Subconcussive Head Impacts in Sport

SUBCONCUSSIVE HEAD IMPACTS IN SPORT 1

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Working Title: Subconcussive Head Impacts in Sport: A Systematic Review of the Evidence

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Lynda Mainwaring, Kaleigh M. Ferdinand Pennock, Sandhya Mylabathula, and Benjamin Z.

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Alavie

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University of Toronto, Toronto, Ontario, Canada

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Acknowledgements

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The authors acknowledge Julian Clarke for his contributions to conceptualizing the paper and his

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help in title and abstract screening.

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Point of contact

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Correspondence concerning this article should be addressed to Lynda Mainwaring, University of

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Toronto, Toronto, Ontario, Canada, M5S 2W6.

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E-mail: [email protected]

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The citation for this article is as follows: Mainwaring, L. Ferdinand-Pennock, K., Myalabuthula, S. & Alavie, B. (2018). Subconcussive head impacts in sport: A systematic reiview of the evidence. Special Edition: International Journal of Psychopshysiology: doi: 10.1016/j.ijpsycho.2018.01.007. [Epub ahead of print]


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The paper can be found online:

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https://www.sciencedirect.com/science/article/pii/S0167876018300126?via%3Dihub

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Abstract

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Purpose: To identify and evaluate the evidence that examines subconcussive impacts in sport-

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specific settings, and address two objectives: a) to determine how ‘subconcussion’ is

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characterized in the current literature, and b) to identify directions for future research.

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Research design: Systematic review.

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Methods and procedures: CINAHL, EMBASE, MedLine, PsycINFO, SportDiscus, and Web of

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Science were searched. All retrieved articles were screened based on criteria developed a priori

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and selected articles were subsequently reviewed and evaluated with three quality assessment

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tools by rotating pairs of reviewers.

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Results: A total of 1966 articles were screened. Fifty-six studies met the inclusion criteria.

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Studies were classified into three main categories based on primary focus: neurobiological,

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neuropsychological, and impact exposure metrics. The neurobiological studies suggested that in

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male athletes, functional and microstructural deterioration was associated with repetitive head

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impacts. There was insufficient to weak evidence for the relationship between repetitive hits to

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the head and deterioration in neurocognitive performance. Studies of impact exposure metrics

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examined various indices, including linear acceleration, rotational acceleration, and location and

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frequency of hits. Insufficient evidence was presented to determine a minimal threshold for

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subconcussive hits. Across all categories of studies there was a lack of consistency and clarity in

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defining and measuring variables related to the concept of ‘subconcussion’.

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Conclusions: Evidence reviewed predominantly from studies of male athletes in contact and

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collision sports identifies that repetitive hits to the head are associated with microstructural and

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functional changes in the brain. Whether these changes represent injury is unclear. We

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determined the term ‘subconcussion’ to be inconsistently used, poorly defined, and misleading.


The terms ‘subconcussive impacts’, ‘head impacts’, or ‘repetitive hits to the head’, without

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inference of injury, are suggested to be more appropriate at this time. Future research is needed

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to characterize the phenomenon in question.

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Keywords: biomarkers, head impact exposure, neurocognitive functioning, repetitive head hits,

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sport, subconcussive impacts

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SUBCONCUSSIVE HEAD IMPACTS IN SPORT 75 76

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Subconcussive head impacts in sport: A systematic review of the evidence The sport concussion literature has grown rapidly in the past two decades. This literature

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spans various related domains and disciplines (including neuropsychology, neurophysiology,

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biomechanics, imaging, education, sport policy, sport psychology, and sport medicine), and

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covers a wide range of topics. One topic garnering increasing attention is repetitive hits to the

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head and their relation to an emerging concept called subconcussion (Bailes, Petraglia, Omalu,

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Nauman, & Talavage, 2013). Research interest and awareness of this phenomenon has risen with

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the surge of interest in chronic traumatic encephalopathy (CTE), which is a postulated long-term

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outcome of multiple repeated blows to the head with or without concussion (McKee et al., 2009;

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Omalu et al., 2005).

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The literature indirectly related to the concept of subconcussion emerged with Martland’s

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opinion that, “...in punch drunk there is a very definite brain injury due to single or repeated

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blows on the head or jaw...” (1928, p.1103). Pudenz and Shelden observed the effects of what

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they called “subconcussive blows” (1946, p.495) in the brains of macaque monkeys surgically

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fitted with a lucite window in the skull. Much later, dementia pugilistica was described by

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Corsellis and colleagues (1973) in a case series of 15 retired boxers. In 1995, Mendez reviewed

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the neuropsychiatry of boxers and reported that these athletes were prone to CTE with exposure

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to repeated head blows.

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Since then, the concepts of subconcussion and subconcussive blows to the head have

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been addressed directly in the sport injury community (e.g., Broglio et al., 2011; Dashnaw,

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Petraglia, and Bailes, 2012; Pellman, et al., 2003; Rutherford et al., 2003) and indirectly with the

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examination of head impacts (e.g., Mihalik, Bell, Marshall, and Guskiewicz, 2007; Mihalik et al.,

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2005). Both the research and conceptual understanding of this phenomenon are in their infancy.

SUBCONCUSSIVE HEAD IMPACTS IN SPORT

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Despite a few excellent reviews on the concept of subconcussion, there remains a lack of

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clarity and many unanswered questions. Bailes and colleagues (2013) provided a comprehensive

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detailed overview of the biophysical, neurophysiological, and neuroradiological evidence in

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support of subconcussion. They defined subconcussion as “a cranial impact that does not result

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in known or diagnosed concussion on clinical grounds” (p. 1236), which may be the result of a

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“slosh phenomenon” (Smith, Bailes, Fisher, Robles, Turner and Mills, 2012) in which rapid

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acceleration-deceleration of the brain occurs. Bailes et al. (2013) noted that cumulative exposure

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to repetitive head hits is the main contributor to subconcussion, and emphasized the need for

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further study.

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Several reviews contribute to the understanding of what Bailes and colleagues refer to as

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the “underrecognized phenomenon” (2013, p. 1236) of subconcussion. A systematic review by

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Tarnutzer, Straumann, Brugger, and Feddermann-Demont (2016) reported on the associations

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between soccer activities and decline in brain structure and function. That review highlighted

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that frequency of heading was not strongly associated with neuropsychological impairments, and

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that studies were plagued with bias (Tarnutzer et al., 2016). In contrast, Rodrigues, Lasmar and

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Caramelli (2016) reviewed the outcomes of soccer heading and brain function. While

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inconclusive, their findings suggested a relationship between heading the ball in soccer and

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abnormal brain structure. Koerte et al., (2015b) provided a review on advanced imaging

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techniques currently used to investigate changes in the brain in two populations exposed to

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repetitive head impacts, athletes and military personnel. The authors reported evidence of

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microstructural alterations from subconcussive head impacts in soccer, hockey and football.

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Koerte et al. called for capitalizing on advanced imaging techniques for studying repetitive head

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trauma, referring to it as a “whole new era” of study (2015b, p. 345).


Davenport et al., (2016) investigated subconcussive head impacts in youth and high

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school-aged male football players. They determined that emerging evidence for relationships

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between neuroimaging data and impact exposure metrics is cause for more focused studies,

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especially in young vulnerable athletes. Most recently, Belanger, Vanderploeg, and McAllister

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(2016) reviewed short-term clinical outcomes of subconcussive head impacts in their formative

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review. They concluded that human studies of neurological and neuropsychological

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consequences of subconcussive blows to the head were limited, but that the evidence, to date,

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suggested that subconcussive blows do not cause significant clinical effects and that any effect

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“is likely to be small or nonexistent” (p.159). Belanger and colleagues concluded that the term

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“subconcussion” referred to an “elusive theoretical construct” (p.160), does not adequately

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describe the phenomenon, and that caution is warranted in suggesting that “brain injury” is

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secondary to subconcussive impacts because of the societal implications.

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All the reviews related to ‘subconcussion’ have made important contributions. However,

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the findings are equivocal regarding the effect of subconcussive impacts on the brain. There

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remains a need to clarify our knowledge, inform directions for future research, and provide a

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clear description of the phenomenon known as ‘subconcussion’. Therefore, the purpose of this

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systematic review is to identify and evaluate the evidence that examines ‘subconcussion’ in sport

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settings, and to address two specific objectives: 1) to determine how ‘subconcussion’ is

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characterized in the current literature, and 2) to identify directions for future research.

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Methods Data Sources and Searches

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The authors searched six databases to November 2016: CINAHL, EMBASE, MEDLINE,

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PsycINFO, SPORTDiscus, and Web of Science. Medical Subject Headings (MeSH) do not exist

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for ‘subconcussion’; therefore, the search string was developed as follows: [((subconcuss* OR

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sub-concuss*) AND (sport OR athlete)) OR ((repetitive OR multiple OR reoccurr* OR recur*

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OR cumulative OR chronic) AND (brain OR head) AND (sport OR athlete))]. These keywords

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were selected to attempt to capture the breadth of terminology used in research related to

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‘subconcussion’.

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Inclusion and Exclusion Criteria

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Only human studies focusing on athletes were included. They were limited to those

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published in English. Exclusion criteria included (a) not directly examining subconcussion, (b)

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non-peer reviewed articles, (c) traumatic brain injury, (d) languages other than English, (e) non-

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sport context (e.g., blast injury), (f) forensic studies, (g) animal models, and (h) review articles.

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Study Selection

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The authors screened the titles, abstracts, and full-texts that resulted from the literature

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search. In total, 1966 abstracts were screened by rotating pairs of researchers, and disagreements

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were resolved by a third rater. Inter-rater reliability was calculated with Cohen’s Kappa (Cohen,

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1960), which can be categorized by strength of agreement from poor (κ = 0.0) to almost perfect

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(κ = 0.81-1.00; Landis and Koch, 1977). Kappa scores were calculated at two different time

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points of the selection process. The two κ-values of 0.90 and 0.94 indicate almost perfect

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agreement between author pairs for title and abstract screening. A second inter-rater reliability

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was calculated on a sample of 20 full-text articles assessed for inclusion by rotating pairs to


ensure consistency and rigour. The three κ-values (0.47, 0.63, 0.74) suggested a moderate to

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substantial level of agreement among raters. Disagreements were resolved through discussion

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and with assessment by a third rater when needed.

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Assessment of Study Quality

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The criteria to determine the methodological quality of the included studies were

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developed by the authors, a priori, and adapted from previous tools. Three quality assessment

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measures were included. First, each study was assessed using a modified version of the Levels of

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Evidence from the Oxford Centre for Evidence-Based Medicine1 (CEBM; Centre for Evidence-

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Based Medicine, 2009). Second, a modified version of the Quality Assessment Tool for

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Quantitative Studies (henceforth referred to as EPHPP-modified; Effective Public Health

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Practice Project, 1998) was used for quality assessment. A third quality assessment tool, the

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Subconcussion-Specific Tool (SST), was developed, adapted from Comper, Hutchison, Magrys,

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Mainwaring, and Richards (2010), to assess subconcussion-specific issues. Studies were

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classified as Category A or B, based on the following five criteria: 1) Was there an attempt to

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define the term ‘subconcussion’? 2) Was the number and/or magnitude of impacts reported? 3)

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Were subjects who sustained a concussion during the study controlled for or excluded from

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analyses? 4) Were subjects with a history of concussion controlled for or excluded from the

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analyses? 5) Did the study analyze sex/gender differences, or acknowledge limitations associated

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with sampling only males or females? Category A papers fulfilled three or more of these criteria.

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Results Identification of Studies The systematic literature search of the six databases yielded a total result of 1986 articles.

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After removing duplicates, 1966 articles remained. A total of 1943 articles were excluded from

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the present review. An updated search in November 2016 yielded an additional 22 articles, and a

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hand-search of references from the screened articles yielded an additional 11 articles. A total of

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56 studies were included for critical appraisal (Figure 1).

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Insert Figure 1 – Screening Flowchart

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Data Extraction and Synthesis

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The following data were extracted from each study: category, definition, population (sex,

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sport, level of play, sample size), control group, purpose, findings, and main measures (Table 1).

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The extracted data, quality assessments and level of evidence ratings of all studies were

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summarized. The included studies were organized into three categories: neurobiological,

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neuropsychological, and head impact exposure (HIE) metrics. A fourth category included in

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Table 1, mixed measures, identified studies that used multiple assessment modalities. Whereas

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all 56 studies in this review investigated outcomes with respect to impact exposure, the studies in

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the HIE section either, exclusively examined kinematics, or directly measured impacts in relation

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to other outcomes. Studies that emphasized a neurobiological or neuropsychological focus were

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included in their respective sections. A visual representation of the categorization of studies is

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presented in Figure 2.

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Insert Figure 2 - Categorization of Included Studies

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Insert Table 1 – Metadata of Reviewed Studies (n=56)

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Study Characteristics Sample size across studies ranged from 9 to 3702 participants. Males only were included

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in 42 studies, females only in 3 studies, and 9 studies included both males and females.

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Sex/gender was not reported in 2 studies. Football (30), soccer (18), hockey (5), boxing/MMA

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(4) and lacrosse (2) were represented across studies.

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There were 42 unique terms used to refer to the term ‘subconcussion’ (Table 2). Seven

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terms (i.e., subconcussive blow, subconcussive head impact, subconcussive impact,

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subconcussion, subconcussive hit, subconcussive head blow, subconcussive brain/head injury)

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were used in four or more articles. One term - subconcussive head impact - was used in 15

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articles. A definition of subconcussion was included in 21 of the 56 included studies. The

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definition provided by Bailes et al., (2013) was most frequently cited (n=10).

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----------------------------------------------------Insert Table 2 – Subconcuss* Terms Used in the Reviewed Studies -----------------------------------------------------Quality and Level of Evidence Methodological quality of the 56 included studies were evaluated according to the three tools. Overall, 7 papers were categorized as strong, 33 as moderate, and 16 as weak using the


EPHPP-modified tool. Evaluation with the SST identified 22 studies as Category A and 34 as

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Category B. Lastly, the included studies were categorized according to the CEBM as follows:

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Level 1A = 1; Level 2A = 17; Level 3A = 6; Level 3B = 13; Level 4 = 19.

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Neurobiological Evidence

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Twenty-one studies with a neurobiological focus were evaluated for quality of evidence.

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In total, 3 studies were rated strong, 15 moderate and 3 weak. One study was identified as level

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of evidence 1A, ten as 2A, one as 3A, four as 3B and five as level 4. For the SST, there were 10

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Category A and 11 Category B studies (Table 1). With respect to outcomes, 17 studies had

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significant results. When evaluated with the EPHPP-modified tool, 3 of those 17 studies were

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rated strong, 12 moderate and 2 weak. With the CEBM, one study was rated as level 1A, eight as

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2A, 1 as 3A, three as 3B, and 4 studies at level 4. For the SST ratings, there were 9 Category A

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and 8 Category B. Two studies had mixed findings, with one rated strong, and the other as weak.

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Both had Category B ratings. One study had a 2A rating with the CEBM, and the other was rated

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as level 4. Finally, two studies had non-significant findings and were both rated moderate, one as

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Category A, and one as B. One study was rated at 2A for level of evidence, and one was 3B.

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Overall, 15/17 (88%) of the studies had significant results and were rated moderate to strong on

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the EPHPP. Eight of those studies were rated level of evidence 2a, and one was rated 1A (53%

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of the studies with significant findings).

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The studies focused predominantly on male athletes in contact or collision sports,

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including football, ice hockey, boxing, and soccer. Level of play varied from youth, to high

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school, to collegiate, through to retired professional players. Seventeen studies did not provide a

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definition of subconcussion whereas four used a definition based on previous literature (Table 1).


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Thirteen studies examined neurophysiological aspects of HIE through imaging (e.g.,

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fMRI, high resolution structural imaging or diffuse tensor imaging [DTI]). DTI uses the

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diffusion of water molecules to evaluate microstructure in both normal and damaged brain tissue.

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Various parameters of diffusion were used to report microstructural changes in the brain—

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fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity

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(MD). FA and MD are typically used to describe overall or average diffusion purported to be

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associated with diffuse axonal injury. All studies detected structural and selected functional

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aspects such as neuronal integrity with exposure to repetitive head impacts. Measurement of

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head impacts varied from direct (Head Impact Telemetry System technology, HITS; Riddell, Inc,

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Rosemont, IL: Simbex, Lebanon, NH) to indirect methods (self-report estimates or diary entries

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of head impacts). Eight of the studies examined varied neurocognitive performance measures

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(e.g., verbal learning, memory, and processing speed). There was a relationship between

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neuronal damage and performance degradation on selected measures, in particular, processing

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speed, verbal learning, and memory.

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Imaging studies measured changes in brain regions including the dorsolateral prefrontal

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cortex, primary motor cortex, cerebellum, basal ganglia, thalamus, caudate, corpus callosum,

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amygdala, hippocampus, and the frontal, parietal, and occipital lobes. There was consistent

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evidence across studies that repetitive blows to the head were associated with deleterious effects

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to the structural integrity of the brain. These included white matter (WM) changes, cortical

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thinning (especially in the parieto-occipital region) and in brain volume (particularly the

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thalamus and caudate). One study found time between subconcussive hits was related to WM

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changes.


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Five studies examined blood biochemistry, one of which studied the neuroprotective

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effects of the nutritional supplement docosahexaenoic acid (DHA). The biochemical markers

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examined included S100B, neuronal specific enolase (NSE), cortisol, creatine kinase, N-acetyl

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aspartate, myo-inositol, glutamate-glutamine, glutathione, and neurofilament light (NFL) protein.

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Details about these metabolites can be found in Table 3. Three studies suggested that

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subconcussive head impacts affect neurochemistry and increased neuroinflammation. Markers of

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neuroinflammation showed promise and a need for further research was emphasized. Two of the

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four studies examining NSE and S100B questioned the suitability of those markers to detect

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brain injury. The study of nutritional supplementation for neuroprotection was rated strong

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methodologically. The authors concluded that 2 grams of DHA (the principal in N-3 long-chain

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polyunsaturated fatty acid in brain tissue) per day was neuroprotective and attenuated NFL

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coincident with increased exposure to head trauma in a sample of football athletes over one

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season.

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Insert Table 3 – Description of Metabolites

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Neuropsychological Correlates of Head Hit-Related Dysfunction Seventeen studies with a primary neuropsychological focus were reviewed. Overall, three

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studies were identified as strong, ten as moderate and four as weak. Using the SST, 4 studies

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were classified as Category A, and 13 as Category B. With the CEBM levels of evidence, two

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studies were rated 2A, four as 3A, six as 3B and five studies were level 4. Eight studies

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attempted to define the term ‘subconcussion’ through a combination of referred literature and

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their own definition. The remaining studies did not provide a definition of the term (Table 1).


Nine studies did not find an association between neuropsychological deficits and

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subconcussive blows, five studies found an association, and three had mixed findings. Of the

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nine studies with nonsignificant findings, two were rated as methodologically strong, six as

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moderate and one as weak with the EPHPP-modified tool. Two of these papers were rated as

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Category A, whereas seven were scored in Category B using SST. Using the CEBM levels of

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evidence, three studies were identified as 3A, four as 3B and two as level 4.

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The five studies with significant findings included three as moderate, and two rated weak.

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One study had an A rating using the SST, and the remaining five were Category B. With respect

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to levels of evidence, one study was rated as 3A, and two each as levels 3B and 4. Finally, of the

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three mixed findings studies, two were Category B papers, rated as strong and weak, with levels

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of evidence of 2A and level 4, respectively. The third paper was a moderate, Category A paper

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with level 2A on the CEBM.

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Twelve papers focused exclusively on male athletes, with eight sampling male football

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players. Three studies had a mix of male and female athletes, one study did not report

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sex/gender, and the remaining study focused on female soccer players. With respect to

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population, studies included athletes at the youth, high school, collegiate, amateur, professional

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and retired levels of play.

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Three of the five papers that found significant results employed a cross-sectional design

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with a cohort of matched controls. Eight papers examined subconcussive changes over the course

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of a single season, ranging from two to four data collection points; of these season-long studies,

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six found no significant neurocognitive changes as a result of subconcussive impacts.

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Seven studies used established neuropsychological test batteries, including Automated Neuropsychological Assessment Metric (ANAM; Levinson & Reeves, 1994), SCAT3 (McCrory


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et al., 2013) and Immediate Post-Concussion Assessment and Cognitive Test (ImPACT; Lovell,

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Collins, Podell, Powell, & Maroon, 2000). The remaining studies compiled measures from

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various domains of functioning, such as memory and attention. One study examined the auditory

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P3b response and cognitive function as a result of subconcussive impacts, with mixed results.

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Another study developed a metric to calculate a cumulative head impact index (CHII) in football

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players, which was used to examine long-term clinical outcomes in the same population.

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Studies also included multiple modalities, including head impact counts, biomarkers, and

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neuroimaging. Table 4 summarizes the results from the most frequently measured cognitive

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functions, organized according to Lezak’s (1995) neuropsychological testing assessment

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classification. These findings were stratified into three sections: a) studies that indirectly

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examined neurocognitive associations with subconcussive impacts; b) studies that directly

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examined associations with HIE variables; and c) studies that examined neurocognitive

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associations with neurobiological markers. This table includes the results of all studies in this

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review where neurocognitive testing served as a primary, secondary or complementary analyses.

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The table summarizes findings in specific cognitive domains: memory, attention and executive

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function. Accordingly, the few studies that provided an overall global score of cognition were

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excluded from the table.

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----------------------------------------------------Insert Table 4 – Summary of Significant Neurocognitive Outcomes from Subconcussive

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Impacts -----------------------------------------------------Head Impact Exposure Metrics


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Eighteen studies with a kinematics focus were evaluated with respect to the CEBM,

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EPHPP-modified, and the SST. One study was identified as strong, eight were moderate, and

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nine were weak. Using the CEBM, 5 studies were identified as level of evidence 2A, 1 was 3A, 3

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were 3B, and 9 were level 4. Eight studies were rated as Category A and ten were Category B

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according to the SST (Table 1).

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There were 8 studies with significant findings, 1 classified as strong, 4 moderate and 3

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weak. Using the SST, 4 studies were identified as Category A and 4 were Category B. With

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CEBM levels of evidence, one study had a rating of 2A, one had 3A, two had 3B, and four had a

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rating of 4. Eight of the studies had mixed findings. Using the CEBM, two studies were rated

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2A, one as 3A, one as 3B, and the remaining four as level 4. With the SST 2 studies were rated

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moderate and 6 were weak. One study had non-significant findings, and was rated as a weak

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level 2A, Category A study. Finally, one of the studies was descriptive, with a level 2A,

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Category A, moderate rating.

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Eight studies investigated HIE metrics to characterize the profile of head impacts in

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specific sports. These measurements included magnitude (linear acceleration, rotational

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acceleration, and force), location of hit (on the head), frequency, impulse, duration, and severity.

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A variety of equipment was used to measure head impacts: HITS, Head Hit Index, observation,

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accelerometers/gyroscopes (in-helmet [e.g., GForce tracker (GForce Tracker, Markham, Ontario,

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Canada)] or on skin [e.g., xPatch (X2 Biosystems)]), and instrumented mouthguards (i1

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Biometrics Inc; Table 5). Reported linear and rotational accelerations are presented in Table 5.

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----------------------------------------------------Insert Table 5 – Linear and Rotational Acceleration of Head Impacts ------------------------------------------------------


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In addition to kinematic measures, (e.g., magnitude and frequency of hits), 10 studies

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reviewed in this section measured a variety of other variables as correlates. The examined

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outcomes included changes in WM, neuropsychological function, biomarkers, oculomotor

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function, and changes in the vestibular system.

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Thirteen studies were conducted with male participants; of these, eleven focused

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exclusively on football. The sex/gender of participants in one study was not reported. Although

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five studies included both males and females, only one provided a sex/gender analysis. The

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included studies addressed football, soccer, lacrosse, and hockey over a range in level of play

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including youth, high school, collegiate, amateur, professional, and retired athletes. Nine studies

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provided a definition of subconcussion (Table 1). Five used the definition provided by Bailes et

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al. (2013), and four defined ‘subconcussion’ in various other ways.

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Discussion The purpose of this review was to systematically search the literature to November 2016

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to identify and evaluate the evidence that examines the phenomenon typically referred to as

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‘subconcussion’ in sport. Within the broader aim, there were two primary objectives: 1) to

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describe how ‘subconcussion’ is characterized, and 2) to identify directions for future research.

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The results of the search led to categorizing the studies into three broad areas of focus:

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neurobiological, neuropsychological, and HIE metrics. Key findings from each area are

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discussed followed by sections on methodological limitations of the reviewed studies, defining

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‘subconcussion’, limitations of our review, future directions for research, and conclusions.

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Neurobiological Markers of Changes in the Brain


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General conclusions distilled from the papers with a biomarker focus are that prolonged

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exposure to repetitive head impacts in sport is associated with both structural and functional

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changes in male athletes. There is insufficient evidence to suggest whether these changes are

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neuroprotective, transient, or permanent. Only one study with a neurobiological focus examined

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female athletes exclusively: Svaldi et al. (2016) determined that cerebrovascular reactivity changes

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soccer players persisted 4-5 months after the season and resolved by 8 months.

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A total of 16 studies examined possible structural changes to the brain after repeated

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exposure to head trauma not associated with symptoms of concussion. Of these, 14 provided

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evidence for structural changes (i.e., WM diffusivity, decreased volume, and cortical thinning) to

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the brain. For example, in a group of boxers, Bernick et al. (2015) found decreased volumes in

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cortical and subcortical brain structures in association with exposure to repetitive head traumas. In

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a group of high school football players, Abbas et al. (2015a) showed that subconcussive head

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impacts were related to both short and long-term deviations in neural connectivity, and that the

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neural reorganization did not return to baseline. They acknowledged that more sophisticated

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methods were needed but suggested their results were cause for concern. Bazarian et al. (2014)

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reported significant changes in WM in college football players over one single football season;

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these changes persisted six months after the season ended. Their study had a small sample and did

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not control for Type 1 errors; therefore, caution is necessary when interpreting these results.

412

Similar to these findings, McAllister et al. (2014) reported a relationship between exposure

413

to head impacts and WM diffusion metrics in a number of brain regions in a sample of varsity

414

athletes. A subsample of these athletes had increased WM median diffusivity in association with

415

decreased scores on verbal learning. The authors suggested that magnitude and frequency of head

416

impacts can modulate WM changes. That finding is supported in the mouse model study by


20

417

Slemmer & Weber (2005) who reported damage to hippocampal cell cultures with 5 or 6 repeated

418

blows over short (2 minute) intervals. In line with McAllister et al. (2014), their results suggested

419

that cumulative damage is dependent on injury severity and intervals between injuries.

420

Overall, the evidence from the studies examining neuroimaging outcome measures

421

suggested microstructural brain changes occur with exposure to repetitive hits to the head. Specific

422

regions of the brain were identified as vulnerable to cumulative impacts to the head. These regions

423

included the thalamus, caudate, corpus callosum (Bernick, et al., 2014; McAllister et al., 2014),

424

amygdala, hypothalamus, hippocampus (McAllister et al., 2014), and the dorsolateral prefrontal

425

cortex (Poole et al., 2015; Talavage et al., 2014). These conclusions resonate with four reviews

426

(Bailes et al., 2013; Davenport, et al., 2016; Koerte et al., 2015b; Rodrigues et al., 2015) that

427

provide preliminary evidence for the relationship between exposure to head impacts and structural

428

and functional changes in the brain on neuroimaging. Belanger et al. (2013) concluded that

429

although the neuroimaging studies that they reviewed showed some structural and functional

430

changes, they were inconsistent across studies. Rutherford et al. (2003), however, concluded that

431

there was no evidence to support that soccer heading causes subconcussive head trauma. In

432

contrast, the majority of imaging studies (93.3%) we reviewed had significant results and were

433

rated moderate to strong on the EPHPP quality assessment. Although further research is required

434

to confirm these results, the evidence is convincing and supports concern for athletes exposed to

435

cumulative head trauma. Our conclusions resonate with recent findings of persisting

436

microstructural abnormalities in WM, as measured by MD and AD on DTI, in a sample of patients

437

with chronic mild traumatic brain injury (Hashim, et al., 2017), and with other findings that

438

showed diffuse WM changes in a sample of retired male football and hockey athletes with a

439

history of sport-related concussion (Tremblay et al., 2014).


21

440

With the exception of Lipton et al. (2013) who had a mixed sample, the majority of studies

441

had male participants, and identified microstructural or functional changes related to HIE. The

442

only study conducted on females showed CVR changes post-season that ultimately resolved

443

(Svaldi et al., 2016). In light of the evidence reviewed, there is cause for concern for males

444

involved in collision and contact sports. There is equal cause for concern for females because they

445

are underrepresented in the research. Conclusions about female brains cannot be drawn by

446

examining male brains or by the sole female-focused study with positive findings. This does not

447

mean, however, that the female brain is not equally, or perhaps more, vulnerable.

448

A range of biochemical markers were examined in the reviewed studies (Table 3). Head

449

impacts were related to significant changes in some metabolite concentrations (e.g., glutamate-

450

glutamine increase; Poole et al., 2015), but not others (e.g., UCH-L1; Puvenna et al., 2014). In

451

general, the literature examining the utility of S100B as a biomarker for effect of subconcussive

452

impact, like that of concussion, is equivocal. Previous studies in boxing found S100B was a marker

453

for brain injury (e.g., Graham et al., 2011). Comparatively, S100B was shown to be an unsuitable

454

biomarker for detecting brain injury (Rogatzki et al., 2016; Straume-Naesheim, et al., 2008;

455

Zetterberg et al., 2007). Other biomarkers, such as NSE, may also be inappropriate for detecting

456

brain trauma (Rogatzki et al., 2016). The evidence suggests that NFL is a promising biomarker of

457

neuroinflammation acutely and after exposure to repetitive head impacts (Neselius et al., 2014;

458

Oliver et al., 2016). This is supported in animal model studies of repetitive head trauma (Shultz,

459

MacFabe, Foley, Taylor, & Cain, 2012) and by human studies of multiple concussions (Di Battista

460

et al., 2016). In the sole nutrition study, 2 grams of DHA per day was shown to be neuroprotective

461

for football players (Oliver et al., 2016). This study was the only RCT in our review. It was

462

particularly compelling as it showed that football players who ingested the supplement over the


22

463

trial period had reduced NFL in relation to repetitive HIE over one season. Overall, the studies on

464

biomarkers show promise and emphasize the need for further exploratory and confirmatory

465

studies.

466

Neuropsychological Correlates of Repetitive Hit-Related Dysfunction

467

The papers with a neuropsychological focus primarily investigated cognitive outcomes of

468

repetitive hits to the head in athletes. The evidence does not fully support a relationship between

469

subconcussive blows and cognitive deficits. There was considerable variability in how the

470

studies measured neurocognitive changes as a result of these repetitive head impacts. Multiple

471

studies inferred that repetitive head impacts occurred in the sport environment; this was a

472

common approach for sports where regular head impacts are expected, such as boxing, soccer,

473

and full-contact sports like football and ice hockey. This indirect approach relied primarily on

474

cohort studies that used self or matched controls, or cross-sectional studies.

475

The three most commonly measured cognitive functions – memory, attention and

476

executive function – had few significant findings, with 28 of 256 (10.9%) assessments

477

demonstrating an association with subconcussive impacts across all neurocognitive studies

478

(Table 4). Of the significant findings, Bernick et al. (2015) concluded only the highest fight

479

exposure scores were related to decreased scores in processing speed. In a cross-sectional study,

480

Tsushima and colleagues (2016) stratified athletes into high and low head contact groups based

481

on prevalence of concussion in specific sports; football players were placed in the high-contact

482

group, while wrestling, soccer baseball, judo, and basketball athletes were considered low-

483

contact. The authors found differences in processing speed and reaction time between the two

484

groups, suggesting an association between high-contact sport and deficits in neurocognitive


functioning. Matser and colleagues (1999) found neurocognitive impairments in planning and

486

memory in amateur soccer players compared to matched non-contact athlete controls.

487

23

The most promising results appear with studies that assess neurobiological markers along

488

with neurocognitive testing. Multiple collaborative studies (Breedlove et al., 2012, 2014; Poole

489

et al., 2014; Talavage et al., 2014) have found associations with memory impairments and

490

neurological functional impairment in subgroups of non-concussed athletes. Identifying athletes

491

without clinically-observed impairment (COI-) but with functionally-observed impairment

492

(FOI+) (Breedlove et al., 2012; Talavage et al., 2014) suggests that neurophysiological changes

493

from repetitive impacts may precede discernable neurocognitive deficits. Many of the studies

494

exploring neurophysiological and neurocognitive outcomes tended to have small sample sizes

495

(e.g., Bang et al., 2016; Bazarian et al., 2012), or lacked matched-controls (e.g. Chrisman et al.,

496

2016; Davenport et al., 2014), and require replication.

497

The significant findings from neuropsychological testing should be interpreted with

498

caution. We would be remiss not to acknowledge a possible reporting error in the study by

499

Matser and colleagues (1999), as initially reported by Rutherford et al. (2003). More broadly, we

500

also note the array of neurocognitive tests employed. There were over 30 unique neurocognitive

501

tests, making it difficult to compare results and draw conclusions. Some studies used

502

neurocognitive tests traditionally used in psychology (e.g., California Verbal Learning Test;

503

Wisconsin Card-Sorting Task) whereas others took a sport-specific approach (e.g., ImPACT;

504

SCAT3). Greater consistency within neurocognitive testing would help improve confidence in

505

the findings. Additionally, concurrent validity could be determined by combining

506

neuropsychological testing with sophisticated imaging techniques such as DTI (Bazarian, Zhu,

507

Blyth, Borrino, & Zhong, 2012). In light of evidence that NP assessment is unable to detect


axonal injuries that are otherwise present (Neselius et al., 2014), a multimodal approach is

509

warranted. As such, the use of neuropsychological testing alone to provide evidence for

510

‘subconcussion’ should be questioned.

511

24

The non-significant neuropsychological findings were associated with varied study

512

designs. For example, both Bang et al. (2016) and Jennings et al. (2015) found no differences in

513

neurocognitive testing in age-matched samples for retired boxers and youth football players,

514

respectively. Other non-significant findings emerged from case control (Munce et al., 2014;

515

Miller, Adamson, Pink, & Sweet, 2007), cross-sectional (Lipton et al., 2003) and retrospective

516

studies (Meehan et al., 2016).

517

The lack of robust findings with neuropsychological measures is incommensurate with

518

the previously discussed positive results of functional and structural changes detected with

519

advanced imaging techniques and biomarkers. The ability for sport-specific NP tests to detect

520

changes between athletes and non-injured controls has been questioned (Randolph, McCrea &

521

Barr, 2005). Neurocognitive measures may not be sufficiently sensitive to detect the subtle

522

cognitive changes associated with repetitive hits to the head. It also may be that subconcussive

523

impacts do not elicit cognitive changes. The effects of subconcussive impacts on cognitive

524

functioning remain unclear and require continued exploration with assessment tools shown to be

525

sensitive and specific to concussion.

526

Head Impact Exposure Metrics

527

The HIE metrics included hit location, magnitude (linear and rotational acceleration, and

528

force), hit duration, and frequency. The relationship between hit location and impairment was not

529

clear across sports. Breedlove et al. (2012) found that hits to the side of the head were more

530

commonly associated with neuropsychological and neurological impairment (but not diagnosed


25

531

concussion) in high school football players. Munce et al. (2014) also examined impact location

532

in youth football athletes. They showed that the front or top of the helmet was the most common

533

location for head hits, but found no associated changes in neurologic function. However,

534

Breedlove et al. (2012) noted that impacts of higher magnitude (denoted as ≥80 g) more

535

commonly occur at the top or front of the helmet. According to Pellman et al. (2003), this level

536

of magnitude may result in concussion. Broglio et al. (2009) provide further support for the

537

greatest linear acceleration at the top of the helmet in high school football. Similarly, the highest

538

linear acceleration and frequency of hits in collegiate lacrosse were associated with the front of

539

the head (Miyashita et al., 2016). More research is needed to clarify the relationship between hit

540

location and impairment across sports, but it appears the front and top of the head are the most

541

vulnerable locations. This is consistent with the findings from Guskiewicz et al., (2003) that

542

National Collegiate Athletic Association football players had a higher propensity for impacts on

543

the top of the head, and those impacts had a higher relative risk for concussion.

544

Most studies examined magnitude of impacts in football, with limited research in hockey,

545

soccer, and lacrosse. Linear and rotational accelerations ranged widely across studies (Table 5).

546

Rotational acceleration was reported less frequently than linear acceleration. Rotational

547

acceleration is considered to be a contributor to concussive injury (Post & Hoshizaki, 2015), and

548

therefore needs to be reported in the context of repetitive hits to the head. In football studies, the

549

mean peak linear acceleration were mostly observed within a 20-30 g’s range, but were reported

550

as high as 940.5 g’s by Kawata et al. (2016a). Some of this variance could be attributed to

551

differences in age or level of play. In soccer, linear acceleration varied from 14.49 g’s (Kawata et

552

al., 2016b) to 50.8 g’s (Dorminy et al., 2015). Reported linear and rotational accelerations may

553

be misrepresented when recorded in a controlled experimental setting compared to game or


practice settings. Measures of linear acceleration, in general, may underestimate true values

555

(Guskiewicz & Mihalik, 2011). There was a dearth of studies in hockey and lacrosse.

556

26

In terms of frequency, Reynolds et al. (2016b) found an increase in head impacts in

557

football games compared to practice, and in practices in which athletes wore full equipment

558

compared to helmets only. Others studies had similar findings showing that youth and high

559

school football athletes experience more hits during games versus practice (Broglio et al., 2009;

560

Munce et al., 2015). Miyashita et al. (2016) provided evidence that men’s collegiate lacrosse

561

athletes received 2.3 times more hits to the head in games versus in practices. Guskiewicz et al.

562

(2003) found that athletes who experience higher frequencies of head impacts were at greater

563

risk for concussion. Similarly, Kawata et al. (2016a) reported that football players with higher

564

head impact frequency experienced near point convergence dysfunction, an oculomotor

565

impairment often associated with concussion. The evidence reviewed suggests that higher head

566

impact frequencies are associated with games more than practices, and that a higher impact

567

frequency may be associated with the risk of injury.

568

There may be a number of factors that contribute to the occurrence of a concussion,

569

including impact frequency, magnitude, location, and duration. Very few studies reported

570

duration of impacts, and the extent to which impact magnitude is associated with concussion or

571

severity of injury is equivocal (Beckwith et al., 2013; Guskiewicz & Mihalik, 2011). The

572

evidence suggests a threshold for concussion ranges from 70-75 g’s (Pellman et al., 2003) to 90

573

g’s or above (Guskiewicz & Mihalik, 2011). Guskiewicz and Mihalik (2011) suggest that it may

574

not be possible to establish a threshold for concussion because of the large variety in impact

575

metric scores.


27

The kinematics studies reviewed employed numerous different tools which may have

577

contributed to the range in values in the reported data. Although existing tools such as the xPatch

578

and the HITS system are useful in characterizing hits, there are significant limitations. For

579

example, accelerometers fitted into helmets only provide estimates of head movement

580

(Breedlove et al., 2012). The use of accelerometers in direct contact to the skin, such as the

581

xPatch, may be more accurate. However, these tools are also flawed because they measure

582

movement of the head and not the brain (Guskiewicz & Mihalik, 2011; Reynolds et al, 2016a).

583

Kinematic tools may also fail to capture force transmissions, such as in the case of body blows.

584

The placement of the accelerometer on the head (e.g., jaw, crown of the head, inside the mouth)

585

varied across studies, and may influence recording accuracy. The consistent use of standardized

586

measures within a particular sport context would likely reduce variation in data and facilitate

587

comparisons of results.

588

Overall, across the HIE studies there was a wide variety of tools, variables, and study

589

designs. In addition, some types of variables were not reported consistently. It was difficult to

590

compare results and provide conclusive statements about HIE metrics associated with

591

subconcussive impacts. This was not surprising since conclusions about various impact exposure

592

metrics related to concussion, such as injury thresholds, remain “elusive” (Guskiewicz et al.

593

2003). More research in relation to each dimension of impact exposure (e.g., linear and

594

rotational acceleration, frequency, severity, hit location) is warranted.

595

Methodological Shortcomings Across Studies

596

There are several common methodological limitations across studies, irrespective of

597

category. Just over half (54%) of the studies were based on small sample sizes and often

598

response and attrition rates were not reported, possibly contributing to sampling biases. With


28

599

respect to statistical analyses, some studies did not correct for multiple comparisons, which

600

inflated the risk of Type 1 errors. Further, control group inadequacies or the absence of a control

601

group created threats to internal validity in some studies. Members of control groups need to be

602

matched on as many variables as possible. The selection of appropriately matched participants or

603

groups of participants can be challenging. Often, intact sport groups are chosen out of

604

convenience, or without careful consideration of the appropriate control principles. From the

605

outset, such groups may not be the best to include in an already compromised quasi-experimental

606

design (Cook & Campbell, 1979).

607

Most studies did not screen for substance abuse, which has been related to WM changes

608

(Arnone, Abou-Saleh, & Barrick, 2007). Future research would benefit from accounting for such

609

confounding variables. Youth and female athletes were severely underrepresented. Most of the

610

research focused on male football players. When females were included, sex or gender analyses

611

were rarely performed. Only three of the reviewed studies focused exclusively on females

612

(Forbes et al., 2016; McCuen et al., 2015; Svaldi et al., 2016). Similarly, few studies included

613

children and adolescents. Not only is there a need for more research in youth, but the research

614

needs to incorporate methods that can tease apart developmental changes in the brain from those

615

that may be related to head impacts. This will be challenging because, as Tarnutzer et al. (2016)

616

suggest, exposure length and intensity in youth studies may not be sufficiently extensive to

617

identify positive findings on testing.

618

A range of indirect methods of estimating heading frequency exposure were used,

619

including self-reported measures, and previously published data as estimates of heading exposure

620

(Montenigro et al., 2017). Objective measurement may be more precise, but it is difficult to

621

acquire lifetime exposure rates other than by self-report or estimations from records.


29

622

Finally, although concussion occurrence during the studied periods was typically

623

controlled, history of concussion was not always addressed. The concussion literature has shown

624

that history of concussion is a risk factor for concussion (Abrahams, Mc Fie, Patricios,

625

Posthumus, & September (2014). However, Lipton et al. (2013) concluded that history of

626

concussion did not contribute to the changes in WM microstructure they associated with heading

627

in soccer. Those findings need replicating, and the potential effects of previous head trauma

628

requires continued study.

629

Defining Subconcussion

630

In our review, we identified numerous terms that were used interchangeably to refer to

631

the concept of subconcussion (Table 2). The terms ranged from describing a hit (e.g.,

632

subconcussive hit, subconcussive blow, subconcussive impact) to characterizing damage or

633

trauma (e.g., subconcussive injury, subconcussive trauma, subconcussion). Consistent with the

634

conclusions made by Belanger et al. (2016) and Svaldi et al. (2016) we noted the ambiguous and

635

misleading use of the term subconcussion.

636

Two critical questions arise from the vague use of the term and the associated

637

implications for research and practice: 1) Is subconcussion an injury? and 2) How do we describe

638

or assess subconcussive impacts? The term subconcussion and variations thereof imply there is

639

an injury resulting from hits to the head. This situates subconcussion on a spectrum between the

640

absence of injury and concussion. As discussed previously, this is problematic, because there is

641

no precise evidence for a minimal threshold for either concussion or a ‘subconcussive’ impact.

642

Concussion is diagnosed by signs and symptoms (McCrory et al., 2013), and subconcussion is

643

recognized by the absence of a concussion diagnosis (Bailes et al., 2013). Defining

644

subconcussion by what it is not does not tell us what it is. At best, the term is a fuzzy


conceptualization. Svaldi et al. explain that previous empirical work “demonstrates that the

646

simple classification of ‘concussion’ (i.e., symptom inducing) and ‘subconcussion’ (i.e., not

647

producing symptoms) is inadequate and possibly misguided” (2016, p.2). It remains unclear

648

whether the functional and microstructural changes identified in our review are distinct from

649

those that are seen with concussion. Forbes, Glutting, and Kaminski (2016) postulate that so-

650

called subconcussive changes may be indicative of concussion. If this is the case, we may need

651

to reconceptualize how we define, and diagnose, concussion.

652

30

Our conclusions, after reviewing the evidence available, are consistent with the

653

arguments put forth by Belanger et al. who suggest the term “head impact” (2016, p.160) is more

654

accurate than subconcussion, and Svaldi et al. who suggest research should focus “on the

655

cumulative effects of the mild, repetitive head trauma experienced” (2016, p. 2). We, too,

656

recommend refraining from using the terms ‘subconcussion’ and ‘subconcussive injuries’. They

657

cannot be operationally defined. With respect to the use of the term ‘subconcussive impact(s)’

658

there is a lack of precision. It is used to refer to any impact that does not result in a concussion.

659

Therefore, we recommend researchers clearly state their intended meaning by constructing an

660

operational definition. And, while the evidence from this review suggests cumulative, long-term

661

damage leads to microstructural and functional changes in male brains, we cannot associate these

662

changes with accrued ‘subclinical injuries’.

663

Limitations

664

We acknowledge a number of limitations in this review. However broad, the review did

665

not capture all of the existing studies on head impacts. This was because our inclusion/exclusion

666

criteria focused exclusively on studies that referred to ‘subconcussion’. A considerable number


of impact studies – while providing important data on subclinical impacts – were focused on

668

concussion, and thus were excluded through our screening process.

669

31

Our selection of quality assessment tools attempted to account for overall methodological

670

quality, and issues specific to the concept of subconcussion as described by Bailes et al., (2013).

671

In setting specific criteria with the SST, the overall level of quality for some studies was reduced.

672

This did not mean that those studies were necessarily of poor methodological quality, but rather,

673

that they did not meet our criteria for a quality study on subconcussion. Similarly, the CEBM

674

identifies RCTs as the gold standard. This established hierarchy of scientific evidence is not

675

always appropriate, particularly in injury research where it is difficult to conduct RCTs.

676

Therefore, the quality assessment scores of some studies were reduced by using the CEBM.

677

Lastly, our inclusion criteria were limited to athletes, and excluded military, forensic, and

678

animal research related to repetitive hits to the head. Nevertheless, we acknowledge the

679

importance of those lines of research. We delimited the review to studies of athletes, mainly

680

because of focus and manageability.

681

Future Directions

682

The literature we reviewed points to a number of important directions for future research.

683

First, clear definitions and directions are required to move the research on the relationship

684

between head impacts and short and long-term consequences forward. Second, specific

685

methodological limitations (identified previously) need to be addressed, and third, substantive

686

issues related to subconcussion require clarification.

687

Future research would benefit from developing well-delineated exposure metrics. For

688

example, Lipton et al. (2013) developed a structured questionnaire to estimate heading exposure

689

as low, medium, or high. Higher exposure thresholds were associated with both imaging and


cognitive changes whereas lower thresholds were not. There are also promising advances

691

addressing cumulative effects of repeated hits to the head, including the development of sport-

692

specific metrics to estimate exposure (Montenigro et al., 2017).

693

32

Moving ahead, the research on repetitive impacts would benefit from accounting for time

694

between successive hits by using specific weighted analyses such as those developed by

695

Merchant-Borna and colleagues (2016). Both retrospective and prospective study designs are

696

needed to consider exposure time (e.g., 1 year, 5 years, etc.). Also, our understanding of the long

697

term effects of repetitive head impacts would be enhanced if longitudinal prospective studies

698

accounted for other relevant variables such as age of first exposure, lifetime exposure rates,

699

history of concussion, history of substance abuse, head impact location ,immune competency,

700

genetics, and individual differences in morphology. The topic and implications are no doubt

701

complex.

702

As previously noted, study designs that use a multimodal approach by combining

703

biomarker and psychological measures, for example, offer promise to capture the complexity of

704

RHI and their effects (c.f., Tarnutzer et al., 2016). Such research, however, requires standardized

705

measures, consistency across protocols and reporting, and of course, sizable funding. A diverse

706

range of researchers from a variety of fields are contributing to the evolving subconcussive

707

impact research. Future studies will benefit from collaborative teams with experts across

708

domains to ensure that all aspects of multidisciplinary research are addressed.

709

While the literature on cumulative effects of repeated hits to the head is growing, we also

710

need further investigation of the consequences of a single impact. McCaffrey, Mihalik, Crowell,

711

Shields, and Guskiewicz (2007) investigated the neuropsychological effects following a single


impact in football, and similar research across sports combined with sophisticated imaging

713

techniques would improve our understanding of isolated impacts.

714

33

After three decades of research primarily focused on male football players and

715

concussions, we recommend that females receive greater attention in the continued investigation

716

of repetitive head impacts. Young athletes also require special attention given the emerging

717

research that suggests recovery time for concussed adolescents is different than that in adults

718

(McCrory et al., 2013). There are many promising avenues for future research.

719 720 721

Conclusions Overall, this review sought to consolidate and evaluate the evidence on subconcussive

722

impacts in sport. The empirical studies reviewed were categorized into three areas:

723

neurobiological, neuropsychological, and impact exposure metrics. The evidence presented

724

identified that repetitive hits to the head in males are associated with deleterious effects to the

725

microstructural integrity and function of the brain. Insufficient evidence was presented to

726

conclude that repetitive head impacts are associated with neurocognitive impairment. It may be

727

that neuropsychological assessment tools are not sufficiently sensitive to detect any subtle

728

changes in cognitive function that emerge from subconcussive impacts. Specific impact

729

thresholds leading to injury could not be identified in males or females. Conclusions about

730

female athletes could not be drawn because they were underrepresented in all categories of

731

study. With respect to terminology, we suggest that the terms ‘subconcussion’ and

732

‘subconcussive injuries’ are vague and cannot be operationalized. As others have emphasized

733

(Belanger et al., 2016; Svaldi et al, 2016), these terms are poorly defined and misleading.


34

Given the evidence reviewed, we conclude the following: 1) exposure to repetitive hits to

735

the head in sport presents the risk of microstructural and functional changes to the brain in male

736

athletes, and 2) prolonged exposure to repetitive head impacts in sport, for both males and

737

females, should be avoided. Further study is essential to advance our understanding of how

738

exposure to head impacts affect the brains of athletes in the short and long-term.

739 740


35

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K. (2007). No neurochemical evidence for brain injury caused by heading in soccer.

1051

British Journal of Sports Medicine, 41(9), 574-577.

1052

1053

1054


49

1055

1056

1057

1058 1059

Footnotes 1

1A= Individual RCT with narrow confidence interval; 1B= All or none RCT; 2A=

1060

Individual cohort study or low quality RCT; 2B= Outcomes research: ecological studies; 3A=

1061

Cross-sectional; 3B= Individual case-control study; 4= Case series (and poor quality cohort and

1062

case-control studies); 5= Expert opinion without explicit critical appraisal

1063

1064

1065

1066

1067

1068

1069

1070

1071


1073

1074

1075 1076

Figures and Tables Figure 1 – Screening flowchart

1077 1078

Figure 2 – Categorization of included studies (N=56)

1079 1080

Table 1 – Metadata of reviewed studies (N=56)

1081 1082

Table 2 – Subconcuss* terms used in the reviewed studies

1083 1084

Table 3 – Description of metabolites

1085 1086

Table 4 – Summary of significant neurocognitive outcomes from subconcussive impacts

1087 1088 1089

1090

1091

Table 5 – Linear and rotational acceleration of head impacts

50


1093

1094

1095

1096 1097

1098

1099

1100

Figure 1 Screening flowchart

51


1102

1103

1104

1105

Figure 2 Categorization of included studies (N=56)

52

Table 1 Metadata of Included Studies (n=56) Citation, year

Definition of subconcussion (yes or no, source)*

Population (sample size, sex, level of play, sport)

Control Group

Primary purpose of study

Main tools/measures**

Findings (y/n, mixed)

SST

CEBM

EPHPP – modified rating

Head Impact Exposure Metrics (n=8) Beckwith et al., 2013

No

1208 high school (6 teams) and collegiate (8 teams) male football players

No

To compare the frequency and severity of head impacts sustained by football players on days with and without diagnosed concussion and to identify the sensitivity and specificity of single impact severity measures to diagnosed injury

HITS; GSI; HIC; FHI

Yes

B

4

Moderate

Broglio et al., 2009

No

35 male high school football players

No

To characterize the location and magnitude of impacts sustained by players during a football season.

HITS

Mixed

B

3A

Moderate

Kerr et al., 2015

No

64 male football players (32 professional and collegiate, 32 collegiate)

No

To calculate head impact exposure estimates

Detailed estimate calculation

Yes

B

4

Weak

McCuen et al., 2015

Yes; 1, 2

29 female high school and 14 female collegiate soccer players

No

To quantify the number and magnitudes of hits sustained by female soccer players over the course of an entire season

xPatch; FHI

Yes

B

4

Moderate

Miyashita et al., 2016

Yes; 1

42 male collegiate lacrosse players

No

To quantify the frequency, magnitude, and location of head impacts among player positions, between practice and games

GForce Tracker; FHI

Mixed

B

2A

Weak

Naunheim et al., 2000

No

132 high school football players, 128 high school hockey players, 23 elite high school soccer players

No

To measure the acceleration of impact experienced by the head of participants during game play

Triaxial accelerometer; GSI

Mixed

B

4

Weak

Reynolds et al., 2016a

Yes; 1, 2

20 male collegiate football players

No

To determine whether subconcussive head impact in collegiate athletes varies with practice type

xPatch ; FHI

Yes

A

4

Weak

Reynolds et al., 2016b

Yes; 1

14 female and 15 male collegiate lacrosse players

No

To quantify head impact associated with practicing and playing collegiate lacrosse athletes while subjects were fitted with wearable accelerometers

xPatch; FHI

Mixed

A

4

Weak

105 previously concussed female high school soccer players

105 matched controls with no history of concussion

To determine whether subconcussive impacts on individuals with a previous concussion history produce declines in neurocognitive performance and concussion-related symptoms

FHI; ANAM

No

A

3B

Moderate

Neuropsychological (n=11) Forbes et al., 2016

Yes; 2

Citation, year



Control Group




SST

CEBM


Jennings et al., 2015

No

44 male youth (8-12) football players

13 baseball players

To determine the effect of a season of subconcussive contact on C-SCAT3 scores in 8-12-year-old males compared to their age matched peers who participated in non-contact sports

C-SCAT3

No

B

4

Moderate

Matser et al., 1999

No

33 amateur soccer players

27 non-contact sport athletes

To determine if neuropsychological dysfunction occurs in amateur soccer players

NP battery

Yes

B

3A

Weak

Meehan et al., 2016

Yes; 1, 2

3702 male and female college alumni, incl. collision, contact and non-contact athletes

No

Whether exposure to collision sports of certain division III alumni was associated with later life quality-of-life measures, particularly those assessing the neurobehavioral symptoms hypothesized to be associated with repeated sub-concussive blows to the head

Neuro-QOL; PROMIS

No

B

3A

Moderate

Miller et al., 2007

Yes; 2


No

To compare neurocognitive scores in collegiate football players who had sustained no known concussion throughout a season of repetitive contact activity

ImPACT; SAC

No

B

3B

Strong

Putukian et al., 2000

No

100 collegiate soccer players (44 male, 56 female)

Own controls

To use neuropsychological instruments to assess the acute cognitive effects of heading a ball

NP battery

No

B

3B

Strong

Ravdin et al., 2003

No

18 male professional boxers

No

To conduct serial neuropsychological testing to professional boxers before and after a boxing match to examine short term recovery from subconcussive head trauma

NP battery

Yes

B

3B

Weak

Rutherford et al., 2009

Yes; 2

73 male university soccer players

77 rugby union players; 104 noncontact sport athletes

Do UK university football club players suffer neuropsychological impairment as a consequence of their football (soccer) play?

FHI; NP battery

No

B

3A

Moderate

Stephens et al., 2010

Yes; 3

48 male youth soccer players

22 rugby players; 16 noncontact athletes

To compare neuropsychological performance between soccer players and controls

WISC-R; CDI-S; NP battery

No

A

3A

Moderate

Tsushima et al., 2016

Yes; 2, 3


100 low contact sport matched athletes

To explore the possible neuropsychological effects of subconcussive head trauma in varied sports among high school athletes with no prior history of concussion.

ImPACT

Yes

A

4

Moderate

Wilson et al., 2015

Yes; 2


7 upper-year football players; 7 athletes

To assess the ability of the auditory P3b paired with a simple visual distracter, to measure subtle changes in cognitive function cause by SCI exposure during the course of a Division I football bowl subdivision season

Auditory P3b

Mixed

B

4

Weak

Citation, year



Control Group




SST

CEBM


Neurobiology (n=10) Abbas et al., 2015a

No


No

To determine the neuro physiological changes in a cohort of high school football athletes with exposure to repeated sub-concussion head impacts

MRI; rs-fMRI

Yes

B

4

Moderate

Abbas et al., 2015b

No


10 non-collision sport athletes

Rs-fMRI study of neurophysiological changes in DMN after repetitive subconcussion blows

DMN; rs-fMRI

Yes

A

2A

Moderate

Bamaç et al., 2011

No

17 professional male soccer players

No

To investigate the effects of heading training of NGR and BDNF in professional soccer players.

NGF; BDNF

Yes

A

2A

Moderate

Bazarian et al., 2012

No

9 male high school hockey and football athletes

6 injured and uninjured athletes

To examine the ability of wild bootstrapping analyses to detect subject specific changes in brain white matter before and after SRC

DTI; FA;MD; SAC; ImPACT

Yes

B

4

Moderate

Chamard et al., 2012

No

45 varsity hockey players (25 male, 20 female)

No

To evaluate the effects of repetitive concussive and subconcussive head impacts on neurometabolic concentrations

MRS; Cr; MI; NAA

Mixed

B

4

Weak

Koerte et al., 2015a

Yes; 1

11 retired professional male soccer players

14 age and gender matched

To examine the long-term effects of RSHI on neurochemistry in athletes with high exposure to RSHI but no HOC

Cr; Cho; Glu; GSH; MI; NAA

Yes

A

2A

Strong

Mussack et al., 2003

No

61 male youth amateur soccer players

58 male amateur soccer players

To evaluate the neuroprotein S100B serum levels in young amateur soccer players early after controlled heading

S100B

Yes

B

4

Weak

Oliver et al., 2016

No


Randomized placebo group

To determine the effects of different does of DHA on serum NFL over 1 football season

Blood NFL

Yes

B

1A

Strong

Rogatzski et al., 2016

Yes; 1, 2


No

To determine if serum concentrations of biochemical markers of brain injury are elevated post football in athletes with subconcussion impacts

NSE; S100B; CK; Cortisol

Mixed

B

2A

Moderate

Zetterberg et al., 2007

No

23 male amateur soccer players

10 age-matched nonathletic

To examine CSF for biochemical markers of neuronal injury after a training session of heading the ball in soccer

CSF; T-Tau; NFL; GFAP; S100B

No

B

3B

Moderate

Mixed Measures (n=27)

Citation, year



Control Group




SST

CEBM


Bang et al., 2016

No

5 retired male boxers

4 age matched

To examine neuronal deficits with imaging of rTBI and relationship b/w NP

UPDRS; CBI Scale; NP battery; MRI; F-FDG PET; FMZ PET; BDHI

Yes

B

3B

Weak

Bazarian et al., 2014

No


5 non-athlete

To characterise magnitude and persistence of RHI induced WM changes

FA; MD; S100B APOA1; HITS; ImPACT; BESS

Yes

A

4

Moderate

Bernick et al., 2015

No

224 male MMA fighters, 93 male boxers

22 age and level of education matched

To evaluate the relationship between repeated blows to the head and structure and function of cognitive performance

CNS vital signs; MRI; FES

Yes

B

2A

Strong

Breedlove et al., 2012

No

23 male high school football athletes

No

To track neurophysiological and biomechanical history over 2 seasons

HITS; NP battery; fMRI

Mixed

B

4

Weak


No


No

To evaluate the possibility of quantifying risk of developing abnormal neurophysiology due to repetitive subconcussive impacts to the head

HITS; ImPACT; fMRI

Yes

B

3B

Moderate

Chrisman et al., 2016

Yes; 3

17 middle school soccer players (10 male, 7 female)

No

To assess the association between head impacts and changes in symptoms, cognitive testing, and advanced neuroimaging

xPatch; FHI; MRI; DTI; NP battery; King-Devick

No

A

2A

Weak

Davenport et al., 2014

No


No

To determine if cumulative impacts to the head produce changes in the brain

HITS; ImPACT; DTI-WM

Yes

A

2A

Moderate

Dorminy et al., 2015

No

16 male college soccer players

No

To examine the effect of soccer heading ball speed on S100B serum concentrations, linear head impact acceleration, and concussion assessment scores

S100B; SCAT2; Triaxial accelerometer; King-Devick

No

A

2A

Moderate

Hwang et al., 2017

Yes; 1,3

20 adult soccer players (any organized level; 15 male, 5 female)

10 adult soccer players (7 male, 3 female)

To investigate possible sensory-motor dysfunction after subconcussive impact on vestibular processing and walking stability

SCAT; GForce Tracker; Galvanic vestibular stimulation

Yes

A

3B

Weak

Kawata et al., 2016a

Yes; 2


No

To investigate whether repetitive subconcussive impacts during preseason football practice season cause changes in NPC

NPC; i1Biometerics Vector mouthguard; FHI; SCAT

Yes

A

4

Moderate

Kawata et al., 2016b

Yes; 2



To examine effects of repetitive subconcussive head impacts on ocular NPC

NPC; Gforce Tracker

Yes

A

2A

Moderate

Citation, year



Control Group




SST

CEBM


Koerte et al., 2015c

No

15 male professional soccer players

15 male, age-matched former professional non-contact athletes

To evaluate cortical thickness in former soccer players with high resolution structural MRI

MRI cortical thickness; NP battery

Yes

B

3B

Moderate

Lipton et al., 2013

No

29 amateur male soccer players and 8 female players

No

To examine the relationship of soccer heading to subclinical evidence of TBI.

NP battery; MRI cortical thickness

Yes

A

3A

Moderate

McAllister et al., 2012

No

214 male and female hockey and football players

45 non-contact sport athletes

Repetitive head impacts sustained over one season would negatively affect cognitive performance, and that change in cognition would be related to head impact exposure

NP battery; ImPACT testing; HITS; FHI

Mixed

A

2A

Moderate

McAllister et al., 2014

No

80 non-concussed male collegiate football and hockey players

79 non-contact sport athlete controls

To determine whether exposure to RHI over a single season affects WM diffusion measures in collegiate contact sport athletes.

DTI; FA; MD; CVLT-II; WRAT-4 HITS

Yes

A

2A

Moderate

McCaffrey et al., 2007

No

43 male college football players

No

To compare baseline measures of balance and neurocognitive testing to follow-up testing 24 hours after sustaining a head impact of 90g and/or 60g

HITS; SOT; ANAM

No

B

4

Weak

MerchantBorna et al., 2016

Yes; 1


No

To develop a novel method of quantifying the cumulative effects of subconcussion head blows during a single season by weighted helmet-based impact measures for time b/w helmet impacts

DTI; HITS

Yes

A

2A

Moderate

Montenigro et al., 2017

Yes; 1, 2

93 male former amateur football players (17 high school, 76 collegiate)

No

To develop a metric to quantify RHI exposure from football (CHII)

CHII; BTACT; BRIEF-A; CES-D; AES;

Yes

B

4

Moderate

Munce et al., 2014

No

10 male youth football players

No

To determine if neurologic deficits consistent with the diagnoses of mild traumatic brain injury could be detected in non-concussed youth football players

Postural stability; KingDevick; ImPACT; PCSS

No

B

3B

Moderate

Munce et al., 2015

No

22 male adolescent football players

No

To examine potential associations between head impact exposure and acute impairment in neurologic function

PCSS; postural stability; KingDevick; mixed NP, HITS; FHI

Descriptive

A

2A

Moderate

Myer et al., 2016

No



The effect of collar wearing during head impact exposure on brain microstructure integrity

GForce Tracker; DTI; FHI

Yes

B

3B

Strong

Neselius et al., 2014

No

30 male elite amateur boxers

25 age-matched

To investigate if neurological assessment can detect cognitive impairment caused by subconcussive trauma

NFL; neurological exam; NP battery

No

B

2A

Strong

Citation, year



Control Group




SST

CEBM


Poole et al., 2014

No


10 male high school non-collision sport

Hypothesized that high school football players would demonstrate significant differences in metabolite concentrations relative to noncollision sport controls

MRS; ImPACT

Mixed

B

3B

Moderate

Poole et al., 2015

No


No

To determine if head collision histories can serve as predictors of sub-concussive changes in brain metabolism for athletes

HITS; MRS

Mixed

B

2A

Weak

Puvenna et al., 2014

Yes; 2

15 male collegiate football athletes

Positive: emergency department mTBI patients; Negative: patients with no history of concussion

To investigate the effects of subconcussive head hits in collegiate football on markers of blood-brain barrier disruption, cerebrospinal fluid leakage, and brain damage

S100B, UCH-L1, Beta-2 transferase; HHI

Mixed

A

4

Moderate

Svaldi et al., 2016

Yes; 2

26 female high school soccer players

12 non-collision female sport athletes

To assess longitudinally-observed changes of CVR in female high school soccer athletes and its relationship to cumulative mechanical loading.

fMRI-CVR; xPatch

Yes

A

3B

Moderate

Talavage et al., 2014

No


8 matched controls

To examine neurocognitive and neurophysiological changes associated with head trauma

HITS; ImPACT; fMRI

Mixed

B

2A

Moderate

*Definition (1=Bailes; 2=other source; 3=study specific) ** AES = Apathy Evaluation Scale; AFE = Age at first exposure; ANAM = Automated Neuropsychological Assessment Metrics; APOA1 = Apolipoprotein 1 allele; BDHI = Buss-Durkee Hostility Inventory; BDNF = brain derived neurotropic factor; BESS = Balance Error Scoring System; BTACT = Brief Test of Adult Cognition by Telephone; BRIEF-A = Behaviour Rating Inventory of Executive Function-Adult Version; CBI Scale = Chronic Brain Injury Scale; CDI-S = Childhood Depression Inventory Short Form; CES-D = Center for Epidemiologic Studies- Depression Scale; C-SCAT 3 = Child Sport Concussion Assessment Tool; CHII = Cumulative head impact index; CHO = Choline; CK = Serum Creatine Kinase; CR = Creatine; CSF = cerebrospinal fluid; CVLT-II = California Verbal Learning Test-II; DMN = Default mode network; DTI = Diffusion Tension Imaging; FA = Fractional anisotropy; FES = Fight Exposure Score; F-FDG PET= F-fluorodeoxyglucose positron emission tomography; FMZ PET = F-flumazenil positron emission tomography; fMRI = functional Magnetic Resonance Imaging; fMRI-CVR = fMRI - cerebrovascular reactivity; FHI = frequency of head impacts; GFAP = Glial Fibrillary Acidic Protein; Glu = glutamate/glutamine; GSH = glutathione; GSI = Gadd Severity Index; HIC = Head Injury Criterion; HITS = Head Impact Telemetry System; HHI = Head Hit Index; ImPACT = Immediate Post-Concussion Assessment and Cognitive Testing; MD = mean diffusivity; MI = myo-inositol; MRI = Magnetic Resonance Imaging; MRS = Magnetic Resonance Spectroscopy; NAA = N-acetyl aspartate; NFL = neurofilament light; NGF = nerve growth factor; NP = neuropsychological testing; NPC = Near Point Convergence; PCSS = Post Concussion Symptom Score; PROMIS = Patient Reported Outcomes Measurement Information System; rs-fMRI = resting state fMRI; S100B = S100 calcium-binding protein B; SAC = Standardized Assessment of Concussion; SCAT = Sport Concussion Assessment Tool; SOT = Sensory Organization Test; T-tau = Total Tau; UCH-L1 = Ubiquitin carboxy-Terminal Hydrolase L1; UPDRS = Unified Parkinson's Disease Rating Scale; WISC-R = Wechsler Intelligence Scale for Children - Revised; WM = White matter; WRAT-4 = Wide Range Achievement Test-4.

Table 2 Subconcuss* terms used in the reviewed studies Subconcuss* terms (number of listed terms)

Number of studies that use the term

Subconcussive head impact (1)

15

Subconcussive blow (1)

14

Subconcussive impact (1)

12

Subconcussion, subconcussive hit (2)

7

Subconcussive head blow, subconcussive brain/head injury (2)

4

Subconcussive damage, subconcussive event, repetitive head impacts, repetitive/repeated head trauma (RHT), subconcussive effect (5)

3

Subconcussive contact, subconcussive head trauma, repetitive subconcussive events, subconcussive forces (4)

2

Subconcussive head hit, subconcussive changes, subclinical brain damage, subconcussive neural injuries, subclinical head impacts, subclinical concussion, subconcussive neurotrauma, repeated impacts [by heading], repetitive minor impacts at subconcussive thresholds, repetitive subconcussive head impacts (RSHI), repeated low level injuries, low level accelerational forces, sub-concussive head insult, subconcussive insult, sub-concussive neuronal insult, subconcussive head impact exposure, subconcussive impact exposure, cumulative head trauma, repetitive traumatic brain injury, repetitive subconcussive mild traumatic brain injury, head impact exposure (HIP), repetitive hits to the head, repetitive heading, repetitive subconcussive trauma, repetitive contact, repetitive blow (27)

1

Table 3 Description of metabolites Metabolites

Description

CK

A marker to estimate muscle damage

Cortisol

A stress hormone

Glutamateglutamine

An abundant excitatory amino-acid present in all neurons. Elevated glutamate levels lead to excitotoxicity, which can be observed in CNS pathologies including traumatic brain injury

GSH

An antioxidant that reduces reactive oxygen species

Myo-inositol

A marker of glial activation

NAA

An amino acid that is used in imaging studies as an indicator of brain pathology

NFL

A protein marker of axonal damage

NSE

Neuron specific enolase is the dominant enolase-isoenzyme found in neuronal and neuroendocrine tissues and when measured in cerebral spinal fluid can indicate neuronal destruction

S100B

S100 calcium-binding peptide B belongs to the S-100 protein family and is often used as a biomarker of glial activation and/or death in central nervous system (CNS) disorders. It is used as a neurochemical marker of traumatic brain injury

Table 4 Summary of significant neurocognitive outcomes from subconcussive impacts Cognitive domain Indirect impact exposurea Direct impact exposureb (Lezak et al., 1995) (significant findings/total (significant findings/total evaluations (%)) evaluations (%))

Memory

Neurobiological markersc (significant findings/total evaluations (%))

Total (significant findings/total evaluations (%))

8/63 (12.7)

0/30 (0)

5/23 (21.7)

13/116 (11.2)

Attention

7/ 67(10.4)

1/35 (2.9)

5/15 (33.3)

13/117 (11.1)

Executive Function

1/10 (10.0)

1/9 (11.1)

0/1 (0)

2/20 (10.0)

Total (significant findings/total evaluations (%))

16/140 (11.4)

2/74 (2.7)

10/39 (25.6)

In each of the three domains, statistically significant findings are compared to the total number of neurocognitive evaluations across all studies. For example, for indirect impacts, eight findings were significant from 63 total assessment scores of memory. Results are pooled from 27 studies that included subscale and composite scores of neurocognitive functioning. a Includes self-reports and estimates of head impact exposure b Includes calculated head impact exposure metrics c Includes imaging and blood-serum analyses

Table 5 Linear and rotational acceleration of head impacts Linear (g)

Rotational (rad/s2)

Tool

Bazarian et al., Football 2014a

Mean peak acceleration (SD): 26.91 (14) to 37.53 (25)

Mean peak acceleration (SD): 1691.95 (1216) to 2071.71 (1578)

HITS (Riddell, Inc, Rosemont, IL: Simbex, Lebanon, NH)

Beckwith et al., 2013b

Football

Mean peak acceleration (SD): 112.1 (35.4)

Mean peak acceleration (SD): 4253 (2287)

HITS


Football

Mean peak acceleration (SD), range: 27.7 (17.5), 10.0-255.6

Broglio et al., 2009c

Football

Mean peak acceleration (SD): Games: 24.76 (15.72) Practices: 23.26 (14.48)

Mean peak acceleration (SD): Games: 1669.79 (1249.41) Practices: 1468.58 (1055.00)

HITS

Kawata et al., 2016ad

Football

Median peak acceleration (range): Low impact group: 99.4 (40.9-478.7) High impact group: 1112.3 (664.6-2735.9) Overall: 940.5 (40.9-2735.9)

Median peak acceleration (range): Low impact group: 7589 (1348-22529) High impact group: 65016 (32143 – 136554) Overall: 56672 (1348-136554)

i1Biometerics Vector mouthguard (i1 Biometrics Inc)

MerchantBorna et al., 2016

Football

Mean peak acceleration (range): 147 (98-180)

Mean peak acceleration (range): 10479 (7612-16661)

HITS

Munce et al., 2014

Football

Mean peak acceleration (range): Total: 25.5 (10.0-175.9) Practice: 25.0 (10.0-175.9) Game: 26.8 (10.0-154.1)

Mean peak acceleration (range): Total: 1691.8 (7.1 – 12322.5) Practice: 1628.6 (7.1-12322.5) Game: 1832.8 (9.5 – 10795.7)

HITS

Study

Sport

HITS

Study

Sport

Linear (g)

Rotational (rad/s2)

Tool

Myer et al., 2016e

Football

Mean peak acceleration (SD): Control group: 38.15 (4.24) to 124.20 (7.96) Study group: 36.58 (3.02) to 125.29 (9.85)

Mean peak acceleration (SD): Control group: 2377.46 (2474.16) to 30065.43 (19850.52) Study group: 1824.30 (1499.10) to 32738.98 (21873.88)

GForce Tracker

Reynolds, et al., 2016af

Football

Mean peak acceleration: Helmet-only practice: 21.7 Shell practice: 28.0 Full-pad practice: 28.8 Games: 28.2

Mean peak acceleration: Helmet-only practice: 3899 Shell practice: 5485 Full-pad practice: 5605 Games: 5560

xPatch

Miyashita et al., 2016g

Lacrosse

Mean peak acceleration: Game: 48.8 Practice: 56.72

Reynolds et al., 2016bh

Lacrosse

Mean peak acceleration (range): Practice: Females: 18.1 (14.2-31.8) Males: 21.3 (14.8-31.2) Game: Females: 14.7* (13.3 – 45.4) Males: 21.1* (17.7-26.3)

Chrisman et al., 2016

Soccer

Median peak acceleration: 18.3

xPatch (X2 Biosystems)

Dorminy et al., Soccer 2015i

At 30 mph: 34.7 ± 6.16 At 40 mph: 49.2 ± 10.09 At 50 mph: 50.8 ± 7.68

Triaxial mouthguard accelerometer (model 35A, Endevco Corporation, San Juan Capistrano, CA, USA)

Kawata et al., 2016b

Soccer

Mean peak acceleration (SD): 14.49 (5.4)

GForce Tracker (GForce Tracker, Markham, Ontario, Canada)

McCuen et al., 2015j

Soccer

Mean peak acceleration (CI): High School: 37.56 (36.9, 38.1) Collegiate: 39.3 (38.8, 39.8)

Mean peak acceleration (CI): High School: 7523 (7390, 7655) Collegiate: 7713 (7606, 7818)

xPatch

McAllister et al., 2012k

Football, hockey

Maximum acceleration (SD), range: 132 (47), 17-324

Maximum magnitude (SD), range: 10255 (3723), 1684-5593784

HITS

McAllister et al., 2014k

Football, hockey

95th percentile mean acceleration (SD), range: 54.8 (11.4), 33.4-88.3

95th percentile acceleration mean (SD), range: 2552 (635), 1481-4540

HITS

Naunheim et. al., 2000l

Football, hockey, soccer

Mean peak acceleration (SD), range: Hockey: 35.0 (1.7), 10.0 – 150 Football: 29.2 (1.0), 10 – 120 Soccer: 54.7 (4.1)

GForce Tracker

Mean peak acceleration: Practice: Females: 3077.1 Males: 3754.9 Game: Females: 2327.6 Males: 3603.1

Note: HITS = Head Impact Telemetry System; HHI = Head Hit Index a These data represent the range of values by player position b These data are associated with concussion c Significant differences between game and practice for both linear and rotational acceleration d Low and high impact groups are significantly different for both linear and rotational acceleration e Three impact group levels f Significant differences between helmet-only practices and all other groups g Game and practice groups are significantly different h Significant differences between males and females in games only i Simulated lab setting j High school and collegiate groups are significantly different for both linear and rotational acceleration k Hockey and football player results are combined l Soccer had a significantly higher peak g, and impacts occurred more frequently in football than in hockey

xPatch

Triaxial accelerometer