criteria and assessment modalities when interpreting and comparing NWO prevalence data.

In a recent investigation, Maitiniyazi et al. (35) endeavored to ascertain the prevalence of NWO within a cohort of 279 young adults (mean age = 21.7 ± 2.1 years). Percentage body fat was assessed utilizing the InBody 770 BIA method.  Obesity classification thresholds were established at 20.0% for males and 30.0% for females.  Parallel to the observed NWO patterns delineated by Zhang and colleagues (57), Maitiniyazi et al. also discerned a higher prevalence of NWO in females (40.1%) as opposed to males (25.5%).  Notably, while these NWO trends align with the patterns identified by Zhang et al. (57), they markedly deviate from the outcomes of the current investigation.  Such discordant findings may find elucidation in the nuanced disparities in the thresholds employed to categorize obesity and the instrumentation deployed for %BF quantification.  Specifically, the divergence in %BF thresholds used for obesity classification emerges as a significant factor. Maitiniyazi et al. (35) employed thresholds different from those of Zhang et al. (57) and the current study, thereby contributing to the observed inconsistencies. Additionally, the equipment utilized to quantify %BF introduces another layer of methodological variation. While Zhang et al. (57) implemented InBody 720 BIA and the current study utilized BOD POD® GS, Maitiniyazi et al. deployed the InBody 770 BIA method.  These divergent methodological approaches underscore the imperative of meticulous consideration when interpreting and comparing NWO prevalence data, highlighting the multifaceted nature of the interplay between obesity thresholds and assessment methodologies in elucidating NWO prevalence.

CONCLUSIONS

This comprehensive investigation contributes significantly to our understanding of NWO prevalence within a young adult population, particularly within the Southeast region of the United States.  The study employed the BOD POD® GS for precise measurement of height, body mass, and percentage body fat, revealing a higher, but not statistically different, prevalence in NWO between males and females.  These results align with similar studies collectively emphasizing the consistent pattern of elevated NWO prevalence in males relative to females.  The study’s alignment with said research investigations further underscores the robustness of the findings, notwithstanding variations in sample size, methodology, and threshold criteria.  Conversely, discrepancies with other research investigations highlight the sensitivity of NWO prevalence to %BF thresholds and assessment modalities.  Despite the divergence in outcomes, these studies collectively reinforce the need for careful consideration of methodological nuances in interpreting and comparing NWO prevalence data.

APPLICATION IN SPORTS

From a practical perspective, the findings emphasize the importance of incorporating regional and demographic variations when assessing NWO prevalence.  Furthermore, the study underscores the relevance of employing standardized methodologies in ensuring consistency and comparability across investigations.  Future endeavors in this domain should continue to explore regional variations, refine %BF threshold criteria, and employ advanced methodologies for accurate NWO characterization.  This knowledge is pivotal for tailoring preventive measures and interventions; more precisely, accurately identifying NWO individuals is an opportunity for clinicians to proactively educate their clients regarding the health risks associated with hypokinetic disease(s), particularly cardiovascular disease(s), metabolic syndrome, and cardiometabolic dysfunction.

References

  1. Alawi, M., Begum, A., Harraz, M., Alawi, H., Bamagos, S., Yaghmour, A., & Hafiz, L. (2021). Dual-Energy X-Ray Absorptiometry (DEXA) Scan Versus Computed Tomography for Bone Density Assessment. Cureus, 13(2), e13261. https://doi.org/10.7759/cureus.13261
  2. Alemán-Mateo, H., Huerta, R. H., Esparza-Romero, J., Méndez, R. O., Urquidez, R., & Valencia, M. E. (2007). Body composition by the four-compartment model: validity of the BOD POD for assessing body fat in Mexican elderly. European journal of clinical nutrition, 61(7), 830–836. https://doi.org/10.1038/sj.ejcn.1602597
  3. Alqarni, A. M., Aljabr, A. S., Abdelwahab, M. M., Alhallafi, A. H., Alessa, M. T., Alreedy, A. H., Elmaki, S. A., Alamer, N. A., & Darwish, M. A. (2023). Accuracy of body mass index compared to whole-body dual energy X-ray absorptiometry in diagnosing obesity in adults in the Eastern Province of Saudi Arabia: A cross-sectional study. Journal of family & community medicine, 30(4), 259–266. https://doi.org/10.4103/jfcm.jfcm_85_23
  4. American College of Sports Medicine. (2020). ACSM’s guidelines for exercise testing and prescription (11th ed.). Wolters Kluwer.
  5. Anderson, K. C., Hirsch, K. R., Peterjohn, A. M., Blue, M. N. M., Pihoker, A. A., Ward, D. S., Ondrak, K. S., & Smith-Ryan, A. E. (2020). Characterization and prevalence of obesity among normal weight college students. International journal of adolescent medicine and health, 35(1), 81–88. https://doi.org/10.1515/ijamh-2020-0240
  6. Bachrach L. K. (2000). Dual energy X-ray absorptiometry (DEXA) measurements of bone density and body composition: promise and pitfalls. Journal of pediatric endocrinology & metabolism: JPEM, 13 Suppl 2, 983–988.
  7. Ballard, T. P., Fafara, L., & Vukovich, M. D. (2004). Comparison of Bod Pod and DXA in female collegiate athletes. Medicine and science in sports and exercise, 36(4), 731–735. https://doi.org/10.1249/01.mss.0000121943.02489.2b
  8. Batsis, J. A., Sahakyan, K. R., Rodriguez-Escudero, J. P., Bartels, S. J., Somers, V. K., & Lopez-Jimenez, F. (2013). Normal weight obesity and mortality in United States subjects ≥60 years of age (from the Third National Health and Nutrition Examination Survey). The American journal of cardiology, 112(10), 1592–1598. https://doi.org/10.1016/j.amjcard.2013.07.014
  9. Bazzocchi, A., Ponti, F., Albisinni, U., Battista, G., & Guglielmi, G. (2016). DXA: Technical aspects and application. European journal of radiology, 85(8), 1481–1492. https://doi.org/10.1016/j.ejrad.2016.04.004
  10. Bilsborough, J. C., Greenway, K., Opar, D., Livingstone, S., Cordy, J., & Coutts, A. J. (2014). The accuracy and precision of DXA for assessing body composition in team sport athletes. Journal of sports sciences, 32(19), 1821–1828. https://doi.org/10.1080/02640414.2014.926380
  11. Brown, A. F., Alfiero, C. J., Brooks, S. J., Kviatkovsky, S. A., Smith-Ryan, A. E., & Ormsbee, M. J. (2021). Prevalence of Normal Weight Obesity and Health Risk Factors for the Female Collegiate Dancer. Journal of strength and conditioning research, 35(8), 2321–2326. https://doi.org/10.1519/JSC.0000000000004064
  12. Clayton, P., Trak-Fellermeier, M. A., Macchi, A., Galván, R., Bursac, Z., Huffman-Ercanli, F., Liuzzi, J., & Palacios, C. (2023). The association between hydration status and body composition in healthy children and adolescents. Journal of pediatric endocrinology & metabolism : JPEM, 36(5), 470–477. https://doi.org/10.1515/jpem-2022-0462
  13. Centers for Disease Control and Prevention. (Year). National Health and Nutrition Examination Survey (NHANES). Retrieved from [URL]
  14. Cota, B. C., Priore, S. E., Ribeiro, S. A. V., Juvanhol, L. L., de Faria, E. R., de Faria, F. R., & Pereira, P. F. (2022). Cardiometabolic risk in adolescents with normal weight obesity. European journal of clinical nutrition, 76(6), 863–870. https://doi.org/10.1038/s41430-021-01037-7
  15. Čuta, M., Bařicová, K., Černý, D., & Sochor, O. (2019). Normal-weight obesity frequency in the Central European urban adult female population of Brno, Czech Republic. Central European journal of public health, 27(2), 131–134. https://doi.org/10.21101/cejph.a5133
  16. Collins, M. A., Millard-Stafford, M. L., Sparling, P. B., Snow, T. K., Rosskopf, L. B., Webb, S. A., & Omer, J. (1999). Evaluation of the BOD POD for assessing body fat in collegiate football players. Medicine and science in sports and exercise, 31(9), 1350–1356. https://doi.org/10.1097/00005768-199909000-00019
  17. Dencker, M., Thorsson, O., Lindén, C., Wollmer, P., Andersen, L. B., & Karlsson, M. K. (2007). BMI and objectively measured body fat and body fat distribution in prepubertal children. Clinical physiology and functional imaging, 27(1), 12–16. https://doi.org/10.1111/j.1475-097X.2007.00709
  18. Dempster, P., & Aitkens, S. (1995). A new air displacement method for the determination of human body composition. Medicine and science in sports and exercise, 27(12), 1692–1697.
  19. Fields, D. A., Hunter, G. R., & Goran, M. I. (2000). Validation of the BOD POD with hydrostatic weighing: influence of body clothing. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity, 24(2), 200–205. https://doi.org/10.1038/sj.ijo.0801113
  20. Franco, L. P., Morais, C. C., & Cominetti, C. (2016). Normal-weight obesity syndrome: diagnosis, prevalence, and clinical implications. Nutrition reviews, 74(9), 558–570. https://doi.org/10.1093/nutrit/nuw019
  21. Frija-Masson, J., Mullaert, J., Vidal-Petiot, E., Pons-Kerjean, N., Flamant, M., & d’Ortho, M. P. (2021). Accuracy of Smart Scales on Weight and Body Composition: Observational Study. JMIR mHealth and uHealth, 9(4), e22487. https://doi.org/10.2196/22487
  22. Gómez-Ambrosi, J., Silva, C., Galofré, J. C., Escalada, J., Santos, S., Gil, M. J., Valentí, V., Rotellar, F., Ramírez, B., Salvador, J., & Frühbeck, G. (2011). Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI. Obesity (Silver Spring, Md.), 19(7), 1439–1444. https://doi.org/10.1038/oby.2011.36
  23. Gómez-Ambrosi, J., Silva, C., Galofré, J. C., Escalada, J., Santos, S., Millán, D., Vila, N., Ibañez, P., Gil, M. J., Valentí, V., Rotellar, F., Ramírez, B., Salvador, J., & Frühbeck, G. (2012). Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. International journal of obesity (2005), 36(2), 286–294. https://doi.org/10.1038/ijo.2011.100
  24. Hung, C. H. (2011). The Association between Body Mass Index and Body Fat in College Students: Asian Journal of Physical Education &Amp; Recreation, 17(1), 18–24. https://doi.org/10.24112/ajper.171883
  25. Murray-Hurtado, M., Martín Rivada, Á., Quintero Alemán, C., Ruiz Alcántara, M. P., & Ramallo Fariña, Y. (2023). Body composition and nutritional status changes in adolescents with anorexia nervosa. Anales de pediatria, 99(3), 162–169. https://doi.org/10.1016/j.anpede.2023.06.015
  26. Hussain, Z., Jafar, T., Zaman, M. U., Parveen, R., & Saeed, F. (2014). Correlations of skin fold thickness and validation of prediction equations using DEXA as the gold standard for estimation of body fat composition in Pakistani children. BMJ open, 4(4), e004194. https://doi.org/10.1136/bmjopen-2013-004194
  27. Jean, N., Somers, V. K., Sochor, O., Medina-Inojosa, J., Llano, E. M., & Lopez-Jimenez, F. (2014). Normal-weight obesity: implications for cardiovascular health. Current atherosclerosis reports, 16(12), 464. https://doi.org/10.1007/s11883-014-0464-7
  28. Jia, A., Xu, S., Xing, Y., Zhang, W., Yu, X., Zhao, Y., Ming, J., & Ji, Q. (2018). Prevalence and cardiometabolic risks of normal weight obesity in Chinese population: A nationwide study. Nutrition, metabolism, and cardiovascular diseases: NMCD28(10), 1045–1053. https://doi.org/10.1016/j.numecd.2018.06.015
  29. Kapoor, N., Furler, J., Paul, T. V., Thomas, N., & Oldenburg, B. (2019). Normal Weight Obesity: An Underrecognized Problem in Individuals of South Asian Descent. Clinical therapeutics, 41(8), 1638–1642. https://doi.org/10.1016/j.clinthera.2019.05.016
  30. Kobayashi, M., Pattarathitwat, P., Pongprajakand, A., & Kongkaew, S. (2023). Association of normal weight obesity with lifestyle and dietary habits in young Thai women: A cross-sectional study. Obesity Pillars (Online), 5, 100055. https://doi.org/10.1016/j.obpill.2023.100055
  31. Kranjac, A. W., & Kranjac, D. (2023). Explaining adult obesity, severe obesity, and BMI: Five decades of change. Heliyon, 9(5), e16210. https://doi.org/10.1016/j.heliyon.2023.e16210
  32. Krugh, M., & Langaker, M. D. (2023). Dual-Energy X-Ray Absorptiometry. In StatPearls. StatPearls Publishing.
  33. Kurmaev, D. P., Bulgakova, S. V., & Treneva, E. V. (2022). Advances in gerontology, 35(2), 294–301.
  34. Madeira, F. B., Silva, A. A., Veloso, H. F., Goldani, M. Z., Kac, G., Cardoso, V. C., Bettiol, H., & Barbieri, M. A. (2013). Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PloS one, 8(3), e60673. https://doi.org/10.1371/journal.pone.0060673
  35. Maitiniyazi, G., Chen, Y., Qiu, Y. Y., Xie, Z. X., He, J. Y., & Xia, S. F. (2021). Characteristics of Body Composition and Lifestyle in Chinese University Students with Normal-Weight Obesity: A Cross-Sectional Study. Diabetes, metabolic syndrome and obesity: targets and therapy, 14, 3427–3436. https://doi.org/10.2147/DMSO.S325115
  36. Manapurath, R. M., Hadaye, R., & Gadapani, B. (2022). Normal Weight Obesity: Role of apoB and Insulin Sensitivity in Predicting Future Cardiovascular Risk. International journal of preventive medicine, 13, 31. https://doi.org/10.4103/ijpvm.IJPVM_139_20
  37. Mohammadian Khonsari, N., Khashayar, P., Shahrestanaki, E., Kelishadi, R., Mohammadpoor Nami, S., Heidari-Beni, M., Esmaeili Abdar, Z., Tabatabaei-Malazy, O., & Qorbani, M. (2022). Normal Weight Obesity and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Frontiers in endocrinology, 13, 857930. https://doi.org/10.3389/fendo.2022.857930
  38. Noreen, E. E., & Lemon, P. W. (2006). Reliability of air displacement plethysmography in a large, heterogeneous sample. Medicine and science in sports and exercise, 38(8), 1505–1509. https://doi.org/10.1249/01.mss.0000228950.60097.01
  39. Oliveros, E., Somers, V. K., Sochor, O., Goel, K., & Lopez-Jimenez, F. (2014). The concept of normal weight obesity. Progress in cardiovascular diseases, 56(4), 426–433. https://doi.org/10.1016/j.pcad.2013.10.003
  40. Passos, A. F. F., Santos, A. C., Coelho, A. S. G., & Cominetti, C. (2023). Associations between Normal-Weight Obesity and Disturbances in the Lipid Profile of Young Adults. Associações entre Obesidade Eutrófica e Alterações no Perfil Lipídico de Adultos Jovens. Arquivos brasileiros de cardiologia, 120(9), e20220914. https://doi.org/10.36660/abc.20220914
  41. Phillips, C. M., Tierney, A. C., Perez-Martinez, P., Defoort, C., Blaak, E. E., Gjelstad, I. M., Lopez-Miranda, J., Kiec-Klimczak, M., Malczewska-Malec, M., Drevon, C. A., Hall, W., Lovegrove, J. A., Karlstrom, B., Risérus, U., & Roche, H. M. (2013). Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype. Obesity (Silver Spring, Md.), 21(1), E154–E161. https://doi.org/10.1002/oby.20263
  42. Pinheiro, A. C. D. B., Filho, N. S., França, A. K. T. D. C., Fontenele, A. M. M., & Santos, A. M. D. (2019). Sensitivity and specificity of the body mass index in the diagnosis of obesity in patients with non-dialysis chronic kidney disease: a comparison between gold standard methods and the cut-off value purpose. Nutricion hospitalaria, 36(1), 73–79. https://doi.org/10.20960/nh.1880
  43. Rakhmat, I. I., Putra, I. C. S., Wibowo, A., Henrina, J., Nugraha, G. I., Ghozali, M., Syamsunarno, M. R. A. A., Pranata, R., Akbar, M. R., & Achmad, T. H. (2022). Cardiometabolic risk factors in adults with normal weight obesity: A systematic review and meta-analysis. Clinical obesity, 12(4), e12523. https://doi.org/10.1111/cob.12523
  44. Ramsaran, C., & Maharaj, R. G. (2017). Normal weight obesity among young adults in Trinidad and Tobago: prevalence and associated factors. International journal of adolescent medicine and health, 29(2), /j/ijamh.2017.29.issue-2/ijamh-2015-0042/ijamh-2015-0042.xml. https://doi.org/10.1515/ijamh-2015-0042
  45. Sabatier, J. P., & Guaydier-Souquieres, G. (1989). Noninvasive methods of bone-mass measurement. Clinical rheumatology, 8 Suppl 2, 41–45. https://doi.org/10.1007/BF02207232
  46. Shea, J. L., King, M. T., Yi, Y., Gulliver, W., & Sun, G. (2012). Body fat percentage is associated with cardiometabolic dysregulation in BMI-defined normal weight subjects. Nutrition, metabolism, and cardiovascular diseases: NMCD, 22(9), 741–747. https://doi.org/10.1016/j.numecd.2010.11.009
  47. Toombs, R. J., Ducher, G., Shepherd, J. A., & De Souza, M. J. (2012). The impact of recent technological advances on the trueness and precision of DXA to assess body composition. Obesity (Silver Spring, Md.), 20(1), 30–39. https://doi.org/10.1038/oby.2011.211
  48. Tseh, W., Caputo, J. L., & Keefer, D. J. (2010). Validity and reliability of the BOD POD® S/T tracking system. International journal of sports medicine, 31(10), 704–708. https://doi.org/10.1055/s-0030-1255111
  49. Vescovi, J. D., Zimmerman, S. L., Miller, W. C., & Fernhall, B. (2002). Effects of clothing on accuracy and reliability of air displacement plethysmography. Medicine and science in sports and exercise, 34(2), 282–285. https://doi.org/10.1097/00005768-200202000-00016
  50. Ward, Z. J., Bleich, S. N., Cradock, A. L., Barrett, J. L., Giles, C. M., Flax, C., Long, M. W., & Gortmaker, S. L. (2019). Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity. The New England journal of medicine, 381(25), 2440–2450. https://doi.org/10.1056/NEJMsa1909301
  51. Wijayatunga, N. N., & Dhurandhar, E. J. (2021). Normal weight obesity and unaddressed cardiometabolic health risk-a narrative review. International journal of obesity (2005), 45(10), 2141–2155. https://doi.org/10.1038/s41366-021-00858-7
  52. Wijayatunga, N. N., Kim, H., Hays, H. M., & Kang, M. (2022). Objectively Measured Physical Activity Is Lower in Individuals with Normal Weight Obesity in the United States. International journal of environmental research and public health, 19(18), 11747. https://doi.org/10.3390/ijerph191811747
  53. Wilson, O. W. A., Zou, Z. H., Bopp, M., & Bopp, C. M. (2019). Comparison of obesity classification methods among college students. Obesity research & clinical practice, 13(5), 430–434. https://doi.org/10.1016/j.orcp.2019.09.003
  54. Yamashiro, K., Yamaguchi, N., Sagawa, K., Tanei, S., Ogata, F., Nakamura, T., & Kawasaki, N. (2023). Relationship of masked obesity to self-reported lifestyle habits, ideal body image, and anthropometric measures in Japanese university students: A cross-sectional study. PloS one, 18(2), e0281599. https://doi.org/10.1371/journal.pone.0281599
  55. Yasuda T. (2019). Anthropometric, body composition, and somatotype characteristics of Japanese young women: Implications for normal-weight obesity syndrome and sarcopenia diagnosis criteria. Interventional medicine & applied science, 11(2), 117–121. https://doi.org/10.1556/1646.11.2019.14
  56. Zapata, J. K., Azcona-Sanjulian, M. C., Catalán, V., Ramírez, B., Silva, C., Rodríguez, A., Escalada, J., Frühbeck, G., & Gómez-Ambrosi, J. (2023). BMI-based obesity classification misses children and adolescents with raised cardiometabolic risk due to increased adiposity. Cardiovascular diabetology, 22(1), 240. https://doi.org/10.1186/s12933-023-01972-8
  57. Zhang, M., Schumann, M., Huang, T., Törmäkangas, T., & Cheng, S. (2018). Normal weight obesity and physical fitness in Chinese university students: an overlooked association. BMC public health, 18(1), 1334. https://doi.org/10.1186/s12889-018-6238-3
  58. Zhu, Y., Maruyama, H., Onoda, K., Zhou, Y., Huang, Q., Hu, C., Ye, Z., Li, B., & Wang, Z. (2023). Body mass index combined with (waist + hip)/height accurately screened for normal-weight obesity in Chinese young adults. Nutrition, 108, 111939. https://doi.org/10.1016/j.nut.2022.111939
  59. Zhu, Y., Wang, Z., Maruyama, H., Onoda, K., & Huang, Q. (2022). Body Fat Percentage and Normal-Weight Obesity in the Chinese Population: Development of a Simple Evaluation Indicator Using Anthropometric Measurements. International journal of environmental research and public health, 19(7), 4238. https://doi.org/10.3390/ijerph19074238
2024-09-23T09:54:55-05:00September 23rd, 2024|Sports Health & Fitness|Comments Off on Prevalence of Normal Weight Obesity Amongst Young Adults in the Southeastern United States

Can there be two speeds in a clean peloton? Performance strategies in modern road cycling

Authors: Karsten Øvretveit1

1K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing,

Corresponding Author:

K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology,
Trondheim, Norway, PB 8905, N-7491 Trondheim, Norway
karsten.ovretveit@ntnu.no

Karsten Øvretveit, MSc3, is a physiologist and PhD candidate at the Norwegian University of Science and Technology (NTNU). His research areas include genetic disease risk, physical performance, motivational dynamics, and human nutrition.

Can there be two speeds in a clean peloton? Performance strategies in modern road cycling

ABSTRACT

In the history of professional cycling, riders have always sought competitive advantages. Throughout 20th century, many relied on performance-enhancing drugs (PEDs) which gave rise to a phenomenon called “two-speed cycling”. Throughout its modern era, professional cycling has seen anti-doping efforts repeatedly intensify on the heels of several large doping scandals. Over the past decade, the sport appears to have transitioned away from large-scale systematic doping and towards novel, legal performance-enhancing strategies, facilitated by a close relationship with scientific, technological, and engineering communities. The tools and technologies available to assess the demands of the sport, the capabilities of the riders, and the role of environmental factors such as wind resistance, altitude, and heat are more refined and comprehensive than ever. Teams and riders are now able to leverage these to improve training, recovery, equipment, race tactics and more, often from a very early age. This review explores several key developments in road cycling and their implications for the modern professional peloton.


Key Words: professional cycling; performance-enhancing drugs; marginal gains; performance analysis

INTRODUCTION

The main pack of riders navigating the road in a cycling race, known as the peloton, comprises a wide range of physiological, anthropometrical, technical, and strategical attributes. The role of each rider in a given race is typically based on strengths, weaknesses, and objectives, and can be modified by injuries, fitness level, personal goals, and unexpected in-race developments. The concept of “cycling at two speeds”, cyclisme à deux vitesses, has historically been used to distinguish between chemically enhanced riders and those who ride clean (134). However, despite increasingly stringent doping controls in professional cycling along with a clear shift in doping culture, the concept of two-speed cycling remains.
Given the well-documented benefits of performance-enhancing drugs (PEDs), there is an expectation that the intensification of anti-doping measures in professional cycling leads to more homogeneous performance levels in the peloton by reducing the number of artificially enhanced riders. Although this may be a reasonable assumption, it discounts the many substantial advances made in training, nutrition, technology, and strategy, as well as the growing talent pool of potential professionals and the early age at which they begin to seriously structure their training, racing, and recovery. These factors can differ greatly between teams and individual riders and thus help maintain the two-speed phenomenon. This review provides a brief history of the PED culture and use in professional cycling, followed by an examination of some of the key developments in the sport that has helped preserve the two-speed phenomenon in a peloton riding within an increasingly strict anti-doping framework.

The performance-enhanced past of the peloton

Drugs have been used to enhance athletic performance for millennia, stretching back to at least the ancient Olympic Games (16). Cycling as a profession emerged among working-class men who likened endurance sports to physically demanding jobs where the use of drugs to aid performance was considered the right thing to do (58). Indeed, doping has been pervasive in professional cycling for over 150 years, throughout most of which it was either legal or not subject to testing (34). For decades, riders doped to simply be able to do the job – faire le métier (33). Then, athlete health became a concern and a major driving force to regulate, if not outright ban the use of certain substances. Drug testing in the Tour de France (TdF), the most prestigious event on the race calendar, began in 1966. Despite this, amphetamines, cortisone, and steroids remained widespread in the professional peloton. It was also around this time that rumors about the use of blood transfusions in athletes began (60). The year after Raymond Poulidor underwent the first drug test in the TdF, Tom Simpson collapsed on the ascent of Mount Ventoux and later passed away due to an unfortunate combination of alcohol, amphetamines, intense heat, and extreme physical exertion. Although this event brought more attention to the use of stimulants and other drugs in cycling and in sports in general (69), doping would persist for decades to follow. Based on interviews with riders on a professional cycling team at the turn of the millennium, psychiatrist Jean-Christophe Seznec (115) asserted that professional cyclists are not only prone to develop an addiction to PEDs, but also recreational drugs, noting the importance of explicitly acknowledging this risk in order to mitigate it.

When professional cycling entered the 90s, the banned yet at that time undetectable erythropoiesis-stimulating agent (ESA) recombinant human erythropoietin (rHuEPO) arrived in the peloton (101), and performances hit a new level. Increasing circulating erythropoietin (EPO) by illegal means has been perceived by some riders and coaches to give an estimated performance boost, without the term “performance” being strictly defined, of 3% to 20% (31, 100, 134, 138). Interestingly, despite its popularity in the peloton, the research literature on the effects of ESAs such as rHuEPO on endurance performance is equivocal. Its effects on hematological values like hemoglobin concentration ([Hb]) and clinical measurements of power and maximal oxygen uptake (V̇O2max) are well-established, but the real-world benefits are not always clear (116, 123).

There are several aspects of professional cycling that are difficult to account for in experimental studies on exogenous EPO, such as the extremely high fitness level of a peaked professional cyclist and the physiological impact of training and racing on parameters such as Hb. A recent randomized controlled trial found no apparent benefit of EPO on relevant performance markers has sometimes been cited to shed doubt on the true effects of the drug (47). However, this study was done in cyclists with an average V̇O2max of 55.6 mL/kg/min, which is substantially lower than their professional counterparts (124). By his own account, former professional Michael Rasmussen saw his hematocrit (Hct) drop from 41% to 36% following the 2002 Giro d’Italia (98), illustrating how blood composition can be severely perturbed by training and racing. Similar values have been observed in other professionals following participation in Grand Tours (17, 89). Using Rasmussen as an example, using rHuEPO to bring this up to 49%, just below the old 50% limit, would represent a relative Hct increase of 36% and result in improved ability to maintain a much higher intensity in training and racing, and consequently greater exercise-induced adaptations.

Throughout the 90s, Grand Tour riders with supraphysiological Hct would traverse France, Italy, and Spain at impressive speeds until it all seemingly came to an end in 1998. Three days before the start of the 85th edition of the TdF, a Festina team car carrying various PEDs was stopped by customs agents at the French-Belgian border. This event marked the start of what later became known as the Festina affair, a major catalyst in cycling’s transition to a cleaner sport. The wake of this scandal saw an increasing number of calls to action against doping, including by the driver of the Festina car (132), with claims of the sport dying unless drastic action is taken. Subsequent large-scale doping cases such as Operación Puerto and the contents of the USADA’s Reasoned Decision Report (10) served as reminders that PEDs were still present in the peloton and strengthened the resolve of those fighting for a cleaner sport.
Although riders are often blamed for the pervasive drug use in cycling, most entered a sport with a lack of top-down anti-doping efforts, leaving them with the difficult choice of either conforming to the culture or competing on unequal terms. One of the most crucial steps towards a cleaner sport is a change in culture among teams and riders. Much, if not most, of the credit should go to the riders themselves, many of which have actively pushed against the use of PEDs for years (46, 50, 59, 85, 130). Today, most doping cases in cycling are among semi-professional riders, whereas the number of riders testing positive at the highest level is approaching zero (88).

Although absence of evidence is not evidence of absence, fewer doping cases at the highest level of cycling suggests that overt, systematic drug use is a thing of the past. Given professional cycling’s checkered history, it would be naïve to think that doping has been eliminated entirely, but the sport does appear to have evolved beyond doping being perceived as all but necessary to gain entry into the professional peloton. Generational shifts not only among riders, but also among governing bodies and team leadership have contributed to an overall firmer stance against doping, removing potentially significant contributors to anti-doping violations (6). There is also indications that the post-Armstrong generation, especially those who started their careers young, are less likely to use PEDs (5), although the evidence is equivocal (64). Additionally, anti-doping technology continues to improve, with recent advances such as gene expression analysis being able to extend the detection window of blood manipulations (28, 133).

Conceptual approaches to legal performance development

It could be argued that the extraordinary performances regularly being on display by the current generation of riders suggest that the dismantling of systematic doping practices has led to progression rather than regression of the sport of cycling. The transition away from prevalent PED use has forced teams and riders to seek out other areas of improvement, some with barely measurable effects, to keep up. Although seeking improvements in many areas is not a new phenomenon in professional cycling, it has received increasing attention over the past decade with the success of Team Sky, now INEOS Grenadiers, and team director, Dave Brailsford, who called this concept “marginal gains”. Brailsford and his team set out to win the TdF within five years with a clean British rider (29). To achieve this, he brought with him the approach he used as a performance director for British Cycling, which had led to considerable success in track cycling. Team Sky was established on the back of British dominance in the Laoshan velodrome during the 2008 Beijing Olympics, where they took home seven gold medals. As he transitioned from the track to the road, Brailsford brought the idea that compiling enough marginal gains could provide a greater performance advantage than PEDs (87).

Although the marginal gain concept came to prominence with Team Sky during one of professional cycling’s most recent avowed shift from banned to legal performance-enhancing strategies, it has been practiced by cyclists since at least the mid-1900s. Italian Fausto Coppi, who rode to multiple victories in the TdF and Giro d’Italia, as well as in one-day classics throughout the 40s and early 50s, was an early adopter of novel diet and training approaches. After World War II, the sport of cycling was anything but advanced and Coppi set out to change that. He worked with Bianchi to develop bikes and other equipment; he adapted his diet to better fuel his riding – not only its contents, but also the timing and amount; and he explored strategies for how to best race as a team (37). Some of these developments would later influence other greats, such as Eddie Merckx, who, among other things, was obsessed with proper bike fit (38). Current director of the French national team, Cyrille Guimard, has also long been known for his application of cutting-edge technology and training methods. One of his former riders, Laurent Fignon, described him as being “right up-to-date. He had files for everything. He was interested in all the lates training methods. Where his protégés were concerned, he would look at the very last detail and even the slightest defect would be corrected. He knew how to ensure everyone had the very best equipment that was on the market: made-to-measure bikes, the newest gadgets.” (32, p. 56).

 The notion that modern riders can surpass past performances solely through legal performance strategies rests on the assumption that these strategies, particularly when combined, are highly effective. Furthermore, a larger pool of athletes and an earlier onset of structured athletic development might amplify these effects. The following section explores the degree of improvement that can be made in the areas of training, nutrition, and technology.

There is not a single anthropometric or physiological characteristic that is completely uniform across high-level cyclists (65, 111). Those with elite potential tend to have stand-out absolute measurements of aerobic fitness and power, but these are attributes that can also be found in cyclists of lower caliber. Elite riders also possess very high power-to-weigh ratios, typically expressed as watts per kilogram (W/kg). An emerging concept that may also distinguish riders of different caliber is durability, i.e., the point and degree of physiological decline during extended exercise (66, 79, 80). Laboratory measurements of key performance determinants such as power-to-weigh ratio, V̇O2max, cycling economy, critical power, and peak power output provide a detailed physiological profile of each individual rider but cannot accurately predict real-life performance.

Training Strategies

Aided by technology, experience, and insights from a growing body of research, training is more refined, structured, and supervised than before, with most, if not all, training sessions serving a specific purpose. Each rider typically follows an individualized training plan that is carried out under comprehensive monitoring of variables such as heart rate, power output, climate, and terrain. These data, along with laboratory measurements, race outcomes, and even psychological variables, are used to adjust volume, frequency, intensity, and/or modality throughout the season. This allows each rider to absorb as much recoverable training volume as possible to optimize physiological adaptations and peak repeatedly for competition while avoiding overtraining. Whereas virtually every single pedal stroke of the modern rider is quantified and analyzed to guide training, racing, and recovery, riders of the past relied more on “feel”, often opting for subjective rather than objective measurements of output. During the 1987 TdF, Laurent Fignon declared his legs to be “functioning again, more or less”, but did not see the value in monitoring his heart rate, explaining that “I lost my temper with those blasted pulse monitors: I handed mine back so that it wouldn’t tell me anything anymore” (32, p. 182).

Although W/kg is often favored as an indicator of riding capacity and a way to quantify cycling performances, a large V̇O2max has long been considered a basic requirement of entry into the professional peloton. Values reported for GC contenders are generally comparable between generations, with the lowest value found in the most dominant TdF rider of all time, albeit with an asterisk (table 1). There are a few caveats to these numbers, such as the validity of the actual measurement, most of which are not described in the research literature but rather in media. Moreover, oxygen uptake does not increase in proportion to body mass and scaling V̇O2max to whole body mass is thus not appropriate when comparing athletes of different body sizes (71). Although some of these values may be exacerbated by PED use, both the baseline level and plasticity of V̇O2max are under considerable genetic influence (15, 86, 135), and WorldTour levels can be reached without doping in those with sufficient genetic predisposition and appropriate stimulus.

Interestingly, there seems to be a physiological trade-off between efficiency and power, where adaptations towards the latter may attenuate the former (72, 113). This phenomenon was observed in Norwegian cyclist, Oskar Svendsen, who once had the highest V̇O2max ever recorded. Svendsen showed promise early by becoming junior time trial champion with less than three years of training and placing high in Tour de l’Avenir. However, despite an incredible V̇O2max of 96.7 ml/kg/min at 18 years of age, Svendsen never became a WorldTour rider. Although his early retirement at age 20 left his potential at the elite level largely unexplored, the reduction in cycling economy he experienced with increased training load could have been resolved as he matured as a rider, as cyclists appear to become more efficient over the span of their careers with little change in V̇O2max (112). If he remained active, Svendsen may eventually have been able to exploit his incredible baseline to reach the proverbial second speed in the modern peloton without chemical assistance. These insights into Svendsen’s physiological profile not only reveal some of the physiological complexities involved in high-level endurance performance, but also serve as an example of the scientific resources available to modern teams and riders that allows for a level of detail in the assessment and follow-up of athletes never seen before at that level of the sport.

Among the many training-related advances in the modern era is a more systematic approach to altitude training. Altitude-mediated erythropoiesis has long been recognized as an exposure that can produce adaptations that improves performance at sea level, as well as acclimatize athletes to sustain performance in hypobaric conditions. There are several ways to approach altitude training and care should be taken to avoid carrying the detrimental effects of prolonged hypoxic exposure, such as reduced cardiac output (Q̇) due to hypovolemia (117), into competition. Today, professional cycling teams rely on both experience as well as past and emerging research to use altitude as an important preparatory measure in various parts of the season. As the individual responses to hypoxic conditions can vary greatly (93), a large hematological response following real or simulated altitude exposure is an important attribute in modern riders. If done properly, altitude training can induce comparable hematological changes to rHuEPO use (table 2), making it a crucial performance-enhancing strategy in the modern peloton. Increasing [Hb] not only improves V̇O2max by improving the oxygen-carrying capacity of blood (43), it also enables sustained work at a higher fraction of maximal capacity (40) and faster V̇O2 kinetics (18), which can be hugely influential in a peloton with limited interindividual difference in V̇O2max.

A more recent strategy to legally induce hematological adaptations is heat acclimation. Prolonged exposure to heat is associated with both increased plasma volume, which can improve stroke volume and consequently Q̇ and V̇O2max, as well as an expansion of total hemoglobin mass (Hbmass) (91). In fact, light exercise in a heated environment five times per week has been shown to increase Hbmass by 3% – 11% in endurance athletes (90, 103, 107). Due to the logistical challenges and cost related to with altitude camp designs such as live high-train low, heat acclimation training may offer a more accessible strategy for riders and teams with less resources, or an additional stimulus to regular stays at altitude.
The mechanistic similarities between synthetic and natural causes of erythropoiesis makes it physiologically possible to harness the benefits of EPO without doping. Voet (132) recounts that pre-scandal Festina riders did not even bring EPO to altitude camps because it was going to be “useless”. Describing his first stay at altitude, formerly enhanced rider, Thomas Dekker, wrote that “[t]he altitude works its magic: the thin air jolts my body into producing extra red blood cells and the Swiss Tour is the first race in ages where I can stay with the pace on the climbs” (25, p. 135), expressing relief that he could hang with the peloton without PEDs. Michele Ferrari, Lance Armstrong’s coach during the height of his career, argues that the effects of EPO on hemoglobin concentration can be achieved through proper altitude training alone (31).

Every rider in the professional peloton possesses rare abilities as cyclists. Given that the sport selects for individuals with above average baseline values of [Hb] and Hct, it may not take much stimulus to maintain a high level. However, compared to simply administering rHuEPO, strategies such as altitude training and heat acclimation are more complex undertakings, partly because of potential drawbacks with that must be accounted for, such as transiently reduced Q̇ and altered dietary requirements. The financial cost associated with prolonged exposure to altitude and/or heat for a professional team is also a considerable barrier, as the finances of teams can differ greatly. In some cases, PED use might simply just be more practical than legal strategies, and not necessarily more powerful.

Improving oxygen delivery and utilization have been main training targets for cyclists throughout most of its history, while resistance training (RT) has been largely neglected. As the impact of both power output and oxygen consumption on cycling performance is intrinsically related to rider weight, maintaining a low body mass has been, and still is, imperative. However, RT with an emphasis on neural adaptations can substantially improve force-generating capacity and reduce the oxygen cost of exercise in athletes without adding unnecessary bulk (51-53, 140). It also helps maintain bone mineral density, which elite cyclists are prone to lose (48, 110). A recent study found that RT with traditional movements and individualized load improved bone mineral density and endurance performance in professional cyclists (126). Moreover, it appeared to improve strength, power, and body composition to a greater degree than short sprint training, a more traditional power training modality for cyclists, supporting the role of structured RT as a part of a professional cyclists overall training program. Indeed, evidence for the benefit of RT on cycling performance has been mounting over the past years (table 3) (62, 102, 104-106, 108, 109, 120, 131, 141). This has contributed to changing the way RT is perceived and applied in the.

An elite physiology is easier to perturb than improve. At the highest level of cycling, large adaptations to training are unlikely to occur in the short term. The full, natural potential of a rider can only be reached via the cumulative effects of proper training and recovery, both of which are highly dependent on proper fueling.
Nutrition, body composition, and supplementation

In Jørgen Leth’s classic documentary, “A Sunday in Hell”, Roger De Vlaeminck can be seen consuming a plate of meat with his team before setting out to defend his multiple Paris–Roubaix victories from the previous years in the 1976 edition, with the narrator explaining that “a rare steak is a good breakfast for what lies ahead” (67). This is in stark contrast to the low-residue diet often consumed by riders in the modern peloton (39). A low-residue diet is characterized by a very low fiber content, which can reduce rider weight and consequently improve race performance (36). This diet is usually combined with a very high carbohydrate intake throughout a race to ensure constant glucose availability, and the reduced satiety that can be associated with low-residue diets may even help a rider maintain energy intake during a race. The exact amount differs between riders, with numbers around 100 g of carbohydrate per hour being a rough estimate that may be exceeded considerably on hard days. The recognition of the added performance benefit of increased carbohydrate intake has given rise to the concept of gut training for athletes (56, 78). Racing hard for hours on end for multiple consecutive days with limited glucose availability is guaranteed to hamper performance compared to a well-fueled athlete; as red blood cells do not convert to adenosine triphosphate; blood doping cannot replace bioenergetic fuel.

There are some examples of riders that leveraged nutrition to increase their performance throughout history, such as Fausto Coppi (37), but in the modern era, all riders pay attention and have access to both nutritionists and chefs, both of which are roles that have become integral parts of professional teams. Riders also have access to more knowledge and tools, such as food apps powered by machine learning (121). The days of training hard during the day following by alcohol consumption in the evening and racing on the weekends are gone, but were reportedly common until fairly recently (25, 54). The culmination of evidence- and experience-based diets in professional cycling has led to better fueling strategies and lower body mass in the peloton and perhaps especially among the best riders.

Although described as “thin as rakes” (132, p. 63), the riders of the 90s were heavy by today’s standard. Laurent Fignon (32) explains that the importance of power-to-weight ratio did not become known among the riders before the mid-80s and that he, until that point, paid little attention to diet. Looking at the top 10 finishers of the TdF for the past four decades, starting with the latest edition, suggest that it is becoming more and more of a requirement for the overall GC placing (table 4). Notably, between 1992 and 2022, the average BMI of the top 10 decreased by 8.1%. This trend seems to generally hold across all Grand Tours for the past decades (118).

Supplements such as creatine and beta-alanine have been shown to improve endurance performance, including in cycling (7, 12, 21, 49, 127, 128). Creatine was introduced to the peloton in the mid-90s but was very expensive at the time. Riders who had access to it could consume up to 30 g the day before a long time trial or a mountain stage in hopes of a performance boost (132). Creatine and beta-alanine are now both affordable and widely used, alongside other supplements such as caffeine, electrolytes, nitrates, various vitamins, and minerals, as well as macronutrient supplements such as protein and carbohydrate.

In recent years, a lot of attention has been devoted to exogenous ketones. It is a contentious supplement that has been embraced some of the strongest teams while being recommended against by the Union Cycliste Internationale (UCI) and the Movement for Credible Cycling (MPCC). Ketones, or ketone bodies, are acetyl-CoA-derived metabolites that are produced by the liver under conditions with reduced glucose availability, such as low-carbohydrate diets, fasting, and during or after hard exercise. Ketone bodies such as β-hydroxybutyrate can spare glycogen by inhibiting glycolysis and acting as an alternative fuel in oxidative phosphorylation, which in turn can improve endurance (19). As with the research on other legal and illegal enhancement strategies, the degree to which exogenous ketones translates to improved exercise performance remains to be fully elucidated (24, 92, 94, 96, 125, 139). Although there may be potential drawbacks with isolated ketone supplementation (82), in conjunction with sodium bicarbonate, which is a weak base that has been used for some time in endurance sports (45), ketone supplementation has been shown to improve power output towards the end of a race simulation by 5% (95), although this effect may be unreliable and warrants further study (97).

Much of the hype surrounding some of the proposed effect of ketones as an energy substrate appears unwarranted, but emerging evidence suggest that it may have intriguing properties as a signaling molecule. A few years ago, it was shown that infusion of ketone bodies increased circulating EPO levels in healthy adults (63). The impact of ketones on EPO is supported by the observation that adherence to a ketogenic diet can increase [Hb] and Hct by ~3%, with the caveat this effect is within the biological variation of these markers (83). Recently, Evans et al. (30) found that ingestion of ketone monoester after cycling exercise increased serum EPO concentration, providing further evidence that it may be the signaling effects rather than nutritional value of ketone supplements confers the greatest performance benefit for professional cyclists.

Technology and equipment
Science tends to be reductionistic by necessity, whereas a cycling race is much more open-ended. There is, however, a certain cycling event that is performed in highly controlled conditions and relies heavily on technological advances that can serves as a good example of marginal gains in modern road cycling: the hour record. In 1972, Eddy Merckx, perhaps the greatest cyclist of all time, rode a distance of 49.431 km to set a new hour record for the first time since the 1950s. Twelve years later, Francesco Moser breached 50 km with an effort totaling 51.151 km, aided by disc wheels and a skin suit. The following years would see various innovative approaches by riders such as Graeme Obree and Chris Boardman, until the UCI decided to revise the rules in 1994 and again in 2014 (table 5). To set his records, Boardman worked closely with Brailsford’s predecessor in British Cycling, Peter Keen, and then later with Brailsford himself after his retirement, on what would be the beginning of British riders’ marginal gains on the track and later in the peloton (14).

From Voigt’s first attempt to Ganna’s latest, the modern hour record has been improved by over 11%. Although Ganna is a multiple World Time Trial champion and likely one of the most suitable riders to attempt the record, the last person to hold the record before him was Daniel Bigham, the only rider on the list that was never a WorldTour rider. Although an accomplished cyclist in his own right, Bigham’s record is a prime example of how far and fast you can get by maximizing the margins, with his record being set at an average power output approximately 100 watts less than Wiggins. Bigham himself puts his performance down to 50% physiology and 50% equipment (137). One of the main aspects Bigham exploited was aerodynamics; his coefficient of aerodynamic drag (CdA) was ~0.15, which is considerably below what is commonly seen in cyclists, including professionals (41).

Aerodynamics is not only relevant when riding fast around a velodrome for an hour, but also one of the most important things to consider when trying to ride fast on a bike in general. At a riding speed of about 54 km/h, close to the average on a flat TdF stage, approximately 90% of the total resistance is aerodynamic resistance (13, 44). Most of the resistance is caused by the rider himself, with common estimates ranging from 60-82% (74), and the rest by other factors such as equipment (22, 73, 77). The importance of minimizing CdA underlies much of the development of modern bike frames, wheels, handlebars, helmets, clothing, and more. In recent years, there has been less emphasis from manufacturers on getting their bikes down to the UCI weight limit of 6.8 kg in favor of more aerodynamic optimizations. This approach is supported by findings showing that simply opting for aerodynamic rather than light wheels will reduce climbing time on 3% – 6% grade hills (57). Steeper hills favor lighter wheels and WorldTour riders often make specific selections of wheelset, gear ratio, and even frameset based on race or stage profile. Some teams take it a step further, such as Jumbo-Visma, who use a portable aero sensor to measure exact wind conditions on race day and make equipment selections accordingly (81).

Since the inception of professional cycling there have been numerous technological advances and there is still a steady flow of innovations reaching the peloton. Some of these become widely adopted, such as aero-optimized gear; some are providing new alternatives without replacing old ones, such as tubeless tires (riders still use a variety of tubed, tubeless, and tubular tires); and others are replacing without immediately improving a function, such as disc brakes. Technology has also enabled more extensive monitoring of athletes, both on and more recently off the bike. For instance, several teams are now measuring body temperature and hydration status, and by analyzing the individual sodium composition sweat, can select the appropriate supplementary amount of sodium for each rider. During very hot days, riders are often seen wearing cooling gear to keep body temperature down. This can not only keep the riders comfortable, but may also benefit their performance in the race by lowering thermal strain (75).

Although professional cycling continues to benefit from science, technology, and engineering, the UCI have rules and regulations in place that ensures that cycling does not, for better or worse, stray too far away from its origins. Although these are subject to change based on new developments, they sometimes can become more restrictive, such as the recent ban on handlebars narrower than 350mm. Riders with the ability and resources to combine effective performance strategies from training, nutrition, recovery, and technology – perhaps especially strategies with small effects that are more likely to be ignored by others – may find themselves able to ride at a different speed than the rest of the peloton.

Merging the margins

Imagine a gifted and durable athlete with an exceptional ability to consume oxygen across all intensity domains, maintain a low body mass, effectively utilize lactate, absorb and recover from a high training load without injury or illness, handle training and race nutrition, thermoregulate in various climates, and respond well to altitude and heat exposure finding his or her way into cycling early in life. Suppose this young cyclist learns to maintain an aerodynamic position on the bike, pedal with an efficient cadence, move seamlessly through the peloton, avoid accidents, calmly handle the pressure of competition, and execute winning moves. Professional cycling selects for individuals with supraphysiological potential from environments that have allowed this potential to be expressed. Then, it awards those who have made it to the starting line and are able make as many performance determinants as possible come together on race day.

Increased professionalism at the highest level of the sport trickles down to the amateur and junior ranks, exposing up-and-coming cyclists to favorable conditions at an earlier age, leading to greater improvements in physiology, psychology, and race craft. Some riders may show incredible promise in some aspects of racing and struggle with others. Oskar Svendsen, V̇O2max world record holder, undoubtedly had one of the greatest physiological potentials ever seen in a rider. However, he admittedly also had technical and tactical challenges: “Cycling is a monotonous sport, yet so complex and driven by tactics that you won’t win races unless you deliver on all those qualities. I came into the sport with good physical qualities, but I struggled most with the tactics and patterns. I did learn a lot in my senior years on Team Joker though, even if I still had a long way to go. Descending down hills was also something I struggled a lot with, and it sapped much of my energy in races.” (99) Svendsen’s career serves as an example of how cycling is not only a physiological sport, but also technical, tactical, and psychological. Recently retired rider, Richie Porte, described former TdF GC winners Chris Froome and Tadej Pogačar as “psychological beasts” and noted that cycling has become increasingly scientific, which does not suit all riders (35). Modern riders are more methodical, data driven, and regimented than before. This reduces the human element of the sport, to the dismay of those claiming that this will increase predictability. Some researchers in the field have also warned against measuring just for the sake of measuring, and advise that rider data should serve a specific purpose (55).

The widely established routine of constant fueling during training and racing not only acutely increase work capacity but also improves subsequent recovery by preventing the rider from becoming completely depleted. This is in stark contrast to the days when reaching for your bottle during a hard training ride, even if it only contained water, was considered a weakness. Paul Köchli, former coach of riders such as Bernard Hinault and Greg Lemond, once said that the art of cycling is to do the right thing at the right moment (27). This is true not only in the context of a race, but indeed for the professional cyclist’s career as a whole. The effects of proper training, nutrition, and recovery accumulate not only throughout a season, but a whole career, benefitting those who consistently do the right things from early on.

Conclusion and future perspectives

In some ways, modern approaches to improving cycling performance represent a first principles approach to cycling and a fundamental challenge of conventions, within the rules and regulations of UCI. It seems to have restored some of the faith in the sport that was once lost with various doping scandals. Given the measurable impacts of legal performance-enhancing strategies, many of which were previously unknown or overlooked, it could be argued that combining these effects can bring a clean rider’s performance close to, or even surpass, that of an enhanced cyclist, assuming a gifted baseline and sufficient degree of adaptability.

Suggesting that it is possible to win at the highest level in cycling without the use of PEDs is not the same as claiming that the sport is completely clean. As others have pointed out, periods that have previously been perceived as clean have later been shown to be anything but (26). This paper covers some of the key legal advances in road cycling that has contributed to elite performances in the modern peloton, while at the same time acknowledging that illegal strategies may still be present.

Much of what was once considered “marginal gains” have now become common in all professional cycling teams. This represents a shift from a culture of doping to a culture of exhaustive continuous improvement, a lot of which is kept under wraps and some that may even be considered a grey area. Effective anti-doping measures contribute to a more level playing field, but not entirely level. The teams with the most resources often get the most talented riders, allowing them to combine the greatest potential with the best strategies. And even still, there are some who favor optimizing riders and their equipment for weight rather than aerodynamics, ignoring the latter to the extent that it becomes a considerable detriment. In an era of professional cycling where individual performances are influenced by a multitude of human and nonhuman factors, which in combination can have profound effects, the existence of two-speed cycling in a clean peloton is not only logical – it should be expected.

Acknowledgments

This work was supported by the Norwegian University of Science and Technology (NTNU). The author would like to thank Dr. Endre T. Nesse and Dr. Fabio G. Laginestra for their comments and feedback on the manuscript.

REFERENCES

  1. Annaheim, S., Jacob, M., Krafft, A., Breymann, C., Rehm, M., & Boutellier, U. (2016). RhEPO improves time to exhaustion by non-hematopoietic factors in humans. European Journal of Applied Physiology, 116(3), 623-633. https://doi.org/10.1007/s00421-015-3322-6
  2. Arnold, R. (2018, 18 January 2018). Egan Bernal: A VO2max of 91… “it’s just a number”. Ride Media. Retrieved 10 January 2023 from https://www.ridemedia.com.au/features/egan-bernal-vo2max-91-just-number
  3. Astolfi, T., Crettaz von Roten, F., Kayser, B., Saugy, M., & Faiss, R. (2021). The Influence of Training Load on Hematological Athlete Biological Passport Variables in Elite Cyclists. Frontiers in Sports and Active Living, 3. https://doi.org/10.3389/fspor.2021.618285
  4. Attia, P., & San-Millán, I. (2022, 1 April 2022). How often should you be doing Zone 5 training? | Iñigo San-Millán, Ph.D. & Peter Attia, M.D. YouTube. Retrieved 10 January 2023 from https://www.youtube.com/watch?v=xuqURs4auc8
  5. Aubel, O., Lefèvre, B., Le Goff, J.-M., & Taverna, N. (2018). Doping risk and career turning points in male elite road cycling (2005–2016). Journal of Science and Medicine in Sport, 21(10), 994-998. https://doi.org/10.1016/j.jsams.2018.03.003
  6. Aubel, O., Lefèvre, B., Le Goff, J. M., & Taverna, N. (2019). The team effect on doping in professional male road cycling (2005-2016). Scandinavian Journal of Medicine & Science in Sports, 29(4), 615-622. https://doi.org/10.1111/sms.13384
  7. Baguet, A., Koppo, K., Pottier, A., & Derave, W. (2010). Beta-alanine supplementation reduces acidosis but not oxygen uptake response during high-intensity cycling exercise. Eur J Appl Physiol, 108(3), 495-503. https://doi.org/10.1007/s00421-009-1225-0
  8. Bailey, M. (2016, 6 May 2016). Greg LeMond: Interview. Cyclist. Retrieved 10 January 2023 from https://www.cyclist.co.uk/in-depth/115/greg-lemond-interview
  9. Bailey, M. (2016, 31 May 2016). Miguel Indurain: the record Tour winner. Cyclist. Retrieved 10 January 2023 from https://www.cyclist.co.uk/in-depth/423/miguel-indurain-the-record-tour-winner
  10. Bell, P., Ten Have, C., & Lauchs, M. (2016). A case study analysis of a sophisticated sports doping network: Lance Armstrong and the USPS Team. International Journal of Law, Crime and Justice, 46, 57-68. https://doi.org/10.1016/j.ijlcj.2016.03.001
  11. Bell, P. G., Furber, M. J., van Someren, K. A., Antón-Solanas, A., & Swart, J. (2017). The Physiological Profile of a Multiple Tour de France Winning Cyclist. Med Sci Sports Exerc, 49(1), 115-123. https://doi.org/10.1249/mss.0000000000001068
  12. Bemben, M. G., & Lamont, H. S. (2005). Creatine supplementation and exercise performance: recent findings. Sports Med, 35(2), 107-125. https://doi.org/10.2165/00007256-200535020-00002
  13. Blocken, B., van Druenen, T., Toparlar, Y., & Andrianne, T. (2018). Aerodynamic analysis of different cyclist hill descent positions. Journal of Wind Engineering and Industrial Aerodynamics, 181, 27-45. https://doi.org/10.1016/j.jweia.2018.08.010
  14. Boardman, C. (2017). Triumphs and Turbulence: My Autobiography. Ebury Press.
  15. Bouchard, C., An, P., Rice, T., Skinner, J. S., Wilmore, J. H., Gagnon, J., Pérusse, L., Leon, A. S., & Rao, D. C. (1999). Familial aggregation of VO(2max) response to exercise training: results from the HERITAGE Family Study. J Appl Physiol (1985), 87(3), 1003-1008. https://doi.org/10.1152/jappl.1999.87.3.1003
  16. Bowers, L. D. (1998). Athletic drug testing. Clin Sports Med, 17(2), 299-318. https://doi.org/10.1016/s0278-5919(05)70082-x
  17. Chicharro, J. L., Hoyos, J., Bandrés, F., Terrados, N., Fernández, B., & Lucía, A. (2001). Thyroid hormone levels during a 3-week professional road cycling competition. Horm Res, 56(5-6), 159-164. https://doi.org/10.1159/000048112
  18. Connes, P., Perrey, S., Varray, A., Préfaut, C., & Caillaud, C. (2003). Faster oxygen uptake kinetics at the onset of submaximal cycling exercise following 4 weeks recombinant human erythropoietin (r-HuEPO) treatment. Pflugers Arch, 447(2), 231-238. https://doi.org/10.1007/s00424-003-1174-0
  19. Cox, Pete J., Kirk, T., Ashmore, T., Willerton, K., Evans, R., Smith, A., Murray, Andrew J., Stubbs, B., West, J., McLure, Stewart W., King, M. T., Dodd, Michael S., Holloway, C., Neubauer, S., Drawer, S., Veech, Richard L., Griffin, Julian L., & Clarke, K. (2016). Nutritional Ketosis Alters Fuel Preference and Thereby Endurance Performance in Athletes. Cell Metabolism, 24(2), 256-268. https://doi.org/10.1016/j.cmet.2016.07.010
  20. Coyle, E. F. (2005). Improved muscular efficiency displayed as Tour de France champion matures. J Appl Physiol (1985), 98(6), 2191-2196. https://doi.org/10.1152/japplphysiol.00216.2005
  21. Crisafulli, D. L., Buddhadev, H. H., Brilla, L. R., Chalmers, G. R., Suprak, D. N., & San Juan, J. G. (2018). Creatine-electrolyte supplementation improves repeated sprint cycling performance: A double blind randomized control study. J Int Soc Sports Nutr, 15, 21. https://doi.org/10.1186/s12970-018-0226-y
  22. Crouch, T. N., Burton, D., LaBry, Z. A., & Blair, K. B. (2017). Riding against the wind: a review of competition cycling aerodynamics. Sports Engineering, 20(2), 81-110. https://doi.org/10.1007/s12283-017-0234-1
  23. CyclingTips. (2016, 15 August 2016). Chris Froome’s lab results analysed: just how good is the three-time Tour de France champion? CyclingTips. Retrieved 10 January 2023 from https://cyclingtips.com/2016/08/chris-froomes-lab-results-analysed-just-how-good-is-the-three-time-tour-de-france-champion
  24. Dearlove, D. J., Harrison, O. K., Hodson, L., Jefferson, A., Clarke, K., & Cox, P. J. (2021). The Effect of Blood Ketone Concentration and Exercise Intensity on Exogenous Ketone Oxidation Rates in Athletes. Medicine & Science in Sports & Exercise, 53(3). https://doi.org/10.1249/MSS.0000000000002502
  25. Dekker, T. (2018). The Descent. Ebury Press.
  26. Dimeo, P. (2014). Why Lance Armstrong? Historical Context and Key Turning Points in the ‘Cleaning Up’ of Professional Cycling. The International Journal of the History of Sport, 31(8), 951-968. https://doi.org/10.1080/09523367.2013.879858
  27. Dower, J. (2014). Slaying the Badger ESPN.
  28. Durussel, J., Haile, D. W., Mooses, K., Daskalaki, E., Beattie, W., Mooses, M., Mekonen, W., Ongaro, N., Anjila, E., Patel, R. K., Padmanabhan, N., McBride, M. W., McClure, J. D., & Pitsiladis, Y. P. (2016). Blood transcriptional signature of recombinant human erythropoietin administration and implications for antidoping strategies. Physiological Genomics, 48(3), 202-209. https://doi.org/10.1152/physiolgenomics.00108.2015
  29. Edworthy, S., & Brailsford, D. (2012). 21 Days to Glory: The Official Team Sky Book of the 2012 Tour de France. HarperSport.
  30. Evans, E., Walhin, J.-P., Hengist, A., Betts, J. A., Dearlove, D. J., & Gonzalez, J. T. (2022). Ketone monoester ingestion increases post-exercise serum erythropoietin concentrations in healthy men. American Journal of Physiology-Endocrinology and Metabolism. https://doi.org/10.1152/ajpendo.00264.2022
  31. Ferrari, M. (2013, 22 January 2013). A bit of history. 53×12. Retrieved 27 December 2022 from https://www.53×12.com/a-bit-of-history
  32. Fignon, L. (2010). We Were Young and Carefree. Yellow Jersey Press.
  33. Fincoeur, B. (2009). Lutte antidopage et cyclisme à deux vitesses: Évolution du rapport au dopage chez les cyclistes belges depuis l’affaire Festina. Revue internationale de criminologie et de police technique, 62.
  34. Fincoeur, B., Gleaves, J., & Ohl, F. (2019). Doping in Cycling: Interdisciplinary Perspectives. Routledge.
  35. Fletcher, P. (2022, 23 December 2022). Richie Porte: Pogacar and Froome are psychological beasts. Cyclingnews. Retrieved 18 January 2023 from https://www.cyclingnews.com/news/richie-porte-pogacar-and-froome-are-psychological-beasts
  36. Foo, W. L., Harrison, J. D., Mhizha, F. T., Langan-Evans, C., Morton, J. P., Pugh, J. N., & Areta, J. L. (2022). A Short-Term Low-Fiber Diet Reduces Body Mass in Healthy Young Men: Implications for Weight-Sensitive Sports. Int J Sport Nutr Exerc Metab, 32(4), 256-264. https://doi.org/10.1123/ijsnem.2021-0324
  37. Fotheringham, W. (2010). Fallen Angel: The Passion of Fausto Coppi. Yellow Jersey Press.
  38. Fotheringham, W. (2013). Half Man, Half Bike: The Life of Eddy Merckx, Cycling’s Greatest Champion. Yellow Jersey Press.
  39. Freeman, R. (2018). The Line: Where Medicine and Sport Collide. Wildfire.
  40. Fritsch, J., Winter, U. J., Reupke, I., Gitt, A. K., Berge, P. G., & Hilger, H. H. (1993). [Effect of a single blood donation on ergo-spirometrically determined cardiopulmonary performance capacity of young healthy probands]. Z Kardiol, 82(7), 425-431. (Einfluss einer einmaligen Blutspende auf die ergospirometrisch bestimmte kardiopulmonale Leistungsfähigkeit bei jungen gesunden Probanden.)
  41. García-López, J., Rodríguez-Marroyo, J. A., Juneau, C.-E., Peleteiro, J., Martínez, A. C., & Villa, J. G. (2008). Reference values and improvement of aerodynamic drag in professional cyclists. Journal of Sports Sciences, 26(3), 277-286. https://doi.org/10.1080/02640410701501697
  42. Gifford, B. (July 2008). Greg LeMond vs. The World. Men’s Journal. Retrieved 10 January 2023 from https://www.mensjournal.com/health-fitness/greg-lemond-vs-the-world-20130318
  43. Gledhill, N., Warburton, D., & Jamnik, V. (1999). Haemoglobin, blood volume, cardiac function, and aerobic power. Can J Appl Physiol, 24(1), 54-65. https://doi.org/10.1139/h99-006
  44. Grappe, F., Candau, R., Belli, A., & Rouillon, J. (1298). Aerodynamic drag in field cycling with special reference to the Obree’s position. Ergonomics, December 1, 1299-1311. https://doi.org/10.1080/001401397187388
  45. Grgic, J., Pedisic, Z., Saunders, B., Artioli, G. G., Schoenfeld, B. J., McKenna, M. J., Bishop, D. J., Kreider, R. B., Stout, J. R., Kalman, D. S., Arent, S. M., VanDusseldorp, T. A., Lopez, H. L., Ziegenfuss, T. N., Burke, L. M., Antonio, J., & Campbell, B. I. (2021). International Society of Sports Nutrition position stand: sodium bicarbonate and exercise performance. J Int Soc Sports Nutr, 18(1), 61. https://doi.org/10.1186/s12970-021-00458-w
  46. Hamilton, T., & Coyle, D. (2012). The Secret Race: Inside the Hidden World of the Tour de France. Bantam Press.
  47. Heuberger, J. A. A. C., Rotmans, J. I., Gal, P., Stuurman, F. E., van ‘t Westende, J., Post, T. E., Daniels, J. M. A., Moerland, M., van Veldhoven, P. L. J., de Kam, M. L., Ram, H., de Hon, O., Posthuma, J. J., Burggraaf, J., & Cohen, A. F. (2017). Effects of erythropoietin on cycling performance of well trained cyclists: a double-blind, randomised, placebo-controlled trial. The Lancet Haematology, 4(8), e374-e386. https://doi.org/10.1016/S2352-3026(17)30105-9
  48. Hilkens, L., van Schijndel, N., Weijer, V., Boerboom, M., van der Burg, E., Peters, V., Kempers, R., Bons, J., van Loon, L. J. C., & van Dijk, J.-W. (2022). Low Bone Mineral Density and Associated Risk Factors in Elite Cyclists at Different Stages of a Professional Cycling Career. Medicine & Science in Sports & Exercise. https://doi.org/10.1249/MSS.0000000000003113
  49. Hill, C. A., Harris, R. C., Kim, H. J., Harris, B. D., Sale, C., Boobis, L. H., Kim, C. K., & Wise, J. A. (2007). Influence of beta-alanine supplementation on skeletal muscle carnosine concentrations and high intensity cycling capacity. Amino Acids, 32(2), 225-233. https://doi.org/10.1007/s00726-006-0364-4
  50. Hincapie, G., & Hummer, C. (2014). The Loyal Lieutenant: Leading Out Lance and Pushing Through the Pain on the Rocky Road to Paris. HarperCollins.
  51. Hoff, J., Berdahl, G. O., & Bråten, S. (2001). Jumping height development and body weight considerations in ski jumping. In Science and skiing II : Second International Congress on Science and Skiing ; St. Christoph a. Arlberg, Austria, January 9-15, 2000. Hamburg: Kovač (Verlag), 2001, S. 403-412, Lit.
  52. Hoff, J., Gran, A., & Helgerud, J. (2002). Maximal strength training improves aerobic endurance performance. Scand J Med Sci Sports, 12(5), 288-295. https://doi.org/10.1034/j.1600-0838.2002.01140.x
  53. Hoff, J., Helgerud, J., & Wisløff, U. (1999). Maximal strength training improves work economy in trained female cross-country skiers. Med Sci Sports Exerc, 31(6), 870-877. https://doi.org/10.1097/00005768-199906000-00016
  54. Hushovd, T., & Ravnåsen, J. (2014). Thor. Schibsted Forlag AS.
  55. Javaloyes, A., & Mateo-March, M. (2022). Only what is necessary: The use of technology in cycling and concerns with its selection and use. Journal of Science & Cycling, 11(3), 1-2. https://doi.org/10.28985/1322.jsc.16
  56. Jeukendrup, A. E. (2017). Training the Gut for Athletes. Sports Med, 47(Suppl 1), 101-110. https://doi.org/10.1007/s40279-017-0690-6
  57. Jeukendrup, A. E., & Martin, J. (2001). Improving Cycling Performance. Sports Medicine, 31(7), 559-569. https://doi.org/10.2165/00007256-200131070-00009
  58. Johnson, M. (2016). Spitting in the Soup: Inside the Dirty Game of Doping in Sports. VeloPress
  59. Kimmage, P. (2007). Rough Ride. Yellow Jersey Press.
  60. Klein, H. G. (1985). Blood transfusion and athletics. Games people play. N Engl J Med, 312(13), 854-856. https://doi.org/10.1056/nejm198503283121311
  61. Kolata, G. (2005, 24 July 2005). Super, Sure, but Not More Than Human. The New York Times. Retrieved 10 January 2023 from https://www.nytimes.com/2005/07/24/weekinreview/super-sure-but-not-more-than-human.html
  62. Kordi, M., Folland, J. P., Goodall, S., Menzies, C., Patel, T. S., Evans, M., Thomas, K., & Howatson, G. (2020). Cycling-specific isometric resistance training improves peak power output in elite sprint cyclists. Scand J Med Sci Sports, 30(9), 1594-1604. https://doi.org/10.1111/sms.13742
  63. Lauritsen, K. M., Søndergaard, E., Svart, M., Møller, N., & Gormsen, L. C. (2018). Ketone Body Infusion Increases Circulating Erythropoietin and Bone Marrow Glucose Uptake. Diabetes Care, 41(12), e152-e154. https://doi.org/10.2337/dc18-1421
  64. Lentillon-Kaestner, V., Hagger, M., & Hardcastle, S. (2011). Health and doping in elite-level cycling. Scandinavian Journal of Medicine & Science in Sports, 22, 596-606. https://doi.org/10.1111/j.1600-0838.2010.01281.x
  65. Leo, P., Simon, D., Hovorka, M., Lawley, J., & Mujika, I. (2022). Elite versus non-elite cyclist – Stepping up to the international/elite ranks from U23 cycling. Journal of Sports Sciences, 40(16), 1874-1884. https://doi.org/10.1080/02640414.2022.2117394
  66. Leo, P., Spragg, J., Mujika, I., Giorgi, A., Lorang, D., Simon, D., & Lawley, J. S. (2021). Power Profiling, Workload Characteristics, and Race Performance of U23 and Professional Cyclists During the Multistage Race Tour of the Alps. International Journal of Sports Physiology and Performance, 16(8), 1089-1095. https://doi.org/10.1123/ijspp.2020-0381
  67. Leth, J. (1977). A Sunday in Hell Steen Herdel Filmproduktion.
  68. Levine, B. D., & Stray-Gundersen, J. (1997). “Living high-training low”: effect of moderate-altitude acclimatization with low-altitude training on performance. J Appl Physiol (1985), 83(1), 102-112. https://doi.org/10.1152/jappl.1997.83.1.102
  69. Ljungqvist, A. (2017). Brief History of Anti-Doping. Med Sport Sci, 62, 1-10. https://doi.org/10.1159/000460680
  70. Llamas, F. (2016, 24 January 2016). La ‘bestia’ que viene. Marca. Retrieved 10 January 2023 from https://www.marca.com/ciclismo/2016/01/24/56a4fa71ca474159048b45c4.html
  71. Lolli, L., Batterham, A. M., Weston, K. L., & Atkinson, G. (2017). Size Exponents for Scaling Maximal Oxygen Uptake in Over 6500 Humans: A Systematic Review and Meta-Analysis. Sports Med, 47(7), 1405-1419. https://doi.org/10.1007/s40279-016-0655-1
  72. Lucía, A., Hoyos, J., Pérez, M., Santalla, A., & Chicharro, J. L. (2002). Inverse relationship between V̇O2max and economy/efficiency in world-class cyclists. Medicine & Science in Sports & Exercise, 34(12). https://doi.org/10.1249/01.MSS.0000039306.92778.DF
  73. Lukes, R. A., Chin, S. B., & Haake, S. J. (2005). The understanding and development of cycling aerodynamics. Sports Engineering, 8(2), 59-74. https://doi.org/10.1007/BF02844004
  74. Malizia, F., Druenen, T., & Blocken, B. (2021). Impact of wheel rotation on the aerodynamic drag of a time trial cyclist. Sports Engineering, 24. https://doi.org/10.1007/s12283-021-00341-6
  75. Marino, F. E. (2002). Methods, advantages, and limitations of body cooling for exercise performance. British Journal of Sports Medicine, 36(2), 89. https://doi.org/10.1136/bjsm.36.2.89
  76. Martin, D. T., Quod, M. J., & Gore, C. J. (2005). Has Armstrong’s cycle efficiency improved? Journal of Applied Physiology, 99(4), 1628-1629. https://doi.org/10.1152/japplphysiol.00507.2005
  77. Martin, J. C., Milliken, D. L., Cobb, J. E., McFadden, K. L., & Coggan, A. R. (1998). Validation of a Mathematical Model for Road Cycling Power. Journal of Applied Biomechanics, 14(3), 276-291. https://doi.org/10.1123/jab.14.3.276
  78. Martinez, I. G., Mika, A. S., Biesiekierski, J. R., & Costa, R. J. S. (2023). The Effect of Gut-Training and Feeding-Challenge on Markers of Gastrointestinal Status in Response to Endurance Exercise: A Systematic Literature Review. Sports Med, 53(6), 1175-1200. https://doi.org/10.1007/s40279-023-01841-0
  79. Mateo-March, M., Valenzuela, P. L., Muriel, X., Gandia-Soriano, A., Zabala, M., Lucia, A., Pallarés, J., & Barranco-Gil, D. (2022). The Record Power Profile of Male Professional Cyclists: Fatigue Matters. International Journal of Sports Physiology and Performance, 17, 1-6. https://doi.org/10.1123/ijspp.2021-0403
  80. Maunder, E., Seiler, S., Mildenhall, M. J., Kilding, A. E., & Plews, D. J. (2021). The Importance of ‘Durability’ in the Physiological Profiling of Endurance Athletes. Sports Medicine, 51(8), 1619-1628. https://doi.org/10.1007/s40279-021-01459-0
  81. Mc Laughlin, R. (2022, 3 August 2022). Has Aerosensor finally cracked at-home aero testing? CyclingTips. Retrieved 17 January 2023 from https://cyclingtips.com/2022/08/has-aerosensor-finally-cracked-at-home-aero-testing
  82. McCarthy, D. G., Bostad, W., Powley, F. J., Little, J. P., Richards, D. L., & Gibala, M. J. (2021). Increased cardiorespiratory stress during submaximal cycling after ketone monoester ingestion in endurance-trained adults. Appl Physiol Nutr Metab, 46(8), 986-993. https://doi.org/10.1139/apnm-2020-0999
  83. McKay, A. K. A., Peeling, P., Pyne, D. B., Welvaert, M., Tee, N., Leckey, J. J., Sharma, A. P., Ross, M. L. R., Garvican-Lewis, L. A., Swinkels, D. W., Laarakkers, C. M., & Burke, L. M. (2019). Chronic Adherence to a Ketogenic Diet Modifies Iron Metabolism in Elite Athletes. Medicine & Science in Sports & Exercise, 51(3). https://doi.org/10.1249/MSS.0000000000001816
  84. Meeuwsen, T., Hendriksen, I. J. M., & Holewijn, M. (2001). Training-induced increases in sea-level performance are enhanced by acute intermittent hypobaric hypoxia. European Journal of Applied Physiology, 84(4), 283-290. https://doi.org/10.1007/s004210000363
  85. Millar, D. (2012). Racing Through the Dark. Orion Publishing Group.
  86. Miyamoto-Mikami, E., Zempo, H., Fuku, N., Kikuchi, N., Miyachi, M., & Murakami, H. (2018). Heritability estimates of endurance-related phenotypes: A systematic review and meta-analysis. Scandinavian Journal of Medicine & Science in Sports, 28(3), 834-845. https://doi.org/10.1111/sms.12958
  87. Moore, R. (2012). Sky’s the Limit: British Cycling’s Quest to Conquer the Tour de France. HarperSport.
  88. Movement for Credible Cycling. (2022). Credibility figures: Continental teams tarnished [Internet; cited 2023 August 2]. Retrieved from: https://www.mpcc.fr/en/credibility-figures-continental-teams-tarnished/.
  89. Mørkeberg, J. S., Belhage, B., & Damsgaard, R. (2009). Changes in blood values in elite cyclist. Int J Sports Med, 30(2), 130-138. https://doi.org/10.1055/s-2008-1038842
  90. Nybo, L., Rønnestad, B., & Lundby, C. (2022). High or hot-Perspectives on altitude camps and heat-acclimation training as preparation for prolonged stage races. Scand J Med Sci Sports. https://doi.org/10.1111/sms.14268
  91. Oberholzer, L., Siebenmann, C., Mikkelsen, C. J., Junge, N., Piil, J. F., Morris, N. B., Goetze, J. P., Meinild Lundby, A.-K., Nybo, L., & Lundby, C. (2019). Hematological Adaptations to Prolonged Heat Acclimation in Endurance-Trained Males. Frontiers in Physiology, 10. https://doi.org/10.3389/fphys.2019.01379
  92. Pinckaers, P. J., Churchward-Venne, T. A., Bailey, D., & van Loon, L. J. (2017). Ketone Bodies and Exercise Performance: The Next Magic Bullet or Merely Hype? Sports Med, 47(3), 383-391. https://doi.org/10.1007/s40279-016-0577-y
  93. Płoszczyca, K., Langfort, J., & Czuba, M. (2018). The Effects of Altitude Training on Erythropoietic Response and Hematological Variables in Adult Athletes: A Narrative Review. Frontiers in Physiology, 9. https://doi.org/10.3389/fphys.2018.00375
  94. Poffé, C., Ramaekers, M., Bogaerts, S., & Hespel, P. (2020). Exogenous ketosis impacts neither performance nor muscle glycogen breakdown in prolonged endurance exercise. Journal of Applied Physiology, 128(6), 1643-1653. https://doi.org/10.1152/japplphysiol.00092.2020
  95. Poffé, C., Ramaekers, M., Bogaerts, S., & Hespel, P. (2021). Bicarbonate Unlocks the Ergogenic Action of Ketone Monoester Intake in Endurance Exercise. Medicine & Science in Sports & Exercise, 53(2). https://doi.org/10.1249/MSS.0000000000002467
  96. Poffé, C., Robberechts, R., Podlogar, T., Kusters, M., Debevec, T., & Hespel, P. (2021). Exogenous ketosis increases blood and muscle oxygenation but not performance during exercise in hypoxia. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 321(6), R844-R857. https://doi.org/10.1152/ajpregu.00198.2021
  97. Poffé, C., Wyns, F., Ramaekers, M., & Hespel, P. (2021). Exogenous Ketosis Impairs 30-min Time-Trial Performance Independent of Bicarbonate Supplementation. Med Sci Sports Exerc, 53(5), 1068-1078. https://doi.org/10.1249/mss.0000000000002552
  98. Rasmussen, M., & Wivel, K. (2013). Gul Feber. People’sPress.
  99. Redford, P. (2018, 2 April 2018). The Fittest Human Ever Quit Sports, Found Happiness. Deadspin Retrieved 10 January 2023 from https://deadspin.com/the-fittest-human-ever-quit-sports-found-happiness-1823998288
  100. Riis, B., & Pedersen, L. S. (2012). Riis: Stages Of Light And Dark. Vision Sports Publishing.
  101. Robinson, N., Giraud, S., Saudan, C., Baume, N., Avois, L., Mangin, P., & Saugy, M. (2006). Erythropoietin and blood doping. Br J Sports Med, 40 Suppl 1(Suppl 1), i30-34. https://doi.org/10.1136/bjsm.2006.027532
  102. Rønnestad, B. (2022). Case Report: Effects of Multiple Seasons of Heavy Strength Training on Muscle Strength and Cycling Sprint Power in Elite Cyclists. Front Sports Act Living, 4, 860685. https://doi.org/10.3389/fspor.2022.860685
  103. Rønnestad, B., Hamarsland, H., Hansen, J., Holen, E., Montero, D., Whist, J. E., & Lundby, C. (2021). Five weeks of heat training increases haemoglobin mass in elite cyclists. Exp Physiol, 106(1), 316-327. https://doi.org/10.1113/ep088544
  104. Rønnestad, B., Hansen, E. A., & Raastad, T. (2010). Effect of heavy strength training on thigh muscle cross-sectional area, performance determinants, and performance in well-trained cyclists. Eur J Appl Physiol, 108(5), 965-975. https://doi.org/10.1007/s00421-009-1307-z
  105. Rønnestad, B., Hansen, E. A., & Raastad, T. (2010). In-season strength maintenance training increases well-trained cyclists’ performance. Eur J Appl Physiol, 110(6), 1269-1282. https://doi.org/10.1007/s00421-010-1622-4
  106. Rønnestad, B., Hansen, E. A., & Raastad, T. (2011). Strength training improves 5-min all-out performance following 185 min of cycling. Scandinavian Journal of Medicine & Science in Sports, 21(2), 250-259. https://doi.org/10.1111/j.1600-0838.2009.01035.x
  107. Rønnestad, B., Hansen, J., Bonne, T., & Lundby, C. (2021). Case Report: Heat Suit Training May Increase Hemoglobin Mass in Elite Athletes. International Journal of Sports Physiology and Performance, 17, 1-5. https://doi.org/10.1123/ijspp.2021-0033
  108. Rønnestad, B., Hansen, J., Hollan, I., & Ellefsen, S. (2015). Strength training improves performance and pedaling characteristics in elite cyclists. Scand J Med Sci Sports, 25(1), e89-98. https://doi.org/10.1111/sms.12257
  109. Rønnestad, B., Hansen, J., & Nygaard, H. (2017). 10 weeks of heavy strength training improves performance-related measurements in elite cyclists. J Sports Sci, 35(14), 1435-1441. https://doi.org/10.1080/02640414.2016.1215499
  110. Sabo, D., Reiter, A., Pfeil, J., Güssbacher, A., & Niethard, F. U. (1996). [Modification of bone quality by extreme physical stress. Bone density measurements in high-performance athletes using dual-energy x-ray absorptiometry]. Z Orthop Ihre Grenzgeb, 134(1), 1-6. https://doi.org/10.1055/s-2008-1037409 (Beeinflussung der Knochenqualität durch extreme körperliche Belastung. Knochendichtemessungen bei Hochleistungssportlern mit der Dual-Energie-Röntgen-Absorptionmetrie.)
  111. Sánchez-Muñoz, C., Mateo-March, M., Muros, J. J., Javaloyes, A., & Zabala, M. (2022). Anthropometric characteristics according to the role performed by World Tour road cyclists for their team. European Journal of Sport Science, 1-8. https://doi.org/10.1080/17461391.2022.2132879
  112. Santalla, A., Naranjo, J., & Terrados, N. (2009). Muscle efficiency improves over time in world-class cyclists. Med Sci Sports Exerc, 41(5), 1096-1101. https://doi.org/10.1249/MSS.0b013e318191c802
  113. Schiffer, T. A., Ekblom, B., Lundberg, J. O., Weitzberg, E., & Larsen, F. J. (2014). Dynamic regulation of metabolic efficiency explains tolerance to acute hypoxia in humans. The FASEB Journal, 28(10), 4303-4311. https://doi.org/10.1096/fj.14-251710
  114. Schuler, B., Thomsen, J. J., Gassmann, M., & Lundby, C. (2007). Timing the arrival at 2340 m altitude for aerobic performance. Scand J Med Sci Sports, 17(5), 588-594. https://doi.org/10.1111/j.1600-0838.2006.00611.x
  115. Seznec, J. C. (2002). Toxicomanie et cyclisme professionnel. Annales Médico-psychologiques, revue psychiatrique, 160(1), 72-76. https://doi.org/10.1016/S0003-4487(01)00133-0
  116. Sgrò, P., Sansone, M., Sansone, A., Romanelli, F., & Di Luigi, L. (2018). Effects of erythropoietin abuse on exercise performance. The Physician and Sportsmedicine, 46(1), 105-115. https://doi.org/10.1080/00913847.2018.1402663
  117. Siebenmann, C., Hug, M., Keiser, S., Müller, A., van Lieshout, J., Rasmussen, P., & Lundby, C. (2013). Hypovolemia explains the reduced stroke volume at altitude. Physiol Rep, 1(5), e00094. https://doi.org/10.1002/phy2.94
  118. Smith, A., Wijnkoop, M. v., Colangelo, J., Buadze, A., & Liebrenz, M. (2023). Body Mass Index trends in men’s Grand Tour cycling events from 1992-2022: Implications for athlete wellbeing and regulatory frameworks. Research Square. https://doi.org/10.21203/rs.3.rs-2568920/v1
  119. Stray-Gundersen, J., Chapman, R. F., & Levine, B. D. (2001). “Living high-training low” altitude training improves sea level performance in male and female elite runners. J Appl Physiol (1985), 91(3), 1113-1120. https://doi.org/10.1152/jappl.2001.91.3.1113
  120. Sunde, A., Støren, Ø., Bjerkaas, M., Larsen, M. H., Hoff, J., & Helgerud, J. (2010). Maximal Strength Training Improves Cycling Economy in Competitive Cyclists. The Journal of Strength & Conditioning Research, 24(8). https://doi.org/10.1519/JSC.0b013e3181aeb16a
  121. Team Jumbo-Visma. (2021, 21 January 2021). Must-have for all riders: the Jumbo Foodcoach app. TeamJumboVisma.com. Retrieved 16 January 2023 from https://www.teamjumbovisma.com/article/material/must-have-for-all-riders-the-jumbo-foodcoach-app/
  122. Thewlis, T. (2023). Chords to cols: How Jonas Vingegaard went from guitars to Grand Tours [Internet]. 2023 July 6 [cited 2023 August 2]; Retrieved from: https://www.cyclingweekly.com/racing/holidays-in-the-mountains-to-hard-graft-in-hantsholm-harbour-the-making-of-jonas-vingegaard.
  123. Trinh, K. V., Diep, D., Chen, K. J. Q., Huang, L., & Gulenko, O. (2020). Effect of erythropoietin on athletic performance: a systematic review and meta-analysis. BMJ Open Sport Exerc Med, 6(1), e000716. https://doi.org/10.1136/bmjsem-2019-000716
  124. Valenzuela, P. L., Alejo, L. B., Ozcoidi, L. M., Lucia, A., Santalla, A., & Barranco-Gil, D. (2023). Durability in Professional Cyclists: A Field Study. Int J Sports Physiol Perform, 18(1), 99-103. https://doi.org/10.1123/ijspp.2022-0202
  125. Valenzuela, P. L., Castillo-García, A., Morales, J. S., & Lucia, A. (2021). Perspective: Ketone Supplementation in Sports-Does It Work? Adv Nutr, 12(2), 305-315. https://doi.org/10.1093/advances/nmaa130
  126. Valenzuela, P. L., Gil-Cabrera, J., Talavera, E., Alejo, L. B., Montalvo-Pérez, A., Rincón-Castanedo, C., Rodríguez-Hernández, I., Lucia, A., & Barranco-Gil, D. (2021). On- Versus Off-Bike Power Training in Professional Cyclists: A Randomized Controlled Trial. Int J Sports Physiol Perform, 16(5), 674-681. https://doi.org/10.1123/ijspp.2020-0305
  127. Van Thienen, R., Van Proeyen, K., Vanden Eynde, B., Puype, J., Lefere, T., & Hespel, P. (2009). Beta-alanine improves sprint performance in endurance cycling. Med Sci Sports Exerc, 41(4), 898-903. https://doi.org/10.1249/MSS.0b013e31818db708
  128. Vandebuerie, F., Vanden Eynde, B., Vandenberghe, K., & Hespel, P. (1998). Effect of creatine loading on endurance capacity and sprint power in cyclists. Int J Sports Med, 19(7), 490-495. https://doi.org/10.1055/s-2007-971950
  129. Vandecapelle, B. (2023). FACTCHECK. “Toen hij 17 was, had hij VO₂max van 97”: waanzinnige cijfers doen de ronde over Vingegaard, maar kloppen ze wel? [Internet]. 2023 July 19 [cited 2023 August 2]; Retrieved from: https://www.hln.be/tour-de-france/factcheck-toen-hij-17-was-had-hij-vomax-van-97-waanzinnige-cijfers-doen-de-ronde-over-vingegaard-maar-kloppen-ze-wel~a340e8a6/.
  130. Vaughters, J. (2019). One-Way Ticket: Nine Lives on Two Wheels. Quercus Editions Ltd.
  131. Vikmoen, O., Ellefsen, S., Trøen, Ø., Hollan, I., Hanestadhaugen, M., Raastad, T., & Rønnestad, B. (2016). Strength training improves cycling performance, fractional utilization of VO2max and cycling economy in female cyclists. Scand J Med Sci Sports, 26(4), 384-396. https://doi.org/10.1111/sms.12468
  132. Voet, W. (2002). Breaking the Chain: Drugs and Cycling: The True Story. Random House.
  133. Wang, G., Durussel, J., Shurlock, J., Mooses, M., Fuku, N., Bruinvels, G., Pedlar, C., Burden, R., Murray, A., Yee, B., Keenan, A., McClure, J. D., Sottas, P.-E., & Pitsiladis, Y. P. (2017). Validation of whole-blood transcriptome signature during microdose recombinant human erythropoietin (rHuEpo) administration. BMC Genomics, 18(8), 817. https://doi.org/10.1186/s12864-017-4191-7
  134. Whittle, J. (2009). Bad Blood: The Secret Life of the Tour de France. Yellow Jersey Press.
  135. Williams, C. J., Williams, M. G., Eynon, N., Ashton, K. J., Little, J. P., Wisloff, U., & Coombes, J. S. (2017). Genes to predict VO2max trainability: a systematic review. BMC Genomics, 18(Suppl 8), 831. https://doi.org/10.1186/s12864-017-4192-6
  136. Witts, J. (2022, 1 August 2022). Rouleur Retrieved 10 January 2023 from https://www.rouleur.cc/blogs/rouleur-performance/recover-like-a-grand-tour-rider
  137. Witts, J. (2022, 1 September 2022). Behind the scenes of Dan Bigham’s Hour Record: Part one Rouleur. Retrieved 17 January 2023 from https://www.rouleur.cc/blogs/the-rouleur-journal/behind-the-scenes-of-dan-bighams-hour-record-part-one
  138. Zenovich, M. (2020). Lance Part 1 ESPN.
  139. Øvretveit, K. (2023). Metabolic and moral effects of exogenous ketones. Norwegian journal of nutrition, 21(2), 33-36. https://doi.org/10.18261/ntfe.21.2.6
  140. Øvretveit, K., & Tøien, T. (2018). Maximal Strength Training Improves Strength Performance in Grapplers. J Strength Cond Res, 32(12), 3326-3332. https://doi.org/10.1519/jsc.0000000000002863
  141. Aagaard, P., Andersen, J. L., Bennekou, M., Larsson, B., Olesen, J. L., Crameri, R., Magnusson, S. P., & Kjær, M. (2011). Effects of resistance training on endurance capacity and muscle fiber composition in young top-level cyclists. Scandinavian Journal of Medicine & Science in Sports, 21(6), e298-e307. https://doi.org/10.1111/j.1600-0838.2010.01283.x
2024-02-22T11:24:51-06:00February 23rd, 2024|Research, Sport Education, Sport Training, Sports Coaching, Sports Health & Fitness, Sports Medicine, Sports Nutrition|Comments Off on Can there be two speeds in a clean peloton? Performance strategies in modern road cycling

Sports Performance: A Comparison of Oral Rehydration Solutions on Hydration Biomarkers in Military Personnel

Authors: Reginald B. O’Hara1 and Brenda Moore2

1Chief, Biochemistry Services Division, Department of Clinical Investigation, William Beaumont Army Medical Center, For Bliss, TX, USA.

2 Moore Enterprises, LLC., Independent Research Contractor, Yellow Springs, OH, USA

Correspondence:

Reginald B. O’Hara, PhD, ACSM-EP
William Beaumont Army Medical Center
Department of Clinical Investigation
Building 18509 Highlander Medics Street
El Paso, TX 79918
reginald.b.ohara.civ@health.mil
210-792-1048

Reginald B. O’Hara, Ph.D., is the Chief of the Biochemistry Services Division at William Beaumont Army Medical Center, Department of Clinical Investigation, Fort Bliss, TX. His research interests focus on the clinical pathology of the disease process, human performance, exertional heat stress, and physiological fatigue and recovery in military personnel.

Brenda Moore, Ph.D., nutrition microbiologist, is retired but continues to support her own research contracting business in Yellow Springs Ohio. Dr. Moore worked as a research nutrition microbiologist under an ORISE contract from 2016-2019 in the United States Air Force School of Aerospace Medicine, WPAFB, OH where she conducted research on thermal stress and dehydration in military personnel.

Sports Performance: A Comparison of Oral Rehydration Solutions on Hydration Biomarkers in Military Personnel

ABSTRACT

Purpose: Exertional heat stress is a serious condition, especially for military personnel working in high-heat and humid climates, such as flight maintainers, flight crew, loadmasters, and Special Operations Forces Operators. Hence, this study assessed the effects of military-approved oral rehydration solutions (ORS) in highly fit military personnel while performing a 10-mile run. The ORS tested were Gatorade (G) and CeraSport (CS), with water (W) as the control. Methods: Fifteen “well-trained” participants (13 male, 2 female) (mean± SD: age, height, weight, % body fat, and VO2 max = 28.07 ± 5.0 yrs., 69.79 ± 4.2 in, 174.4 ± 21.53 lbs., 13.7 ± 6.7%, and 52.9 ± 5.0 mL/kg/min, respectively) completed three separate 10-mile treadmill runs, separated by a one-week recovery period. Hydration biomarkers were measured at baseline, post, and 20-minute run pause increments during each 10-mile testing trial run. Study investigators measured the following hydration biomarkers 1) Body weight change (BWC), 2) hematocrit (Hc), 3) exercise heart rate (HR), 4) blood glucose (BG), 5) blood lactate concentration ([BLa¯] b), 6) hemoglobin (Hb), and 7) total urinary output (UO). Results: No statistical difference occurred in the hydration biomarkers, likely due to the large volume (1500 mL) of fluid consumed. While no significant differences in BG were detected between G and CS, both CS and G values were significantly higher than water (p< 0.05) throughout the study. Additionally, blood lactate concentrations ([BLa¯] b) were lower during the last 40 minutes of the study when CS was consumed in comparison to G, approaching significance (p= 0.09) at Time 7 (T7).Conclusions: Outcomes from the present study provide preliminary evidence that consumption of CS in the same volume and time as G results in the preservation of BG values with lower sugar and carbohydrate consumption and without a concurrent rise in blood lactate. The present study showed that although there were no statistical differences in hydration biomarkers, potential differences may be more clearly extricated in future studies conducted in varying environmental conditions, such as higher temperatures and humidity, with a larger sample size, or a more prolonged exercise period.

Keywords: rehydration solutions, biomarkers, physical exertion, sports, military personnel

(more…)
2024-01-12T15:29:30-06:00January 12th, 2024|Research, Sports Health & Fitness|Comments Off on Sports Performance: A Comparison of Oral Rehydration Solutions on Hydration Biomarkers in Military Personnel
Go to Top