Skip to main content
NCBI home page
International Journal of Sports Physical Therapy logoLink to International Journal of Sports Physical Therapy
. 2019 Apr;14(2):237–252.

INJURY INCIDENCE IN COMPETITIVE CROSS-COUNTRY SKIERS: A PROSPECTIVE COHORT STUDY

Sonya G A Worth 1,✉, Duncan A Reid 2, Alan B Howard 3, Sharon M Henry 4
PMCID: PMC6452571  PMID: 30997276

Abstract

Background/Purpose

Endurance sports, including cross-country skiing, require long hours of repetitive training potentially increasing the chance of injury, yet injury incidence and risk factors for adult cross-country skiers remain relatively unexplored. Data for elite adult north American competitive cross-country skiers is unexplored. A 12 month prospective surveillance study was undertaken to calculate the injury incidence and exposure of cross-country skiers. Injuries by anatomic location and mechanism of injury were calculated. Further, the relationships between new injury and the participant's demographics and physical assessment parameters were examined. The aims of this study were to determine the injury incidence and any risk factors for injury in elite adult north American cross-country skiers.

Methods

Elite cross-country skiers (35 men, 36 women) self-reported demographics, injury history, and injury and training surveillance monthly over 12 months. t-tests compared the mean number of injuries per individual, per 1,000 training/exposure hours between anatomic regions, type of injuries, and seasons. Spearman's correlation analyses tested the relationship between new injury and Movement Competency Screen (MCS) score, past injury, total training time, and running training time. To determine if new injury could be predicted from any demographic data, intake physical measures, or, monthly injury, training and racing data, a regression model was developed.

Results

Overall, 58% of participants (18 men, 23 women) completed the study, and reported 3.81 injuries per 1,000 training/exposure hours. Over 12-months, lower extremity injury incidence (2.13) was higher than upper extremity (0.46) and trunk injury incidence (0.22) (p < 0.05). Non-traumatic/overuse injury incidence (2.76) was higher than acute injury incidence (1.05) (p < .05). Non-ski-season injury incidence (5.25) was not statistically higher than ski-season injury incidence (2.27) (p = 0.07). New injuries were positively correlated with previous injury (p < 0.05), but not with any other variables (p > 0.05).

Conclusion

In this year-long monthly survey of injuries and training load in elite adult north American cross-country skiers, new injuries were positively correlated with previous injury. Lower extremity, and non-traumatic/overuse injuries had the highest incidence rates. There was no significant correlation between new injuries and physical assessment parameters or training load.

Level of Evidence

Level 3, Prospective Longitudinal Cohort Study

Keywords: Injury burden, Injury rate, Movement System, Nordic skiing

INTRODUCTION

Endurance athletes such as cross-country skiers, distance runners, swimmers, cyclists, and rowers require high levels of aerobic power and endurance1–4 gained from the long hours of repetitive training required to excel at their chosen sport. Previous injury and a high volume of repetitive training are two commonly reported injury risk factors in endurance athletes.5,6 There is considerable variation in injury reporting methods used when studying endurance athletes, leading to poor consensus about the incidence of injury and the risk factors for injury in endurance sports.5,7–13

Cross-country skiing is an endurance sport requiring a range of skiing techniques to cover various types of terrain efficiently. There are two distinct cross-country ski styles, skating and classic, each with sub-techniques.14 Injuries, and injury risk factors in cross-country skiers have been studied using a variety of methodologies.1,5,6,8–11,15–22 Previous injury,6 and a high volume of training5,6 are the only identified risk factors for injury in cross-country skiers.

Consistent reporting of injury incidence is necessary for effective monitoring of injury and injury prevention strategies.23 Two studies11,24 have reported injury incidence for cross-country skiers. In the earlier study of 149 cross-country skiers with an average age of 22.7 years11 the injury incidence rate was 2.10 injuries per 1,000 exposure hours. In the later study of 74 cross-country skiers with an average age of 17 years24 the injury incidence rate was 2.5 injuries per 1,000 exposure hours. The injury studies thus far have surveyed injury and training using one-time retrospective 12 month surveys5,6,16,25,26 or one-time in person interviews,19 or more recently weekly surveys for 12 months.24 Retrospective approaches can be significantly affected by personal recall bias,11,27 and thus, the results should be interpreted with caution. The current study used monthly injury and training surveys for 12 consecutive months to replicate the methodology used in a previous injury incidence study of elite rowers13 with the aim to minimize the burden of weekly reporting while continuing to reduce the recall bias of the one-time 12 month methodologies. The monthly reporting should improve the accuracy of injury and training reporting, and thus result in more accurate measures of injury incidence and contribute to improved understanding of injury risk.

There may be other risk factors for injury in cross-country skiers related perhaps to movement habits or muscular conditioning that have yet to be detected. One way to examine the potential contribution of habitual movement patterns to cross-country skier injury is to use movement screening. Historically, pre-season screening of athletes, with comparison to established norms, involved impairment level tests for joint range of motion, muscle length, and strength to determine which athletes required further evaluation to optimise their physical functioning. Investigating the relationship between injury and impairment level muscular measures such as hamstring length and trunk muscle endurance ratio contributes to baseline movement pattern data in cross-country skiers.

The Movement Competency Screen (MCS) is a reliable whole body movement screen28–33 that can effectively evaluate whole body movement in military recruits,33 dancers,31,34 netball players,32 and rowers.29 A low MCS score has been correlated with elevated injury risk in high performance dancers,31,34 but not in rowers29 or military recruits.33 This is the first study to use the MCS to evaluate movement competency in cross-country skiers.

The aims of this study were to determine the injury incidence and any risk factors for injury in elite adult north American cross-country skiers. A prospective 12-month study was undertaken to collect monthly injury and training load data. Secondarily, injury reports were compared with selected early season physical assessment measures, as well as history of injury, length of career in cross-country skiing, and training hours.

Finally, the injury incidence data from the monthly surveys were used to determine whether there are differences between the mean injury incidence rates during the ski-season and the non-ski-season, for traumatic and non-traumatic injuries, and for injuries by anatomic location.

METHODS

Study design

A prospective longitudinal cohort study was conducted for 12 consecutive months. Ethical approval was granted by the Institutional Review Boards of the participating institutions. All participants received verbal and written study information and then gave written informed consent. This study replicated the methodology used in previous studies exploring injury risk factors for rowers29 and dancers.34 The ski-season for this study was defined as December 2014 to April 2015 based on reported dates of on-snow training, and ski races.

Participants & the enrolment process

A convenience sample of 71 professional or University NCAA (National Collegiate Athletic Association) level cross-country skiers enrolled in this study (35 men, 36 women, age 18-27 years, mean age 20.66 years).

Participant enrolment occurred on a rolling schedule between August and December of 2014 at ski team meetings. After signing the consent form and receiving a unique identifier, participants completed an electronic intake demographic survey (Appendix 1). In addition, they completed the first of 12 electronic monthly injury and training surveys (Appendix 2). Finally, the participant's performance on the chosen physical assessment tests (MCS, hamstring muscle length, and trunk flexor and extensor muscle endurance) were recorded.

Injury Surveillance

To maintain blinding, the REDCap (Research Electronic Data Capture)35 electronic data capture software, was used to create, administer, and manage all of the submitted surveys. The survey questions were developed by reviewing the current literature, by discussing cross-country skier injury with the coaches of potential study participants, and by reviewing the survey used in a previous injury incidence study conducted on elite rowers.29

Consistent with the study design and methodology being replicated,29 an injury was defined as any episode of pain, ache, or discomfort that lasted for longer than one week (seven days), or an injury that caused the athlete to miss or modify any training or racing sessions.29 Participants selected the location of their injury from a list of body parts or regions, and a free text box was available to further describe their injuries. Prior to data analysis the body parts were grouped into regions: lower extremity, upper extremity, and trunk. The intake survey was self-reported using the electronic REDCap software (Appendix 1), and included: age, gender, handedness, weight, height, level of competition, type of skiing, age when the participant began cross-country skiing, previous injuries, current injuries, current medications, and occupation. All intake data were retained for initial descriptive analysis.

Using the e-mail address provided by the participants when enrolling, the REDCap software distributed the monthly self-report survey (Appendix 2) to each participant for 12 consecutive months. The REDCap software was programmed to send weekly reminder e-mails to each participant until their survey was completed or the next monthly survey was delivered. The monthly variables of interest were any changes in medications or occupation since the last survey, amount and type of training, amount and type of racing/competing, type and severity of new or ongoing injury, effect of injury on training and/or racing. To ensure results spanned a full calendar year, the data from participants who responded to nine or more of the surveys (18 men, 23 women) were retained for longitudinal analysis of training, racing, and injury reports.

Total monthly training load for each participant for each training activity was calculated from the number of training sessions as well as the duration (in minutes) of each training session. All variables from the intake survey, intake physical measurements, and the monthly surveys were considered to be potential risk factors for predicting new injury. Statistical analyses and clinical considerations were used to determine which variables were relevant risk factors.

Physical assessment testing

A set of physical measurement tests were performed at the start of the data collection period to determine the relationship between these physical measures and the report of a new injury during the 12-month study period. These tests included the Movement Competency Screen (MCS),28,32 the Active Straight Leg Raise (ASLR) hamstring length test left and right,36 the Biering-Sorensen back extensor muscle endurance test,37 and the McGill trunk flexor muscle endurance test.38 The tests were selected to replicate methodology used to study rowers29 and dancers,34 and thus allowed comparison of results among different sports. The standardised parameters for performing and recording these tests have been described previously.28,32,36–38 The ASLR test was modified according to previous research,36 and as such, the hip flexion range of motion was recorded for each leg as an indication of the length of the hamstring muscles. The McGill trunk flexor muscle endurance test time was standardized to 360 seconds, and the Biering-Sorenson trunk extensor muscle endurance time was standardized to 180 seconds. The trunk muscle endurance ratio was calculated as flexor to extensor ratio using the standardized scores.38

Statistical Analyses

The characteristics of the study population were established using descriptive analysis of intake and monthly data, injury prevalence, injury incidence, and training/exposure hours. Two sample t-tests were used to explore gender differences in the demographic and intake physical measurement data. Injuries were grouped into 3 body regions: 1) the lower extremity including the hip, thigh, knee, lower leg, foot and ankle; 2) the upper extremity including the shoulder, upper arm, elbow, forearm, wrist and hand; and 3) the trunk including the head, neck, upper back, low back and pelvis. Injury incidence was calculated as the number of new injury reports per participant, per 1,000 hours of training/exposure.

Training hours were calculated per participant by multiplying the number of training sessions by the average duration of each training session for that activity per week, and per month. Total training/exposure hours, or specific activity hours, could then be summed per participant. To determine if new injury could be predicted from any demographic data, intake physical measures, or, monthly injury, training and racing data, a regression model was developed. The regression model included age, BMI, gender, number of years of competition, age the participant began competing, past injury report, trunk muscle endurance ratio, hamstring length, MCS score, average monthly time training, average monthly time running, average monthly time roller skiing, average monthly time cycling, average monthly time skiing, and average monthly time lifting weights. The final regression model included variables with statistically significant correlation coefficients, or that were considered clinically important to new injuries based on clinical experience, the review of current literature, or had been included by investigators who used a similar research methodology.29,34 The variables represented in the final model were past injury, total training hours, running hours, and MCS score. Spearman's correlation was used to determine if new injury during the 12 months of the study was correlated with past injury, total training hours, running hours, or MCS score. A t-test was used to determine if mean injury incidence per participant per 1,000 hours of training was significantly different between the competitive ski-season and the non-ski-season, and acute/traumatic and non-traumatic/overuse injuries. While this study was not powered to detect differences in mean injury incidence per participant per 1,000 training hours among body regions, we explored these differences in order to better describe the characteristics of the participants and when analysing injury incidence in relation to the current literature.

RESULTS

Participants

Due to participant availability and enrolment time constraints, a total of 71 participants enrolled in the study. Forty-one participants (57.7%) completed sufficient monthly surveys (9/12) to be included in the injury incidence and correlation analysis. Men and women in this study were not significantly different except for the men being significantly taller and heavier than the women (Table 1). From all the prior injuries noted by participants, lower extremity injuries were reported more frequently than other regions (Table 1).

Table 1.

Participant demographics at enrolment

Variable Men (SD) n = 35 Women (SD) n = 36 p-value
Mean age (years) 21.15 (2.48) 20.18 (1.92) 0.07
Mean height (cm) 177.87 (6.82) 168.46 (6.69)  < 0.05*
Mean weight (kg) 71.14 (7.26) 62.32 (7.06)  < 0.05*
Mean BMI (kg/m2) 22.45 (1.41) 21.93 (1.74) 0.17
Mean age began competitive skiing (years) 11.6 (2.90) 12.0 (2.74) 0.55
Mean years skiing 11.4 (5.04) 11.1 (5.45) 0.85
Number of participants with past history of injury 80% 28/35 80.6% 29/36 0.95
Number of participants reporting previous trunk injury 12 7 0.50
Number of participants reporting previous upper extremity injury 10 9 0.50
Number of participants reporting previous lower extremity injury 21 23 0.50
Number of participants injured at time of enrolment 25.7% 9/35 27.8% 10/36 0.84
Open in a new tab

Note: Previous injury by body region is qualitative data from free text survey question “list previous injuries”. Many participants reported multiple previous injuries. Only previous injuries from major body regions are reported here.

Physical assessment test results

Observed MCS scores ranged from 10/21 to 18/21, with a median score of 13/21. Men had significantly higher MCS scores than women (p < .05) (Table 2). Men also scored higher than women on the individual MCS movements of the push-up, and the twist (of the two-part lunge and twist movement). There were no significant gender differences between the mean scores on the ASLR test, the Biering-Sorenson test, the McGill test, or the trunk muscle endurance ratio (p > 0.05) (Table 2).

Table 2.

MCS and muscular measures scores, all participants at enrolment.

MCS and muscular scores Men (SD) Women (SD) p-value
N 35 36
MCS Score 14.43 (1.46) 12.58 (1.40)  < .05*
Right hamstring length (degrees) 73.69 (12.27) 75.53 (10.99) .51
Left hamstring length (degrees) 72.66 (10.82) 76.11 (11.35) .19
McGill trunk flexor endurance time (seconds) 227.00 (107.24) 226.42 (112.18) .98
Biering-Sorenson trunk extensor endurance time (seconds) 123.91 (28.11) 133.51 (33.95) .13
Trunk muscle endurance ratio (flexor/extensor) 0.97 (0.55) 0.85 (0.40) .25
Open in a new tab

MCS = Movement Competency Screen; SD = standard deviation. * = significant at p = .05.

New injury characteristics

In total, 90 new injuries were reported by 27 of the 41 participants who completed the study over the 12 month period (13 men, 14 women). More injuries were reported for the lower extremity (58) than the upper extremity (19) or the trunk (12) (Figure 1). Men reported a near even distribution of upper and lower extremity injuries, whereas women reported more lower than upper extremity injuries (Figure 1).

Figure 1.

Figure 1.

Open in a new tab

Number of new injuries by anatomic location and gender (numbers in bars show number of injuries by gender).

Ankle and foot injuries accounted for 39.7% of all lower extremity injuries and 25.6% of all new injuries (Figure 1). Shoulder injuries accounted for 36.8% of all upper extremity injuries and 7.8% of all new injuries (Figure 1). Twenty-six of the new injuries were classified as traumatic. Non-ski-season new injuries numbered 67 compared to 23 during the ski-season.

Injury Incidence

The mean injury incidence was 3.81 new injuries per participant per 1,000 hours of training. There was a significantly higher incidence of lower extremity injuries (2.13) than other body regions (upper extremity 0.46, trunk 0.22) (p < .05). There was a significantly higher incidence of non-traumatic injuries (2.76) than traumatic injuries (1.05) (p < .05). There was a higher incidence of injuries during the non-ski-season (5.25), but it was not statistically different from injury incidence during the ski-season (2.27) (p = .07) (Table 3).

Table 3.

Mean injury incidence per participant per 1,000 hours training.

Type of injury Injury incidence Type of injury Injury incidence p-value
All injuries 3.81
LE 2.13 UE 0.46  < .05*
LE 2.13 Trunk 0.22  < .05*
Overuse/Non-traumatic 2.76 Acute/traumatic 1.05  < .05*
Off season 5.25 Ski season 2.27 .07
Open in a new tab

LE = lower extremity; UE = upper extremity

* = significant at p < .05

Training Load

Participants in the current study recorded mean training/exposure hours of 52–56 hours per participant per month, for approximately 600 hours per participant per year.

Correlations with new injury

New injury was positively correlated with previous injury (p = .04), but not with any of the remaining variables: career length (p = .54), training hours (p = .30), running hours (p = .30), roller ski training hours (p = .93), trunk muscle endurance (p = .97), hamstring length (right side p = .17, left side p = .36), or MCS score (p = .63).

Prediction of risk factors for sustaining a new injury

A generalized linear model (GLM) was used to determine the relationship between a new injury and possible risk factors. Possible risk factors included age, gender, BMI, years skiing, age began skiing, past injury, MCS score, hamstring length, trunk muscle endurance ratio, monthly total training hours, monthly running hours, monthly cycling hours, monthly roller ski hours, and monthly ski hours. The best fit GLM included past injury, total training hours, running hours, and MCS score. These variables were included for their statistical significance, their clinical relevance to new injuries, or if they had been included in models from previous similar studies of rowers29 and dancers.34 Past injury was a significant predictor of new injury when accounting for training hours, running hours, and MCS score in the model (p < .05) (Table 4).

Table 4.

Generalized Linear Model.

Variable B Std error 95% Confidence interval Wald Chi-Square Sig.
Lower Upper
Total training time 0.00 0.01 -0.02 0.03 0.01 0.92
Total run time 0.08 0.12 -0.16 0.31 0.40 0.53
Past injuries 1.34 0.67 0.03 2.66 4.02  < 0.05*
MCS Score -0.01 0.14 -0.28 0.27 0.00 0.97
Open in a new tab

Note: * = significant at .05 level

DISCUSSION

This cohort of elite adult north American cross-country skiers have an injury incidence of 3.18 injuries per participant per 1,000 exposure hours. New injury was positively correlated with a history of injury (p = .04), but not with any other study variables (p > .05). The new injuries reported in this study occurred during non-skiing activities. Taken together these results highlight the importance of injury prevention in all activities that elite endurance athletes participate in not just their chosen competitive sport.

Participants

The demographics of skiers in this study were similar to those published in other cross-country ski injury incidence studies of adults.6,11,39 The rate of completion in this study was 57.7%, a minimum of 9 out of the 12 monthly self-report surveys were needed for a participant to be considered complete. Eighty percent (80%) of the skiers in this study had a history of injury, with lower extremity injuries being the most common, consistent with prior studies.6,8,11,16,20,24,25,40 Significance levels in the statistical analyses and their interpretations presented below should be considered in conjunction with the knowledge that 57.7% of participants completed the study.

Overall injury incidence and prevalence, and training load

The overall injury incidence rate in this study was 3.81 injuries per participant per 1,000 exposure hours. This is a comparable but slightly higher rate than previously reported rates of 2.10 – 2.79 injuries per 1,000 exposure hours for cross-country skiers, swimmers, long distance runners, and dancers.11,24,31 However other recently published studies have demonstrated that cross-country skiers have the lowest rates of injury relative to other endurance sports (long distance running, swimming, cycling, orienteering), and winter Olympic athletes.8,9,11,24,41 The discrepancy in incidence rate between this study and the two previously published cross-country ski incidence rate studies11,24 may be explained by the difference in participant age, and the large difference in sample sizes (41 participants aged 18-27 years in the current study, versus 74 aged 16-19 years,24 and 149 aged 18-28 years11), and perhaps by the differences in sampling frequency of this study (monthly for 12 months, versus weekly for 12 months,24 versus one retrospective survey for the 12 previous months11) leading to different reporting accuracy of injury frequency and training hours. The higher training loads in this study (600 hours per year, versus 509 hours per year,24 versus 552 hours per year11) may have also influenced the injury incidence.

Although not statistically significant, more injuries occurred during the non-ski-season when subjects were running and roller skiing, not snow skiing. The observation of injuries occurring in an exercise mode other than the athlete's specific competitive sport has been reported before,11 and may be an important factor to consider in future injury prevention strategies. Movement quality and sport-specific skill in cross-training activities may be as important as it is in the athlete's competitive sport when evaluating injury cause and injury prevention.

Injuries by gender & anatomic region

Women in this study reported a greater percentage of lower extremity injuries (90.5% of all their new injuries), than men (42.6% of all their new injuries), a pattern that is consistent with other endurance sports.50 Across genders, the incidence rate for lower extremity injuries was higher than any other body part, consistent with the previously reported cross-country skier injury incidence patterns,6,24 and injury prevalence data for endurance sports.8,20 The high prevalence of foot and ankle injuries in this study (40%) was consistent with the reported mechanisms of injury (fall or trip), the participant's sporting activities (running, skiing, roller skiing), and previously reported studies.11,20,24 Similar to other studies, acute foot and ankle injuries reported during this study occurred more often while running than skiing.11,24 To reduce injuries in elite cross-country skiers, emphasis must be placed on safety, movement quality, technique, and training programs across all activities, not just their competitive sport of cross country skiing. Given the high foot and ankle injury rates, even moderate reductions to foot and ankle injuries would significantly reduce injury prevalence and incidence in cross-country skiers. Number of injuries reported in this study for the lower back, trunk and upper extremity, were too low to analyse; however, these areas would be worth exploring in future studies.

Predictors & risk factors of new injuries

New injuries were positively correlated with previous injury which is consistent with the current endurance sport literature.13,24,26,42 Considering this result, injury reduction strategies in endurance sports should focus on preventing the initial injury, as well as monitoring those athletes who have been injured previously and are therefore at an elevated risk for subsequent injury. Contrary to previous reports,5,13,43,44 training/exposure time did not correlate with new injury in this study. In the current study, mean training/exposure hours reported were approximately 600 hours per participant per year (52-56 hours per participant per month). These exposure hours fell below the 700 hours per year previously reported to be to be a risk factor for any new injury in cross-country skiers, swimmers and distance runners.6

Past injury report was a significant predictor of new injury in the risk factor analysis that included overall training time, run time, and MCS score. Due to insignificant statistical relationships between new injury and the remaining independent variables, little else can be concluded from the risk factor analysis.

Study limitations

Retrospective survey studies have two main categories of recall bias: the loss of information due to failure to recall the event (memory decay), and the tendency to remember events in the past as if they occurred closer to the present than they actually did (telescoping effect).11 It has been suggested that regardless of sampling frequency the retrospective period of a survey should not extend beyond seven days in order to minimise the risk of recall bias.27 Therefore, although the monthly survey frequency aimed to reduce the effects of recall bias, the 30 day period between surveys could still be considered a limitation.

A larger study population of cross-country skiers with the monthly survey frequency that lasted for, at least, two competitive seasons and the intervening off-season would increase the statistical power of the study. As it is, the statistical analyses should be interpreted with care as the low number of participants and the low number of new injuries reported have impacted the statistical outcomes.

Future research

Investigating relationships between injuries to specific parts of the body and then subsequent injuries to those same areas, especially areas of overuse sustained by cross-country skiers could improve targeted rehabilitation strategies.

Recording whether participants met their pre-determined seasonal performance goals, and exploring the relationship between performance and seasonal injury, training, and movement patterns may be a more engaging method of surveillance for athletes and coaches, while also providing researchers, coaching, and medical staff with practical information for subsequent individual and group injury prevention strategies.

Prior to enrolling in this study, 80% of the elite cross-country skiers, all under the age of 22, had sustained at least one injury. Focusing on technique and movement quality in the developing athlete especially in cross training activities such as running and roller skiing could have a large impact on reducing injuries in the future. Identifying existing problems (such as side differences in strength and balance) and assessing the junior athlete's movement competence to ensure it is appropriate for their sport may be important in preventing the first sport or training related injury.

CONCLUSIONS

In this year-long monthly survey of injuries and training load in elite adult north American cross-country skiers, new injuries were positively correlated with previous injury, but did not correlate with training load, or any physical assessment tests, including the MCS score in this first report of MCS scores in elite cross-country skiers. Lower extremity, and non-traumatic/overuse injuries had the highest incidence rates. The lower extremity injuries occurred during non-skiing sports used as fitness training when snow skiing was not possible. Coaching staff should ensure each athlete possesses skill proficiency in all sports/activities included in their training program to reduce injury risk. Coaching and medical staff should also consider each skier's lifetime injury history, when determining which athletes would benefit from further medical team assessments prior to beginning a training programme. Finally, targeted training of developing skiers could be important in long-term injury reduction – an area for future research.

Appendix 1. Intake Questionnaire

graphic file with name ijspt-14-237-A001.jpg

graphic file with name ijspt-14-237-A002.jpg

graphic file with name ijspt-14-237-A003.jpg

graphic file with name ijspt-14-237-A004.jpg

graphic file with name ijspt-14-237-A005.jpg

Appendix 2. Monthly Questionnaire

graphic file with name ijspt-14-237-A006.jpg

graphic file with name ijspt-14-237-A007.jpg

graphic file with name ijspt-14-237-A008.jpg

graphic file with name ijspt-14-237-A009.jpg

graphic file with name ijspt-14-237-A010.jpg

graphic file with name ijspt-14-237-A011.jpg

graphic file with name ijspt-14-237-A012.jpg

graphic file with name ijspt-14-237-A013.jpg

graphic file with name ijspt-14-237-A014.jpg

REFERENCES

  • 1.Renstrom P Johnson RJ. Cross-country skiing injuries and biomechanics. Sports Med. 1989;8(6):346-370. [DOI] [PubMed] [Google Scholar]
  • 2.Sandbakk Ø Holmberg H-C. A reappraisal of success factors for Olympic cross-country skiing. Int J. 2013. [DOI] [PubMed] [Google Scholar]
  • 3.Holmberg HC. The elite cross-country skier provides unique insights into human exercise physiology. Scand J Med Sci Sport. 2015;25:100-109. [DOI] [PubMed] [Google Scholar]
  • 4.Stöggl T Mueller E Ainegren M Holmberg HC. General strength and kinetics: fundamental to sprinting faster in cross country skiing? Scand J Med Sci Sport. 2011;21(6):791-803. [DOI] [PubMed] [Google Scholar]
  • 5.Bahr R Andersen SO Loken S Fossan B Hansen T Holme I. Low back pain among endurance athletes with and without specific back loading--a cross-sectional survey of cross-country skiers, rowers, orienteerers, and nonathletic controls. Spine. 2004;29(4):449-454. [DOI] [PubMed] [Google Scholar]
  • 6.Ristolainen L Kettunen JA Waller B Heinonen A Kujala UM. Training-related risk factors in the etiology of overuse injuries in endurance sports. J Sports Med Phys Fit. 2014;54(1):78-87. [PubMed] [Google Scholar]
  • 7.Clarsen B Krosshaug T Bahr R. Overuse Injuries in Professional Road Cyclists. Am J Sports Med. 2010;38(12):2494-2501. [DOI] [PubMed] [Google Scholar]
  • 8.Clarsen B Bahr R Heymans MW, et al. The prevalence and impact of overuse injuries in five Norwegian sports: Application of a new surveillance method. Scand J Med Sci Sport. 2014:25:323-330. [DOI] [PubMed] [Google Scholar]
  • 9.Engebretsen L Steffen K Alonso JM, et al. Sports injuries and illnesses during the Winter Olympic Games 2010. Br J Sports Med. 2010;44(11):772-780. [DOI] [PubMed] [Google Scholar]
  • 10.Soligard T Steffen K Palmer-Green D, et al. Sports injuries and illnesses in the Sochi 2014 Olympic Winter Games. Br J Sports Med. 2015;49(7):441-447. [DOI] [PubMed] [Google Scholar]
  • 11.Ristolainen L Heinonen A Turunen H et al. Type of sport is related to injury profile: A study on cross country skiers, swimmers, long-distance runners and soccer players. A retrospective 12-month study. Scand J Med Sci Sport. 2010;20(3):384-393. [DOI] [PubMed] [Google Scholar]
  • 12.Kerr ZY Baugh CM Hibberd EE Snook EM Hayden R Dompier TP. Epidemiology of National Collegiate Athletic Association men's and women's swimming and diving injuries from 2009/2010 to 2013/2014. Br J Sports Med. 2015;49(7):465-471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Newlands C Reid D Parmar P. The prevalence, incidence and severity of low back pain among international-level rowers. Br J Sports Med. 2015. 49(14):951-956. [DOI] [PubMed] [Google Scholar]
  • 14.Marsland F Lyons K Anson J Waddington G Macintosh C Chapman D. Identification of Cross-Country Skiing Movement Patterns Using Micro-Sensors. Sensors. 2012;12(4):5047-5066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Bergstrom KA Brandseth K Fretheim S Tvilde K Ekeland A. Back injuries and pain in adolescents attending a ski high school. Knee Surg Sports Traumat Arthrosc. 2004;12:80-85. [DOI] [PubMed] [Google Scholar]
  • 16.Blut D Santer S Carrabre J Manfredini F. Epidemiology of Musculoskeletal Injuries Among Elite Biathletes: A Preliminary Study. Clin J Sports Med. 2010;20(4):322-324. [DOI] [PubMed] [Google Scholar]
  • 17.Boyle JJ Johnson RJ Pope MH. Cross-country ski injuries: a prospective study. Iowa Orthop J. 1981;1:41. [Google Scholar]
  • 18.Butcher JD Brannen SJ. Comparison of injuries in classic and skating Nordic ski techniques. Clin J Sports Med. 1998;8(2):88-91. [DOI] [PubMed] [Google Scholar]
  • 19.Flørenes TW Nordsletten L Heir S Bahr R. Injuries among World Cup ski and snowboard athletes. Scand J Med Sci Sport. 2012;22(1):58-66. [DOI] [PubMed] [Google Scholar]
  • 20.Orava S Jaroma H Hulkko A. Overuse injuries in cross-country skiing. Br J Sports Med. 1985;19(3):158-160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Smith M Matheson GO Meeuwisse WH. Injuries in Cross-Country Skiing. Sports Med. 1996;21(3):239-250. [DOI] [PubMed] [Google Scholar]
  • 22.Ueland Ø B K. Occurrence and trends in ski injuries in Norway. Br J Sports Med 1998;32:299-303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Finch C. A new framework for research leading to sports injury prevention. J Sci Med Sport. 2006;9(1-2):3-9. [DOI] [PubMed] [Google Scholar]
  • 24.von Rosen P Floström F. Frohm A. Heijne A. Injury patterns in adolescent elite endurance athletes participating in running, orienteering, and cross-country skiing. Int J Sports Phys Ther. 2017;12(5). [PMC free article] [PubMed] [Google Scholar]
  • 25.Østerås H Garnæs KK Augestad LB. Prevalence of musculoskeletal disorders among Norwegian female biathlon athletes. Open access journal of sports medicine. 2013;4:71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Foss IS Holme I Bahr R. The Prevalence of Low Back Pain Among Former Elite Cross-Country Skiers, Rowers, Orienteerers, and Nonathletes A 10-Year Cohort Study. Am J Sports Med. 2012;40(11):2610-2616. [DOI] [PubMed] [Google Scholar]
  • 27.Clarsen B Myklebust G Bahr R. Development and validation of a new method for the registration of overuse injuries in sports injury epidemiology: the Oslo Sports Trauma Research Centre (OSTRC) Overuse Injury Questionnaire. Br J Sports Med. 2013;47(8):495-502. [DOI] [PubMed] [Google Scholar]
  • 28.Kritz M. Development, reliability and effectiveness of the Movement Competency Screen (MCS). Doctoral Thesis, Auckland University of Technology. 2012.
  • 29.Newlands C. Low back pain incidence in New Zealand rowers and its relationship with functional movement patterns, Auckland University of Technology; 2013.
  • 30.Dewhirst R Mandara E Ellis D Worth SA Reid DA Henry SM. The Movement Competency Screen in Adolescent Nordic Skiers: A Pilot Study. Orthopaedic Section Poster Presentation OPO1150. 2015.
  • 31.Lee L Reid D Cadwell J Palmer P. Injury incidence, dance exposure and the use of the movement competency screen (MCS) to identify variables associated with injury in full-time pre-professional dancers. Int J Sports Phys Ther. 2017;12(3):352. [PMC free article] [PubMed] [Google Scholar]
  • 32.Reid DA Vanweerd RJ Larmer PJ Kingstone R. The inter and intra rater reliability of the Netball Movement Screening Tool. J Sci Med Sport. 2015;18(3):353-357. [DOI] [PubMed] [Google Scholar]
  • 33.Milbank EJ Peterson DD Henry SM. The Reliability and Predictive Ability of the Movement Competency Screen in a Military Population. Sport J. 2016(8-25-2016). [Google Scholar]
  • 34.Lee L. Injury incidence and the use of the Movement Competency Screen (MCS) to predict injury risk in full-time dance students at the New Zealand School of Dance (NZSD): A prospective cohort study.: Health and Rehabilitation Research Institute, Faculty of Health and Environmental Science, Auckland University of Technology; 2015.
  • 35.Harris PA Taylor R Thielke R Payne J Gonzalez N Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Cook G Burton L Hoogenboom B. Invited Clinical Commetary. Pre-participation Screening: The use of Fundamental Movements as an Assessment of Function - Part 2. N Am J Sports Phys Ther. 2006;1(3). [PMC free article] [PubMed] [Google Scholar]
  • 37.Biering-Sorensen F. Physical measurements as risk indicators for low-back trouble over a one-year period. Spine. 1984;9(2):106-119. [DOI] [PubMed] [Google Scholar]
  • 38.McGill SM Childs A Liebenson C. Endurance times for low back stabilization exercises: clinical targets for testing and training from a normal database. Arch Phys Med Rehabil. 1999;80(8):941-944. [DOI] [PubMed] [Google Scholar]
  • 39.Ristolainen L Heinonen A Waller B Kujala UM Kettunen JA. Gender Differences in Sport Injury Risk and Types of Inju-Ries: A Retrospective Twelve-Month Study on Cross-Country Skiers, Swimmers, Long-Distance Runners and Soccer Players. J Sports Sci Med. 2009;8(3):443-451. [PMC free article] [PubMed] [Google Scholar]
  • 40.Alricsson M Werner S. Self-reported health, physical activity and prevalence of complaints in elite cross-country skiers and matched controls. J Sports Med Phys Fit. 2005;45(4):547-552. [PubMed] [Google Scholar]
  • 41.Ruedl G Schobersberger W Pocecco E, et al. Sport injuries and illnesses during the first Winter Youth Olympic Games 2012 in Innsbruck, Austria. Br J Sports Med. 2012;46(15):1030-1037. [DOI] [PubMed] [Google Scholar]
  • 42.Fulton J Wright K Kelly M, et al. Injury risk is altered by previous injury: a systematic review of the literature and presentation of causative neuromuscular factors. Int J Sports Phys Ther. 2014;9(5):583-595. [PMC free article] [PubMed] [Google Scholar]
  • 43.Van Mechelen W Twisk J Molendijk A Blom B Snel J Kemper HC. Subject-related risk factors for sports injuries: a 1-yr prospective study in young adults. Med Sci Sports Exerc. 1996;28(9):1171-1179. [DOI] [PubMed] [Google Scholar]
  • 44.O’Connor FG Deuster PA Davis J Pappas CG Knapik JJ. Functional movement screening: predicting injuries in officer candidates. Med Sci Sports Exerc. 2011;43(12):2224-2230. [DOI] [PubMed] [Google Scholar]

Articles from International Journal of Sports Physical Therapy are provided here courtesy of North American Sports Medicine Institute

RESOURCES