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A preliminary investigation into a qualitative assessment tool to identify

athletes with high knee abduction moments during cutting: Cutting
Movement Assessment Score (CMAS).

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 A preliminary investigation into a
qualitative assessment tool to identify
athletes with high knee abduction
moments during cutting : Cutting
Movement Assessment Score (CMAS)
Jones, PA, Donelon, T and Dos'Santos, T

Title A preliminary investigation into a qualitative assessment tool to identify

athletes with high knee abduction moments during cutting : Cutting
Movement Assessment Score (CMAS)
Authors Jones, PA, Donelon, T and Dos'Santos, T
Type Article
URL This version is available at: http://usir.salford.ac.uk/44271/
Published Date 2017

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 1 A preliminary investigation into a qualitative
2 assessment tool to identify athletes with high knee
3 abduction moments during cutting: Cutting
4 Movement Assessment Score (CMAS).
5

8 Paul A. Jones1, Thomas Donelon1 and Thomas Dos’Santos1

1
9 Directorate of Sport, Exercise and Physiotherapy, University of Salford.

10

11 Corresponding Author: Dr. Paul Jones. Directorate of Sport, Exercise and

12 Physiotherapy, University of Salford, Allerton Building, Frederick Road
13 Campus, Salford, Greater Manchester, United Kingdom, M6 6PU.

14 Tel: (+44) 161 295 2371. Email: P.A.Jones@salford.ac.uk

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22

1
23 Summary
24

25 Given the limited accessibility of 3D motion analysis for injury screening of athletes, there is

26 a need to develop a field-based screening tool to identify athletes with ‘at-risk’ cutting

27 mechanics. The aim of this preliminary study was to assess the validity of a qualitative

28 assessment tool for cutting (CMAS) to estimate the magnitude of peak knee abduction

29 moments (KAM) against ‘Gold Standard’ 3D motion analysis. The presented CMAS was

30 able to rank cutting trials based on the magnitude of KAM. Thus, is a potential method to

31 identify athletes who generate high KAM during cutting.

32 Keywords: Anterior Cruciate Ligament; Knee Abduction Moments; Injury Screening

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51 Introduction
52 Cutting is an action often associated with non-contact ACL injuries in field and court

53 based sports such as soccer [4] and handball [23]. This is due to the propensity of generating

54 high knee abduction (valgus) and rotational moments when the foot is planted [17], which

55 could lead to increased ACL strain [24, 25]. Whilst the efficacy of screening tests to identify

56 ‘at-risk’ athletes for specific injuries is debated [1], it is important as strength and

57 conditioning coaches and sports rehabilitators to have a battery of assessments to provide an

58 ‘injury profile’ of an athlete. If an athlete underachieves in certain related qualities, steps can

59 be taken in training to address these deficiencies to provide an overall more rounded and

60 robust athlete. It is unlikely that one single factor can predict injury alone [1]. Part of such a

61 battery of assessments with regard to non-contact ACL injuries, should include some

62 assessment of movement quality during relevant sports actions. In regard to non-contact ACL

63 injuries, identifying athletes with poor lower limb mechanics in sports where there are large

64 weight acceptance (braking) forces can be considered important.

65 To date, most literature has examined landing tasks such as the drop jump to identify

66 ‘at-risk’ athletes despite some sports (i.e., soccer) reporting cutting or changing direction to

67 be the most common action associated with non-contact ACL injury in females[4]. Hewitt et

68 al., [6] using 3D motion analysis prospectively found that females who went on to injure their

69 ACL had significantly greater knee abduction angles and moments during a drop jump than

70 non-injured volleyball players. Although more recent research [15] found such an approach

71 was unable to identify at-risk athletes for ACL injury in elite soccer and handball players;

72 which questions the efficacy of the approach to find ‘at-risk’ athletes, but may also suggest

73 that the screening task needs to reflect the movement demands of the sport. Nevertheless, the

74 accessibility, time and financial costs will limit the widespread application of 3-Dimensional

75 analysis to find athletes with poor movement quality, which has led authors to suggest the use

3
76 of simplified 2D analysis of drop jumps focusing on estimates of frontal plane knee motion

77 [19, 29]. Moreover, Padua et al. [20] have developed and validated a qualitative analysis tool

78 for a drop jump involving 2D video capture in the frontal and sagittal planes. Although,

79 mixed evidence has been reported with regard to the efficacy of the Landing Error Scoring

80 System (LESS) [26, 21] to prospectively predict ACL injury. This may suggest that the use of

81 landing tasks may fail to identify athletes with at-risk cutting mechanics. Furthermore, there

82 is also mixed evidence available to suggest whether examination of landing mechanics could

83 identify athletes with poor cutting mechanics [9,13]. For instance, it is suggested that landing

84 tasks maybe better at identifying athletes with poor knee control during cutting, but the

85 ability to identify athletes with high KAMs during cutting from landing is more difficult due

86 to the differing technical demands of each task [9]. Thus, it is likely that assessment of

87 movement quality of cutting alongside landing mechanics is needed to further develop the

88 injury profile of an athlete in cutting and landing sports.

89 Field-based measures evaluating cutting mechanics have also relied on 2D estimates

90 of frontal plane knee motion. McLean et al. [18] investigated whether a 2D assessment of

91 knee valgus motion relates to knee valgus motion identified from 3D analysis during a 35-60°

92 side-step, side-jump and shuttle-run (180º turn). 2D estimates correlated well with 3D data

93 for the side-step (R2 = 0.58) and side-jump (R2 = 0.64), but did not correlate with the shuttle-

94 run, highlighting the difficulty in assessing knee valgus motion 2-dimensionally in the frontal

95 plane with more vigorous horizontal changes of direction. Furthermore, such a method only

96 examines knee valgus motion and does not evaluate the range of technical factors that are

97 associated with high KAM [3, 8,10,11,12, 17, 27]. Hence, a qualitative screening tool that

98 examines many aspects of poor cutting mechanics maybe more informative for practitioners.

99 Therefore, the aim of this preliminary study is to assess the validity of a qualitative screening

4
100 tool for cutting (Cutting Movement Assessment Score) to estimate the potential magnitude of

101 KAMs against the ‘Gold Standard’ 3D motion analysis.

102

103 Methods

104 Participants

105 With institutional ethical approval, 8 University level team (mean ± SD; age: 20.1 ±1.1

106 years, height: 1.63 ± 0.09 m, mass: 54.0 ± 6.9 kg) sport female athletes participated in this

107 study. For inclusion in the study, all athletes had played their respective sport for a minimum

108 of 5 years and regularly performed 1 game and 2 structured skill based sessions per week. All

109 players were right leg dominant. All players were free from injury during the course of the

110 study and none of the player’s had suffered prior traumatic knee injury such as anterior

111 cruciate ligament injury. Data collection took place during the players pre-season. Written

112 informed consent was provided by all subjects.

113 Cutting Movement Assessment Score

114 Table 1 presents a qualitative technique analysis tool to estimate the magnitude of

115 KAMs during cutting (Cutting Movement Assessment Score - CMAS) based on research

116 pertaining to technique determinants of KAM during 45-90° cutting. If an athlete during

117 cutting exhibits any of the characteristics in Table 1 they are awarded a score. It is

118 hypothesised that the greater the total score the greater the potential magnitude of KAM.

119

120

121

122

123

5
124 Table 1. A qualitative technique analysis tool to determine the magnitude of knee
125 abduction moments during cutting – Cutting Movement Assessment Score (CMAS).
Variable Observation Score
Penultimate contact
Backward inclination of the trunk Y/N Y=0/ N=1
Final Contact
Wide lateral leg plant Y/N Y=2/N=0
Hip in an initial internally rotated position Y/N Y=1/N=0
Initial knee ‘valgus’ position Y/N Y=1/N=0
Inwardly rotated foot position Y/N Y=1/N=0
Frontal plane trunk position relative to intended L/U/M L=2/U = 1/M=0
direction; Lateral (L), Upright (U) or Medial (M).
Trunk upright or leaning back throughout contact Y/N Y=1/N=0
Limited Knee Flexion during final contact Y/N Y=1/N=0
Excessive Knee ‘valgus’ motion during contact Y/N Y=1/N=0
Total Score /11
126

127 The CMAS examines both the penultimate and final contact during the cutting tasks.

128 For penultimate contact a ‘backward inclination of the trunk relative to the planted foot’ is

129 considered in order to increase horizontal braking forces during penultimate contact, based on

130 research [11] that has found an association between average horizontal ground reaction forces

131 (GRF) during penultimate contact and KAMs during final contact. For the final contact,

132 ‘wide lateral leg plant’ and ‘frontal plane trunk position’ are considered major determinants

133 of KAMs [3, 8, 12, 10]; and thus, are given a greater weighting. Previous research has found

134 that a wide-lateral foot plant is associated with high KAMs [3, 27, 12, 10] as such a technical

135 characteristic may create a GRF vector acting laterally outside the knee with greater distances

136 of foot plant creating a greater moment arm and thus, KAM. Lateral trunk flexion has also

137 been associated with increasing KAMs during cutting [3, 8, 12, 10], as a laterally flexed trunk

6
138 towards the planted leg side shifts the athletes weight laterally creating a laterally directed

139 force vector, increasing the moment arm relative to the knee joint and thus, KAMs.

140 Other considerations for the final foot contact include ‘initial knee valgus position’, which

141 has been found in several studies to be associated with KAMs [17, 12, 10]. An increased knee

142 abduction angle at initial contact has an effect of placing the knee more medial to the

143 resultant GRF vector and thus, increases the lever arm of the resultant GRF vector relative to

144 the knee joint leading to an increased KAM. Furthermore, Sigward and Powers [27] found

145 both initial foot progression angle and initial hip internal rotation angle were significantly

146 related to KAMs, as such a position could lead to a more medially positioned knee in relation

147 to the GRF vector [27] and thus, are both considered within the tool. Finally, overall knee

148 valgus motion during final contact and trunk inclination throughout final contact, with the

149 latter considered to potentially increase the overall knee joint load due an increased lever arm

150 of the trunk relative to the knee.

151 Experimental Procedures

152 The procedures are similar to the methods of Jones et al. [10] and are summarised here. Prior

153 to data collection, reflective markers (14 mm spheres) were placed on bony landmarks of

154 each athlete [10], along with 4 marker ‘cluster sets’ (lightweight plastic shell) placed on the

155 upper back, both thighs and shanks, which approximated the motion of the segments during

156 dynamic trials.

157 Following a static trial, each athlete performed 5 trials of a between 60-90° cutting task

158 (Figure 1) which involved sprinting through a set of timing gates (Brower, Draper, UT)

159 positioned at hip height 5 m from the centre of the plate and then after contacting the centre

160 of the force platform with the right foot cut to the left through a second set of timing gates

161 positioned 3 m away. The performance times were used to monitor performance between

7
162 trials. For each trial, three-dimensional motion data using 10 Qualisys Oqus 7 infrared

163 cameras (240 Hz) operating through Qualisys Track Manager Software v2.8 and ground

164 reaction force (GRF) data from two AMTI force platforms (sampling at 1200 Hz) were

165 collected. This arrangement allowed data to be collected for both penultimate and final

166 contact. Simultaneously, 2 Casio EXF-1 cameras (Casio, Tokyo, Japan) sampling at 30 Hz

167 were positioned 5 m away from the force platforms in frontal and sagittal planes. Greater

168 video sampling rates could not be used as floodlights would have been required to enhance

169 lighting, which would have then impacted on 3D motion data collection. Video footage was

170 subsequently viewed in Quintic Biomechanics v26 (Coventry, UK) for qualitative analysis

171 using the CMAS (Table 1).

172

173 Figure 1. Plan view of the experimental set-up. The task involves subjects approaching 5

174 m towards a turning point on the 2nd of 2 force platforms. At the turning point, subjects

175 cut to the left between timing cells positioned 3 m away and 60 to 90° from the original

176 direction of travel.

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177 A lower extremity and trunk 6 degrees of freedom kinematic model was created for

178 each participant from the static trial. This model included the trunk, pelvis, thighs, shanks and

179 feet using Visual 3D software (C-motion, version 3.90.21, Gothenburg, Sweden) and is

180 described in more detail elsewhere [10]. The local coordinate system was defined at the

181 proximal joint centre for each segment. The static trial position was designated as the

182 participant’s neutral (anatomical zero) alignment, and subsequent kinematic measures were

183 related back to this position. KAMs were calculated using an inverse dynamics approach [30]

184 through Visual3d software (C-motion, version 3.90.21) and represented as external moments.

185 Trials were disqualified if the subjects slid or missed the force platform that went

186 unnoticed during data collection. This resulted in a total of 36 trials considered acceptable for

187 both 3D and qualitative video analysis. Trials were time normalised for each participant, with

188 respect to ground contact time. Initial contact was defined as the instant after ground contact

189 that the vertical GRF was higher than 20 N and end of contact was defined as the point where

190 the vertical GRF subsided past 20 N. Joint coordinate and force data were smoothed with a

191 Butterworth low pass digital filter with cut-off frequencies of 12Hz and 25Hz, respectively.

192 Cut off frequencies were selected based on a residual analysis [30] and visual inspection of

193 the data.

194

195 Statistical Analysis

196 To determine inter and intra-rater reliability, 8 trials (1 from each subject) were

197 randomly selected by one experimenter. One lead researcher (TD) viewed and graded each

198 trial on two separate occasions and compared (intra-rater reliability), whilst another lead

199 researcher (PJ) viewed and graded each trial once and compared to the other lead researcher

200 (inter-rater reliability). Intra-class correlation co-efficients (ICC) for total score were

201 determined. For each item within the CMAS and total score, percentage agreements

9
202 (agreements /agreements + disagreements × 100) and Kappa co-efficients were calculated.

203 Kappa co-efficients were calculated using the formula; Κ = Pr(a) – Pr(e) / 1 – Pr(e), where

204 Pr(a) = relative observed agreement between raters; Pr(e) = hypothetic probability of chance

205 agreement, using the observed data to calculate the probabilities of each observer randomly

206 saying each category [5]. The kappa co-efficient was interpreted based on the following scale

207 of Landis and Koch [16]: 0.01-0.2 (slight); 0.21-0.4 (fair); 0.41-0.6 (moderate), 0.61-0.8

208 (good) and 0.81-1.0 (excellent).

209 The relationship between CMAS and the ‘gold standard’ determination of peak KAM

210 during the final contact of the cutting task from 3D motion analysis for all available trials was

211 explored using Spearman’s rank correlation due to the non-parametric nature of the

212 qualitative data. Correlations were evaluated as follows: trivial (0.0-0.09), small (0.10 –

213 0.29), moderate (0.30 – 0.49), large (0.50 – 0.69), very large (0.70 – 0.89), nearly perfect

214 (0.90 – 0.99), and perfect (1.0) [7].

215

216 Results

217 Moderate to excellent intra- and inter-rater agreement was observed (Table 2). Excellent

218 intra- and inter-rater ICC for total score was also observed (Intra-rater = 0.922; Inter-rater =

219 0.913).

220

221

222

223

224

225

226

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227 Table 2. Intra and inter-rater agreement for CMAS criteria and total score.

Variable/ screening tool criteria Intra-rater reliability Inter-rater reliability

% Agreement Κ % Agreement Κ
Backward inclination of the trunk 100 1.00 100 1.00
(penultimate contact)

Wide lateral leg plant 87.5 0.60 100 1.00

Hip in an initial internally rotated position 87.5 0.75 87.5 0.75

Initial knee ‘valgus’ position 87.5 0.60 100 1.00

Inwardly rotated foot position 100 1.00 100 1.00

Frontal plane trunk position relative to 75 0.62 62.5 0.40

intended direction; Lateral (L), Upright
(U) or Medial (M).

Trunk upright or leaning back throughout 100 1.00 87.5 0.75

final contact

Limited knee flexion during final contact 100 1.00 100 1.00

Excessive knee ‘valgus’ motion during 100 1.00 87.5 0.71

final contact

Total 93 0.87 92 0.85

228

229 Figure 2 shows a linear relationship between CMAS and KAM’s. Mean ± SD KAM

230 from each trial of all 8 subjects and the respective CMAS were 0.80 ± 0.52 Nm·kg-1 and 4.5

231 ± 2.1, respectively. Spearman’s correlation revealed a significant large association between

232 CMAS and KAMs (ρ = 0.633; p < 0.001).

11
 2.5

Peak knee abduction moment (Nm/kg)

2

1.5

0.5

0
0 2 4 6 8 10 12
233 CMAS

234 Figure 2. Scatter plot for the relationship between CMAS with peak knee abduction
235 moments.
236

237 Discussion

238 The aim of this preliminary study was to assess the validity of a qualitative movement

239 assessment tool for cutting (CMAS) to estimate the potential magnitude of KAMs against the

240 ‘Gold Standard’ 3D motion analysis. The preliminary results suggest that the presented

241 CMAS was able to rank cutting trials based on the magnitude of KAM. Thus, the CMAS can

242 be considered a potential method to identify ‘at-risk’ athletes who generate high KAM during

243 cutting and could be used in a battery of assessments for an athlete from ‘cutting’ sports to

244 develop an injury profile of the athlete. The CMAS also demonstrated excellent inter and

245 intra-rater reliability and agreement.

246 The efficacy and efficiency of injury prevention protocols could be improved

247 considerably if they are designed specifically for predetermined at-risk athletes, with defined

248 neuromuscular control deficits. Whilst screening for specific injury is difficult [1],

12
249 practitioners require a battery of tests to develop an athlete profile that provides an

250 assessment of risk factors that could inform training prescription. Central to such a battery of

251 tests is an assessment of movement quality that relates to common actions in the sport

252 associated with non-contact injury. Mixed evidence has been reported regarding the efficacy

253 of using 3D motion analysis of drop jumping [6,15] to prospectively predict ACL injured

254 athletes and may be partly explained by the need to assess athletes performing common

255 actions that are associated with injury and occur frequently in change of directions sports,

256 rather than just purely focus on landing tasks. Furthermore, 3D motion analysis is difficult to

257 apply for widespread evaluation of athletes. Whilst relationships have been found with regard

258 to knee motion between landing and changing direction [9, 13], when considering knee joint

259 loads, lower or absent relationships have been observed [9, 13]; highlighting the need for

260 field-based assessments of cutting or change of direction mechanics. Current field-based

261 measures evaluating change of direction mechanics from 2D video analysis can approximate

262 frontal plane knee motion for shallow angles of direction change only and have not been

263 shown to predict knee joint loads [18]. The results of the present study suggest that the

264 CMAS has potential to identify athletes with ‘at-risk’ cutting mechanics and could be used in

265 a battery of assessments for an athlete from ‘cutting’ sports to develop an injury profile of the

266 athlete. The use of the CMAS can specifically identify biomechanical or neuromuscular

267 control deficits in athletes, which can then be targeted via appropriate training and

268 conditioning.

269 One benefit of CMAS proposed in this study is that it evaluates an action (cutting)

270 that is common in many sports such as soccer [2] and netball [28], whereas the drop jump is

271 seldom performed in isolation during sport, as this action is effectively an assessment of an

272 athlete’s reactive strength. Furthermore, cutting and change of direction actions have been

273 associated with non-contact ACL injuries in soccer [4] and handball [23], whereas bilateral

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274 landings are associated with non-contact ACL injury in basketball [14]. Thus, the CMAS

275 proposed in this study may serve well for athlete assessment in sports where cutting and

276 change of direction actions are common. Further work is required to develop the CMAS

277 particularly with a greater sample of athletes to determine whether the tool is capable of

278 discriminating between athletes exhibiting poor to excellent cutting technique. Previous

279 research using the LESS [21] found that 5 was an optimal cut-off score to identify at-risk

280 athletes for non-contact ACL injury with 86% sensitivity and 64% specificity. Therefore, a

281 longitudinal study is required to identify a potential cut-off score for the CMAS to identify

282 ‘at-risk’ athletes and whether the tool can subsequently predict injury.

283 The present study involved team sport athletes with a range of ability levels,

284 therefore, research is required to establish whether the tool can discriminate between athletes

285 of different ability levels. In terms of the method of data collection, the intra- and inter-rater

286 agreements revealed lower percentage agreements for frontal plane trunk position. This was

287 partially due to the difficulty in viewing this variable in the frontal plane when athletes have a

288 slightly rotated trunk or pelvis into the intended direction of travel. The authors recommend

289 placing an additional camera 45° to the original direction of travel in order to improve the

290 view of variables in the frontal plane when some level of rotation prior to or at initial contact

291 of final footfall takes place. A further limitation of this study was that due to the need for

292 additional lighting and to avoid this impacting the 3D motion capture only 30 Hz video

293 recordings were gathered. Use of greater sampling rates (>100 Hz) would enable more

294 precise identification of key instances during cutting manoeuvres and therefore, further

295 enhance validity and reliability of the CMAS. The authors recommend using greater

296 sampling frequencies (if available) in practice.

297 Finally, another limitation of the present study is that the intra- and inter-rater

298 reliability and agreements were based on Biomechanics researchers carrying out the

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299 investigation. Further work is required to quantify intra- and inter-rater reliability with a

300 range of applied practitioners such as strength and conditioning coaches, sports rehabilitators

301 and physiotherapists to be able to apply the CMAS in the field.

302

303 Practical Applications for Strength & Conditioning

304 The large association between KAMs from 3D motion analysis and CMAS found in

305 the present study support the association of the technique characteristics identified in the

306 CMAS (Table 1) to KAMs during cutting, and therefore, could also act as a guide for

307 technique development for athletes where the goal is for injury prevention. A unique aspect

308 of this study is that technical guidelines for safer cutting are provided where currently there

309 are no guidelines available on how to safely cut. This tool offers a template to enable

310 practitioners to coach safer cutting technique. However, it should be highlighted that some of

311 these technique aspects may be detrimental to performance. For instance, a wide lateral foot

312 plant may facilitate the direction change by helping to generate medial GRF’s, but would

313 result in an initial increase in KAM. Further research is required to better understand the

314 conflict between performance and injury risk for cutting, which may further inform the

315 CMAS presented here.

316 A note of caution in using the CMAS is that practitioners should not only focus on

317 total score but the actual criteria where the athlete scored points. A low score doesn’t

318 necessarily mean that a player has perfect and safe technique. For example, an athlete may

319 only score two points on the CMAS, however, this score maybe for lateral trunk flexion,

320 which has been stated as one of the theories of increased risk of ACL injury [22], as such this

321 deficit in trunk control displayed by an athlete should not be ignored and the athlete should

322 still receive specific training and conditioning.

15
323 Acknowledgements

324 The authors would like to thank Laura Smith and Natasha Brown for assistance with data

325 collection for this study.

326

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