Eleven studies investigated workers and students including coal miners (Dobson et al., 2017(Dobson et al.,, 2018a(Dobson et al.,, 2018b, metal refining plant workers (Akbar-Khanzadeh, 1998), health professionals (Anderson et al., 2021 Getie et al., 2021), engineering students (Franciosa et al., 2013), fishermen (Jensen & Laursen 2011), students from Quanzhou Normal University (Peng et al., 2020), from University of Salford (Melvin et al., 2019) and a college community campus (Burke, 2012). Four studies did not describe the age range or average ages of their participants. 2017Pace et al., 2020 Peng et al., 2020 Saxton et al., 2020 Schuh et al., 2011 Sinclair et al., 2016 Sterzing et al., 2013 Thordarson et al., 2001 Thordarson, Ebramzadeh, Moorthy, et al., 2005 Van Alsenoy et al., 2019 West et al., 2020 Witana et al., 2009 Zhang et al., 2019 Zwaferink et al., 2020Zwaferink et al.,, 2021. Two studies investigated children (aged less than 13 years) (Herbaut et al., 2019 Matthias et al., 2021), three studies investigated adolescents (13-18 years) (Lam et al., 2019 Muniz & Bini, 2017 Park et al., 2017), 90 studies investigated adult populations (age 19þ years) (Anderson et al., 2021 Apps et al., 2015 Arezes et al., 2013 Arts et al., 2014 Au & Goonetilleke, 2007 Bergstra et al., 2015 Bishop et al., 2020 Branthwaite et al., 2014 Burke, 2012 Bus et al., 2009 Cha, 2020 Chan et al., 2020 Che et al., 1994 Chee-Kidd & Vivek, 2009 Chu et al., 2014 Chuter et al., 2016 Clinghan et al., 2008 Colonna et al., 2015 Cornwall & McPoil, 2017 Crews & Candela, 2018 Davis et al., 2013 Day & Hahn, 2020 Dinato et al., 2015 Dobson et al., 2017Dobson et al.,, 2018bDobson et al.,, 2018a2020 Franciosa et al., 2013 Frecklington et al., 2019 Fuchs et al., 2020 Gao & Abeysekera, 2000 Getie et al., 2021 Govindasamy et al., 2020 Grier et al., 2011 Haene et al., 2012 Hennessy et al., 2007 Herbaut et al., 2016 Hintzy et al., 2015 Hoerzer et al., 2016 Hofer et al., 2014 Hollawell & Baione, 2015 Hong et al., 2005Hong et al.,, 2016Hurst et al., 2017 Isherwood et al., 2021 Janson et al., 2021 Jensen & Laursen 2011 Jerosch et al., 1995 Jiang et al., 2016 Jordan & Bartlett, 1995 Kong & Bagdon, 2010 Koska & Maiwald, 2020 Lam et al., 2011Lam et al.,, 2017Lane et al., 2014 Lindorfer et al., 2019aLindorfer et al.,, 2019bLindorfer et al.,, 2020Liu et al., 2021 Llana et al., 2002 L opez-Moral et al., 2020 Luczak et al., 2020 Luo et al., 2009 Melia et al., 2021 Melvin et al., 2019 Meyer et al., 2018 Mills et al., 2010Mills et al.,, 2018Mo et al., 2020 M€ undermann et al., 2001Nigg et al., 1986 Okholm Kryger et al. Only nine studies did not report on the gender ratio of the participants. Clinicians and researchers can incorporate the RUN-CAT to optimise running shoe comfort in athletes. The RUN-CAT demonstrates excellent reliability, acceptable measurement error and can discriminate between footwear models. Subject nominated MID values ranged from 9.3 to 9.9 mm. The SEM of the comfort score was 2.8 points and the MDD90 was 6.5 mm. Reliability of the overall comfort score was good to excellent (within-day ICC 0.88, between-day 0.70) with all four components displaying good reliability (ICC >0.70). Of the 19 initial items, after item reduction, four were included in the final tool: heel cushioning, shoe stability, forefoot cushioning and forefoot flexibility. A three-stage process was used to develop a new running footwear comfort assessment tool (RUN-CAT): (i) a survey of 282 runners to identify meaningful items of comfort, (ii-a) field testing of 100 runners who assessed the comfort of different shoes, (ii-b) item reduction using bootstrap aggregation and weightings using multiple regressions to identify a final set of items, and (iii) defining test-retest reliability, standard error of measurement (SEM), minimal detectable difference (MDD90) and minimal important difference (MID) values for the final tool. Comfort is important for running shoe prescription in athletes to enhance performance and potentially decrease injury risk.
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