Developing common demographic data elements to include in future editions of the SCAT and Child SCAT: a modified international Delphi study.

BACKGROUND

The Sport Concussion Assessment Tool (SCAT) and Child SCAT are the 'gold standard' assessment tools for a suspected sport-related concussion (SRC). A number of 'modifiers' (eg, previous history of concussion) of a SRC have been identified. These may influence how the SCAT/Child SCAT results should be interpreted.

OBJECTIVES

To achieve consensus, via an international panel of SRC experts, on which athlete/player and parent/caregiver demographic variables should be considered for inclusion in future editions of the SCAT/Child SCAT respectively.

METHODS

A two-round modified Delphi technique, overseen by a steering committee, invited 41 panellists to achieve expert consensus (≥80% agreement). The first round utilised open questions to generate demographic variables; the second round used a five-point ordinal item to rank the importance of including each variable in future editions of the SCAT/Child SCAT.

RESULTS

15 experts participated in at least one Delphi round. 29 athlete/player and eight parent/caregiver variables reached consensus for inclusion in the SCAT, whereas two parent/caregiver variables reached consensus for exclusion. 28 athlete/player and four parent/caregiver variables reached consensus for the Child SCAT, whereas two parent/caregiver variables reached consensus for exclusion. Key categories of variables included the following: concussion/sport details, personal medical conditions and family medical history.

CONCLUSION

This study provides a list of athlete/player and parent/caregiver demographic variables that should be considered in future revisions of the SCAT/Child SCAT. By considering (and ultimately likely including) a wider and standard set of additional demographic variables, the Concussion in Sport experts will be able to provide clinicians and researchers with data that may enhance interpretation of the individual's data and the building of larger datasets.