Procedures for assessing psychological predictors of injuries in circus artists: a pilot prospective study
© Shrier et al.; licensee BioMed Central Ltd. 2014
Received: 26 September 2013
Accepted: 4 June 2014
Published: 11 June 2014
Research on psychological risk factors for injury has focused on stable traits. Our objective was to test the feasibility of a prospective longitudinal study designed to examine labile psychological states as risk factors of injury.
We measured psychological traits at baseline (mood, ways of coping and anxiety), and psychological states every day (1-item questions on anxiety, sleep, fatigue, soreness, self-confidence) before performances in Cirque du Soleil artists of the show “O”. Additional questions were added once per week to better assess anxiety (20-item) and mood. Questionnaires were provided in English, French, Russian and Japanese. Injury and exposure data were extracted from electronic records that are kept as part of routine business practices.
The 43.9% (36/82) recruitment rate was more than expected. Most artists completed the baseline questionnaires in 15 min, a weekly questionnaire in <2 min and a daily questionnaire in <1 min. We improved the formatting of some questions during the study, and adapted the wording of other questions to improve clarity. There were no dropouts during the entire study, suggesting the questionnaires were appropriate in content and length. Results for sample size calculations depend on the number of artists followed and the minimal important difference in injury rates, but in general, preclude a purely prospective study with daily data collection because of the long follow-up required. However, a prospective nested case-crossover design with data collection bi-weekly and at the time of injury appears feasible.
A prospective study collecting psychological state data from subjects who train and work regularly together is feasible, but sample size calculations suggest that the optimal study design would use prospective nested case-crossover methodology.
KeywordsEpidemiology Injury Psychological risk factors Prospective
Participation in physical activities is associated with an increase in quality of life , enhanced academic performance [2–4], a positive impact on behavior [5–7], better mental health , better physical health [9, 10], and decreased obesity . Despite its benefits, an increase in physical activity is also associated with an increased injury risk. An estimated 20.6 million children are injured each year in the USA , and 559,000 in Canada . Of those receiving medical attention for sports and recreation related injuries in the US, one fifth of schoolchildren and more than one quarter of working adults experience one or more days of lost time from school or work . In addition to these short-term effects, injured athletes may have an increased risk of long-term sequelae such as osteoarthritis with concomitant reduction of physical capacity [15–17]. A better understanding of the causes of sport injuries and their recurrences can therefore have a significant impact on athlete health and associated care costs.
In addition to physical, equipment, or procedural causes of activity-related injuries, psychological risk factors may play an important role. Although early psychological research was often limited to theoretical tests of single variables (e.g., personality) more recent work has utilized a multi-variable stress and sport injury model . Some findings indicate the experience of stressful life events can increase injury rates [18, 19], although its impact varies across sports  and is often moderated by social support and coping styles [20, 21]. Subsequently, other researchers amended the theory and utilized a variety of different scales to assess life events and coping, focusing on either sport or athletes .
Despite this work, a literature review found only “limited scientific knowledge”  while suggesting that situational-dependent emotional states can also influence injury. Although high anxiety may worsen performance and increase the risk of injury, its impact is quite variable among athletes, even among those with similar skills competing in the same event [23, 24]. Studies based on Hanin’s Individual Zones of Optimal Functioning (IZOF)  have found that 25-45% of athletes actually require high anxiety to achieve good or optimal performances, yet to date no prospective research has examined sport injury using this perspective (one study used a retrospective design ).
Unpleasant moods are also associated with increased injury risk [20, 26]. However, these authors utilized a single baseline assessment of mood, often with a considerable lag time between psychological assessment and injury. Assessment of mood at regular intervals would be far more efficacious given the dynamic nature of athletic training load, mood disturbance , and overtraining syndrome [27, 28]. Depression, irrespective of whether it is the consequence of a negative life event (e.g., divorce) or is sport related (e.g., overtraining), would be expected to compromise the ability to concentrate and focus , and therefore increase injury risk .
Other psychological variables associated with injury include bodily self-perceptions (e.g., fatigue) , coping skills and life events , again assessed with only limited longitudinal research. Fawkner and colleagues  assessed daily hassles in 98 athletes weekly over a 13-week season. Injured athletes reported increased hassles in the week prior to injury compared with uninjured athletes, but the analysis grouped participants by outcome and then observed exposures without considering that both exposure and covariates varied over time, a known source of potential bias [34, 35]. Ivarsson and colleagues conducted two studies that included assessing whether the Daily Hassles Questionnaire predicted injury in Swedish Division 4–6 (competitive) and Premier League (professional) soccer players who completed the questionnaire weekly over a three-month period [36, 37]. Unfortunately biases may have been introduced because the analyses could not account for time-varying nature of exposure or other covariates, and did not assess any state constructs aside from the Daily Hassles Questionnaire. In a third study on elite junior soccer players , the same group found differences in a trajectory analysis comparing Daily Hassles and Uplifting events among injured and non-injured subjects.
These initial studies have been informative, but there are important limitations to the methods that were used to address the role of psychological states that are believed to change frequently over time when the outcome is injury. These are explained below in more depth but in brief, analyses that compare scores for injured versus non-injured subjects address the question whether psychological factors differ between these two groups, rather than whether the varying psychological states within an individual affect injury risk. Because psychological states will correlate with psychological traits (and psychological traits are risk factors for injury), the above analyses did not address the question we are posing. If the psychological state is truly a risk factor within an individual, then psychological interventions on the day of competition would be promising to explore. Otherwise, one should focus on longer-term interventions that address the more stable psychological traits that have already been identified as risk factors (although differentiating causal from non-causal risk factors remains to be determined).
Injury research strategies to minimize bias
When time-varying exposures and covariates are considered important, one could use two general approaches to minimize bias. First, one could ask patients to recall how they felt before the injury. The control group could consist of non-injured participants at the time of injury (case–control study, where control participants are sampled at the time a case is diagnosed, and therefore can later be considered cases), or the same participants recalling their “states” a few days earlier (i.e., case-crossover study) . However, these study designs are subject to potential recall biases.
Alternatively in a prospective design one measures psychological variables regularly (e.g. daily or weekly), where the analytical approach could be a longitudinal repeated-measures design (e.g. time-series) or a nested case-crossover analysis. In this approach sample size requirements are dependent on both the frequency of injury events and the correlation of psychological states over time (higher sample sizes if states are relatively stable because there is less information on the changing exposures). However, this option involves logistical challenges associated with frequent collection of psychological data. Some psychological questionnaires may evoke scepticism among athletes, staff and coaches , and there may be variations due to translation , need to minimize time commitment [40, 41], sport context, and participants’ understanding for the need to avoid response distortion (either faking good or bad) . Completing daily questionnaires may result in fatigue, with answers provided in an unvarying or random manner , or drop-out. There are also challenges in managing daily data collection, and transmitting it to a central data management unit in any sport setting. Therefore, it is paramount to establish procedures and practices that facilitate these processes.
As a consequence, the primary objective of this pilot study was to test the feasibility of a prospective study specifically designed to test if psychological states are predictive of injury. More specifically, our objectives were to determine 1) time required to complete psychological questionnaires distributed at baseline (assessing traits), daily (assessing states), and once per week (assessing states including an additional questionnaire to assess mood); 2) optimal distributing and collecting of questionnaires to be completed daily or once per week; 3) questionnaire comprehension and acceptability and; 4) intra-individual correlation for daily psychological states necessary for sample size calculations in a larger longitudinal repeated-measures investigation.
The study population were artists from the show “O” produced by Cirque du Soleil (CDS). These artists come from a variety of athletic backgrounds including swimming, diving, gymnastics and acrosport, as well as performing arts including dancers and musicians. We chose this population because injury records and exposures are stored electronically, and conducting the study appeared feasible. Overall injury rates have been estimated to be 9.7 injuries per 1000 artist-exposures (similar to Men’s NCAA Basketball ), with 4.4% of injuries resulting in missing 15 or more performances (approximately equivalent to 10 days) . We therefore expect these results to be useful in other athletic contexts involving regular psychological assessments for an extended period.
Preliminary study development
Constructs and associated questionnaires used to measure traits and states
STAI-Y2 : A validated 20-item questionnaire.
STAI-Y1 Best : A validated 20-item questionnaire measuring state anxiety relative to the time the artist had their best performance.
Ability to cope, Frustration
Ways of Coping : A validated 66-item questionnaire to identify the thoughts and acts used to cope in a specific stressful event. There are eight sub-scales in the student-sample scoring we used (problem focused, wishful thinking, detachment, seek social support, focusing on the positive, self blame, tension reduction and keeping to oneself). We chose the student sample scoring system because we felt that the problems associated with a production company environment (e.g. changeover of personnel, deadlines, new training) are more closely related to what students experience compared to a sample of stable community workers.
Profile of Mood States (POMS) : A validated 65-item questionnaire that includes subscales for tension, depression, anger, vigour, fatigue and confusion. There is considerable evidence that this global measure of mood disturbance is closely associated with the stress of athletic training  as well as relative success in athletics and other realms of performance with a significant physical component .
Conflicts, Anger, Frustration
Training Distress Scale : A validated 7-item 5-point Likert scale questionnaire assessing specific and general mood states. Items generally come from the Profile of Mood States  Other items related to the construct of anger/frustration (identified by CDS management identified as potentially important) include “bad-tempered” and “peeved”.
STAI-Y1 : A validated 20-item questionnaire measuring state anxiety.
Single-item Likert question .
Single-item 7-point Likert question : This question examines an artist’s confidence about their upcoming performances that day. Variations of this single item are considered valid alternatives to more time consuming assessments of self-confidence , and are associated with anxiety and performance .
Single-item Likert question .
Fatigue: We used a 1-item validated question .
General well-being: We used a 7-item Likert scale question (from Very very good to Very very bad) .
Sleep: 1-item questionnaire about the amount and quality of sleep .
Sickness within the last 24 hours.
Muscle soreness: 7-item Likert scale questions from Very Very Good to Very Very Sore [42, 49] for whole body, legs and arms. The responses to these items have been found to be correlated to both more comprehensive psychological questionnaires , biological markers of stress such as cortisol  and objective measures of athletic performance .
Table 1 illustrates the constructs identified as most likely to be important through the interviews with CDS personnel and a review of the literature, questionnaires chosen from the literature where they existed, and timing/frequency for questionnaire distribution. The constructs included trait and state anxiety, trait ability to cope, trait and state mood, and state confidence, where traits were measured at baseline and states were measured daily. We also included questions related to several state behavioural habits believed to influence injury risk. Our objective was to have artists complete the baseline questionnaires within 15 min, weekly questionnaires within 2 min and daily questionnaires within 1 min. Details of the specific questionnaires are described below.
Approximately one month prior to the study, we approached all CDS artists ≥ 18 years of age working at the show “O” located in Las Vegas, Nevada. During a regular weekly meeting, we gave a brief 10-min presentation explaining the general principles of the study, and distributed consent forms. We emphasized that we could prepare reports for each individual showing how their daily profiles changed over time. In addition, because artists often rely on each other (e.g. catcher and flyer in a trapeze act), we emphasized that the information may be important to the team, even if the individual did not feel it was personally important (which would be analogous in some team sport contexts). Artists were asked to submit signed consent forms within one week if they were interested in participating.
Questionnaire timing and processes
We provided the questionnaires in the four principal languages of the artists: English, French, Japanese and Russian. We used existing translations where available, and used the professional CDS translation service when necessary. At least one artist with conversational English language skills and whose first language was the translated language reviewed all translated questionnaires. Although we recognize that cultural differences exist between populations using these languages that might increase the heterogeneity of responses between participants, this should not affect our ability to reach our primary objectives of determining feasibility, and variance estimates for sample size calculations of a definitive study.
After discussions with the stage manager and artistic directors, baseline questionnaires including State-Trait Anxiety Inventory (STAI) , Ways of Coping [40, 47] and Profile of Mood States (POMS)  were distributed at the time the artist provided informed consent, and were completed at home over the ensuing 2–4 weeks. On each working day, we placed a short “states” questionnaire (see Additional file 1) that included either 7 or 9 items (i.e. 2 additional questions included on the “weekly” questionnaire) at each artist’s make-up station (each had their own station). The items included in these questionnaires came from validated questionnaires where possible and included the constructs conflicts, anxiety, self-confidence, sleep and physical capacity (Table 1). Artists completed the questionnaire prior to the day’s performances, and dropped them into a locked drop-box or handed them to the supervising therapist. To minimize missing data, distributed questionnaires included the date and the artist’s identification number. Completed forms were scanned and emailed to the primary investigator for data entry.
Artists were encouraged to provide feedback throughout the study, with special reference to the time required to complete questionnaires, undesirable questions, logistical problems, workload, and possible duration for a definitive study.
Injury and exposure data
To track injury and exposure data, we used the same methods as our previous studies . In brief, CDS therapists use electronic charting for all injuries and treatments (provided free of charge to artists) through in-house injury tracking software. We classified injuries according to commonly used criteria in the sport injury literature [56–58] and we have used in the past with the same company  and are common in sport medicine epidemiology: Medical Attention (injury reported to a show therapist) and Time-Loss-1 (TL-1) if the injury resulted in at least one missed performance. Anytime an artist misses a performance, it must be documented as either a work-related injury (included in our analyses and for which the artist is covered by health insurance), or a personal health condition (excluded from our analyses). Exposure data were obtained from in-house software that keeps track of when artists are performing.
As a pilot study designed primarily to assess feasibility, we provide descriptive statistics only. For continuous variables, we report the median and interquartile region (IQR) because the data were skewed, and we report percent for categorical variables. In addition, we report the direct feedback elicited during weekly meetings. Our sample size was determined by the number of artists within the show and was not modifiable. For practical considerations due to vacation time, we used an 8-week period that began one week after a vacation and ended within two weeks of the next vacation.
To calculate sample sizes for the definitive study, we used linear mixed models and generalized estimating equations (GEE) to account for the intra-participant correlations for the psychological variables over days (stability of states across time), and took a precision-based approach for the estimate via simulations based on psychological daily data that corresponded to our observed data. The observed injury rates (shown in results) were much less than previous studies in the general CDS population. Given the short time frame of the pilot study, we felt the results from our previous study would provide a more reliable estimate. We estimated a baseline TL-1 injury rate of 1.6 injuries per 1000 artist-exposures for the company , and a minimal important difference (MID) of 25% (i.e. 1.2 injuries per 1000 artist-exposures). We calculated sample sizes based on at least a 2-category change in an ordinal scale (all daily questions used essentially ordinal data). We varied the number of artists (between 180–1000) and the number of observation days (40–250) to explore different assumptions (always setting alpha to 0.05, 2-sided). All simulations and calculations were conducted in the open-software R  with the lme4  and geepack  libraries. In addition, we estimated sample size for a case-crossover study using a matched case–control design using free open source statistical software  and the following parameter values: 2-sided alpha = 0.05, power = 0.8, 10:1 ratio of controls to cases, risk of exposure (categorized dichotomously) in controls at 0.3, and an MID of 25% and 50% increased risk of exposure in cases.
Mean (sd) for each of the psychological questionnaires administered at baseline*
Ways of coping
Seek social support
Focusing on the positive
Keep to self
Distributing and collecting questionnaires
To facilitate the process, identification numbers on questionnaires were numbered in the same order as the participating artists’ make-up tables, but were not sequential (i.e. two proximal artists might be 63 and 66) in order to enhance confidentiality. Distributing the questionnaires required ~10 min and was usually done ~2 hours before the night’s first performance. In general, feedback from the artists suggested the ability to simply drop completed questionnaires in a locked box located near the door was well received.
Time to complete questionnaires
The baseline questionnaire was completed in the targeted time (<15 min) by more than 60% of artists but required more than 30 min by two artists. We expected the weekly questionnaire to be completed in <2 min and the daily questionnaire to be completed in <1 min, but ~20% of artists required more time. That said, 45-50% of the artists required less than half the expected time. When we cross-correlated the results from the different questionnaires, the two participants requiring >30 min for the baseline questionnaire were not the same participants requiring longer periods for the weekly or daily questionnaires. Of the seven participants requiring >2 min for the weekly questionnaire, the time required for the daily questionnaire was <30 s for two artists, between 31–120 s for three artists, and >2 min for two artists.
Acceptability and comprehension of the questionnaires
There were no questions that artists felt should not be asked. No artist felt the workload onerous, but a couple of artists felt it was bothersome to answer the same questions daily. Of the 26 participants who answered how much longer they would be willing to continue completing the questionnaires if the study was repeated, five responded as long as needed, five responded ~2-3 months, two responded one month, two simply said they would continue for now, three were unsure and nine said they would no longer continue.
Based on artist feedback, we made important changes to the text and format of the daily questionnaire within the first couple of weeks (see Additional file 1). The original versions of questions 5 and 6 were re-worded to clearly indicate they referred to the upcoming performance not yet done. We also added “current role” to question 6 to clearly indicate we were interested in the optimal level of anxiety relevant to their current role at CDS.
Despite our encouragement for feedback, the first question of the daily questionnaire (overall well-being, “How do you feel today?” with 7-point Likert response from Very very good to Very very bad) was left unanswered on 68/1297 collected questionnaires; several artists provided feedback saying it was difficult to give an “overall” assessment. The question regarding sleeping was left unanswered 42/1297 times, likely due to questionnaire formatting because we did not receive any comments. We believe adding a dotted line from the question to the answer choices might draw the artists’ eyes to the question-answer group.
Injuries during pilot study
Over 2660 artist-performances, there were a total of nine Medical Attention injuries (3.4 injuries per 1000 artist-performances) and three TL-1 injuries 1.1 TL-1 injuries per 1000 artist-performances.
Sample size calculations
Intra-participant correlations were higher than expected, and three artists had no variability within the study for some psychological factors. With the low TL-1 injury rate and low variability, sample size calculations for both the linear mixed and GEE models were very high, with the linear mixed model predictably showing slightly less power than the GEE results. For example, using 180 artists measured over 100 days would provide only 13% power under a GEE model. Increasing the number of artists to 1000 only increases power to 30%. Increasing the number of days of observation increases power as well, but following 750 artists for 250 days yields only 30% power, and following 1000 artists for 250 days yields only 61% power.
Estimated Intra-class correlations (ICC) 1 and calculated sample sizes 2 for variables measured daily over the entire study
Estimated sample size2
Ill past 24 hours
Whole body soreness
If instead one were interested in medical attention injuries, the estimated injury rate from previous studies is ~10 per 1000 performances. In this context, we would need to follow 210 artists for ~10-16 months (82,800 to 130,1000 artist performances) for an increased risk of 25%, but only ~ 3–5 months (24,200 to 38,900 artist performances) for an increased risk of 50%. Increasing the number of artists through improved recruitment per show, or by including additional shows would further decrease the amount of time required to complete the study.
This study examined procedures and methods to be used in a larger prospective study investigating the role of psychological states on injury. It required participants to complete psychological questionnaires of varying length on a daily basis and the 45% recruitment yield was higher than anticipated. There were also no dropouts among the artists who agreed to participate, indicating they found the workload and questionnaires to be generally acceptable, with the noted exception of providing an overall assessment of well-being.
To maximize acceptability and participation by the artists and their support staff, we used a participatory research approach . We sought input from the stakeholder’s key personnel in both the identification of the theoretical model and selection of psychological measures. We used an iterative process to ensure we captured all the salient features, and to prioritize the traits and states of most interest. To minimize dropouts, we targeted specific questionnaire completion times based on our previous experience in other similar studies, and on the opinions of the stakeholder. During the study, we altered the formatting and wording of some questions based on participants’ feedback.
In general, the majority of artists completed the questionnaires within the target time and we obtained data on state anxiety, mood, self-confidence, and physical factors that will be presented in other reports. From informal conversations, some artists were more sensitive to nuances within their own psychological states and required more time to distinguish among the various choices in the questionnaires. For interval validity, the artist should be consistent in her/his approach throughout the course of the study. We would expect the acceptable time to complete questionnaires in other activities or sports to be generally similar, but there may be important differences based on the participating group’s culture, and research requirements. Therefore, we encourage investigators using protocols that require repeated psychological assessments to engage stakeholders to build support and determine acceptable questionnaire lengths, and other important insights. We chose to prioritize our data on the assessment of labile states, moods and feelings rather than on distal events (e.g. conflict with management, which is a cause of mood). We did not include self-efficacy [64, 65] because the analyses are expected to be confounded by ability, and therefore any interpretation would be questionable even if it also affects injury risk itself. Nor could we explore an artist’s body awareness (common in dancers), and nutrition habits. Although we have data on sleep patterns, CDS artists work at night and some regularly (or irregularly) take naps; these interim periods of sleep could theoretically reduce the effect of a poor night’s sleep.
Sample size calculation
Sample size requirements for a definitive prospective study on time loss injuries is prohibitively large, but using an MID of 50% increased risk, one could use the case-crossover design with prospectively collected control data to investigate the role of psychological states by following 210 artists for ~2-2.5 years for TL-1 injuries, and for ~3-5 months for medical attention injuries. These large sample size requirements (both in number of participants, and duration of study which could result in subject fatigue) represent an important challenge for any study investigating the role of psychological states and injury. Sample size requirements would be reduced if the injury rate is higher than 10 injuries per 1000 exposures, the MID is higher than 50%, or the correlation of states over time is lower than in our study.
Our very large sample size calculations appear to conflict with previous prospective studies showing statistically significant results for psychological factors as risk factors. However, as previously mentioned, there are important differences between the studies. First, the TL-1 injury rate in previous data from our artists was 1.5 injuries per 1000 artist-performances. The injury rates estimated from the various published studies (assuming 6 exposures per week per athlete) were approximately 5.1 injuries per 1000 athlete-exposures (games or practices) , 6.9 injuries per 1000 athlete-exposures , 11.1 injuries per 1000 athlete-exposures , and 4.9 injuries per 1000 athlete-exposures . The much higher injury rate in elite soccer compared to circus artists in our data suggests sample size calculations would be closer to our calculated Medical Attention injury definition. Second, our analyses address the question whether certain psychological states are risk factors for injury among participants who get injured (it is not possible to measure the effect in those that never get injured). Other published studies on psychological states [33, 36–38] addressed whether psychological factors distinguish between athletes who get injured and athletes who do not get injured. The distinction is important because if the psychological state is important in those that get injured, interventions for the day of competition or training are promising for injury prevention. However, if the psychological state is not important in those that get injured and is just a marker for a psychological trait, then longer-term interventions must be designed to change the more stable psychological traits (if these are indeed causal factors rather than markers for true causal risk factors). Third, the previous studies compared injured to non-injured subjects, which is a case–control analysis. Because they did not use incidence-density sampling, the results may overestimate the effect if adverse psychological states decrease during the study. Finally, previous studies have focused on the Daily Hassles questionnaire (measured weekly) whereas we focused on daily states related to confidence, anxiety and mood. Ivarsson  estimated the ICC for the Daily Hassles questionnaire to be 73% in elite junior soccer players, which is higher than the ICC for the variables we report in Table 3. This suggests the required sample size would be higher rather than lower for questions addressing whether the psychological state affects injury risk in those that get injured.
Although retrospective case-crossover design is possible, the risk of recall bias is high. Another alternative is to use a hybrid approach where one prospectively collects “control” exposure data on psychological states only once or twice per week, and contrast this with psychological state data collected for the “case-event” after the injury occurred. In this study design, some of the cases would happen to occur on days where pre-performance exposure data had already been collected. Thus, one could estimate the magnitude of “case” recall bias by comparing exposures collected retrospectively after the injury with exposure data collected prospectively before the performance. There is research demonstrating athletes can provide accurate retrospective reports on past performances, at least for anxiety and pre-performance moods .
Finally, recent technological advances allow data to be entered directly via smartphones and tablets. Although these methods may appear promising, surveys on electronic devices are sometimes read/interpreted differently from paper-based systems, and response rates may or may not be improved [66–68]. Investigators should evaluate the advantages and disadvantages of all methods within the context of their study.
We found that that the procedures used to implement systematic psychological monitoring of CDS artists were successful with respect to time to complete, distribution, acceptability and comprehension of questionnaires. The same methods could be implemented in other sport contexts where injury and exposures are closely monitored, and participants have their personal reserved space (e.g. in a dressing room) to receive and complete questionnaires. Challenges for recruitment and retention were primarily related to the degree of willingness to repeatedly complete questionnaires. Additional challenges include optimal questionnaire formatting, prioritizing which psychological constructs to study, and the limited number of questions due to time constraints. Finally, in the population studied, sample size calculations suggest several years of follow-up would be necessary to measure states that change daily given the injury rates. Sample sizes would be lower if injury rates were higher, or correlations between daily states lower.
- Ekstrand J, Gillquist J: Soccer injuries and their mechanisms: a prospective study. Med Sci Sports Exerc. 1983, 15: 267-270. 10.1249/00005768-198315030-00014.View ArticlePubMedGoogle Scholar
- Dwyer T, Sallis JF, Blizzard L, Lazarus R, Dean K: Relation of academic performance to physical activity and fitness in children. Pediatr Exerc Sci. 2001, 13 (3): 225-237.Google Scholar
- Sallis JF, McKenzie TL, Kolody B, Lewis M, Marshall S, Rosengard P: Effects of health-related physical education on academic achievement: project SPARK. Res Q Exerc Sport. 1999, 70 (2): 127-134. 10.1080/02701367.1999.10608030.View ArticlePubMedGoogle Scholar
- Tremblay MS, Inman JW, Willms JD: The relationship between physical activity, self-esteem and academic achievement in 12-year-old children. Pediatr Exerc Sci. 2000, 12 (3): 312-323.Google Scholar
- Escobedo LG, Marcus SE, Holtzman D, Giovino GA: Sports participation, age at smoking initiation, and the risk of smoking among US high school students. JAMA. 1993, 269 (11): 1391-1395. 10.1001/jama.1993.03500110059035.View ArticlePubMedGoogle Scholar
- Kulig K, Brener ND, McManus T: Sexual activity and substance use among adolescents by category of physical activity plus team sports participation. Arch Pediatr Adolesc Med. 2003, 157 (9): 905-912. 10.1001/archpedi.157.9.905.View ArticlePubMedGoogle Scholar
- Pate RR, Trost SG, Levin S, Dowda M: Sports participation and health-related behaviors among US youth. Arch Pediatr Adolesc Med. 2000, 154 (9): 904-911. 10.1001/archpedi.154.9.904.View ArticlePubMedGoogle Scholar
- Kirkcaldy BD, Shephard RJ, Siefen RG: The relationship between physical activity and self-image and problem behaviour among adolescents. Soc Psychiatry Psychiatr Epidemiol. 2002, 37 (11): 544-550. 10.1007/s00127-002-0554-7.View ArticlePubMedGoogle Scholar
- Janz K: Physical activity and bone development during childhood and adolescence. Implications for the prevention of osteoporosis. Minerva Pediatr. 2002, 54 (2): 93-104.PubMedGoogle Scholar
- Janz KF, Dawson JD, Mahoney LT: Increases in physical fitness during childhood improve cardiovascular health during adolescence: the Muscatine Study. Int J Sports Med. 2002, 23 (Suppl 1): S15-S21.View ArticlePubMedGoogle Scholar
- Hill JO, Leon AS: Physical Activity, Body Weight, and Body fat Distribution. Physical Activity and Cardiovascular Health A National Consensus. Edited by: Leon AS. 1997, Champaign, Il: Human Kinetics, 88-97.Google Scholar
- Danseco ER, Miller TR, Spicer RS: Incidence and costs of 1987–1994 childhood injuries: demographic breakdowns. Pediatrics. 2000, 105 (2): E27-10.1542/peds.105.2.e27.View ArticlePubMedGoogle Scholar
- Pless IB, Millar W: Unintentional Injuries in Childhood: Results from Canadian Health Surveys. Edited by: Publications HC. 2000, Ottawa, Ontario K1A 0K9: Health Canada, 23-68.Google Scholar
- Conn JM, Annest JL, Gilchrist J: Sports and recreation related injury episodes in the US population, 1997–99. Inj Prev. 2003, 9 (2): 117-123. 10.1136/ip.9.2.117.View ArticlePubMedPubMed CentralGoogle Scholar
- Turner AP, Barlow JH, Heathcote-Elliott C: Long term health impact of playing professional football in the United Kingdom. Br J Sports Med. 2000, 34 (5): 332-336. 10.1136/bjsm.34.5.332.View ArticlePubMedPubMed CentralGoogle Scholar
- Roos H, Lindberg H, Gärdsell P, Lohmander LS, Wingstrand H: The prevalence of gonarthrosis and its relation to meniscectomy in former soccer players. Am J Sports Med. 1994, 22 (2): 219-222. 10.1177/036354659402200211.View ArticlePubMedGoogle Scholar
- Lindberg H, Roos H, Gärdsell P: Prevalence of coxarthrosis in former soccer players. Acta Orthop Scand. 1993, 64: 165-167. 10.3109/17453679308994561.View ArticlePubMedGoogle Scholar
- Williams JM, Andersen MB: Psychosocial antecedents of sport injury: review and critique of stress and injury model. J Appl Sport Psych. 1998, 10: 5-25. 10.1080/10413209808406375.View ArticleGoogle Scholar
- Williams JM, Andersen MB: Psychosocial Antecedents of Sports Injury and Interventions for Risk Reduction. Handbook of Sport Psychology. Edited by: Tenenbaum G. 2007, Eklund RC: Wiley, 379-403.Google Scholar
- Junge A: The influence of psychological factors on sports injuries. Review of the literature. Am J Sports Med. 2000, 28 (5 Suppl): S10-S15.View ArticlePubMedGoogle Scholar
- Petrie T: Coping skills, competitive anxiety, and playing status: moderating effects on the life stress injury-relationship. J Sport Exerc Psych. 1993, 15: 261-274.Google Scholar
- Petrie T: Psychological antecedents of athletic injuries: the effects of life stress and social support on female collegiate gymnasts. Behav Med. 1992, 18: 127-138. 10.1080/08964289.1992.9936963.View ArticlePubMedGoogle Scholar
- Hanin YL: Individual Zones of Optimal Functioning (IZOF) Model: Emotion-Performance Relationships in Sport. Emotion in Sports. Edited by: Hanin YL. 2000, Champaign, Il: Human Kinetics, 65-90.Google Scholar
- Hanin YL: Emotion in Sports: An Individualized Approach. Encyclopedia of Applied Psychology. Edited by: Spielberger CD. 2004, Oxford, UK: Elsevier Academic Press, 739-750.View ArticleGoogle Scholar
- Devonport TJ, Lane AM, Hanin YL: Emotional states of athletes prior to performance-induced injury. J Sports Sci Med. 2005, 4: 382-394.PubMedPubMed CentralGoogle Scholar
- Galambos SA, Terry PC, Moyle GM, Locke SA, Lane AM: Psychological predictors of injury among elite athletes. Br J Sports Med. 2005, 39 (6): 351-354. 10.1136/bjsm.2005.018440.View ArticlePubMedPubMed CentralGoogle Scholar
- Raglin JS, Kentta G: A Psychological Approach to Understanding and Preventing Overtraining Syndrome. Handbook of Sports Medicine and Athletic Health. Volume 3. Edited by: Echemendia R, Moorman CTI. 2011, Santa Barbara, CA: Praeger, 93-111.Google Scholar
- Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, Raglin J, Rietjens G, Steinacker J, Urhausen A: Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Med Sci Sports Exerc. 2013, 45 (1): 186-205. 10.1249/MSS.0b013e318279a10a.View ArticlePubMedGoogle Scholar
- Austin MP, Ross M, Murray C, O’Carroll RE, Ebmeier KP, Goodwin GM: Cognitive function in major depression. J Affect Disord. 1992, 25 (1): 21-29. 10.1016/0165-0327(92)90089-O.View ArticlePubMedGoogle Scholar
- Raglin JS: Psychological factors in sport performance: the Mental Health Model revisited. Sports Med. 2001, 31 (12): 875-890. 10.2165/00007256-200131120-00004.View ArticlePubMedGoogle Scholar
- Kentta G, Hassmen P, Raglin JS: Mood state monitoring of training and recovery in elite kayakers. Eur J Sports Sci. 2006, 4: 245-253.View ArticleGoogle Scholar
- Johnson U, Ivarsson A: Psychological predictors of sport injuries among junior soccer players. Scand J Med Sci Sports. 2011, 21: 129-136. 10.1111/j.1600-0838.2009.01057.x.View ArticlePubMedGoogle Scholar
- Fawkner HJ, McMurrary NE, Summers JJ: Athletic injury and minor life events: a prospective study. J Sci Med Sport. 1999, 2 (2): 117-124. 10.1016/S1440-2440(99)80191-1.View ArticlePubMedGoogle Scholar
- Moodie EE, Stephens DA: Marginal Structural Models: unbiased estimation for longitudinal studies. Int J Public Health. 2011, 56 (1): 117-119. 10.1007/s00038-010-0198-4.View ArticlePubMedGoogle Scholar
- Mittleman MA, Maclure M, Robins JM: Control sampling strategies for case-crossover studies: an assessment of relative efficiency. Am J Epidemiol. 1995, 142 (1): 91-98.PubMedGoogle Scholar
- Ivarsson A, Johnson U: Psychological factors as predictors of injuries among senior soccer players. A prospective study. J Sports Sci Med. 2010, 9: 347-352.PubMedPubMed CentralGoogle Scholar
- Ivarsson A, Johnson U, Podlog L: Psychological predictors of injury occurrence: A prospective investigation of professional swedish soccer players. J Sport Rehab. 2013, 22: 19-26.Google Scholar
- Ivarsson A, Johnson U, Lindwall M, Gustafsson H, Altemyr M: Psychosocial stress as a predictor of injury in elite junior soccer: a latent growth curve analysis. J Sci Med Sport. 2013, doi:10.1016/j.jsams.2013.10.242. [Epub ahead of print]Google Scholar
- Berglund B, Safstrom H: Psychological monitoring and modulation of training load of world-class canoeists. Med Sci Sports Exerc. 1994, 26 (8): 1036-1040.View ArticlePubMedGoogle Scholar
- Raglin JS, Hanin YL: Competitive Anxiety. Emotion in Sports. Edited by: Hanin YL. 2000, Champaign, Il: Human Kinetics, 93-111.Google Scholar
- Raglin JS, Morgan WP: Development of a scale for use in monitoring training-induced distress in athletes. Int J Sports Med. 1994, 15 (2): 84-88. 10.1055/s-2007-1021025.View ArticlePubMedGoogle Scholar
- O’Connor PJ, Morgan WP, Raglin JS: Psychobiologic effects of 3 d of increased training in female and male swimmers. Med Sci Sports Exerc. 1991, 23 (9): 1055-1061.View ArticlePubMedGoogle Scholar
- Turner PE, Raglin JS: Variability in precompetition anxiety and performance in college track and field athletes. Med Sci Sports Exerc. 1996, 28 (3): 378-385.PubMedGoogle Scholar
- Hootman JM, Dick R, Agel J: Epidemiology of collegiate injuries for 15 sports: summary and recommendations. J Athl Train. 2007, 42 (2): 311-319.PubMedPubMed CentralGoogle Scholar
- Shrier I, Meeuwisse WH, Matheson GO, Wingfield K, Steele RJ, Prince F, Hanley J, Montanaro M: Injury patterns and injury rates in the circus arts: an analysis of 5 years of data from Cirque du Soleil. Am J Sports Med. 2009, 37 (6): 1143-1149. 10.1177/0363546508331138.View ArticlePubMedGoogle Scholar
- Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA: State-Trait Anxiety Inventory for Adults. 1983, Palo Alto, CA: Consulting Psychologists Press Inc.Google Scholar
- Folkman S, Lazarus RS, Dunkel-Schetter C, DeLongis A, Gruen RJ: Dynamics of a stressful encounter: cognitive appraisal, coping, and encounter outcomes. J Pers Soc Psychol. 1986, 50 (5): 992-1003.View ArticlePubMedGoogle Scholar
- McNair DM, Lorr M, Dropplemann LF: Profile of Mood States Manual. 1992, San Diego, CA: Educational and Testing ServiceGoogle Scholar
- Morgan WP, Costill DL, Flynn MG, Raglin JS, O’Connor PJ: Mood disturbance following increased training in swimmers. Med Sci Sports Exerc. 1988, 20 (4): 408-414. 10.1249/00005768-198808000-00014.View ArticlePubMedGoogle Scholar
- Spielberger CD, Gorsuch RL, Lushene PE, Vagg PR, Jacobs GA: Manual for the State-Trait Anxiety Inventory (Form Y). 1983, Palo Alto, CA: Consulting Psychologist PressGoogle Scholar
- Davey HM, Barratt AL, Butow PN, Deeks JJ: A one-item question with a Likert or Visual Analog Scale adequately measured current anxiety. J Clin Epidemiol. 2007, 60 (4): 356-360. 10.1016/j.jclinepi.2006.07.015.View ArticlePubMedGoogle Scholar
- Lundqvist C, Kentta G, Raglin JS: Directional anxiety responses in elite and subelite young athletes: intensity of anxiety symptoms matters. Scand J Exerc Sci Sports. 2011, 21 (6): 853-862. 10.1111/j.1600-0838.2010.01102.x.View ArticleGoogle Scholar
- Lee C, Bobko P: Self-efficacy beliefs: comparison of five measures. J Appl Psychol. 1994, 79: 364-369.View ArticleGoogle Scholar
- van Hooff ML, Geurts SA, Kompier MA, Taris TW: “How fatigued do you currently feel?” Convergent and discriminant validity of a single-item fatigue measure. J Occup Health. 2007, 49 (3): 224-234. 10.1539/joh.49.224.View ArticlePubMedGoogle Scholar
- Raglin JS, Koceja DM, Stager JM, Harms CA: Mood, neuromuscular function, and performance during training in female swimmers. Med Sci Sports Exerc. 1996, 28 (3): 372-377.PubMedGoogle Scholar
- Fuller CW, Molloy MG, Bagate C, Bahr R, Brooks JHM, Donson H, Kemp SPT, McCrory P, McIntosh AS, Meeuwisse WH, Quarrie KL, Raftery M, Wiley P: Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union. Clin J Sport Med. 2007, 17: 177-181. 10.1097/JSM.0b013e31803220b3.View ArticlePubMedGoogle Scholar
- Dick R, Putukian M, Agel J, Evans TA, Marshall SW: Descriptive epidemiology of collegiate women’s soccer injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2002–2003. J Athl Train. 2007, 42 (2): 278-285.PubMedPubMed CentralGoogle Scholar
- Emery CA, Meeuwisse WH, Hartmann SE: Evaluation of risk factors for injury in adolescent soccer: implementation and validation of an injury surveillance system. Am J Sports Med. 2005, 33 (12): 1882-1891. 10.1177/0363546505279576.View ArticlePubMedGoogle Scholar
- Core Team R: R: A language and environment for statistical computing v. 2.7.1. Vienna, Austria: R Foundation for Statistical Computing; 2007
- Bates D, Maechler M, Bolker B: lme4: Linear Mixed-Effects Models Using S4 Classes. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing. 2011, Vienna, Austria, 0999375-39Google Scholar
- Yan J, Højsgaard S, Halekoh U: Geepack: Generalized Estimating Equation Package. R: A language and environment for statistical computing R Foundation for Statistical Computing. 2011, Vienna, Austria, 10-18Google Scholar
- Dupont WD, Plummer WD: PS power and sample size program available for free on the Internet. Control Clin Trials. 1998, 19: 589-601. 10.1016/S0197-2456(98)00037-3.View ArticlePubMedGoogle Scholar
- Macaulay AC, Commanda LE, Freeman WL, Gibson N, McCabe ML, Robbins CM, Twohig PL: Participatory research maximises community and lay involvement. North American Primary Care Research Group. BMJ. 1999, 319 (7212): 774-778. 10.1136/bmj.319.7212.774.View ArticlePubMedPubMed CentralGoogle Scholar
- Feltz DL, Lirgg CD: Self-Efficacy Beliefs of Athletes, Teams and Coaches. Handbook of Sport Psychology. Edited by: Singer RN, Hausenblas HA, Janelle CM. 2001, New York, NY: John Wiley & Sons, 340-361. 2Google Scholar
- Shrier I, Halle M: Psychological predictors of injuries in circus artists: an exploratory study. Br J Sports Med. 2011, 45: 433-436. 10.1136/bjsm.2009.067751.View ArticlePubMedGoogle Scholar
- Verhagen E, Clarsen B, Bahr R: A peek into the future of sports medicine: the digital revolution has entered our pitch. Br J Sports Med. 2014, 48: 739-740. 10.1136/bjsports-2013-093103.View ArticlePubMedGoogle Scholar
- Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, Riley WT, Shar A, Spring B, Spruijt-Metz D, Hedeker D, Honavar V, Kravitz R, Lefebvre RC, Mohr DC, Murphy SA, Quinn C, Shusterman V, Swendeman D: Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013, 45 (2): 228-236. 10.1016/j.amepre.2013.03.017.View ArticlePubMedPubMed CentralGoogle Scholar
- Ekegren CL, Gabbe BJ, Finch CF: Injury reporting via SMS text messaging in community sport. Inj Prev. 2014, doi: 10.1136/injuryprev-2013-041028. [Epub ahead of print]Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/14/77/prepub
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