George Minoso2024-11-14T07:40:15-06:00

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

George Minoso2024-11-14T07:40:15-06:00November 15th, 2024|COVID-19, Research, Sports Exercise Science, Sports Health & Fitness|

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.

George Minoso2024-11-04T11:58:48-06:00

Cupping Therapy Treatment on Range of Motion

George Minoso2024-11-04T11:58:48-06:00November 4th, 2024|Research, Sports Exercise Science, Sports Medicine|

Authors: ¹Rachele E. Warken, ²Erik Reid, & ³Christopher M. Harp

¹Northern Kentucky University, Highland Heights, Kentucky, USA

Corresponding Author:

Rachele Warken is an associate professor and the director of the graduate Athletic Training Program at Northern Kentucky University. She is also a certified athletic trainer. Rachele has a bachelor’s degree from Northern Kentucky University and a master’s and doctoral degree from the University of Hawaii, Manoa.

Abstract

Purpose:The purpose of this study was to assess the effects of cupping therapy and passive stretching on shoulder internal and external rotation in healthy male high school athletes. Methods: Participants included nine high school male football players recruited from a local private high school. An eight minute cupping therapy treatment was completed on one arm, while passive shoulder stretching was completed on the other. Pre and post intervention measurements were taken for shoulder internal and external rotation and analyzed. Results: Analysis revealed that shoulder internal rotation range of motion post intervention were significantly higher than at pre intervention (p = 0.003), but there was no significant difference between shoulder internal rotation between the cupping therapy group and passive stretching group (p = 0.879). Similarly, shoulder external rotation range of motion post intervention was significantly higher than at pre intervention (p=0.021), but there was no significant difference between the cupping therapy group and passive stretching group (p = 0.621). Conclusions: The results of this study conclude that a cupping therapy treatment was as effective as a passive stretching treatment at increasing shoulder internal and external rotation in healthy high school males. Application in Sports: Cupping therapy is widely used by clinicians and athletes for a variety of reasons. Although this study this study did not find that cupping therapy is superior to passive stretching in healthy high school aged males, it did demonstrate that this intervention is as effective as passive stretching and provides the clinician with an additional method of treatment.

Key Words: Passive Stretching, Myofascial Decompression, Rehabilitation

Introduction

            Injuries to the shoulder and elbow are very common among athletes, especially in sports that require forceful overhead activities. Range of motion deficits, specifically in shoulder internal and external rotation, have been linked to both shoulder and elbow injury. Previous research has indicated that athletes with a passive shoulder internal rotation deficit greater than 25° in their dominant shoulder compared to their non-dominant shoulder were at four to five times greater risk of upper extremity injury than those with less than a 25° deficit (10). Additionally, a total range of motion (shoulder internal rotation plus external rotation) of less than 160° also resulted in an increased the risk of upper extremity injury (2). As a result, clinicians and athletes consistently work to improve shoulder rotation range of motion with the goal of decreasing shoulder and elbow injuries.

            Common methods to increase shoulder rotation include passive stretching and self-stretching. These stretches place slow and controlled tension on the soft tissue and have been shown to increase range of motion, improve flexibility, reduce the risk of injury, and improve blood circulation (1). Recently, the use of cupping therapy has gained popularity, especially in the athletic population as a result of prominent athletes advocating its use. Cupping therapy is an ancient Chinese technique that utilizes either glass or plastic cups along with fire or a vacuum pump to create negative pressure, drawing the skin and underlying tissue into the cup during treatment (9). The negative pressure developed during the treatment is thought to help reduce pain and inflammation, improve blood flow, facilitate the healing process and strengthen the immune system (6 ,8, 9).

Cupping therapy or myofascial decompression as it is commonly known in Western medicine is often used in sports medicine settings to increase range of motion. It is thought that the increase in blood flow to the muscle during a cupping therapy treatment increases tissue temperature causing tissues to become more elastic, resulting in greater range of motion (3). Although commonly used, there is currently limited research demonstrating the effectiveness of cupping therapy on improving range of motion. Previous research analyzing the effectiveness of cupping therapy on improving spine range of motion found that the cupping therapy intervention increased cervical and lumbar spine flexion range of motion following treatment (7, 11, 14). When cupping therapy was applied to other areas of the body differing results were found. When a cupping therapy treatment was applied to the gastrocnemius, an increase in dorsiflexion range of motion was identified (4). When cupping therapy was applied to the hamstring muscle group, researchers found that the cupping therapy treatment provided similar improvements in range of motions as more standard methods such as passive stretching (5, 8, 12) or found no improvement in range of motion (9, 13). To our knowledge, there is no previous research available that assess the effectiveness of cupping therapy on the upper extremity. Therefore, the purpose of this study was to assess the effects of cupping therapy and passive stretching on shoulder internal and external rotation in healthy male high school athletes. It was hypothesized that cupping therapy will result in greater shoulder internal and external range of motion values than the passive stretching technique.

Methods

Study Design

            This study utilized a cross-sectional design, and all data were collected in the athletic training clinic of a local boy’s private high school. The dependent variables include internal and external shoulder range of motion. The independent variables include the treatment types (cupping therapy and passive stretching) and the time the measurements were taken (pre-intervention and post-intervention). This study was approved by University’s Institutional Review Board.

Participants

            Participants in this study included male high school football athletes recruited from a local boy’s private high school. A total of nine participants completed the study. Participant demographic information including age, height and weight are listed in Table 1. The inclusionary criteria for this study were healthy male high school athletes who were cleared for full athletic participation. The exclusionary criteria for this study included those who did not have full medical clearance for athletic participation, had shoulder surgery within the past year, or currently have shoulder pain.

INSERT Table 1. Participant demographics.

Table 1
Participant demographics (mean ± SD)
	N	Mean	±	SD
Age (yrs)	9	15.89	±	0.60
Height (in)	9	70.00	±	2.35
Weight (lbs)	9	188.89	±	39.43

Instrumentation

A standard twelve inch goniometer was used to measure internal and external rotation range of motion of the shoulder prior to and following the interventions. For the cupping therapy intervention, five plastic cups and pumping handle were used (Hansol Cupping Therapy Equipment Set, Hansol Medical Equipment, Seoul Korea).

Procedures

All testing occurred in the athletic training room at the local all boy’s private high school. Each participant (and their parent/guardian) completed the informed consent and assent forms prior to testing. During testing, age, height, weight, dominant arm, and previous shoulder injury information were collected. Each participant completed both the cupping therapy intervention and passive stretching intervention, one on each arm. The interventions were randomly assigned to each arm (dominant/non-dominant).

Prior to any intervention, passive shoulder internal and external rotation range of motion were assessed in both shoulders with a goniometer while the participant was lying supine, with their shoulder abducted to 90°,their elbow flexed to 90° and their shoulder in neutral rotation. Two measurements in each direction were taken and the values were averaged and used in the statistical analysis.

Following the pre intervention measurements, the cupping therapy intervention was performed with the patient lying prone. Lotion was applied to the posterior shoulder, scapula, and upper back to act as a lubricant for the cups. Five cups, each two inches in diameter were then applied to the muscle bellies of the posterior and lateral deltoid, infraspinatus, the middle portion of the trapezius and the rhomboid major and given three pumps each. The cups remained in place for eight minutes and then removed. Following removal of the cups, shoulder internal and external rotation range of motion was measured again with a goniometer.

Prior to the stretching intervention, the participant was asked to perform a warm-up of the arm being stretched. The warm-up consisted of passive self-stretching into flexion, extension, internal and external rotation, and completing rows with an elastic band. Following the warm-up, the researcher manually stretched the shoulder in both internal and external rotation with the participant in the supine position. The researcher held each stretch for 30 seconds, switching between stretching internal and external rotation for a total of three stretches in each direction. Following the stretching treatment, shoulder internal and external rotation range of motion were measured with a goniometer.

Statistical Analysis

A two-way analysis of variance was used to assess the differences between interventions (cupping therapy and passive stretching) and time period (pre-intervention and post-intervention) was completed for each dependent variable (shoulder internal rotation and shoulder external rotation). A priori alpha levels were set at p < 0.05 for statistical significance. All statistical analyses were performed using SPSS Version 28 (SPSS, Inc, Chicago, IL).

Results

A total of nine male high school athletes participated in this study. The demographic information is included in Table 1. The two-way analysis of variance revealed that shoulder internal rotation range of motion post intervention were significantly higher than at pre intervention (p = 0.003). There was no significant difference between shoulder internal rotation between the cupping therapy group and passive stretching group (p = 0.879), nor was there a significant interaction (F(1, 32) = 0.094, p = 0.761) (Table 2). Similarly, the two-way analysis of variance for shoulder external rotation range of motion post intervention was significantly higher than at pre intervention (p=0.021). There was no significant difference between the cupping therapy group and passive stretching group (p = 0.621), nor was there a significant interaction (F(1, 32) = 0.061, p = 0.806) (Table 3).

Table 2
Shoulder Internal Rotation Range of Motion (deg, mean ± SD)
	Pre-Intervention			Post-Intervention
Cupping Therapy	65.33	±	4.85	73.67	±	6.78
Passive Stretching	64.00	±	11.60	74.11	±	9.91

Table 3
Shoulder External Rotation Range of Motion (deg, mean ± SD)
	Pre-Intervention			Post-Intervention
Cupping Therapy	80.78	±	9.20	88.11	±	9.73
Passive Stretching	78.22	±	9.23	87.22	±	11.95

Discussion

The purpose of this study was to examine the effectiveness of a cupping therapy treatment on increasing shoulder internal and external rotation. The results of this study found that both the cupping therapy intervention and the passive stretching intervention significantly increased shoulder rotation, however there was no difference between the interventions. To our knowledge, this was the first study to examine the use of cupping therapy to increase range of motion at the shoulder. Previous authors examined different areas of the body and found differing results.

Markowski et al. (7) conducted a study analyzing the effects of cupping therapy on lumbar flexion in participants with chronic low back pain. They found that one cupping therapy treatment significantly improved lumbar flexion range of motion. This study did not include a control group, so it is not clear if a cupping therapy treatment is superior to more standard ways of increasing range of motion such as passive stretching of the low back. Similarly, a study by Yim et al. (14) examined the difference between a cupping therapy treatment and McKenzie stretching exercises on cervical spine range of motion in healthy participants. They found that that the cupping treatment increased cervical spine range of motion to greater degree than the McKenzie stretching exercises indicating that cupping therapy applied to the cervical spine region was a superior to other standard stretching techniques.

A study by Hammons and McCullough (4) examined the effects of a cupping therapy treatment on dorsiflexion range of motion in individuals with delayed onset muscle soreness (DOMS) in their gastrocnemius muscle. They found that cupping therapy significantly increased dorsiflexion range of motion in individuals with DOMS compared to a control group. Although a control group was used in this study, this group did not receive any treatment, so although cupping therapy increased dorsiflexion, it is not clear if a cupping therapy treatment is superior to other methods of increasing range of motion.

Several studies have examined the effectiveness of cupping therapy in the hamstring muscle group. Kim et al. (5) compared cupping therapy to passive stretching in the hamstring group. They found that both interventions significantly increased hamstring range of motion, however there was no difference between groups. Murray et al. (8) found that cupping therapy significantly increased hamstring range of motion, but similar to other studies, they did not use a control group so it is unclear if the increased observed following the cupping therapy treatment was superior to other methods of increasing range of motion. Warren et al. (12) conducted a study on hamstring flexibility and compared a cupping therapy treatment to a self-mobilization treatment using a foam roller, in individuals with tight hamstrings. Similar to others, they also found that both groups had significant improvements in range of motion, but the individual treatments were not significantly different.

Finally, a study by Williams et al. (13) also looked at the effect of cupping therapy compared to a control group on hamstring flexibility. The control group did not receive any treatment. Unlike other previous research, they found that a cupping therapy treatment did not increase hamstring range of motion. Similarly, a study by Schafer et al. (9) compared hamstring flexibility in a cupping therapy group, a sham group and a control group and found that none of the groups significantly increased hamstring range of motion following treatment.

Conclusion

This is the first study to specifically examine the effects of cupping therapy on increasing shoulder internal and external rotation. The results of this study found that cupping therapy increased both shoulder internal and external rotation, but was not superior to passive stretching. Cupping therapy is a common practice among clinicians and athletes and is used for a variety of reasons. This study adds to the previous literature that indicates that cupping therapy could be a useful tool, among others to increase shoulder internal and external rotation. Future research could focus on individuals with shoulder rotation deficits, functional limitations and pain. In this population, it is possible that cupping therapy could be a superior method for increasing range of motion and function as well as decreasing pain.

Applications in Sport

            Cupping therapy is widely used by clinicians and athletes for a variety of reasons. This study concludes that the use of cupping therapy is one possible method for increasing shoulder internal and external rotation. Although the results indicated that cupping therapy is not superior to passive stretching for increasing shoulder range of motion in healthy, high school aged male athletes, it is one tool that could be used. Although not analyzed in this study, cupping therapy has been shown to help with pain and inflammation. In theory, in an athlete suffering from a shoulder pain and decreased range of motion, a clinician may choose cupping therapy over passive stretching, because cupping therapy may increase shoulder range of motion, and it may also help with pain.

References

1. Bryant, J., Cooper, D. J., Peters, D. M., & Cook, M. D. (2023). The effects of static stretching intensity on range of motion and strength: A systematic review. Journal of Functional Morphology & Kinesiology, 8(2), 37.
2. Bullock, G. S., Faherty, M. S., Ledbetter, L., Thigpen, C. A., & Sell, T. C. (2018). Shoulder range of motion and baseball arm injuries: A systematic review and meta-analysis. Journal of Athletic Training, 53(12), 1190–1199. https://doi.org/10.4085/1062-6050-439-17
3. Chi, L.-M., Lin, L.-M., Chen, C.-L., Wang, S.-F., Lai, H.-L., & Peng, T.-C. (2016). The effectiveness of cupping therapy on relieving chronic neck and shoulder pain: A randomized controlled trial. Evidence-Based Complementary and Alternative Medicine : eCAM, 2016, 7358918. https://doi.org/10.1155/2016/7358918
4. Hammons, D., & McCullough, M. (2022). The effect of dry cupping on gastrocnemius muscle stiffness, range of motion and pain perception after delayed onset muscle soreness. Alternative Therapies in Health and Medicine, 28(7), 80–87.
5. Kim, J.-E., Cho, J.-E., Do, K.-S., Lim, S.-Y., Kim, H.-J., & Yim, J.-E. (2017). Effect of cupping therapy on range of motion, pain threshold, and muscle activity of the hamstring muscle compared to passive stretching. Korean Society of Physical Medicine, 12(3), 23–32. https://doi.org/10.13066/kspm.2017.12.3.23
6. Liu, W., Piao, S., Meng, X., & Wei, L. (2013). Effects of cupping on blood flow under skin of back in healthy human. World Journal of Acupuncture – Moxibustion, 23(3), 50–52. https://doi.org/10.1016/S1003-5257(13)60061-6
7. Markowski, A., Sanford, S., Pikowski, J., Fauvell, D., Cimino, D., & Caplan, S. (2014). A pilot study analyzing the effects of Chinese cupping as an adjunct treatment for patients with subacute low back pain on relieving pain, improving range of motion, and improving function. The Journal of Alternative and Complementary Medicine, 20(2), 113–117. https://doi.org/10.1089/acm.2012.0769
8. Murray, D., & Clarkson, C. (2019). Effects of moving cupping therapy on hip and knee range of movement and knee flexion power: A preliminary investigation. Journal of Manual & Manipulative Therapy, 27(5), 287–294. https://doi.org/10.1080/10669817.2019.1600892
9. Schafer, M. D., Tom, J. C., Girouard, T. J., Navalta, J. W., Turner, C. L., & Radzak, K. N. (2020). Cupping therapy does not influence healthy adult’s hamstring range of motion compared to control or sham conditions. International Journal of Exercise Science, 13(3), 216–224.
10. Shanley, E., Rauh, M. J., Michener, L. A., Ellenbecker, T. S., Garrison, J. C., & Thigpen, C. A. (2011). Shoulder range of motion measures as risk factors for shoulder and elbow injuries in high school softball and baseball players. American Journal of Sports Medicine, 39(9), 1997–2006.
11. Sya’id, A., & Fatarona, A. (2020). Cupping care effectiveness on flection range of motion. STRADA Jurnal Ilmiah Kesehatan. STRADA Jurnal Ilmiah Kesehatan, 9(2), 1539–1544.
12. Warren, A. J., LaCross, Z., Volberding, J. L., & O’Brien, M. S. (2020). Acute outcomes of myofascial decompression (cupping Therapy) compared to self-myofascial release on hamstring pathology after a single treatment. International Journal of Sports Physical Therapy, 15(4), 579–592.
13. Williams, J. G., Gard, H. I., Gregory, J. M., Gibson, A., & Austin, J. (2019). The effects of cupping on hamstring flexibility in college soccer players. Journal of Sport Rehabilitation, 28(4), 350–353. https://doi.org/10.1123/jsr.2017-0199
14. Yim, J., Park, J., Kim, H., Woo, J., Joo, S., Lee, S., & Song, J. (2017). Comparison of the effects of muscle stretching exercises and cupping therapy on pain thresholds, cervical range of motion and angle: A cross-over study. Physical Therapy Rehabilitation Science, 6(2), 83–89. https://doi.org/10.14474/ptrs.2017.6.2.83

George Minoso2024-10-24T15:50:50-05:00

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

George Minoso2024-10-24T15:50:50-05:00October 25th, 2024|Research, Sports Health & Fitness, Sports Nutrition|

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

Corresponding Author:

*CORRESPONDING AUTHOR:  

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)	2	0	2
Normal BMI (18.5 – 24.9 kg∙m-2)	181	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-2)	71	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

George Minoso2024-10-21T09:45:40-05:00

Low Energy Availability (LEA) in Male Athletes: A Review of the Literature

George Minoso2024-10-21T09:45:40-05:00October 23rd, 2024|Book Reveiws, Research, Sports Nutrition|

Authors:Brandon L. Lee¹

¹The Department of Exercise, Health, and Sport Sciences, Pennsylvania Western University

Corresponding Author:

Brandon L. Lee, MS, RD, CCRP
10263 4th Armored Division Dr.
Fort Drum, NY 13603
leebl18@outlook.com
315-772-0689

Brandon L. Lee, MS, RD, CCRP is a Holistic Health and Fitness (H2F) Dietitian for the U.S. Army Forces Command and a Doctor of Health Science (DHSc) student at Pennsylvania Western University. Brandon’s research interests include energy systems and metabolism, energy availability, andragogical methods for adult learning, and reflective practice to enhance learning in formal education..

ABSTRACT

Purpose: Low energy availability (LEA) is a physiological state when there is inadequate energy to meet the demands placed on the body, often through physical activity, exercise, or sports. LEA can impact any athlete engaged in a sport with low energy intake or excessive energy expenditure. LEA is a precursor to the onset of The Male Athlete Triad (MAT) and Relative Energy Deficiency in Sport (RED-S). There is no defined low energy availability threshold specific to male athletes engaged in high-energy expenditure sports leading to MAT and RED-S. This literature review evaluates the literature on the relationship between LEA and signs or symptoms of MAT and RED-S to establish a low energy availability threshold specific to male athletes engaged in high-energy expenditure sports.

Methods: The Pennsylvania Western University library electronic database was used for the literature search. Search terms included “male athletes”, “low energy availability”, “male athlete triad”, “relative energy deficiency in sport”, and “energy deficiency”. Research studies included cross-sectional, experimental, systematic reviews, meta-analyses, case studies, and some narrative and literature reviews. Studies must have been peer-reviewed and published within five years of the literature search (12/2018- 12/2023).

Results: A review of the literature shows that it is difficult to determine a LEA threshold due to present research gaps and inconsistent findings related to health and performance consequences. Based on the results of experimental studies, practitioners can expect an LEA threshold of 20-25kcal per kilogram (kg) of fat-free mass (FFM) per day in male athletes engaged in high energy-expenditure sports.

Conclusions: Athletes engaged in sports that lead to inadequate energy intake or high energy expenditure are at risk for LEA, MAT, and RED-S. Experimental research on the LEA threshold in athletes engaged in physiologically demanding sports is the greatest research gap. Based on present findings, male athletes may have an LEA threshold of <30kcal/kg of FFM/day.

Applications in Sport: Healthy nutritional practices are essential to sports performance. Interdisciplinary sports performance teams must collaborate with nutrition professionals to develop effective LEA prevention, screening, and intervention protocols.

Keywords: energy intake, energy deficiency, energy expenditure of exercise, male athlete triad, relative energy deficiency in sport, sports nutrition

Low Energy Availability (LEA) in Male Athletes: A Review of the Literature

Energy availability (EA) is the energy dedicated to body system functions. In sports nutrition, energy availability is defined as the amount of energy remaining to support an athlete’s bodily functions after energy expenditure of exercise (EEE) is deducted from energy intake (EI) (2). Health and athletic performance issues arise when athletes have inadequate energy intake or excessive energy expenditure, depleting their EA. The designated term for this is low energy availability (LEA). LEA is defined as a physiological state when there is inadequate energy to meet the demands placed on the body, often through physical activity, exercise, or sports (23). Causes of LEA include obsessive causes (disordered eating or eating disorders), intentional causes (attempts to modify body mass or composition), and inadvertent causes (byproduct of high EEE) (1).
LEA can impact any athlete engaged in a sport with low energy intake or excessive energy expenditure. LEA is most common in sports of high intensity, duration, volume, or frequency and in sports that emphasize low body weight/fat, aesthetics, or thinness, including distance cycling and running, triathlons, tactical (i.e., military), swimming, gymnastics, wrestling, bodybuilding, martial arts, boxing, soccer, tennis, rowing, horse racing, and volleyball. LEA is a precursor to the onset of both The Male Athlete Triad (MAT) and Relative Energy Deficiency in Sport (RED-S), two conditions that result in weakened physiological functions, with the former focused on reproductive and bone health decline (22). The problem is the prevalence of LEA among male athletes participating in high-energy expenditure sports, leading to potential health and performance issues. Additionally, there is no defined low energy availability threshold specific to male athletes engaged in high-energy expenditure sports leading to MAT and RED-S (3, 4, 5, 9, 11, 14, 17, 22, 26).
This literature review aims to evaluate the literature on the relationship between LEA and signs or symptoms of MAT and RED-S to establish a defined low energy availability threshold specific to male athletes engaged in high-energy expenditure sports. This literature review will report on LEA’s impact on health, body composition, athletic performance; establish LEA thresholds, and address research gaps.

RELATIVE ENERGY DEFICIENCY IN SPORT (RED-S)
LEA is a common precursor to many health and athletic performance issues. In 2014, the International Olympic Committee (IOC) developed a consensus statement titled “Beyond the Female Athlete Triad: Relative Energy Deficiency in Sport (RED-S)” and established RED-S as a new condition that refers to diminished physiological processes due to relative energy deficiency. The most current IOC RED-S models show that RED-S can impact the following systems: immunological, menstrual/reproductive function and bone health (related to athlete triad), endocrine, metabolic, hematological, growth and development, psychological, cardiovascular, and gastrointestinal. Moreover, another IOC RED-S model shows the potential performance effects of RED-S, including decreased endurance performance, increased injury risk, decreased training response, impaired judgment, decreased coordination, decreased concentration, irritability, depression, decreased glycogen stores, and decreased muscle strength (19). Much of the research on the impact of LEA and the cascade of events that lead to RED-S has primarily been conducted on female athletes, and the findings are extrapolated to their male counterparts; however, this is changing.

MALE ATHLETE TRIAD
The Male Athlete Triad (MAT) was first introduced in the 64th Annual Meeting of the American College of Sports Medicine (ACSM) in 2017 (6). MAT has comprised three essential components: LEA (sometimes referred to as energy deficiency), impaired bone health, and suppression of the hypothalamic-pituitary-gonadal (HPG) axis (22).
Prevention and treatment methods of MAT hinge on the EA or energetic status of the athlete at risk. Nattiv et al. (2021) explain that energy deficiency or LEA is confirmed when one of the following metabolic adaptations is presented: reduced RMR compared to body size or fat-free mass (FFM), unintentional weight loss resulting in a new low set point, underweight body mass index (BMI), and reduced metabolic hormones such as triiodothyronine (T3), leptin, and several more. Hypogonadotropic hypogonadism can manifest as oligospermia (deficiency of sperm in the semen) or decreased libido (reduced sexual drive). Lastly, poor bone health can manifest as osteopenia, osteoporosis, or bone stress injury (22).
The energetic status of the athlete can vary greatly depending on frequency, intensity, duration, type of sport, volume, and progression. Nattiv et al. (2021) have surmised that male athletes engaged in leanness sports typically have low energy intake compared to recommended amounts from the Institute of Medicine Daily Recommended Intakes or Food and Agriculture Organization of the United Nations/World Health Organization. Unfortunately, male leanness sports or weight-class athletes potentially consume up to 1000kcal/day less than required to support their exercise demands (22). Athletes consistently at risk for MAT include runners and cyclists, primarily if they compete in long-distance competitions.

Cardiovascular Health
Cardiovascular health (CVH) is essential to every athlete engaged in any sport. A healthy cardiovascular system effectively moves blood from one location to another to transport oxygen-containing blood cells for muscular activity. Langan-Evans et al. (2021) studied the impact of incorporating daily fluctuations in LEA on cardiorespiratory capacity via treadmill test in one combat athlete preparing to make weight for competition. The athlete experienced microcycle EA fluctuations ranging from 7 to 31 kcal per kilogram (kg) of FFM/day (mean EA of 20kcal/kg of FFM/day) for seven days and did not experience any significant changes in resting heart rate, cardio output, or overall CVH (14). Theoretically, LEA would have significant structural, conduction, repolarization, and peripheral vascular effects on CVH (17). However, a scant amount of research establishes any correlation between CVH and LEA, and primary research studies conducted within the past five years have yet to establish causation between the two.
On the other hand, Fagerberg (2018) has found that EA <25kcal/kg FFM over six months in bodybuilders preparing for a competition can impact CVH by reducing heart rate. According to Fagerberg (2018), low body fat percentages in bodybuilders worsen CVH risk (4). This heart rate reduction, paired with low body fat, is likely a physiological adaptation to conserve energy and sustain life. There needs to be more consistency in the literature regarding the impact of LEA on CVH.

Physiological Health
LEA and RED-S are both physiological and psychological health risks. Sports that emphasize leanness (e.g., cycling) or have weight divisions (e.g., combat sports) often place additional mental stress on athletes to perform well and possess a specific physique. For example, Schofield et al. (2021) found that male cyclists are at risk for LEA and RED-S due to rigid weight management practices, desire for leanness, disordered eating and eating disorders, and body dissatisfaction (26).
Elevating psychological health is commonly conducted via a questionnaire or interview. Langbein et al. (2021) explored the subjective experience of RED-S in endurance athletes through semi-structured, open-ended interviews. The first male participant commented on hitting “rock bottom” and the body’s sensitivity to energy intake changes. In addition, the other male athlete appeared to have a transactional relationship with food and exercise, void of any joy or performance goals. Both male athletes reported negative psychological consequences regarding RED-S; these consequences included increased rates of irritability because they were obsessed with food and exercise and feelings of helplessness and despair (15).
Perelman et al. (2022) also examined the male athlete’s psychological state by evaluating and intervening on body dissatisfaction, drive for muscularity, body-ideal internalization, and muscle dysmorphia. Male athlete participants (n=79) were from various sports, including baseball, golf, soccer, swimming, track and field, volleyball, and wrestling. The results showed that group sessions focused on reframing ideal body perception, the consequences of RED-S, encouraging positive self-talk, and reviewing strategies to modify energy balance healthfully can significantly reduce body dissatisfaction, body-ideal internalization, and drive for muscularity (p < .05) (24). The results demonstrate the value of understanding, supporting, and guiding an athlete’s psychological state toward personal health and satisfaction.

Reproductive Health
Functional hypogonadotropic hypogonadism is one of the three primary pillars of the MAT. LEA can induce disruptions to the hypothalamic-pituitary-gonadal (HPG) axis, resulting in functional hypogonadotropic hypogonadism. Signs of hypogonadotropic hypogonadism include (1) reductions of testosterone (T) and luteinizing hormone (LH), (2) decreased T and responsiveness of gonadotropins to gonadotropin-releasing hormone (GnRH) stimulation after training, (3) alterations in spermatogenesis, and (4) self-reported data on decreased libido and sexual desire (22). Most current research studies examine free and total T as an indicator of HPG axis suppression. Lundy et al. (2022) categorize low total T (<16nmol/L) and low free T (<333 pmol/L) as primary indicators for LEA (16).
A significant contribution to this area comes from the work by Jurov et al. (2021) who conducted a non-randomized experimental study with a crossover design to investigate the reproductive health impacts of progressively reducing EA by 50% for 14 days in well-trained and elite endurance male athletes. The results demonstrated a positive correlation between T levels and measured EA; as EA declined, so did T (9).
The empirical evidence on the causal relationship between LEA and T has been growing over recent years, with studies such as one conducted by Dr. Iva Jurov and colleagues. In three progressive steps, their quasi-experimental study reduced EA (via increasing EEE and controlling EI) in well-trained and elite male endurance athletes. Participants had statistically significant T changes starting at the 50% EA reduction phase with a mean EA of 17.3 ± 5.0kcal/kg of FFM/day for 14 days (p < 0.037). Furthermore, T levels continued to significantly decline at 75% EA reduction phase with a mean EA of 8.83 ± 3.33 for ten days (p < 0.095) (10). Conversely, in another quasi-experimental study by Jurov et al. (2022b), endurance male athletes had their EA reduced by 25% by increasing EEE and controlling EI for 14 days. The mean EA was 22.4 ± 6.3kcal/kg of FFM/day. The results show no significant changes to T levels, potentially indicating that a greater EA reduction was required to induce change (11).
Stenqvist et al. (2020) conducted four weeks of intensified endurance training designed to increase aerobic performance and determine the impact of T and T: cortisol ratio on well-trained male athletes. After the four weeks of intensified endurance training, the results showed that total T significantly increased by 8.1% (p=0.011) while free T (+4.1%, p=0.326), total T: cortisol ratio (+1.6%, p=0.789), and free T: cortisol ratio (-3.2%, p=0.556) did not have significant changes when compared to baseline (27). It is complex to determine the EA threshold defined by HPG axis suppression. Research on LEA and suppression of the HPG axis (i.e., T reduction) have demonstrated varied results based on athlete EA study design features (e.g., high EEE intensity or low EI duration); however, endurance athletes remain at the highest risk (18, 22, 26).

Bone Health
The last pillar of the MAT is osteoporosis with or without bone stress injury (BSI). Impaired bone health is most common in athletes in sports that have low-impact loading patterns, such as cycling, swimming, or distance running. Bone mineral density (BMD) is the primary measurement method to evaluate overall bone health and risk for osteoporosis. Dual-energy x-ray absorptiometry (DXA) is the gold standard for assessing bone density, but quantitative computed tomography (QCT) is also emerging as an equally acceptable alternative. In outpatient or rehabilitation settings, frequency of DXA scans is recommended no sooner than every ten months to allow for detectable changes in bone mineral density (17).
Risk factors for low BMD include LEA, low body weight (<85% of ideal body weight), hypogonadism, running mileage >30/week, and a history of stress fractures (22). In addition to BMD, other indicators of bone health include bone mineral content (BMC), markers of bone formation including β-carboxyl-terminal cross-linked telopeptide of type I collagen (β-CTX), bone alkaline phosphatase, and osteocalcin, and markers of bone resorption including amino-terminal propeptide of type-1 procollagen (P1NP), tartrate-resistant acid phosphatase, and carboxy-terminal collagen cross-links (4, 17). Studies will occasionally implement biomarkers such as Vitamin D and calcium to evaluate dietary intake and risk of BSI or osteoporosis.
What is the prevalence of low BMD in athletes? Tam et al. (2018) evaluated the bone health and body composition of elite male Kenyan runners (n=15) compared to healthy individuals. The results showed that 40% of Kenyan runners have Z-scores indicating low bone mineral density in their lumbar spine for their respective age (z-score <−2.0). This study did not measure energy availability with bone mineral density (29). However, based on previous research, low bone mineral density may have LEA origins.
Heikura et al. (2018) studied the BMD of middle- and long-distance runners and race walkers and found that athletes had an LEA (21kcal/kg of FFM/day) (7). Athletes with a moderate EA generally had better z-scores than the LEA athletes; however, the differences were not statistically significant. Similarly, Õnnik et al. (2022) found that high-level Kenyan male distance runners had an average EI of 1581kcal, and male controls had an average EI of 1454kcal per day. The male athletes did not show a statistically significant difference in BMD (p = 0.293) compared to the male control group, with only one runner (out of 20) at risk for osteoporosis (lumbar spine z-score <1.0) (23).
Cyclists are at the highest risk for poor bone health due to chronic LEA, reduced osteogenic simulation, and low levels of impact or resistance (26). Keay et al. (2018) assessed the efficacy of a sport-specific EA questionnaire and clinical interview (SEAQ-I) in British professional cyclists at risk of developing RED-S. Based on the results of the SEAQ-I, 28% (n=14) were identified with LEA, and 44% of the cyclists had low lumbar spine BMD (z-score <-1.0) (p< 0.001). Also, cyclists with a history of lack of load-bearing sports or activities had the lowest BMD (p= 0.013) (13). This study demonstrates a clear association between LEA and reduced lumbar spine BMD in professional cyclists.
In a randomized controlled trial, Keay et al. (2019) investigated the efficacy of an educational intervention with British competitive cyclists to improve energy availability and bone health. The researchers induced LEA by 25% (mean EA of 22.4 ± 6.3kcal/kg of FFM/day) for 14 days. Athletes who implemented nutritional strategies (provided by nutrition professionals) to improve EA and strength training strategies to improve skeletal loading saw lumbar spine BMD improvements. Mean vitamin D levels significantly improved from pre-season (90.6 ± 23.8 nmol/L) to post-season (103.6nmol/L; p=0.0001). Calcium, correct calcium, and alkaline phosphatase had no statistically significant changes between pre-season and post-season (12). Keay et al. have established the prevalence of LEA and poor bone health in cyclists and demonstrated nutrition education efficacy for BMD improvements. Noteworthy findings such as these help to raise awareness in the cycling community and can inform preventative or rehabilitative strategies.

BODY COMPOSITION
Body composition is the distinction between fat mass and fat-free mass. Fat-free mass includes water, tissue, organs, bones, and muscle (e.g., skeletal muscle). Body composition control and maintenance are essential for an athlete’s health, performance, and mindset. Research measurements of body composition include weight, body mass index, body fat percentage, lean mass, and water content. According to Lundy et al. (2022), a body mass index <18.5 kg/m2 is a primary indicator of LEA; this suggests body composition changes in response to LEA (16).
What is the impact of LEA on body composition? Stenqvist et al. (2020) implemented a four-week intensified endurance training designed to increase aerobic performance and elevate body composition’s impact on well-trained cyclists. The results did not show statistically significant changes in energy intake, body weight, fat mass, or fat-free mass. Body weight loss was potentially averted due to reduced resting metabolic rate as a protective mechanism (27). Whereas Stenqvist et al. (2020) focused on increasing EEE, Jurov et al. (2021) attempted to induce LEA via EI manipulation. Jurov et al. (2021) progressively reduced EA by 50% for 14 days in well-trained and elite endurance male athletes; the results showed no significant changes in body mass and fat-free mass (9).
Regarding resistance training and LEA, Murphy and Koehler (2022) conducted a meta-analysis to quantify the discrepancy in lean mass accretion between interventions providing resistance training in an energy deficit and those without an energy deficit. The literature findings demonstrated lean mass gains impairment in athletes resistance training in an energy deficit compared to those training without an energy deficit (significantly, p = 0.02). The results also surmised that an energy deficit of as much as 500kcal/day could impede lean mass gains (21).
Roth et al. (2023) evaluated the impact of a relatively high- versus moderate volume resistance training program on alterations in lean mass during caloric restriction in male weightlifters. The results showed that whole-body lean mass significantly declined in both groups (high and moderate volume groups) following six weeks of energy restriction. The high-volume group had an EA of 31.7 ± 2.8kcal/kg of FFM/day, and the moderate-volume group had an EA of 29.3 ± 4.2kcal/kg of FFM/day (25). Both studies demonstrate that muscle hypertrophy is unattainable in the presence of LEA.
Furthermore, Murphy and Koehler (2020) found that three days of caloric restriction at an EA of 15kcal/kg of FFM/day in recreational weightlifters resulted in significant reductions in weight (p<0.01), fat mass (p<0.01), and lean mass (p<0.001). Also, the total mass loss was significant (p<0.01) when compared to a control group (EA of 40kcal/kg of FFM/day) (20). The results of studies focused on resistance training and caloric restriction hold applicability for athletes in sports that rely on lean mass gains while manipulating EI, such as bodybuilding (4).

CARDIORESPIRATORY ENDURANCE
Cardiorespiratory endurance (CRE) is the ability of the lungs, heart, and blood vessels to deliver sufficient oxygen to cells to meet the physiological demands of exercise and physical activity (8). Evaluating maximal oxygen uptake or VO2max is a standard CRE measure. A VO2 max of 67.9 ± 7.4 mL/kg/min is categorized as a high fitness level (28).
What is the impact of induced LEA on CRE performance outcomes? Jurov et al. (2021) investigated the endurance performance impact of progressively reducing energy availability by 50% for 14 days in well-trained and elite endurance male athletes. The researchers increased EEE to achieve a mean energy availability of 17.3 ± 5 kcal/kg of FFM/day. The results showed lowered EA reduced endurance performance, as indicated by respiratory compensation point (RC) and VO2max. Jurov et al. (2022b) reduced EA by 25% (by increasing EEE and controlling EI) in trained endurance male athletes and monitored for aerobic performance changes. The results showed that inducing LEA by 25% (mean EA of 22.4 ± 6.3kcal/kg of FFM/day) for 14 days reduced hemoglobin levels, indirectly impacting VO2max and aerobic performance (11). Beyond research conducted by Dr. Iva Jurov and colleagues, there is insufficient experimental research on LEA and CRE.

MUSCULAR STRENGTH AND ENDURANCE
In recent years, few experimental studies have evaluated the impact of LEA on muscular strength, endurance, and athletic performance. Research on athletic performance and LEA has shown that endurance athletes with an EA of 17.3 ± 5 kcal/kg of FFM/day show no reductions in agility t-tests, power output, or countermovement jump results, indicating no association with EA (9). Also, Jurov et al. (2022b) found that a mean EA of 22.4 +/- 6.3kcal/kg of FFM/day in endurance male athletes for 14 days results in significant changes to explosive power (countermovement jump) but not agility t-tests (11).
Furthermore, Jurov et al. (2022a) also reduced EA (via increasing exercise energy expenditure and controlling energy intake) in male endurance athletes to evaluate performance and muscular power impact. The results showed significant reductions in explosive power (measured via vertical jump height test) at a mean EA of 22.4, 17.3, and 8.82 kcal/kg of FFM/day. Based on these findings, athletes reach the LEA threshold after a long time in an energy-deficient state, such as ten to 14 days (10).
However, Stenqvist et al. (2020) aimed to measure peak power in male cyclists after four weeks of intensified endurance training. The results showed that the cyclists significantly improved their peak power output (4.8%, p < 0.001) and functional threshold power (6.5%, p < 0.001) measured via stationary bike. Possibly, the EEE of the intervention was insufficient to induce LEA but instead induced the Specific Adaptation to Imposed Demands (SAID) principle in the athletes (27).
Regarding weightlifters, Murphy and Koehler (2022) studied whether energy deficiency impairs strength gains in response to resistance training. This research study was a meta-analysis of randomized controlled trials. The study findings showed that strength gains were comparable between resistance training groups in either an energy deficit or a balance state. These results demonstrated that low energy availability for prolonged periods (i.e., RED-S) did not impede strength output (21). There are a few studies that report bodybuilders with strength declines with estimations of EA <20 kcal/kg of FFM/day (4). The theory remains that inadequate energy intake will inevitably reduce muscular strength and output.

LOW ENERGY AVAILABILITY THRESHOLD
To date, optimal EA levels and the threshold for LEA in male athletes are under investigation. However, many research studies are cross-sectional, only demonstrating a correlation between athletes and energy availability (e.g., LEA commonly found in endurance athletes). The scant number of current experimental studies often fail to induce LEA and thereby fail to establish clear LEA thresholds.
To prevent LEA and subsequent conditions such as RED-S and MAT, athletes need to maintain their energy availability. Primarily, athletes need to ensure adequate EI and carefully manage their EEE. Current EA “zones” for female athletes are also applied to male athletes until experimental research can demonstrate a need for separate guidelines. EA >45kcal/kg of FFM/day supports body mass gain and maintains healthy physiological functions; 45kcal/kg of FFM/day is optimal for weight maintenance and healthy physiological functions; 30-45kcal/kg of FFM/day is considered suboptimal and at-risk for reduced physiological functions; and ≤30kcal/kg of FFM/day is considered low energy availability (1, 3, 4, 9, 10, 14, 17, 26).
Research by Jurov and colleagues has demonstrated mixed results regarding performance outcomes, body composition, and bone health (9, 10, 11). Mean energy availability in those studies ranged between 17-22 kcal/kg of FFM/day (9, 11). Based on their research findings, Jurov and colleagues have proposed a range of 9-25kcal/kg of FFM/day (mean value of 17kcal/kg of FFM/day) for an LEA threshold (10).
Regarding performance and body composition outcomes, Murphy and Koehler (2020) conducted a randomized, single-blind, repeated-measures crossover trial that showed three days of caloric restriction at an EA of 15kcal/kg of FFM/day induced considerable anabolic resistance to a heavy resistance training bout (20).
In a case study by Langan-Evans et al. (2021), an EA of 20kcal/kg per FFM/day led to weight loss and fat loss without signs of MAT and RED-S. However, an EA of <10kcal/kg of FFM/day did result in signs and symptoms of MAT and RED-S, including disruptions to the hypothalamic-pituitary-gonadal axis, resting metabolic rate (measured), and resting metabolic rate (ratio) (14). Additionally, some LEA thresholds may need to be sport-specific. For instance, Fagerberg et al. (2018) suggest an LEA threshold of 20-25kcal/kg of FFM/day for male bodybuilders with a lower body fat percentage (4). Research to establish EA zones and an LEA threshold for male athletes continues, and guidelines primarily still consider ≤30kcal/kg of FFM/day appropriate for male athletes. However, some researchers have also contested that male athletes can go lower before exhibiting signs and symptoms of MAT and RED-S.

RESEARCH GAPS
There are sizable research gaps regarding LEA and RED-S. First, this literature was unable to address the impact of LEA on endocrine, metabolic, hematological, and gastrointestinal health due to insufficient research published in the past five years. Mountjoy et al. (2018) identified the following research gaps: (1) lack of practical tools to measure and detect LEA and RED-S, (2) lack of validated prevention interventions for RED-S, (3) RED-s in male athlete research, (4) health and performance consequences of RED-S research, and (5) lack of evidence-based guidelines for treatment and return-to-play for athletes with RED-S. Research gaps focused on male athletes with MAT are even more prominent (19).

Moreover, Fredericson et al. (2021) listed several research gaps that need scientific attention, including screening protocols to detect MAT in adolescent and young males, identification of MAT energetic and metabolic impact factors, prevalence of DEED in male athletes with MAT, evaluating the efficacy and effectiveness of clearance and return-to-play protocols, risk assessment for BSI and poor bone health, prevalence of MAT in military recruits, health interventions on the prevention and treatment of MAT, and lastly, cutoff values (or threshold) for LEA (5). Addressing these research gaps would enable sports and health practitioners to effectively prevent and treat LEA, RED-S, and MAT, ensuring athlete health and sports performance.

SUMMARY
LEA is defined as a physiological state when there is inadequate energy to meet the demands placed on the body, often through physical activity, exercise, or sports (23). LEA can impact any athlete engaged in a sport with low energy intake or excessive energy expenditure. LEA is a precursor to the onset of both The Male Athlete Triad (MAT) and Relative Energy Deficiency in Sport (RED-S), two conditions that result in weakened physiological functions, with the former focused on reproductive and bone health decline (22).

Recent literature has shown mixed results on LEA’s impact on immunological health, metabolic markers, bone health, body composition, cardiorespiratory endurance, and muscular strength and endurance. There has been little evidence to connect LEA and endocrine, metabolic, hematological, and gastrointestinal health. However, a notable causal relationship exists between LEA and psychological health and reproductive health. Currently, there is still no defined low energy availability threshold specific to male athletes, however, EA zones from 15-25kcal/kg of FFM/day may be appropriate based on current literature (4, 20, 10, 18, 22, 26).

APPLICATION TO SPORT
Healthy nutritional practices are essential to sports performance. Interdisciplinary sports performance teams must collaborate with nutrition professionals such as Registered Dietitians accredited by the Commission on Dietetic Registration to develop effective LEA prevention, screening, and intervention protocols. Preventative measures must prioritize energy availability, modify sporting culture to encourage energy intake, and mitigate barriers to calorie- and nutrient-dense foods in male athletes. Screening protocols must include EA evaluations based on dietary intake, exercise energy expenditure, and fat-free mass measured via DXA or bioelectrical impedance analysis. Male athletes with an EA ≤20-25kcal/kg of FFM/day must receive nutritional guidance to reduce health and performance impairments. Intervention protocols must be enacted when LEA is confirmed and should primarily focus on increasing energy intake, decreasing energy expenditure, and addressing other associated aspects such as psychological health. Athletes, coaches, and practitioners must raise LEA awareness, dispel energy consumption stigmas, and foster an environment where food and nutrition fuel peak performance.

ACKNOWLEDGEMENTS
This work was supported by the Pennsylvania Western University Department of Exercise, Health, and Sport Sciences. The author would like to thank Dr. Marc Federico and Dr. Brian Oddi for their guidance and feedback on the manuscript

References

1. Burke, L., Deakin, V., Minehan, M. (2021). Clinical sports nutrition. (6th ed.). Sydney, Australia: McGraw Hill Education.
2. Burke, L. M., Lundy, B., Fahrenholtz, I. L., & Melin, A. K. (2018). Pitfalls of conducting and interpreting estimates of energy availability in free-living athletes. International Journal of Sport Nutrition & Exercise Metabolism, 28(4), 350–363. https://doi.org/10.1123/ijsnem.2018-0142
3. Egger, T., & Flueck, J. L. (2020). Energy availability in male and female elite wheelchair athletes over seven consecutive training days. Nutrients, 12(11). https://doi.org/10.3390/nu12113262
4. Fagerberg, P. (2018). Negative consequences of LEA in natural male bodybuilding: A review. International Journal of Sport Nutrition & Exercise Metabolism, 28(4), 385–402. https://doi.org/10.1123/ijsnem.2016-0332
5. Fredericson, M., Kussman, A., Misra, M., Barrack, M. T., De Souza, M. J., Kraus, E., Koltun, K. J., Williams, N. I., Joy, E., & Nattiv, A. (2021). The male athlete triad- A consensus statement from the female and male athlete triad coalition part II: Diagnosis, treatment, and return-to-play. Clinical Journal of Sport Medicine, 31(4), 349–366. https://doi.org/10.1097/JSM.0000000000000948
6. Hattori, S., Aikawa, Y., & Omi, N. (2022). Female athlete triad and male athlete triad syndrome induced by low energy availability: An animal model. Calcified Tissue International, 111(2), 116–123. https://doi.org/10.1007/s00223-022-00983-z
7. Heikura, I. A., Uusitalo, A. L. T., Stellingwerff, T., Bergland, D., Mero, A. A., & Burke, L. M. (2018). Low energy availability is difficult to assess, but outcomes have a large impact on bone injury rates in elite distance athletes. International Journal of Sport Nutrition & Exercise Metabolism, 28(4), 403–411. https://doi.org/10.1123/ijsnem.2017-0313
8. Hoeger, W. W., Hoeger, S. A., Hoeger, C. I., & Fawson, A. L. (2019). Lifetime physical fitness & wellness: A personalized program (15th ed.). Boston, MA: Cengage Learning.
9. Jurov, I., Keay, N., & Rauter, S. (2021). Severe reduction of energy availability in controlled conditions causes poor endurance performance, impairs explosive power, and affects hormonal status in trained male endurance athletes. Applied Sciences (2076-3417), 11(18), 8618. https://doi.org/10.3390/app11188618
10. Jurov, I., Keay, N., & Rauter, S. (2022a). Reducing energy availability in male endurance athletes: A randomized trial with a three-step energy reduction. Journal of the International Society of Sports Nutrition, 19(1), 179–195. https://doi.org/10.1080/15502783.2022.2065111
11. Jurov, I., Keay, N., Spudić, D., & Rauter, S. (2022b). Inducing LEA in trained endurance male athletes results in poorer explosive power. European Journal of Applied Physiology, 122(2), 503–513. https://doi.org/10.3389/fendo.2020.512365
12. Keay, N., Francis, G., Entwistle, I., & Hind, K. (2019). Clinical evaluation of education relating to nutrition and skeletal loading in competitive male road cyclists at risk of RED-Ss (RED-S): 6-month randomised controlled trial. BMJ Open Sport & Exercise Medicine, 5, 1–8. https://doi.org/10.1136/bmjsem-2019-000523
13. Keay, N., Francis, G., & Hind, K. (2018). Low energy availability assessed by a sport-specific questionnaire and clinical interview indicative of bone health, endocrine profile and cycling performance in competitive male cyclists. BMJ Open Sport & Exercise Medicine, 4(1), e000424. https://doi.org/10.1136/bmjsem-2018-000424
14. Langan-Evans, C., Germaine, M., Artukovic, M., Oxborough, D. L., Areta, J. L., Close, G. L., & Morton, J. P. (2021). The psychological and physiological consequences of LEA in a male combat sport athlete. Medicine & Science in Sports & Exercise, 53(4), 673–683. https://doi.org/10.1249/MSS.0000000000002519
15. Langbein, R. K., Martin, D., Allen-Collinson, J., Crust, L., & Jackman, P. C. (2021). “I’d got self-destruction down to a fine art”: A qualitative exploration of relative energy deficiency in sport (RED-S) in endurance athletes. Journal of Sports Sciences, 39(14), 1555–1564. https://doi.org/10.1080/02640414.2021.1883312
16. Lundy, B., Torstveit, M. K., Stenqvist, T. B., Burke, L. M., Garthe, I., Slater, G. J., Ritz, C., & Melin, A. K. (2022). Screening for low energy availability in male athletes: Attempted validation of LEAM-Q. Nutrients, 14(9), 1873. https://doi.org/10.3390/nu14091873
17. McGuire, A., Warrington, G., & Doyle, L. (2020). LEA in male athletes: A systematic review of incidence, associations, and effects. Translational Sports Medicine, 3(3), 173–187. https://doi.org/10.1002/tsm2.140
18. Moris, J. M., Olendorff, S. A., Zajac, C. M., Fernandez-del-Valle, M., Webb, B. L., Zuercher, J. L., Smith, B. K., Tucker, K. R., & Guilford, B. L. (2022). Collegiate male athletes exhibit conditions of the male athlete triad. Applied Physiology, Nutrition & Metabolism, 47(3), 328–336. https://doi.org/10.1139/apnm-2021-0512
19. Mountjoy, M., Sundgot-Borgen, J., Burke, L., Ackerman, K. E., Blauwet, C., Constantini, N., Lebrun, C., Lundy, B., Melin, A., Meyer, N., Sherman, R., Tenforde, A. S., Torstveit, M. K., & Budgett, R. (2018). International olympic committee (IOC) consensus statement on relative energy deficiency in sport (RED-S): 2018 update. International Journal of Sport Nutrition & Exercise Metabolism, 28(4), 316–331. https://doi.org/10.1123/ijsnem.2018-0136
20. Murphy, C., & Koehler, K. (2020). Caloric restriction induces anabolic resistance to resistance exercise. European Journal of Applied Physiology, 120(5), 1155–1164. https://doi.org/10.1007/s00421-020-04354-0
21. Murphy, C., & Koehler, K. (2022). Energy deficiency impairs resistance training gains in lean mass but not strength: A meta‐analysis and meta‐regression. Scandinavian Journal of Medicine & Science in Sports, 32(1), 125–137. https://doi.org/10.1111/sms.14075
22. Nattiv, A., De Souza, M. J., Koltun, K. J., Misra, M., Kussman, A., Williams, N. I., Barrack, M. T., Kraus, E., Joy, E., & Fredericson, M. (2021). The male athlete triad- A consensus statement from the female and male athlete triad coalition part 1: Definition and scientific basis. Clinical Journal of Sport Medicine, 31(4), 335–348. https://doi.org/10.1097/JSM.0000000000000946
23. Õnnik, L., Mooses, M., Suvi, S., Haile, D. W., Ojiambo, R., Lane, A. R., & Hackney, A. C. (2022). Prevalence of triad-red-s symptoms in high-level Kenyan male and female distance runners and corresponding control groups. European Journal of Applied Physiology, 122(1), 199–208. https://doi.org/10.1007/s00421-021-04827-w
24. Perelman, H., Schwartz, N., Yeoward, D. J., Quiñones, I. C., Murray, M. F., Dougherty, E. N., Townsel, R., Arthur, C. J., & Haedt, M. A. A. (2022). Reducing eating disorder risk among male athletes: A randomized controlled trial investigating the male athlete body project. International Journal of Eating Disorders, 55(2), 193–206. https://doi.org/10.1002/eat.23665
25. Roth, C., Schwiete, C., Happ, K., Rettenmaier, L., Schoenfeld, B. J., & Behringer, M. (2023). Resistance training volume does not influence lean mass preservation during energy restriction in trained males. Scandinavian Journal of Medicine & Science in Sports, 33(1), 20–35. https://doi.org/10.1111/sms.14237
26. Schofield, K. L., Thorpe, H., & Sims, S. T. (2021). Where are all the men? LEA in male cyclists: A review. European Journal of Sport Science, 21(11), 1567–1578. https://doi.org/10.1080/17461391.2020.1842510
27. Stenqvist, T. B., Torstveit, M. K., Faber, J., & Melin, A. K. (2020). Impact of a 4-week intensified endurance training intervention on markers of RED-S (RED-S) and performance among well-trained male cyclists. Frontiers in Endocrinology, 11. https://doi.org/10.3389/fendo.2020.512365
28. Sui, X., LaMonte, M. J., & Blair, S. N. (2007). Cardiorespiratory fitness as a predictor of nonfatal cardiovascular events in asymptomatic women and men. American Journal of Epidemiology, 165(12), 1413–1423.
29. Tam, N., Santos-Concejero, J., Tucker, R., Lamberts, R. P., & Micklesfield, L. K. (2018). Bone health in elite Kenyan runners. Journal of Sports Sciences, 36(4), 456–461. https://doi.org/10.1080/02640414.2017.1313998

Qshequilla Parham Mitchell2024-10-11T13:41:23-05:00

Order of passive and interactive sports consumption and its influences on consumer emotions and sports gambling

Qshequilla Parham Mitchell2024-10-11T13:41:23-05:00October 11th, 2024|Research, Sports Studies|

Authors:Anthony Palomba¹, Angela Zhang², and David Hedlund³

¹Department of Communication, Darden School of Business, University of Virginia, Charlottesville, VA, USA
²Department of Public Relations, Gaylord College of Journalism and Mass Communication, The University of Oklahoma, Norman, OK, USA
³Department of Sport Management, Collings College of Professional Studies, St. John’s University, Queens, NY, USA

Corresponding Author:

Anthony Palomba

100 Darden Blvd.

Charlottesville, VA, 22903

Anthony Palomba is an assistant professor of business administration at the Darden School of Business at the University of Virginia. He is fascinated by media, entertainment, and advertising firms. First, his research explores how and why audiences consume entertainment, and strives to understand how audience measurement can be enhanced to predict consumption patterns. Second, he studies how technological innovations influence competition among entertainment and media firms. Third, he is interested in incorporating machine learning and artificial intelligence tools to better understand consumer and firm behaviors.

Angela Zhang is an assistant professor in public relations. Her research interests span both corporate crisis communication and disaster risk communication in natural and manmade disasters. Her research primarily aims to understand how people process crisis and risk information and how we can communicate better during crises. For example, her work examines how linguistic cues in crisis messages affect people process crisis information, how and why risk information is propagated on social media, and how users communicate and cope on social media after crises. For corporate crisis communication, her research examines effectiveness of crisis prevention strategies such as CSR and DEI communication, as well as crisis response strategies.

Dr. Hedlund is an Associate Professor and the Chairperson of the Division of Sport Management, and he has more than twenty years of domestic and international experience in sport, esports, coaching, business and education. As an author, Dr. Hedlund is the lead editor of the first textbook ever published on esports titled Esports Business Management, and he has more than 30 additional journal, book chapter and related types of publications, in addition to approximately 50 research presentations. In recent years, Dr. Hedlund has acted as a journal, conference and book reviewer for sport, esports and business organizations from around the world, and he is an award-winning reviewer and editorial board member for the International Journal of Sports Marketing and Sponsorship.

ABSTRACT

This study explores how alternating between video game and television experiences influences consumer emotions and subsequent decision-making. Findings indicate that playing a video game after watching a video clip enhances positive emotions (H1 supported) and affects post-experiment betting scores based on pre-experiment gambling bets (H2 supported). Winning teams in video games and elevated positive emotions also positively influence post-experiment betting scores (H3 and H4 partially supported). The interaction effect shows that the sequence of media consumption (TV to video game) increases betting scores (H5 supported). The study contributes to understanding how appraisal tendency theory and mood management theory explain the impact of media consumption order on sports gambling decisions. Video games, as interactive stimuli, elevate consumer moods and influence betting behavior more than passive viewing. Practically, integrating video game and video clip data aids comprehensive audience measurement and targeted advertising strategies, advancing algorithmic forecasting in enhancing consumer engagement and decision-making.

Key Words: Mood management, Appraisal tendency theory, sports, gambling, video games

  INTRODUCTION

            The NFL is one of the most powerful media and entertainment brands in the marketplace, routinely curating legions of television and online video viewers for every annual season. In 2019, it averaged about 16.5 million viewers per game, roughly 33% above the 12.43 million viewing average for the top six non-sports programs (Porter, 2021). Additionally, over the last thirty years, the Madden NFL video game franchise has introduced generations to simulated immersive engagement. The legalization of sports gambling (Cason et al., 2020) has expanded how consumers can further engage with the NFL. NFL executives have discussed using mobile cell phones to aid sports fans in stadiums to make live bets throughout the course of a game (Martins, 2020). Audiences can watch the NFL and NFL game day content on the Xbox One, including up to date news and highlights from select NFL teams (Tuttle, 2016). Given these diverse modes of engagement, consumers often switch across a multitude of different activities. This frequent medium switching can significantly impact their moods and, subsequently, how they execute various tasks, including sports gambling. The phenomenon of media multitasking, where consumers engage with multiple forms of media simultaneously, complicates how they regulate their moods and make subsequent decisions (Deloitte, 2018). Younger consumers, in particular, are more inclined to switch between media than older consumers (Beuckels et al., 2021).

            The increasingly diverse modes of engagement with the NFL, spanning from live game viewing and video game simulations to real-time betting, have led to a phenomenon of frequent media switching among consumers. This constant toggling between different platforms and activities can significantly impact their emotional states, subsequently influencing their decision-making processes, including those related to sports gambling. While previous research has examined task switching in general contexts (Yeykelis, Cummings, & Reeves, 2014) and the impact of media multitasking on advertising (Garaus, Wagner, & Back, 2017), the specific application of appraisal tendency theory to understand how these rapid emotional shifts induced by media switching affect sports gambling behaviors remains largely unexplored. Moreover, social media use while viewing television, a phenomenon that has grown in the last decade, has reconfigured the commodification of audiences, and has also created different markets to understand how consumers multi-task, and how to measure audience engagement (Kosterich & Napoli, 2016). Uniquely, social media may be used to track propensity to make season ticket purchases (Popp et al., 2023) among other sports consumption activities (Du et al., 2023). Recent studies have implicated the legalization of sports gambling as potentially increasing fandom and engagement among fans, and can further elevate communication across stakeholders involved in a sports event (Stadder & Naraine, 2020).

There is a gap in understanding, however, how consumer judgments and decisions are informed by emotions (Han, Lerner, & Keltner, 2007). Understanding this dynamic is critical for comprehending the evolution of fandom and identifying how sports teams can further engage fans. As consumers navigate between watching games, participating in video game simulations, and placing live bets, their engagement strategies and emotional states may significantly influence their decisions and loyalty. By examining these interactions, sports organizations can develop more effective methods to maintain and enhance fan engagement in an increasingly digital and interconnected world.

            The implications of this study are broad and vast for academics along with sports and entertainment managers. The complex nature of media switching in sports consumption furthers our understanding of how affective disposition theory may be applied toward the multi-platform and multi-activity nature of modern sports engagement. It could lead to the development of a more nuanced understanding of how affective dispositions are formed and how they influence decision-making in this context. Microsoft (parent brand of Xbox console series) and the NFL have an agreement in which the NFL can provide fantasy football scores and updates on Xbox One consoles and allow fans to stream certain NFL games from their Xbox One consoles (Chansanchai, 2016). Additionally, Microsoft is able to trace not only what consumers play on Xbox One consoles, but also what TV or SVOD viewing apps fans engage to view content. Together, disparate information on video game play and video viewing can be combined to further identify trends in cross-platform sports consumption behavior and inferred consumer emotional states, which can help illuminate how consumer judgement surrounding sports gambling may be impacted.

NFL INDUSTRY

            The National Football league has been a celebrated sports league in the United States and abroad over the last one hundred years. It draws the highest attendance per professional sports game in the United States, at about sixty-six thousand, and during its 2019 season, it hosted nearly sixteen million total viewers per game (Gough, 2021). The total revenue of all NFL teams was slightly over $15 billion in 2019, and average franchise value was just over $3 billion in 2020. Sports betting on Super bowls alone in Nevada accrued nearly $160 million in 2020 (Gough, 2021). While there are no clear figures regarding sports merchandise sales, NFL revenue by team in 2019 was led by the Dallas Cowboys ($980 million), New England Patriots ($630 million), NY Giants ($547 million) and Houston Texans ($530 million) through last place Las Vegas Raiders ($383 million) (Gough, 2020).

            Aside from tickets, television revenue, and merchandise, the NFL has produced different avenues to engage fan bases. The league has recently embraced sports partnerships with Caesars Entertainment, Draft Kings and FanDuel. This allows these three external partners to engage in retail and online sports betting and engage with fans as well, using sports content from NFL media, as well as data, to market these experiences to fans (NFL, 2021). In fact, the NFL is expected to earn just over $2 billion annually from the sports gambling marketplace (Chiari, 2018). The NFL’s current TV media deals across CBS, ABC/ESPN, NBC, and Fox earn it just over $10 billion per season (Birnbaum, 2021). Arguably, one of the NFL’s highest profile merchandise revenue streams comes from its partnership with Electronic Arts (EA) to release an annual, updated version of Madden NFL, generating roughly $600 million annually for EA (Reyes, 2021). By embracing diverse engagement avenues, the NFL not only diversifies its revenue streams but also caters to the evolving preferences of modern sports consumers. This multi-faceted approach reflects the league’s recognition of the complex interplay between media consumption, mood, and fan behavior, ultimately enhancing the overall fan experience in an increasingly digital and interconnected world.

NFL FOOTBALL AS A VIDEO GAME EXPERIENCE: MADDEN NFL

            There are few video games that possess the dominance and market monopolization as does the Madden NFL franchise. It exists as the only simulated NFL football video game available to consumers (Sarkar, 2020), and it is markedly popular among consumers. In fact, for the last twenty years, every Madden NFL video game installation has debuted as the top selling U.S. game in August each year (Wilson, 2022). The video game franchise itself has blossomed into its own celebrated video game season, as video game play expectedly rises during August in anticipation for the upcoming NFL season (Skiver, 2022). Madden NFL fans have been found to be more devoted and knowledgeable about the NFL. Additionally, they are less likely to miss viewing football games on Sundays, as 42% have stated they never miss a football game due to external activities. They are likely to attend at least one NFL game each annual season (IGN Staff, 2012).

            Video gamers’ moods and subsequent judgment may be impacted by their own experiences. Video game play is an immersive experience, as the required technology helps to transport users into a digital world. The level of presence that is achieved can amplify mediated environment perceived quality, user effects, as well as overall experience (Tamborini & Bowman, 2010). Consumer familiarity with video game play may also influence how they experience presence (Lachlan & Krcmar, 2011). Consumers who view NFL games and play NFL video games may experience wins and loss outcomes in both passive and interactive manners. Sports video game play is motivated by possessing deep passion for the sport, gaming interest, entertainment value, competition, and identifying with the team or sport itself (Kim & Ross, 2006). Consumer emotions can be volatile during sports engagement, as winning and losing can impact overall game satisfaction (Yim & Byon, 2018). Emotions are tied to sports engagement in a primal manner, as consumers vicariously live through sports athletes and align themselves with sports teams, invoking a type of tribalism (Meir & Scott, 2007).

MOOD MANAGEMENT THEORY

               Mood management theory concerns how consumers may manage their own moods through consumption of different mediums. Zillmann (1988) states that there are several traits that may impact whether a medium may repair or enhance a particular mood. First, there is the excitatory potential, or how exciting a message may be for consumers. Second, there is absorption potential, which examines how well a media message will be absorbed by an individual. Third, there is the semantic affinity, which relates to the connection from the current participant mood to a media message, which can moderate the impact of absorption potential. Finally, there is hedonic valence, in which pleasant messages can interrupt consumers’ bad moods (Zillmann, 1988). This study is focused on exploring how consumers’ gambling decisions are influenced by their experiences, both positive and negative, related to predicting scores between teams, and placing a bet on them. Specifically, it aims to investigate the impact of semantic affinity and the excitatory potential of stimuli involved in the process on consumer decision-making in gambling contexts.

               Sports viewing or sports video game play can lead to evaluated states of physiological and psychological arousal, stirring hostile or expressive responses to game outcomes. Arousal has been found to be precipitated by aggressive or hostile states (Zillman, 1983), based on events during the game (Berkowitz, 1989). Hostility can be traced to the dissatisfaction with an outcome, or inability to attain a desired goal. Viewing violent sports competition can also heighten hostility and create greater inclinations toward aggressive behavior. Participants who had high identification with America and viewed an American boxer against a Russian boxer were found to have elevated blood pressure compared to those who had low identification with American (Branscombe & Wann, 1992). Additionally, spectators that have high team identification have higher levels of happiness compared to those with low team identification. The way a message is delivered can impact the effect of a message on consumers, as there are distinct characteristics related to each medium (Dijkstra, Buijtels & van Raaij, 2005).

               Mood management is clearly influential as to how participants respond to video and video game play. Participants who may feel frustration may feel further frustration from viewing violent content (Zillmann & Johnson, 1973). One study by Bryant and Zillmann illustrated that participants who view violent sports did not experience mood repair (Donohew, Sypher, & Higgens,1988). Fulfillment of intrinsic needs can influence selection of video games with varying levels of participant demand (Reinecke et al., 2012). Television has been found to reduce boredom and stress among consumers (Bryant & Zillmann, 1984). In managing moods, this can also impact subsequent decision-making, sometimes surreptitiously and without awareness from participants.

APPRAISAL TENDENCY THEORY

            Appraisal tendency theory considers how different types of emotions within similar valences (e.g., anger and fear) may impact judgement. There are two types of influences that may impact how consumers make judgments. Integral emotion is based on individual experiences that might preempt but be relevant to a subsequent decision. Differently, incidental emotion is due to conceivably irrelevant though impactful elements that can inform decision-making, which may include being influenced by traffic, watching television, or engaging in other non-relevant actions. These influences can carry over to the decision-making process (Schwarz & Clore, 1983; Bodenhausen, Kramer, & Susser, 1994). Moreover, consumers who are angry tend to perceive less risk from engaging in new situations (Han, Lerner, & Keltner, 2007).

            Integral emotion is under examination in this study, as an outcome from a related medium stimulus can impact a subsequent decision that is likely informed by that stimulus. After finding that they have won in a video game, it may be that consumers are less inclined to bet against the team that they just lost against. This subjective pain(joy) based on the first stimulus may be stronger from playing a video game than from viewing a sports clip. Moreover, consumers may seek variety in consumption decisions when they are induced to a negative emotion (Chuang, Kung, & Sun, 2008). Therefore, subsequent decision-making may be informed by the order of passive and interactive media consumed by each individual.

MEDIUM MODALITY

               Mediums that engage multiple senses are likely to lead to impactful communication with consumers (Jacoby, Hoyer & Zimmer, 1983). Television offers engagement through visual and auditory senses, while gaming stimulates both but creates an immersive experience, in which consumers are transported into a virtual world (Kuo, Hiler, & Lutz, 2017). Differently, consumers do not have control over passive mediums such as television, as the content is predetermined and is under the yolk of the sender, creating different delivery systems (Van Raaij, 1998). Video game play offers opportunities for players to speed up game play, based on gaming flexibility as well as how quickly a consumer can finish tasks. Video game play is positioned to evoke cognitive responses, through the speed of information dissemination, since the consumer possesses more control over the experience. Conflated with the demanded attention from video game play, consumers will likely have greater affective responses from video game play than from video viewing (Dijkstra, Buijtels, & van Raaij, 2005).

               In consideration of this study, it follows that the simulated aspect of video game play can further influence decision-making. Consumers are inclined to experience improved decision-making and risk assessment through video game play (Reynaldo et al., 2020), as well as cognitive tasks (Chisholm & Kingstone, 2015). Video game play may also induce lowered physiological stress (Russoniello, O’Brien, & Parks, 2009), and emotional regulation (Villani et al., 2018). While there is scant research surrounding video game play simulations and making subsequent real-life decisions, it is ostensibly clear that video game play can heighten and sharpen decision-making skills as well as emotion regulation. Consumers who are attentive toward a simulated video game play experience may be influenced by its outcome in making a subsequent decision. This can include perceiving the winning team in the simulated game as likely to beat the same opposing team in a real-life match up.

H1: Consumers who play a video game (view a video clip) first will be more inclined to have lower (higher) positive emotions.

SPORTS GAMBLING

               Recently, sports gambling has become legalized or recent legislation has been passed to make it legal in 50% of states in the United States (Rodenberg, 2021). While fans have placed bets on horse-racing and even major league sports, its legalization provides a lawful and safe forum for myriad fans to place bets on teams. However, since many gamblers may not invest time in understanding spreads and other esoteric metrics that gambling managers may use to measure likelihoods of outcomes, playing a Madden NFL game can serve consumers to anticipate potential outcomes in real life match ups. Madden NFL’s algorithms have been harvested in the past to predict Super Bowl outcomes. In fact, EA typically runs one hundred simulations to predict which team will win each year in the Super Bowl (Wiedey, 2020). Additionally, fans are also able to make wagers on major league baseball simulated video games (Cohen, 2020). Younger sports fans may be more inclined to play Madden NFL games as a way to simulate outcomes, and become more familiar with teams to anticipate actual game outcomes. Additionally, sports gamblers are betting on simulated sports, in which Madden NFL video games are simulated through the popular video game streaming site Twitch, and consumers are able to bet on the outcome (Campbell, 2021).   

               Previous studies have highlighted why consumers engage in sports gambling. One study found that consumers engage in sports gambling to seek out social interaction and relaxation through engagement with betting apps, though their effect on problematic gambling and non-problematic gambling varied across these dimensions (Whelan et al., 2021). Consumers may seek out consumer purchases as a way to blunt negative emotions, or may further satiate their positive mood by pursuing purchases that bring them joy. Video game play can engender excitatory potential, stimulating arousal levels and inspiring consumers in negative moods to make consumer purchases or execute notably different gambling bets. The heightened arousal levels experienced by consumers during video game play can create greater vacillation in subsequent decision-making, including sports gambling bets. Tangentially related to this, if a consumer is in a positive mood, this optimism may impact their inclination to bet more on a sports match up. Additionally, the order of engaging a passive medium versus an interactive medium is critical to analyze. Video game play can heighten immersion in content, and provide further confidence in a team. Consumers may be able to participate in high-scoring video game match ups. Additionally, consumers may be spurred to bet on characters with whom they have virtual relationships (Palomba, 2020). Finally, video game play can lead to experiencing dopamine release, leading to greater felt pleasure (Koepp et al., 1998). Together, these may lead consumers to have greater optimism for post-betting scores.

H2: Consumer pre-experiment bet scores will have an anchoring effect and still inform post-experiment bet scores.

H3: The team that wins in the video game will have a greater positive relationship with post experiment bet scores than the team with the highest score in the video clip.

H4: Consumers who experience strong positive (negative) emotions after viewing a video clip will positively (negatively) influence post-experiment bet scores.

H5: Consumption order and time will have an interaction effect that when consumption order is VG to TV, betting scores will decrease from pre-betting to post-betting (pre-betting will be higher than post-betting); when consumption order is TV to VG, betting scores will increase from pre-betting to post-betting (pre-betting will be lower than post-betting).

METHOD

               A 4×2 experiment was conducted here, in which participants were exposed to one of four different video clips, and one of two outcomes in a video game play match up. The New York Giants and Dallas Cowboys were the two teams that were selected for this experiment. Since this experiment took place in the mid-Atlantic region, it was believed that participants were less inclined to like either team. Moreover, these two teams have a storied and high-profile rivalry between them. For the video stimulus, participants were exposed to a randomized video clip highlighting a matchup between the NY Giants and Dallas Cowboys, in which one of four scenarios appeared: a) The NY Giants win by a wide margin (20 points), b) The NY Giants win by a slim margin (3 points), c) The Dallas Cowboys win by a slim margin (3 points), and d) The Dallas Cowboys win by a wide margin (20 points). Each video clip was about five minutes long. The video game stimulus involved playing a Madden NFL video game match up on an Xbox One video game console between the NY Giants and Dallas Cowboys. Participants were able to select which team they desired to play as and in which stadium to play in. The quarters in the Madden NFL game were kept at the default setting of six minutes each, ensuring participants experienced immersion but also maintained the experience to be similar to viewing the video clip.

               Participants in the A condition (VG to TV) first played the video game followed by viewing the video clip, and participants in the B condition (TV to VG) first viewed the video clip followed by the video game play. as well as playing a Madden NFL session implicating both teams. After each condition, participants were asked to evaluate their current emotions. After the video clip, participants were asked to state the final score and which team won in the clip to ensure that they were paying attention to the clip itself. Moreover, after the video game condition, participants were asked to state which team they played as, the final score, as well as what sports stadium they played in.

MEASURES

               To measure fandom, a scale from (Wann, 2002) was used here. It consisted of statements regarding self-assessment of fandom, including statements such as “I consider myself to be a football fan,” “My friends see me as a football fan,” and “I believe that following football is the most enjoyable form of entertainment.” It was measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale.

               To measure current emotions, a scale from Diener and Emmons (1984) was used here. The scale consisted of emotions statements including “joy,” “pleased,” “enjoyment,” “angry,” and other emotion statements. It was measured on a 1 (not at all) to 7 (extremely much) Likert scale.

               It was believed that the current emotions scale, though exhaustive, did not capture extreme aggression that may be felt by sports fans. An ancillary aggression scale (Sinclair 2005; Spielberger, 1999) was used here. The scale consisted of aggression statements including “I feel like yelling at somebody,” “I am mad,” and “I feel like banging on the table.” It was measured on a 1 (not at all) to 5 (extremely) Likert scale.

               To measure for team identification, a scale by Naylor, Hedlund, and Dickson (2017) was used here. The scale consisted of statements including “I know a lot of information about my favorite National Football League team,” “I am very knowledgeable about my favorite National Football League team,” and “I am very familiar with my favorite National Football League team.” It was measured on a 1 (not at all) to 5 (extremely) Likert scale.

               To measure for commitment to team, a scale by Hedlund, Biscaia, and Leal (2020) was used here. The scale consisted of statements including “I am a true fan of the team,” “I am very committed to the team,” and “I will attend my team’s games in the future.” It was measured on a 1 (not at all) to 5 (definitely) Likert scale.

               To measure for brand loyalty toward Madden NFL, a scale by Yoo and Donthu (2001) was used here. The scale consisted of statements including “I consider myself to be loyal to Madden football,” “Madden football would be my first football video game choice,” and “The likely quality of Madden NFL is extremely high.” It was measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale.

RESULTS

               Descriptive analytics were run to break down video clip and video game play exposure to participants. After data-cleaning was executed, one hundred and thirteen participants (n=113) remained for analysis. 63.7% of participants were male. Additionally, across ethnicity, participants were Caucasian (58.4%), Asian-American (16.8%), African-American (8.8%), Hispanic (2.7%) and also identified as other races (13.3%). Among participants’ favorite NFL teams, they included the Washington Commodores (16.8%), New England Patriots (8.0%), and Philadelphia Eagles (8.0%). Less participants were fans of the New York Giants (4.4%) and Dallas Cowboys (1.8%). To gain a sense of faith participants had among each team, participants were asked to imagine making a bet between a pre bet on an imagined match up between the NY Giants and Dallas Cowboys. Participants on average placed the Dallas Cowboys (M=25.77, SD=9.102) past the NY Giants (M=20.67, SD=8.715) and bet roughly $14.37 on average.

               Across all video clips, participants viewed the Giants winning by a lot (23.4%), Giants winning by a little (28.7%), Cowboys winning by a lot (25.5%), and Cowboys winning by a little (22.3%). Participants viewed the Giants winning 49.5% of the time and the Cowboys winning 50.5% of the time. In relation to video game difficulty level exposure, 51.3% of participants were exposed to pro-level difficulty (2/4 level of difficulty), and 48.7% were exposed to all-pro level difficulty (3/4 level of difficulty). This was done to ensure that Madden football players felt challenged and greater immersion during video game play (Csikszentmihalyi, 1975; Falstein, 2005; Nacke, 2012; Missura, 2015). 50.9% of participants played as the Dallas Cowboys, and 49.1% played as the NY Giants. In the video game itself, the Dallas Cowboys won 64% of the time, and the NY Giants won 36% of the time. Finally, participants won 74.8% of the time. Moreover, 58% of participants elected to play in NY Giants home stadium, MetLife Stadium, and 42% elected to play in AT&T Stadium, the Dallas Cowboys’ home stadium. Before analyses could be conducted, it was necessary to run factor analyses to reduce the amount of emotion statements necessary for analyses. For all factor analyses across pre-experimental mood, post video mood, and post video game mood, varimax rotations were run.

               For post video emotions, the factor analysis had a KMO of .895 and the Bartlett’s Test of Sphericity was statistically significant. The first factor loading had 12.717 eigenvalue and explained 48.913% of variance in the data. The first loading, violent, included I feel like kicking somebody (.919), I feel like hitting someone (.908), I feel like breaking things (.880), I feel like pounding somebody (.880), and I feel like yelling at somebody (.874) and had a Cronbach’s alpha score of .972. The second factor loading had an eigenvalue of 5.022 and explained 19.317% of variance in the data. This scale, entitled irritated, included frustrated (.865), annoyed (.835), angry (.820), depressed (.800), and sad (.768), and had a Cronbach’s alpha score of .928. The third factor loading had an eigenvalue of 2.311 and explained 8.890% of variance in the data. This scale, entitled positive, included pleased (.919), joy (.914), glad (.904), delighted (.900), and fun (.898) and had a Cronbach’s alpha score of .953.

               For post video game emotions, a factor analysis was run. The KMO =.879 and the Bartlett’s test of sphericity was statistically significant. The first factor loading had an eigenvalue of 13.119, and it explained 50.458% of variance in the data set. The first factor loading, violent, included I feel like hitting someone (.866), I feel like breaking things (.858), I feel like banging on the table (.853), I feel like pounding somebody (.840) and I feel like kicking somebody (.840) with a Cronbach’s alpha score of .965. The second factor loading had an eigenvalue of 4.640 and explained 17.846% of variance in the data set. This scale, positive, included joy (.915), glad (.910), delighted (.897), pleased (.884), and fun (.860), and possessed a Cronbach’s alpha score of .952.  The third factor loading had an eigenvalue of 1.783 and explained 6.858% of variance in the data set. This scale, irritated, included gloomy (.832), depressed (.798), sad (.747), anxious (.628), and angry (.531) and had a Cronbach’s alpha score of .905.

               There was emotional variance across mediums (Table 1). Paired T-tests were run across an assortment of feelings here. For most of the emotions that were measured for in this experiment, participants generally felt better after playing the video game against viewing the clip itself across both conditions. For instance, in total, joy (M=4.38, SD=1.928), glad (M=4.45,

SD=1.785), and delighted (M=4.32, SD=1.904) all increased across all conditions after the video game play condition. Hypothesis 1 is supported here.

Table 1

Emotion variance across mediums.

	Total			TV to VG			VG to TV
	Pre stimulus	Post video clip	Post video game	Pre stimulus	Post video clip	Post video game	Pre stimulus	Post video game	Post video clip
Joy	4.04(1.614)	3.75(1.864)*	4.38(1.928)***	4.33(1.492)	4.46(1.691)	4.98(1.742)*	3.73(1.689)	3.79(1.933)	3.04(1.768)**
Pleased	4.28(1.623)	4.63(1.665)	4.66(1.824)***	4.44(1.524)	4.63(1.665)	5.02(1.794)	4.13(1.717)	4.30(1.798)	3.54(1.629)***
Fun	4.48(1.553)	5.09(1.491)	5.46(1.705)***	4.61(1.449)	5.09(1.491)***	5.46(1.705)	4.34(1.654)	4.88(1.585)**	3.37(1.902)***
Glad	4.35(1.535)	3.81(1.827)***	4.45(1.785)***	4.70(1.414)	4.39(1.677)	4.89(1.723)*	4.00(1.584)	4.00(1.748)	3.21(1.796)**
Delighted	3.88(1.700)	3.83(1.827)	4.32(1.904)**	4.11(1.666)	4.26(1.798)	4.71(1.755)*	3.66(1.719)	3.93(1.980)	3.39(1.765)*
Contented	4.97(1.555)	4.39(1.775)***	4.55(1.729)	5.11(1.655)	4.82(1.754)	4.80(1.793)	4.84(1.449)	4.30(1.640)*	3.95(1.699)
Angry	1.45(1.106)	1.37(.771)	1.58(1.333)	1.38(1.001)	1.38(.702)	1.46(1.144)	1.52(1.206)	1.70(1.501)	1.36(.841)
Anxiety	2.33(1.550)	1.67(1.060)***	1.62(1.133)	2.25(1.338)	1.77(1.062)*	1.45(.851)*	2.41(1.745)	1.79(1.345)***	1.57(1.059)
Frustrated	1.88(1.309)	1.69(1.115)	2.10(1.682)**	1.77(1.079)	1.66(1.100)	1.84(1.424)	1.98(1.507)	2.36(1.882)	1.71(1.140)**
Depressed	1.76(1.187)	1.46(.958)***	1.45(.928)	1.71(1.107)	1.43(.892)**	1.38(.822)	1.80(1.271)	1.52(1.027)*	1.48(1.027)
Annoyed	1.86(1.293)	1.76(1.050)	2.13(1.688)*	1.59(.949)	1.66(.920)	1.91(1.621)	2.13(1.526)	2.34(1.740)	1.86(1.167)*
Sad	1.74(1.334)	1.42(.866)**	1.44(.918)	1.80(1.470)	1.38(.676)*	1.39(.908)	1.68(1.193)	1.48(.934)	1.46(1.026)
Gloomy	1.75(1.151)	1.50(.977)**	1.40(.895)	1.77(1.191)	1.41(.781)***	1.32(.741)	1.73(1.120)	1.48(1.027)*	1.59(1.141)
*p < .05; **p < .01; ***p < .001.

To test hypotheses 2-4, multiple linear regressions were running for predicting consumer post experiment score bets in table 2 and table 3. In table 2, Across both conditions, pre bet Giants score (β=.413, p<.001), pre bet Cowboys score (β=-.269, p<.012), and video Giants score (β=.225, p<.021) explained 34.6% of variance toward estimating Giants post experiment bet score. In the TV to VG condition, pre bet Giants score (β=.505, p<.003), pre bet Cowboys score (β=-.442, p<.008) explained 35.5% of variance toward estimating Giants post experiment bet score. In the VG to TV condition, pre bet Giants score (β=.430, p<.018) and Giants winning in VG (β=-.583, p<.024) explained 28.9% of variance toward estimating Giants post experiment bet score.

Table 2

Consumer post bets – Giants.

	NY Giants Total			NY Giants TV to VG			NY Giants VG to TV
	Beta	Sig.		Beta	Sig.		Beta	Sig.
Pre bet Giants score	.413	.001***		.505	.003**		.430	.018*
Pre bet Cowboys score	-.269	.012*		-.442	.008**		-.009	.969
Winning team in VG	-.282	.071		-.213	.442		-.583	.024*
Did player win in VG	.011	.921		-.276	.113		.334	.112
Team played as in VG	.010	.942		.190	.513		-.110	.610
Sports arena played in VG	.083	.462		.073	.695		.261	.296
Video Cowboy score	-.056	.550		-.089	.543		-.039	.801
Video Giants score	.225	.021*		.262	.096		.285	.079
VG Giants score	-.120	.388		.073	.738		-.281	.256
VG Cowboys score	-.115	.376		-.174	.409		-.009	.966
VC Violent	.085	.569		.138	.668		.011	.960
VC Irritated	-.012	.919		.007	.971		-.186	.458
VC Positive	-.079	.550		-.170	.397		-.043	.836
VG Violent Actions	-.164	.276		-.290	.423		.051	.815
VG Positive Actions	-.110	.454		.028	.894		-.207	.399
VG Irritated	-.006	.962		-.101	.608		.023	.937
F	3.814			2.448			2.068
R	.685			.775			.748
R²	.346			.355			.289
Significance	.001			.021			.048

               In table 3, across both conditions, pre bet Cowboys score (β=.467, p<.001), Cowboys winning in video game (β= .342, p<.038), and video Cowboy score (β=.226, p<.024) explained 27.4% of variance toward estimating Giants post experiment bet score. In the TV to VG condition, pre bet Cowboys score (β=.394, p<.014), Cowboys winning in video game (β= .613, p<.029), Cowboys video score (β=.352, p<.020), Giants video game score (β=.470, p<.034), and feeling positive after viewing the video clip (β=.476, p<.020), explained 38.8% of variance toward estimating Giants post experiment bet score. In the VG to TV condition, Cowboys winning in the video game (β=.469, p<.035), Cowboys video score (β= .276, p<.047), Giants video game score (β=-.517, p<.021), Cowboys video game score (β=-.450, p<.022), and feeling violent after the video clip (β=-.583, p<.011) explained 46.2% of variance toward estimating Cowboys post experiment bet score. Together, these results supported hypothesis 2 and provided partial support for hypotheses 3 and 4.

Table 3

Consumer post bets – Cowboys.

	Dallas Cowboys Total			Dallas Cowboys TV to VG			Dallas Cowboys VG to TV
	Beta	Sig.		Beta	Sig.		Beta	Sig.
Pre bet Giants score	-.138	.195		.045	.767		-.277	.072
Pre bet Cowboys score	.467	.001***		.394	.014*		.370	.071
Winning team in VG	.342	.038*		.613	.029*		.469	.035*
Did player win in VG	.098	.422		.063	.705		.288	.115
Team played as in VG	-.066	.662		-.294	.300		-.263	.169
Sports arena played in VG	.004	.972		-.221	.231		-.242	.266
Video Cowboy score	.226	.024*		.352	.020*		.276	.047*
Video Giants score	-.120	.236		-.169	.261		.058	.675
VG Giants score	.009	.953		.470	.034*		-.517	.021*
VG Cowboys score	-.128	.349		-.129	.529		-.450	.022*
VC Violent	-.180	.255		.312	.323		-.538	.011*
VC Irritated	.099	.443		-.011	.954		.226	.303
VC Positive	.098	.484		.476	.020*		.088	.629
VG Violent Actions	.090	.569		-.547	.127		.160	.406
VG Positive Actions	-.033	.830		-.098	.634		-.287	.182
VG Irritated	-.019	.895		.383	.054		-.102	.684
F	3.008			2.663			3.251
R	.641			.788			.817
R²	.274			.388			.462
Significance	.001			.013			.004

               To answer the fifth hypothesis, a mixed between-within subjects analysis of variance was conducted to understand the effects of consumption order (TV to VG vs. VG to TV) and game results (NY giant wins a lot vs. Cowboy wins a lot) on participants’ sports betting scores on the two teams (NY Giants and Dallas Cowboys, respectively), across two time periods (pre- and post-experiment).

               For betting scores on NY Giants, a significant interaction effect was found between time and order (Wilks’ Lambda = .89, F (1, 35) = 4.54, p=.04). Both pre and post-betting scores for those under the order condition TV to VG ( = 15.83, SD=5.79 and = 18.72, SD=8.10) scored lower than those under the VG TO TV conditions ( = 23.57, SD=9.67 and = 20.19, SD=8.54). Betting scores for NY Giant has increased for order TV to VG ( = 15.83, SD=5.79 to = 18.72, SD=8.10) but betting scores for order VG to TV has decreased ( = 23.57, SD=9.67 to = 20.19, SD=8.54). However, the main effects for time were not significant, nor were the interaction effects between time and game results, and between time, game results, and order (Figure 1). For betting scores on Dallas Cowboys, no significant main effects or interaction effects were found on any of the variables.

Figure 1

Pre-betting and post-betting scores.

DISCUSSION

               This study worked to demonstrate how toggling between video game and television experiences could influence consumer emotions and inform subsequent decision-making. Consumers who played a video game after viewing a video clip were more inclined to feel positive (H1 supported). Pre-experiment gambling bets informed post experiment bet scores (H2 supported). There was some evidence that suggested winning teams in video games held a positive influence over post experiment bet scores (H3 partially supported) and that high levels of positive emotions also held a positive influence over post experiment bet scores (H4 partially supported). Finally, there was an interaction effect in which consumption order and time, in which betting scores will increase in the TV to VG condition (H5 supported). Together, the evidence illustrates how powerful the order of medium engagement is for consumers, and that these particular sequences can not only impact post-moods, but also decision-making among consumers.

               This study contributes to the understanding of how appraisal tendency theory and mood management theory further elucidate the influence of media consumption sequencing on subsequent sports gambling decision-making. Specifically, the sequential order of media engagement was found to affect consumers’ semantic affinities between their recent media exposures (such as watching sports clips or engaging in video game sports simulations) and their subsequent decisions regarding sports wagering, albeit to a limited extent. Additionally, consumers’ moods were elevated by video game play, compared to viewing sports clips, supporting the excitatory potential of interactive stimuli here (Zillmann, 1988; Reinecke et al., 2012). In particular, the winning team in a video game simulation was able to impact post-consumer scores for the Cowboys, and moderately impact post-consumer scores for the Giants. This illustrates that video game simulations can be used to inform subsequent decision-making including estimating a team’s score during a post bet, an advancement of appraisal tendency theory. Previously, this had not been applied to mixed media modality studies, and this illustrates that previous media consumption activities can impact subsequent decision-making. Overall, post-betting scores were elevated in part based on the video game to television media consumption order, illustrating the anchoring effect established from consumers’ first playing video game match ups. Additionally, while pre bets can inform how consumers may produce bets after engaging in media, playing simulated video games can be impactful, whether it is the final score or which team won. It should be stated that the bulk of consumers played as the Cowboys, which may illustrate why the Giants winning in the video game held a negative relationship toward the Giants post bet score. It may be that for some consumers, there is interest in proving a simulation wrong, whereas others are positively informed by this experience.

               In regards to mood management theory, in particular semantic affinity and excitation potential, consumer moods were elevated during video game play. From a passive to an interactive activity, this illustrates that this can further intensify emotional valences across positive (e.g. joy, pleased, fun) and negative (gloomy, annoyed) states. This furthers our understanding of how order of media consumption can impact particular moods for consumers. Having agency over an experience, and allowing consumers to co-create their own experiences while playing a simulated matchup further elevates positive feelings. Differently viewing video clips can evoke a range of emotions in consumers, including contentment as well as feelings of anxiety, depression, sadness, or gloominess. A passive entertainment experience that does not include consumers in the co-creation process (especially if their favorite team is not featured in the clip itself), can create dower moods among consumers. Only 6% of participants possessed affinity for either the Cowboys or the Giants, which did not improve mood during video viewing. However, video game play was able to overcome this obstacle and uplift moods.

PRACTICAL IMPLICATIONS

               Integrating video game data with video clip data collection facilitates the development of a comprehensive media audience measurement approach. This approach enables practitioners to gauge engagement across both passive and interactive consumption modes. Additionally, it contributes to establishing a new market information framework (Meyer & Rowan, 1977), potentially minimizing analytical redundancies as consumers’ behaviors are tracked seamlessly across various media platforms. The technological disruption of multi-tasking, task-switching, and sequential tasking have created multiple opportunities to measure audiences differently, particularly as 5G becomes widely available in NFL stadiums. Verizon has recently stated that its 5G ultra Wideband service can ensure connectivity for fans during live games (Ashraf, 2023). The ability to engage smart phone devices in a sports stadium allows audiences to gain a sense of how audiences are responding to a game, which may include measuring the amount of bets. For homebound patrons, consolidating data sets in a cohesive and aggregated fashion enables the development of advanced algorithms for forecasting. This helps in deciphering the audience’s mindset based on their past media consumption patterns leading up to watching an NFL game or engaging in Madden NFL gameplay. Currently, Amazon offers X-Ray for Thursday Night Football fans, which is a sophisticated graphical overlay that allows fans to follow statistics in real time along with generated two-minute highlight reels (Forristal, 2023). Therefore, calcified sport consumer profiles and proclivities for communication with each other can be further facilitated through these strategies (Kirkwood, Yap, & Xu, 2018).

               This creates a vehicle for programmatic strategy advertising and public relations, by which automated advertisements and public relations addresses can be targeted toward participants after an activity in order to enhance or repair a sports fan experience. More attention from consumers may be given to positive television advertisements that follow engaging programming rather than calm programming (Lee, Potter & Han, 2023). Consumers gain greater joy on spending money on experiential products including sports events (Nicolao, Irwin, & Goodman, 2009) and so consumers may seek out experiences more so than merchandise. Moreover, the ability to track consumer behavior in virtual spaces has implications for how advertisements may be placed and how consumers may engage with them (Ahn, Kim & Kim, 2022).  The order of consumption can aid practitioners in elevating video game play. Not only can it impact post betting video game scores, but it can also enhance positive moods for consumers. In particular, consumers who experience their own team or a favored team winning in a video game or simulated match up may feel delighted or joy, which may subsequently encourage them to increase the post experience bet score for one or both teams. This can therefore encourage more risk taking among consumers, and perhaps even more spending for that matter. Furthermore, when fans experience negative emotions after their favorite team loses a live match, the NFL team can strategically encourage them to replay the matchup in Madden NFL. This allows fans to reimagine the live game, thus re-writing the experience itself, and mitigating any temporary damage to brand loyalty or equity.

LIMITATIONS AND FUTURE STUDIES

               There were several limitations in this study. First, most participants were inclined to push for in favor of the Cowboys in the pre-bet. Recall that the NY Giants pre bet score was less (M=20.67, SD=8.715) compared to the Dallas Cowboys (M=25.77, SD=9.102). This indicates markedly more confidence in the Dallas Cowboys’ abilities among the participants. However, while the Cowboys won 64% of the time in the video game, participants only played as them for roughly 51% of the time. Moreover, 58% of participants elected to play in the NY Giants arena. Consequently, many participants were surprised by losses to the NY Giants when playing in the Giants’ stadium. Future studies should consider allowing participants to play as their favorite teams or testing various types of advertisements on them. It may also be valuable to examine how participants respond to playing in stadiums that are geographically close to or far from their hometowns. Additionally, investigating how the order of media consumption affects consumer behavior related to memorabilia, tickets, and other sports-related purchases offers a promising area for academic research.

REFERENCES

• Ahn, S., Kim, J., Kim, J. (2022). The bifold triadic relationships framework: A theoretical primer for advertising research in the Metaverse. Journal of Advertising, 51(5), 592-607.

• Ashraf, C. (2023, October 19). Verizon’s 5G Ultra Wideband keeps football fans connected in all 30 NFL stadiums. Verizon News Center. Retrieved May 28, 2024, from https://www.verizon.com/about/news/verizon-5g-ultra-wideband-football-fans-connected-30-nfl-stadiums

• Berkowitz, L. (1989). Frustration-aggression hypothesis: Examination and reformulation. Psychological Bulletin, 106(1), 59-73.

• Beuckels, E., De Jans, S., Cauberghe, V., Hudders, L. (2021). Keepin gup with media multitasking: An eye-tracking study among children and adults to investigate the impact of media multitasking behavior on switching frequency, advertising attention, and advertising effectiveness. Journal of Advertising, 50(2), 197-206.

• Birnbaum, J. (2021, March 19). NFL’s new TV deals will hand teams $300 million a year, and still won’t drive franchise values higher. Forbes. Retrieved February 20, 2023, from https://www.forbes.com/sites/justinbirnbaum/2021/03/19/nfls-new-tv-deals-hand-teams-300-million-per-year-and-still-wont-drive-franchise-values-higher/?sh=4f0127794f04
• Bodenhausen, G., Kramer, G., & Susser, K. (1994). Happiness and stereotypic thinking in social judgment. Journal of Personality and Social Psychology, 66, 621-632.

• Branscombe, N., & Wann, D. (1992). Physiological arousal and reactions to outgroup members during competitions that implicate an important social identity. Aggressive Behavior, 18, 85-93.

• Bryant, J., & Zillmann, D. (1984). Using television to alleviate boredom and stress: Selective exposure as a function of induced excitational states. Journal of Broadcasting, 28(1), 1-20.

• Campbell, S. (2021, January 21). How to bet on simulation sports: Sim sports betting guide. In OddsShark. Retrieved April 29, 2021, from https://www.oddsshark.com/esports/sim-sports/how-to-bet

• Cason, D., Lee, M., Lee, J., Yeo, I., Arner, E. (2020). The impact of legalization of sports gambling: How motivation, fandom, and gender influence sport-related consumption. International Journal of Sport Communication, 13(4), 643-654.

• Chansanchai, A. (2016, October 10). Partnership between Microsoft and NFL continues to score high for players, coaches, and fans. In Microsoft Features. Retrieved April 27, 2021, from https://news.microsoft.com/features/partnership-between-microsoft-and-nfl-continues-to-score-high-for-players-coaches-and-fans/

• Chiari, M. (2018, September 5). Nielsen Report: NFL could make $2.3 billion a year from gambling market. Bleacher Report. Retrieved February 20, 2023, from https://bleacherreport.com/articles/2794282-nielsen-report-nfl-could-make-23-billion-a-year-from-gambling-market

• Chisholm, J., & Kingstone, A. (2015). Action video games and improved attentional control: Disentangling selection – and response-based processes. Psychonomic Bulletin & Review, 22, 1430-1436.

• Chuang, S., Kung, C., & Sun, Y. (2008). The effects of emotions on variety-seeking behavior. Social Behavior and Personality, 36(3), 425-432.

• Cohen, A. (2020, February 5). MLB fans can now bet on virtual games powered by Sportradar. In SportTechie. Retrieved April 27, 2021, from https://www.sporttechie.com/mlb-baseball-fans-sports-betting-virtual-games-sportradar-data

• Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass Publishers.

• Deloitte. (2018). Deloitte Digital Media Trends 2018: Executive Summary. Deloitte. Retrieved May 15, 2024, from file:///Users/palombaa/Downloads/tmt-global-deloitte-digital-media-trends-survey-2018.pdf

• Diener, E., & Emmons, R. (1984). The independence of positive and negative affect. Journal of Personality and Social Psychology, 47(5), 1105-1117.

• Dijkstra, M., Buijtels, H., & Van Raaij, W. (2005). Separate and joint effects of medium type on consumer responses: A comparison of television, print, and the Internet. Journal of Business Research, 58, 377-386.

• Donohew, L., Sypher, H., Higgins, E. (1988). Communication, social cognition, and affect. Lawrence Erlbaum Associates.

• Du, J., Mamo, Y., Floyd, C., Karthikeyan, N., James, J. (2023). Machine learning in sport social media research: Practical uses and opportunities. International Journal of Sport Communication, 17(1), 97-106.

• Falstein, N. (2005). Understanding fun – the theory of natural funativity. In S. Rabin (Ed.), Introduction to game development. Hingham, MA: Charles River Media.

• Forristal, L. (2023, August 24). Amazon brings new AI-driven features to Thursday Night Football. Tech Crunch. Retrieved May 28, 2024, from https://techcrunch.com/2023/08/24/amazon-prime-video-ai-features-thursday-night-football/

• Garaus, M., Wagner, U., Back, A. (2017). The effect of media multitasking on advertising message effectiveness. Psychology & Marketing, 34(2), 138-156.

• Gough, C. (2020, October 2). Revenue of National Football League (NFL) teams 2019 Published by Christina Gough, Oct 2, 2020 The statistic ranks all franchises of the National Football League according to revenue in 2019. In Statista. Retrieved April 29, 2021, from https://www.statista.com/statistics/193553/revenue-of-national-football-league-teams-in-2010/

• Gough, C. (2021, March 29). National Football League (NFL) – Statistics & Facts. In Statista. Retrieved April 29, 2021, from https://www.statista.com/topics/963/national-football-league/#:~:text=The%20NFL%20is%20also%20the,total%20throughout%20the%202019%20season

• Han, S., Lerner, J., & Keltner, D. (2007). Feelings and consumer decision making: The appraisal-tendency framework. Journal of Consumer Psychology, 17(3), 158-168.

• Hedlund, D. P., Biscaia, R., & Leal, M. D. (2020). Classifying Sport Consumers: From Casual to Tribal Fans. In C. Wang (Ed.), Handbook of Research on the Impact of Fandom in Society and Consumerism (pp. 323-356).

• IGN Staff. (2012, June 14). Study finds playing Madden NFL creates more passionate & knowledgeable football fans. In IGN. Retrieved April 30, 2021, from https://www.ign.com/articles/2009/08/20/study-finds-playing-madden-nfl-creates-more-passionate-knowledgeable-football-fans

• Jacoby, J., Hoyer, W., Zimmer, M. (1983). To read, view, or listen? A cross-media comparison of comprehension. Current Issues and Research in Advertising, 6(1), 201-217.

• Kim, Y., & Ross, S. (2006). An exploration of motives in sport video gaming. Journal of Sports Marketing & Sponsorship, 8, 34-46.

• Kirkwood, M., Yap, S., Xu, Y. (2018). An exploration of sport fandom in online communities. International Journal of Sport Communication, 12(1), 55-78.

• Koepp, M. J., Gunn, R. N., Lawrence, A. D., Cunningham, V. J., Dagher, A., Jones, T., Brooks, D. J., Bench, C. J., Grasby, P. M. (1998). Evidence for striatal dopamine release during a video game. Nature, 393, 266-268.

• Kosterich, A., & Napoli, P. (2016). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254-271.

• Kuo, A., Hiler, J., & Lutz, R. (2017). From Super Mario to Skyrim: A framework for the evolution of video game consumption. Journal of Consumer Behavior, 16, 101-120.

• Lachlan, K., & Krcmar, M. (2011). Experiencing presence in video games: The role of presence tendencies, game experience, and time spent in play. Communication Research Reports, 28(1), 27-31.

• Lee, M., Potter, R., Han, J. (2023). Motivational system approach to understand ad processing following various game outcomes. Sport Management Review, 26(4), 517-539.

• Martins, D. (2020, December 11). Betting lounges vs. mobile betting in stadiums and arenas. In BettersInsider. Retrieved April 27, 2021, from https://www.bettorsinsider.com/news/2020/12/10/betting-lounges-vs-mobile-betting-in-stadiums-and-arenas-are-a-new-frontier-for-sports-gambling

• Meir, R., & Scott, D. (2007). Tribalism: Definition, identification, and relevance to the marketing of professional sports franchises. International Journal of Sports Marketing & Sponsorship, 8(4), 330-346.

• Meyer, J., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340-363.

• Missura, O. (2015). Dynamic difficulty adjustment. Doctoral dissertation, Bonn: University Rheinischen Friedrich-Wilhelms, Bonn

• Nacke, L. (2012). Flow in Games: Proposing a Flow Experience Model.

• Naylor, M., Hedlund, D., & Dickson, G. (2017). Team identification full circle: The important of cognition, evaluation, and affect. International Journal of Sport Management, 18, 573-592.

• NFL. (2021, April 15). NFL announces tri-exclusive official sports betting partners. In NFL. Retrieved April 29, 2021, from https://www.nfl.com/news/nfl-announces-tri-exclusive-sports-betting-partners

• Nicolao, L., Irwin, J., & Goodman, J. (2009). Happiness for sale: Do experiential purchases make consumers happier than material purchases? Journal of Consumer Research, 36(2), 188-198.

• Palomba, A. (2020). How high brand loyalty consumers achieve relationships with virtual worlds and its elements through presence. Journal of Media Business Studies, 17(3/4), 243-260.

• Popp, N., Du, J., Shapiro, S., Simmons, J. (2023). Using artificial intelligence to detect the relationship between social media sentiment and season ticket purchases. International Journal of Sport Communication, 17(1), 17-31.

• Porter, R. (2021, January 30). TV long view: Even in a down year, the NFL’s ratings are untouchable. In The Hollywood Reporter. Retrieved April 27, 2021, from https://www.hollywoodreporter.com/live-feed/tv-long-view-nfl-ratings-dominance-2020#:~:text=In%202019%2C%20the%20league%20averaged,biggest%20entertainment%20shows%20only%20widened.

• Reinecke, L., Tamborini, R., Grizzard, M., Lewis, R., Eden, A., Bowman, N. (2012). Characterizing mood management as need satisfaction: The effects of intrinsic needs on selective exposure and mood repair. Journal of Communication, 62, 437-453.

• Reyes, L. (2021, December 29). The story on how John Madden came to be involved with wildly popular EA sports NFL video game. USA Today. Retrieved February 20, 2023, from https://www.usatoday.com/story/sports/nfl/2021/12/29/madden-nfl-video-game-ea-sports/9044657002/

• Reynaldo, C., Christian, R., Hosea, H., Gunawan, A. (2021). Using video games to improve capabilities in decision making and cognitive skill: A literature review. Procedia Computer Science, 179, 211-221.

• Rodenberg, R. (2021, April 7). United States of sports betting: An updated map of where every state stands. In ESPN. Retrieved April 27, 2021, from https://www.espn.com/chalk/story/_/id/19740480/the-united-states-sports-betting-where-all-50-states-stand-legalization

• Russoniello, C., O’Brien, K., Parks, J. (2009). The effectiveness of casual video games in improving mood and decreasing stress. Journal of Cyber Therapy & Rehabilitation, 2(1), 53-66.

• Sarkar, S. (2020, March 10). 2k returns to making NFL video games, but not a Madden competitor. Polygon. Retrieved May 28, 2024, from https://www.polygon.com/2020/3/10/21172310/nfl-2k-sports-football-video-games-deal-ea-madden

• Schwarz, N., & Clore, G. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513-523.

• Sinclair, J. (2005). Exploration of state and trait anger, anger expression, and perfectionism in collegiate springboard divers. [Master’s thesis, Georgia Southern University]. Electronic Theses and Dissertations.

• Skiver, K. (2022, August 18). Madden 23 early access: Here are two ways to play before the 2022 release. In Sporting News. Retrieved November 2, 2022, from https://www.sportingnews.com/us/nfl/news/madden-23-early-access-play-2022-release-date/ndegqutcgw0tez5dvhk1jqnt#:~:text=Mid%2DAugust%20is%2C%20traditionally%2C,game%20early%20via%20early%20access

• Spielberger, C. (1999). Manual for the state-trait anger expression inventory-2. Odessa, FL: Psychological Assessment Resources.

• Stadder, E., & Naraine, M. (2020). Place your bets: An exploratory study of sports-gambling operators’ use of Twitter for relationship marketing. International Journal of Sport Communication, 13(2), 157-180.

• Tamborini, R., Bowman, N., Eden, A., Grizzard, M., Organ, A. (2010). Defining media enjoyment as the satisfaction of intrinsic needs. Journal of Communication, 60(4), 758-777.

• Tuttle, W. (2016, September 9). Watch football your way on Xbox. In Xbox Wire. Retrieved April 28, 2021, from https://news.xbox.com/en-us/2016/09/09/watch-football-your-way-on-xbox/

• Van Raaij, W. (1998). Interactive communication: Consumer power and initiative. Journal of Marketing Communications, 4(1), 1-8.

• Villani, D., Carissoli, C., Triberti, S., Marchetti, A., Gilli, G., Riva, G. (2018). Videogames for emotion regulation: A systematic review. Games for Health Journal, 7(2), 85-99.

• Wann, D. (2002). Preliminary validation of a measure for assessing identification as a sport fan: The Sport Fandom Questionnaire. International Journal of Sport Management, 3, 103-115.

• Whelan, E., Laato, S., Islam, A., & Billieux, J. (2021). A casino in my pocket: Gratifications associated with obsessive and harmonious passion for mobile gaming. PLoS One, 16(2), 1-16.

• Wiedey, B. (2020, January 31). Madden Super Bowl prediction 2020: Can video game correct recent history with Super Bowl 54. In Sporting News. Retrieved April 27, 2021, from https://www.sportingnews.com/us/nfl/news/madden-super-bowl-prediction-2020-history/11dyx2slhr41m1sqixa06v00y7

• Wilson, J. (2022, August 20). Madden debuts as top-selling game in U.S. for 23rd consecutive year. In The Esports Observer. Retrieved November 2, 2022, from https://www.sportsbusinessjournal.com/Esports/Sections/Media/2022/09/NPD-Group-August-2022-video-game-sales-Madden-NFL-2023.aspx

• Yeykelis, L., Cummings, J., Reeves, B. (2014). Multitasking on a single device: Arousal and the frequency, anticipation, and prediction of switching between media content on a computer. Journal of Communication, 64(1), 167-192.

• Yim, B., & Byon, K. (2018). The influence of emotions on game and service satisfaction and behavioral intention in winning and losing situations: Moderating effect of identification with the team. Sport Marketing Quarterly, 27, 93-106.

• Yoo, B., & Donthu, N. (2001). Developing and validating a multidimensional consumer-based brand equity scale. Journal of Business Research, 52, 1-14.

• Zillmann, D. (1983). Transfer of excitation in emotional behavior. In J. T. Cacioppo & R. E. Petty (eds.), Social psychophysiology: A sourcebook. New York: Guilford, pp. 215–240.

• Zillmann, D. (1988). Mood management through communication choices. American Behavioral Scientist, 31(3), 327-340.

• Zillmann, D., & Johnson, R. (1973). Motivated aggressiveness perpetuated by exposure to aggressive films and reduced by exposure to nonaggressive films. Journal of Research in Personality, 7(3), 261-276.

The authors thank the Institute for Business in Society at the Darden School of Business for research support.