Comparison of Internal Training Load Between Soccer Athletes Demonstrating “Excellent” and “Poor” Movement Quality

Tara Condon (Second Year Masters Student in Athletic Training) is the author of this week’s EXSS Impact Post.  Tara presented her work at the Second World Congress of Sports Physical Therapy (Optimal Loading in Sport), which was held at the Titanic Belfast in Northern Ireland.

Why did you do this study?

taraSports medicine clinicians can use clinical movement assessments to identify individuals who may be at increased risk of suffering a lower extremity (LE) musculoskeletal injury.1,2  Injury risk identification is often performed during pre-season screenings, which includes the assessment of an athlete’s biomechanics.2 Baseline clinical movement screenings (such as the Landing Error Scoring System (LESS)) can be used as valuable tools to help identify athletes with high-risk movement strategies. The LESS is a reliable and valid screening tool used to identify individuals at greater risk for LE injury.3,4 Clinicians can monitor injury risk via training load (TL) evaluation throughout a season.5,6 It is currently unknown if biomechanics influence in-season TLs. The aim of this investigation was to examine how an athlete’s LE movement quality influences a season’s relative TL (acute:chronic workload ratio (ACWLR)) in American collegiate soccer players over the course of one traditional season.

What did you do and what did you find in this study?

lessFourteen American male collegiate soccer players participated in this pilot study as part of their standard care. Prior to the season, athletes performed 3 jump-landings from a 30-cm high box to a target line placed ½ of the individual’s height from the front of the box. Athletes were instructed to jump from the box, land in front of the line and then immediately jump for maximum height. A Microsoft® depth sensor capable of capturing human movement – was used instead of standard video cameras. Specialized online software, Physimax®, was used to analyze jump-landing performance. Physimax’s® movement assessment algorithms have been identified to be a valid and reliable for identifying movement errors during the LESS.

Daily session RPE (sRPE) were collected during the traditional season utilizing a modified Borg scale of perceived exertion. sRPE and duration were used to calculate internal TLs and weekly ACWLR7. The season’s trends can be observed in the figure below. ITL data was collected as whole integer on the RPE scale from 1-10 and duration recorded to the nearest minute. ACWLRs were calculated starting five weeks into each season. Fig. 2 represents the team’s average absolute weekly TL, as well as the average weekly ACWLR for the season. We compared early season absolute weekly TL, and early season ACWLR between individuals with poor movement profiles (LESS scores (8-11)) and excellent movement profiles (LESS scores (≤ 4)).


Our results suggest that movement quality associated with musculoskeletal injury risk may influence early season TLs in collegiate male soccer athletes. sRPE x duration is a pragmatic, cost effective means of tracking loads (ITL) experienced by an athlete. There is a lack of data surrounding American collegiate field sport athletes. There are no data that examine the relationships between biomechanical risk factors and training load. Future investigations with larger sample sizes should explore the influence of movement quality on training load responses.


How do these findings impact the public?

With a rise in sport participation rates and injury incidence, there is an increased demand to properly plan training sessions in order to effectively manage the loads imposed upon athletes during sport participation.8,9  Within the current literature, no investigation currently includes a meaningful sample of American collegiate athletes5. There is an increased need to include a wider variety of different sports and demographics in TL research in order to establish a more complete risk profile between different sports, gender and age groups.5,10 Thus, the aims of our future research project will be to examine the associations between the relative rates of internal training load, LE movement quality and how they interactively influence injury risk in American collegiate soccer athletes throughout two traditional seasons.


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  2. Conley KM, Bolin DJ, Carek PJ, et al. National athletic trainers’ association position statement: Preparticipation physical examinations and disqualifying conditions. J Athl Train. 2014;49(1):102-120.
  3. Padua DA, Marshall SW, Boling MC, et al. The Landing Error Scoring System (LESS) is a valid and reliable clinical assessment tool of jump-landing biomechanics: The JUMP-ACL study. Am J Sports Med. 2009;37(10):1996-2002.
  4. Padua DA, DiStefano LJ, Beutler AI, et al. The landing error scoring system as a screening tool for an anterior cruciate ligament injury-prevention program in elite-youth soccer athletes. J Athl Train. 2015;50(6): 589-595.
  5. Drew MK, Finch CF. The relationship between training load and injury, illness and soreness: A systematic and literature review. Sports Med. 2016;46(6):861-883.
  6. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109-115.
  7. Malone S, Owen A, Newton M, et al. The acute:chonic workload ratio in relation to injury risk in professional Soccer. J Sci Med Sport. 2017; 20(6): 561-565.
  8. Gabbett TJ. The Development and Application of an Injury Prediction Model for Noncontact, Soft-Tissue Injuries in Elite Collision Sport Athletes. J Strength Cond Res. 2010;24(10):2593-2603.
  9. Orchard JW, James T, Portus M, Kountouris A, Dennis R. Fast bowlers in cricket demonstrate up to 3- to 4-week delay between high workloads and increased risk of injury. Am J Sport Med. 2009;37(6):1186-1192.
  10. Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48(8):708-712.