Received: 17 January 2019 | Revised: 30 April 2019 | Accepted: 1 May 2019 DOI: 10.1002/ajim.22992 RESEARCH ARTICLE Multisystem afflictions in former National Football League players Timothy P. Morris1 | Caitlin McCracken2 | Aaron Baggish2,3 | Marc Weisskopf2,4 | Ross Zafonte DO 2,5 | Herman A. Taylor MD 2,6 | Lee M. Nadler MD 2,7 | Frank E. Speizer MD 2,3,8 | Alvaro Pascual‐Leone 1,2,9 1 Berenson‐Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 2 Football Players Health Study at Harvard University, Harvard Medical School, Boston, Massachusetts 3 Cardiovascular Performance Program, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 4 Department of Environmental Health and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts 5 Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts 6 Department of Medicine, Morehouse School of Medicine, Cardiovascular Research Institute, Atlanta, Georgia 7 Department of Medicine, Harvard Medical School, Boston, Massachusetts 8 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 9 Institut Guttmann, Universitat Autònoma de Barcelona, Badalona, Spain Correspondence Alvaro Pascual‐Leone, Berenson‐Allen Center Abstract for Noninvasive Brain Stimulation and Background: The long‐term health consequences of participation in American style Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical football (ASF) are not well understood. Center, 330 Brookline Ave (KS‐158), Boston, Methods: We conducted a retrospective cohort study of men who had played in the NFL MA 02215. Email: apleone@bidmc.harvard.edu after 1960. Participants were studied using a standardized self‐administered questionnaire designed to determine both the exposure history to ASF and the prevalence of chronic Funding information National Football League Players Association; pain, sleep apnea, cardiometabolic disease, and neurocognitive impairment. Logistic National Institutes of Health, Grant/Award regression and negative binomial regression models were used to assess associations Numbers: R01MH115949, R01MH111875, R01MH117063, P01AG031720, between age, ethnicity, body‐mass index during professional football career, field position, R24AG06142, R21AG051846, and football career duration with individual and multiple afflictions. R01MH100186; Harvard Clinical and Translational Science Center, Grant/Award Results: In this cohort of former NFL players (n = 3745), approximately one quarter of the Number: UL1 RR025758 eligible former players (27%) reported two or more medical afflictions (chronic pain, cardiometabolic disease, sleep apnea, or neurocognitive impairment). Career duration was significantly associated with an increase in the number of comorbidities. Age, race, and body‐mass index were associated with all affliction categories, other than neurocognitive impairment, which was similarly prevalent in middle‐aged players and older players. Earlier age when first playing the sport was protective against cardiometabolic affliction. Conclusions: Former NFL players report significant combinations of cross‐system afflictions. Future work will be required to determine mechanistic underpinnings. However, attention to the whole player, rather than specific organ systems seems critical to improve long‐term health outcomes in former ASF professional athletes. --------------------------------------------------------------------------------------------------------------------------- This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. © 2019 The Authors American Journal of Industrial Medicine Published by Wiley Periodicals, Inc. Am J Ind Med. 2019;62:655-662. wileyonlinelibrary.com/journal/ajim | 655 656 | MORRIS ET AL. KEYWORDS aging athlete, epidemiology, football (American), medical aspects of sport 1 | INTRODUCTION Football League from 1960 to present. Before 1960, there were substantial changes to the rules and equipment used by players. The long‐term health implications of participation in American style Specifically, the transition to the helmet with a hard, plastic shell was football (ASF) is under significant debate. Previous research has complete and well‐established by 1960, although changes to the described arthritis,1 cardiometabolic abnormalities,2 pain,3 sleep interior padding and facemask continue until the present day. Former apnea,4 and neurological issues.5 High profile cases of prominent players were defined as any player that touched the NFL (training former ASF athletes and their health burdens are reported on camp, rookie camp, practice squad, or drafted by an NFL team), and mainstream media outlets, which have furthered both the public and were no longer playing in the NFL. After testing that there was no scientific discourse. There is a prevalent notion that former ASF difference in response pattern to email or paper mailed question- players have poorer long‐term health outcomes despite the under- naires, we sent email or paper surveys to 13 200 former players with standing that early participation in sport and exercise can predict either a home or email address that did not return as undeliverable. 6 7 later‐life behaviors and protect against certain medical afflictions. Any duplicate responses (those who returned both a paper and Body‐mass index (BMI), age, race and sex are all well‐established risk electronic survey) were removed. Of the 13 200 reachable players factors for conditions such as cardiometabolic disease, sleep apnea and (as of 2015), 3745 completed the survey (March 2017; ~28% dementia in the general population,8,9 and may represent predictive risk response rate). To estimate the representativeness of our sample in factors in former ASF players. Furthermore, the unique demands of regard to the wider former NFL player population (who did not participation in professional ASF may place players at greater risk for respond to our questionnaire) we calculated the difference in our certain medical conditions later in life, whereby football‐specific variables study variables from our sample compared to the nonresponders via such as position, career injuries, and age at first participation in the sport data available from Pro‐Football Reference (PFR), an online source for have been associated with certain medical conditions.10-12 historical data regarding NFL teams, players, scores and leaders.15 The A large majority of previous research on the health of former ASF study was approved by an institutional review board. players has been limited to the study of a single condition. Our previous work has demonstrated that former NFL players report afflictions in specific categories of medical conditions, including pain, sleep apnea, 2.2 | Player characteristics cardiometabolic disease, and neurocognitive impairment.13 Emerging We categorized age (reported in years) into groups of 34 or younger evidence suggests that cross‐correlations between certain medical (24‐34), 35 to 54, and 55 or older. Race was divided into three conditions, such as sleep apnea and cardiovascular pathology (impaired categories; White, Black and other, where other includes American vascular and left ventricular diastolic function),14 may occur in ASF Indian/Alaskan Native, Native Hawaiian/Pacific Islander, Asian, players also. Thus, the study of individual medical conditions in isolation Hispanic, and other. Professional body‐mass index (BMI) was may not capture the full extent to which ASF participation can affect calculated (body weight [average annual during professional playing long‐term health and affliction in former players. time] in kilograms divided by height [current] in meters squared). The aim of this study was two‐fold: to quantify multisystem afflictions in former NFL players and to assess the impact of player characteristics and career history on the presence of both single and 2.3 | Football exposure multisystem conditions. We hypothesized that former ASF players Football exposures examined included position played, number of would show afflictions in more than one biological system, and that seasons played in NFL, and age at which first began to play organized the characteristics of football exposure would be linked to single and ASF. Position played was categorized as linemen (offensive and multisystem comorbidities in cardiometabolic disease, sleep apnea, defensive) and nonlinemen (all other positions). Linemen were those chronic pain, and neurocognitive impairment. participants who had exclusively played this position during their career. Position was categorized this way based on expected 2 | METHODS differences in body habitus and composition, as well as hit frequency/type. This categorization is consistent with the literature 2.1 | Study design suggesting linemen may be at higher risk of a number of medical conditions.1,16 The number of professional seasons played was The present study collected and analyzed questionnaire data from a reviewed for outliers, anyone reporting more than 15 years of cohort of former National Football League (NFL) players. Ques- professional play (N = 100), not exclusive to the NFL, was verified tionnaires asked players to self‐report their demographics, exposure using PFR. Where there was a discrepancy of over or under three to the game, and medical diagnoses. An attempt was made to contact professional years self‐reported from public records reviewed (PFR) all living former players who participated in the American or National the item was set to missing (N = 40). The age at which players first MORRIS ET AL. | 657 began organized football was restricted to ages 4 to 25 to remove T A B L E 1 Player sample characteristics outliers (N = 10). Age (y), n/N (%) 34 or younger 465/3743 (12) 2.4 | Affliction categories 35‐54 1528/3743 (41) 55 or older 1750/3743 (47) Four affliction categories were created based on the medical a Race, n/N (%) conditions highlighted via former player advisors and the medical and scientific literature. These were: sleep apnea, chronic pain, White 2183/3698 (59) cardiometabolic disease, and neurocognitive impairment. These Black 1370/3698 (37) b affliction categories were assessed via self‐reported answers to the Other 145/3698 (4) questionnaire regarding medical diagnoses. As misconceptions about Position, n/N (%) health and disease are common,17 we attempted to avoid misclassi- Linemenc 1093/3733 (29) fication of medical conditions by asking a player if they had ever been Nonlineman 2640/3733 (71) prescribed a medication for a given condition. For medical conditions Number of afflictions, n/N (%) less likely to be susceptible to misconceptions, such as Alzheimer 0 1561/3669 (43) disease, heart attack, or stroke, we asked “Has a health care provider 1 1132 (31) ever told you that you have any of the following diagnoses or health 2 623/3669 (17) outcomes?”. The affliction categories were defined as follows: Sleep 3‐4 353/3669 (9) affliction was defined as having been diagnosed with sleep apnea. 2 Professional BMI, mean (SD), kg/m 30.4 (4.2) Pain affliction was defined as reporting that pain medication for Number of seasons played, mean (SD) 6.7 (3.8) chronic pain was prescribed by a medical provider and was still being taken at the time of survey response. Cardiometabolic affliction was Age when started football, mean (SD), y 11.8 (3.1) a defined as either being diagnosed with a heart attack or stroke by a Subjects had the option to select more than one category. b Other includes American Indian/Alaskan Native, Native Hawaiian/Pacific medical provider, or currently taking medication for at least two of Islander, Asian, Hispanic, other, and multirace. the following three medical conditions: diabetes, hypertension, and c Only played the offensive or defensive line. Special teams were classified high cholesterol. Neurocognitive affliction was defined as being as nonlinemen. diagnosed by a medical provider with dementia or chronic traumatic encephalopathy (CTE) or having ever been prescribed by a physician 3 | RESULTS medication for memory loss. Examination of multisystem comorbidity was characterized by assessing those players who met the criteria for 3.1 | Player demographics one, two, three, or all four affliction categories. At the time of analysis, a total of 3745 former players had responded to our questionnaire. Most respondents were White (59%), with Black 2.5 | Statistical analysis players representing 37% and “other” representing 4% of the total Analysis of individual affliction types were compared using logistic cohort. Linemen (offensive and defensive) represented 30% of regression to assess the independent associations of player character- respondents. The mean age ± standard deviation of the respondents istics and football exposure, adjusting for all covariates, with affliction was 52 ± 14 years. Average weight during professional play was (afflicted or not afflicted). Results are reported as odds ratios where 239 ± 42 lbs; the mean BMI during professional play was 30 ± 4. significance of P < 0.05 is measured by a confidence interval that does not Respondents played an average of 7 ± 4 years professionally and were include one. Due to overdispersion in the affliction count, a negative 12 ± 3‐years of age when they first played organized football (Table 1). binomial regression model was used to assess the relationship of player characteristics and the number of simultaneous afflictions. Regression 3.2 | Individual affliction associations coefficients represent the log of the expected count of the dependent variable for each one‐unit change in the predictor variables. We present A quantification of individual medical conditions reported by our cohort the exponentiated coefficients (called mean ratio [MR]), which indicates of former players is reported elsewhere.13 Age was significantly the fold change in the count as the exposure variable increases by one associated with greater odds of sleep apnea, chronic pain, and unit. We used χ2 statistics and t tests to test the difference in study cardiometabolic affliction, in which older players (55 years and above) variables between our responders and nonresponders (from PFR) as an were significantly more likely to be afflicted in these categories compared estimate of sample representativeness. For this representativeness to younger players (Table 2). Players over 35 years of age, however, had estimation, age from PFR (where only date of birth is provided) was similar odds of neurocognitive affliction compared to younger players. calculated by assuming that the nonresponders filled out the ques- Race and BMI were also predictive of afflictions in all categories (Table 2). tionnaire on the last date a responder did. Statistical analyses were Black players were approximately one and a half times more likely to performed in Stata, version 14 (Statacorp LLC, TX). report afflictions in all categories compared to White players, and those 658 | MORRIS ET AL. T A B L E 2 Logistic regression coefficients for player characteristics on individual health domains Sleep affliction Pain affliction Cardiac affliction Neurocognitive affliction OR (95% CI)c OR (95% CI)c OR (95% CI)c OR (95% CI)c Age, y 34 & under Referent group Referent group Referent group Referent group 35‐54 2.99 (2.09‐4.27) 1.76 (1.32‐2.36) 3.33 (2.20‐5.06) 2.16 (1.50‐3.11) 55 and above 5.64 (3.91‐8.15) 3.44 (2.56‐4.63) 13.03 (8.57‐19.82) 2.75 (1.89‐3.99) Ethnicitya White Referent group Referent group Referent group Referent group Black 1.56 (1.30‐1.86) 1.67 (1.42‐1.97) 1.57 (1.32‐1.86) 1.70 (1.41‐2.05) b Other 1.94 (1.33‐2.83) 0.87 (0.59‐1.30) 0.87 (0.57‐1.33) 1.20 (0.77‐1.86) Position Nonlinemen Referent group Referent group Referent group Referent group Linemen 1.10 (0.88‐1.39) 1.05 (0.85‐1.30) 0.88 (0.70‐1.10) 0.99 (0.77‐1.28) Professional BMI (5‐unit increase), kg/m2 1.67 (1.46‐1.92) 1.26 (1.11‐1.42) 1.38 (1.21‐1.58) 1.05 (0.91‐1.21) Per professional season played 1.00 (0.98‐1.03) 1.02 (0.99‐1.04) 0.99 (0.96‐1.01) 1.01 (0.99‐1.04) Per year of age when started football 1.01 (0.99‐1.04) 0.99 (0.96‐1.01) 1.04 (1.01‐1.07) 1.02 (0.99‐1.05) Abbreviations: BMI, body‐mass index; CI, confidence interval; OR, odds ratio. a Subjects had the option to select more than one category. b Other includes American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, Asian, Hispanic and other. c Adjusted (for all other covariates) odds ratio; 95% CIs are significant in those which do not include 1.0. who listed “other” for ethnicity were almost twice as likely to report sleep 3.3 | Multisystem afflictions apnea compared to White players. Number of seasons played was Approximately 27% of players were categorized as having more essentially unrelated to any of the afflictions. However, older the age than one affliction and 9% had more than two, (Figure 1A). Figure when participation in organized ASF first began increased the odds of 1B illustrates the combinations of afflictions reported by those cardiometabolic affliction significantly but marginally by 4% (Table 2). F I G U R E 1 Prevalence of multiple afflictions within the cohort of former ASF players. A greater numerical proportion of players reported at least one affliction than none (A). The combinations of multisystem afflictions for those who report two and three afflictions, respectively, are shown in (B). The most common coupling of multisystem afflictions were pain, cardiometabolic (CM), and sleep apnea. ASF, American style football; NFL, National Football League [Color figure can be viewed at wileyonlinelibrary.com] MORRIS ET AL. | 659 T A B L E 3 Player characteristics and number of health domain disease, or neurocognitive impairment. Age was a significant predictor afflictions (0‐3)a of all afflictions apart from neurocognitive afflictions. Middle‐aged Mean Ratio former players were just as likely to report neurocognitive impairment (95% CI)d P as older players, a finding which contributes to the growing under- Age, y standing of an association between ASF participation and neurocogni- 34 and under Referent group tive impairment that is above and beyond that which can be explained 35‐54 2.03 (1.73‐2.39) <.001 by aging alone. Multisystem afflictions were prevalent among our 55 and above 3.38 (2.88‐3.97) <.001 cohort with just over a quarter of former players reporting two or more afflictions. This prevalence of multisystem afflictions is higher than the Raceb one reported in the general population (21%).18,19 Career duration was White Referent group marginally predictive of multiple afflictions, whereas earlier age when Black 1.36 (1.27‐1.47) <.001 first starting organized ASF appeared to be protective against Otherc 1.10 (0.92‐1.31) .67 cardiovascular affliction. Position Previous research on the health of former and current ASF Nonlinemen Referent group players has mostly been limited to the study of single system Linemen 1.03 (0.94‐1.13) .49 afflictions.1,2,4,20 While our previous work13 confirmed the presence Professional BMI (5‐unit increase), 1.19 (1.13‐1.25) <.001 of chronic health conditions (pain, cardiometabolic disease, sleep kg/m2 apnea, and neurocognitive impairment) previously described in the Per season played 1.01 (1.00‐1.02) .01 literature,1-5 the results of this paper are novel and notable. We Per year of age when started football 1.01 (1.00‐1.02) .06 found that more than one‐quarter of former players reported Abbreviations: BMI, body‐mass index; CI, confidence interval. afflictions in multiple cross‐system morbidities. Multisystem afflic- a 3 = 3+ due to small cell issues in the four categories. tions are key contributors to overall morbidity, long‐term disability, b Subjects had the option to select more than one category. poorer functional quality of life, treatment complications, and c Other includes American Indian/Alaskan Native, Native Hawaiian/Pacific mortality.18 Emerging evidence suggests that coupling of cross‐ Islander, Asian, Hispanic and other. d Adjusted mean ratio; 95% CIs. system comorbidities within ASF players may be particularly prevalent from a rather early age.14 Our results reveal the most common coupling of multisystem afflictions between cardiometa- with two and three afflictions, respectively. Cardiometabolic and bolic, sleep apnea, and pain. This is consistent with a recent study of pain afflictions were the most common comorbid afflictions for collegiate ASF athletes that found impairments in measures of those with two types of afflictions (23%), whereas for those arterial stiffness and left ventricular diastolic function in those with reporting three afflictions, cardiometabolic, pain, and sleep apnea sleep disordered breathing (defined as ≥5 on the apnea‐hypopnea (36%) were the most common. index), compared to those without sleep disordered breathing.14 The The MR of multisystem afflictions was significantly higher in middle‐ understanding that cross‐system comorbidities are present in former aged players and older players compared to players aged 34 or younger ASF players emphasizes the importance of targeted screening for after adjusting all other variables in the model (Table 3). That is, players multiple afflictions as well as the importance of future work to aged over 54 years were found to have a mean fold increase in the identify linking pathobiological processes. number of multisystem afflictions roughly three times greater than those The etiology of the development of multisystem comorbidity in aged 34 and under. Black players were also significantly more likely to former ASF players is unknown and the physiological correlates are have more afflictions compared to White players. Linemen did not have a likely to be complex, an understanding of which may lead to novel greater fold increase in multiple afflictions compared to nonlinemen, but risk factors for postretirement diseases. In the general population for every 5‐unit increase in professional BMI, a 19% increase in age, race, and body habitus are common risk factors for individual multisystem afflictions was found. In contrast to the individual afflictions afflictions 8,21-23 and our results suggest these are similar in former not being associated with the number of professional seasons played, players. ASF participation is associated with atypical player pheno- having multiple afflictions was significantly associated with professional types unique to the game that may also contribute to the seasons played. While an older age at which players began to play development of multisystem afflictions. High training‐associated organized football was associated with more afflictions, this did not reach static hemodynamic stress and low aerobic conditioning (especially statistical significance at the 0.05 level. in linemen) is common,24 as is deliberate body‐mass gain13 and high‐ calorie diets,25 routine use of nonsteroidal anti‐inflammatory drugs,26 and the use and misuse of opioids.3 Repetitive blunt trauma 4 | D IS C U S S IO N to both the body and head is also an inherent aspect of participation in ASF caused by player‐player impacts and the culminative effect of More than half of the former NFL players in our cohort reported having more years of this type of exposure appears to increase the number at least one affliction, either sleep apnea, chronic pain, cardiometabolic of later‐life multisystem afflictions. 660 | MORRIS ET AL. Whereas older age predicted cardiometabolic, pain and sleep apnea nonparticipants using data publicly available on ProReference Football, afflictions, it was not predictive of neurocognitive impairment. Younger we found the mean age was likely to be representative of the overall (35‐54years) and older (≥55 years) players had similar odds of reporting population (53.3 ± 14.1 for responders compared to 51 ± 12.1 for this affliction. An assumed link between football exposure and cognitive nonresponders). Although significant differences in weight impairment in the public eye may cause bias regarding memory loss (232.8 ± 38.3 pounds for responders, 229.8 ± 39.9 pounds for medication usage among former players (for which ~70% met the nonresponders), years played (6 ± 3.6 for responders, 5.2 ± 3.5 for fulfillment criteria for this affliction category) and a potential selection nonresponders), and the proportion of linemen compared to nonline- bias in our study (those players who are more impaired/more men (28.5% linemen in the responders group, 20.8% linemen in the concerned) may have increased the prevalence rate of this affliction nonresponders group) existed. As such, because of these differences category. Nevertheless, given that the age‐standardized prevalence of we must view the quantification of overall prevalence of the medical all cause dementia in over 60‐year‐olds is between 5% and 7%,27 the conditions among our sample of former NFL players with caution. We proportion of players who reported neurocognitive affliction in our believe that the exposure‐outcome relationships are less likely to cohort (17%; see Churchil et al13) is significantly large enough to still be have been affected based on selection bias as this would require a concern even if neurocognitive affliction is absent in all of the eligible former players to be more or less likely to enter the study based on former players not recruited into this study. Neurocognitive impairment both their affliction status and a certain demographic or football 5 associated with repetitive head trauma and a trend towards early exposure status. Longitudinal follow‐up studies and biological onset of Alzheimer’s disease compared to the US general population11 mechanistic insights are necessary to unveil the true extent of these have previously been reported in former NFL players. While long- afflictions in former NFL players and to develop strategies to itudinal studies are required to fully establish the incidence rate of mitigate the burden of affliction. dementia diagnosis, our results further corroborate the associations In summary, we show that former NFL players report both between ASF participation and neurocognitive issues, especially among individual as well as comorbid afflictions across multiple domains. We younger former players. In this context, the finding of high prevalence of found that while some factors inherent in those players who start the multisystem comorbidities is potentially particularly relevant since, for game earlier seem protective against cardiometabolic afflictions, example, sleep apnea can accelerate the progression of dementia.28 other factors present a clear risk to early neurocognitive issues. Most Previous research has suggested that prognostic implications of relevant for long‐term wellbeing, we found a high prevalence of certain risk factors for cardiometabolic disease in the general multisystem afflictions. Quantification of intricate, multisystem 29 30 31 population, such as obesity, hypertension, and arterial stiffness afflictions and the understanding of cross‐system correlates of are comparable in ASF players.2,4 Certain protective factors for comorbidity in former ASF players ought to lead to enhanced cardiometabolic afflictions however, such as adherence to sport and targeted screening and treatment. exercise, are likely to be present in athletes, and especially present in those who begin playing ASF earlier. Early‐life patterns can predict AC KNO WL EDG M EN TS later‐life behaviors, such as physical exercise,6 which is associated with a 20% to 30% reduction in cardiovascular disease.7 Our results The authors thank the study participants, advisors, and staff of the suggest that those players who avoided early‐life comorbidities and Football Players Health Study at Harvard University. The authors who began ASF participation earlier were less likely to develop would also like to thank Rachel Grashow (HSPH) for her generous cardiometabolic afflictions in later life. Consequently, while certain contribution to the work regarding her analysis of the representa- factors associated with the game are related to other comorbidities, tiveness of our sample. early participation in ASF may promote certain lifestyle factors, or be related to certain body habitus (larger players who may have been CON F LI CTS OF I NTERE ST drafted into the game later, purely based on body size), which are ultimately protective against the development of cardiometabolic All of the other authors named on this study are either partially or afflictions. fully supported by the Football Players Health Study at Harvard The findings here should be interpreted in light of their University which is, in turn, sponsored by the NFLPA . The NFLPA limitations. While we attempted to minimize misclassification of had no role in the design and conduct of the study; collection, medical conditions with our survey questions, accuracy of reporting management, analysis, and interpretation of the data; preparation, health conditions is subject to the limitations of self‐reported data. review, or approval of the manuscript; and decision to submit the Nonetheless, accuracy in the reporting of certain health conditions manuscript for publication. Other authors report no potential or through this type of survey study design has been reported in other actual conflicts of interest. populations.32 The selection bias regarding the type of former player Dr. A. Pascual‐Leone serves on the scientific advisory boards who enrolls in the study is also a limitation. It is not possible to know for Neosync, Neuronix, Starlab Neuroscience, Neuroelectrics, whether players more or less afflicted in the medical conditions Magstim Inc, Constant Therapy, and Cognito; and is listed as an studied were more or less likely to enroll. We had a response rate of inventor on several issued and pending patents on the real‐time ~28%. When we compared our participants to the contacted integration of transcranial magnetic stimulation with MORRIS ET AL. | 661 electroencephalography and magnetic resonance imaging. He study. Am J Prev Med. 2005;28(3):267‐273. https://doi.org/10.1016/j. amepre.2004.12.003 also serves on the editorial board for Circulation, and serves as 7. McKinney James, Lithwick DanielJ, Morrison BarbaraN, et al. The an associate editor for Medicine Science Sports & Exercise. Dr. health benefits of physical activity and cardiorespiratory fitness. Zafonte serves on the Scientific Advisory Board of Myomo, Oxeia BCMJ. 2016;58(3):131‐137. http://www.bcmj.org/articles/health‐ Biopharma, ElMINDA and Biodirection. He also evaluates benefits‐physical‐activity‐and‐cardiorespiratory‐fitness Accessed 1 April, 2018 patients in the MGH Brain and Body‐TRUST Program, which is 8. Li KK, Kushida C, Powell NB, Riley RW, Guilleminault C. Obstructive funded by the NFL Players Association. sleep apnea syndrome: a comparison between Far‐East Asian and White men. Laryngoscope. 2000;110(10 pt 1):1689‐1693. https://doi. org/10.1097/00005537‐200010000‐00022 A UT HO R C ONT RI BU TIO NS 9. Pencina MJ, D’Agostino RB, Larson MG, Massaro JM, Vasan RS. Predicting the 30‐year risk of cardiovascular disease: the Framing- APL, FS, LN, HT, RZ, MW, and AB participated in the conception of ham Heart Study. Circulation. 2009;119(24):3078‐3084. https://doi. the study and its design. CM contributed to the data acquisition and org/10.1161/CIRCULATIONAHA.108.816694 CM and MW performed the data analysis. TM, CM, MW, AB, and APL 10. Miller MA, Croft LB, Belanger AR, et al. Prevalence of metabolic contributed to the interpretation of the work. TM drafted the syndrome in retired National Football League players. Am J Cardiol. 2008;101(9):1281‐1284. https://doi.org/10.1016/j.amjcard.2007.12. manuscript and CM, MW, AB, FS, and APL critically revised the work. 029 All authors gave final approval for the work. 11. Guskiewicz KM, Marshall SW, Bailes J, et al. Association between recurrent concussion and late‐life cognitive impairment in retired professional football players. Neurosurgery. 2005;57(4):719‐726. DIS CLOS URE BY AJIM EDITO R OF RECORD discussion 719‐726 12. Alosco ML, Kasimis AB, Stamm JM, et al. Age of first exposure to John Meyer declares that he has no conflict of interest in the review American football and long‐term neuropsychiatric and cognitive out- and publication decision regarding this article. comes. Transl Psychiatry. 2017;7(9):e1236. https://doi.org/10.1038/tp. 2017.197 13. Churchill TW, Krishnan S, Weisskopf M, et al. Weight gain and E TH I C S A P P R O V A L A N D IN F O R M E D C O N S E N T health affliction among former National Football League players. Am J Med. 2018;131:1491‐149. https://doi.org/10.1016/j. The institutional review board of Beth Israel Deaconess Medical amjmed.2018.07.042 Center, Boston, approved this study and all participants provided 14. Kim JH, Hollowed C, Irwin‐Weyant M, et al. Sleep‐disordered written consent before participating in the research. breathing and cardiovascular correlates in college football players. Am J Cardiol. 2017;120(8):1410‐1415. https://doi.org/10.1016/j. amjcard.2017.07.030 OR CID 15. Pro Football Statistics and History. Pro‐Football‐Reference.com. https://www.pro‐football‐reference.com/. 2017. Accessed 4 Decem- Timothy P. Morris http://orcid.org/0000-0002-7560-3821 ber, 2017 Ross Zafonte http://orcid.org/0000-0002-1050-796X 16. Croft LB, Belanger A, Miller MA, Roberts A, Goldman ME. Comparison of National Football League linemen versus nonlinemen of left ventricular mass and left atrial size. Am J Cardiol. 2008;102(3):343‐ REFERENC ES 347. https://doi.org/10.1016/j.amjcard.2008.03.065 17. Parker R. Health literacy: a challenge for American patients and their 1. Golightly YM, Marshall SW, Callahan LF, Guskiewicz K. Early‐onset health care providers. Health Promot Int. 2000;15(4):277‐283. https:// arthritis in retired National Football League players. J Phys Act Health. doi.org/10.1093/heapro/15.4.277 2009;6(5):638‐643. https://doi.org/10.1123/jpah.6.5.638 18. Vogeli C, Shields AE, Lee TA, et al. Multiple chronic conditions: 2. Selden MA, Helzberg JH, Waeckerle JF, et al. Cardiometabolic prevalence, health consequences, and implications for quality, care abnormalities in current National Football League players. Am J management, and costs. J Gen Intern Med. 2007;22(3):391‐395. Cardiol. 2009;103(7):969‐971. https://doi.org/10.1016/j.amjcard. https://doi.org/10.1007/s11606‐007‐0322‐1 2008.12.046 19. Xuan J, Kirchdoerfer LJ, Boyer JG, Norwood GJ. Effects of 3. Cottler LB, Ben Abdallah A, Cummings SM, Barr J, Banks R, Forchheimer comorbidity on health‐related quality‐of‐life scores: an analysis of R. Injury, pain, and prescription opioid use among former National clinical trial data. Clin Ther. 1999;21(2):383‐403. https://doi.org/10. Football League (NFL) players. Drug Alcohol Depend. 2011;116(1‐3):188‐ 1016/S0149‐2918(00)88295‐8 194. https://doi.org/10.1016/j.drugalcdep.2010.12.003 20. Cottler LB, Ben Abdallah A, Cummings SM, Barr J, Banks R, Forchheimer 4. George CFP, Kab V, Levy AM. Increased prevalence of R. Injury, pain, and prescription opioid use among former National sleep‐disordered breathing among professional football Football League (NFL) players. Drug Alcohol Depend. 2011;116(1):188‐ players. N Engl J Med. 2003;348(4):367‐368. https://doi.org/10. 194. https://doi.org/10.1016/j.drugalcdep.2010.12.003 1056/NEJM200301233480422 21. Franklin KA, Lindberg E. Obstructive sleep apnea is a common 5. Amen DG, Newberg A, Thatcher R, et al. Impact of playing American disorder in the population—a review on the epidemiology of sleep professional football on long‐term brain function. J Neuropsychiatry apnea. J Thorac Dis. 2015;7(8):1311‐1322. https://doi.org/10.3978/j. Clin Neurosci. 2011;23(1):98‐106. https://doi.org/10.1176/jnp.23.1. issn.2072‐1439.2015.06.11 jnp98 22. Senaratna CV, Perret JL, Lodge CJ, et al. Prevalence of obstructive 6. Telama R, Yang X, Viikari J, Välimäki I, Wanne O, Raitakari O. sleep apnea in the general population: a systematic review. Sleep Med Physical activity from childhood to adulthood: a 21‐year tracking Rev. 2017;34:70‐81. https://doi.org/10.1016/j.smrv.2016.07.002 662 | MORRIS ET AL. 23. Young T, Shahar E, Nieto FJ, et al. Sleep Heart Health Study Research 29. Pérez AP, Muñoz JY, Cortés VB, Velasco P, de P. Obesity and Group. Predictors of sleep‐disordered breathing in community‐ cardiovascular disease. Public Health Nutr. 2007;10(10A):1156‐1163. dwelling adults: the Sleep Heart Health Study. Arch Intern Med. https://doi.org/10.1017/S1368980007000651 2002;162(8):893‐900. 30. Mannino DM, Thorn D, Swensen A, Holguin F. Prevalence and 24. Levine BD, Baggish AL, Kovacs RJ, Link MS, Maron MS, Mitchell JH. outcomes of diabetes, hypertension, and cardiovascular disease in Eligibility and disqualification recommendations for competitive chronic obstructive pulmonary disease. Eur Respir J. 2008;32:962‐ athletes with cardiovascular abnormalities: task force 1: classification 969. https://doi.org/10.1183/09031936.00012408 of sports: dynamic, static, and impact: a scientific statement from the 31. Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardio- American Heart Association and American College of Cardiology. vascular events and all‐cause mortality with arterial stiffness: a Circulation. 2015;132(22):e262‐e266. https://doi.org/10.1161/CIR. systematic review and meta‐analysis. J Am Coll Cardiol. 2010;55(13): 0000000000000237 1318‐1327. https://doi.org/10.1016/j.jacc.2009.10.061 25. Jonnalagadda SS, Rosenbloom CA, Skinner R. Dietary practices, 32. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. attitudes, and physiological status of collegiate freshman football Agreement between self‐report questionnaires and medical record data players. J Strength Cond Res. 2001;15(4):507‐513. was substantial for diabetes, hypertension, myocardial infarction and 26. Holmes N, Cronholm PF, Duffy AJ, Webner D. Nonsteroidal anti‐ stroke but not for heart failure. J Clin Epidemiol. 2004;57(10):1096‐1103. inflammatory drug use in collegiate football players. Clin J Sport Med. https://doi.org/10.1016/j.jclinepi.2004.04.005 2013;23(4):283‐286. https://doi.org/10.1097/JSM.0b013e318286d0fa 27. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement J. 2013;9(1):63‐75. https://doi.org/10.1016/j.jalz. How to cite this article: Morris TP, McCracken C, Baggish A, 2012.11.007 e2 et al. Multisystem afflictions in former National Football 28. Gosselin N, Baril A‐A, Osorio RS, Kaminska M, Carrier J. Obstructive League players. Am J Ind Med. 2019;62:655‐662. https://doi. sleep apnea and the risk of cognitive decline in older adults. Am J org/10.1002/ajim.22992 Respir Crit Care Med. 2018;199:142‐148. https://doi.org/10.1164/ rccm.201801‐0204PP August