Perfectionism and attitudes towards doping in junior athletes

Recent theory and research suggest that perfectionism is a personal factor contributing to athletes’ vulnerability to doping (using banned substances/drugs to enhance sporting performance). So far, however, no study has examined what aspects of perfectionism suggest a vulnerability in junior athletes. Employing a cross-sectional design, this study examined perfectionism and attitudes towards doping in 129 male junior athletes (mean age 17.3 years) differentiating four aspects of perfectionism: perfectionistic strivings, perfectionistic concerns, parental pressure to be perfect, and coach pressure to be perfect. In the bivariate correlations, only parental pressure showed a positive relationship with positive doping attitudes. In a multiple regression analysis controlling for the overlap between the four aspects, perfectionistic strivings additionally showed a negative relationship. Moreover, a structural equation model examining the relationships between all variables suggested that coach pressure had a negative indirect effect on attitudes towards doping via perfectionistic strivings. The findings indicate that perceived parental pressure to be perfect may be a factor contributing to junior athletes’ vulnerability to doping, whereas perfectionistic strivings may be a protective factor

Perfectionism and burnout in junior athletes: A three-month longitudinal study

Perfectionism in sports has been shown to be associated with burnout in athletes. Whether perfectionism predicts longitudinal changes in athlete burnout, however, is still unclear. Using a two-wave cross-lagged panel design, the present study examined perfectionistic strivings, perfectionistic concerns, and athlete burnout in 101 junior athletes (mean age 17.7 years) over 3 months of active training. When structural equation modeling was employed to test a series of competing models, the best-fitting model showed opposite patterns for perfectionistic strivings and perfectionistic concerns. Whereas perfectionistic concerns predicted increases in athlete burnout over the 3 months, perfectionistic strivings predicted decreases. The present findings suggest that perfectionistic concerns are a risk factor for junior athletes contributing to the development of athlete burnout whereas perfectionistic strivings appear to be a protective factor

Athletes’ perfectionism and reasons for training: Perfectionistic concerns predict training for weight control

Exercise and training for sports are associated with a number of psychological and health benefits. Research on exercise, however, suggests that such benefits depend on the reasons why individuals participate in sport. The present study investigated whether individual differences in perfectionism predicted different reasons for training and examined four dimensions of perfectionism (perfectionistic strivings, perfectionistic concerns, coach pressure to be perfect, parental pressure to be perfect) and three reasons for training (avoidance of negative affect, weight control, mood improvement) in 261 athletes (mean age 20.9 years). Regression analyses showed that perfectionistic concerns positively predicted avoidance of negative affect and weight control, whereas perfectionistic strivings positively predicted mood improvement. The findings suggest that individual differences in perfectionism help explain why athletes train for different reasons

Determining optimal cadence for an individual road cyclist from field data

The cadence that maximises power output developed at the crank by an individual cyclist is conventionally determined using a laboratory test. The purpose of this study was two-fold: (i) to show that such a cadence, which we call the optimal cadence, can be determined using power output, heart-rate, and cadence measured in the field and (ii) to describe methodology to do so. For an individual cyclist's sessions, power output is related to cadence and the elicited heart-rate using a non-linear regression model. Optimal cadences are found for two riders (83 and 70 revolutions per minute, respectively); these cadences are similar to the riders’ preferred cadences (82–92?rpm and 65–75?rpm). Power output reduces by approximately 6% for cadences 20?rpm above or below optimum. Our methodology can be used by a rider to determine an optimal cadence without laboratory testing intervention: the rider will need to collect power output, heart-rate, and cadence measurements from training and racing sessions over an extended period (>6 months); ride at a range of cadences within those sessions; and calculate his/her optimal cadence using the methodology described or a software tool that implements it

Inter-observer agreement of thoracolumbar fascia morphology: an exploratory analysis of ultrasound images

BACKGROUND: Ultrasound imaging (USI) has been shown to be a valid method to investigate the morphology of the thoracolumbar fascia (TLF) [1]. A USI-based study has demonstrated that the TLF of subjects with chronic lower back pain (LBP) is on average 25% thicker and more disorganised compared to a control group [1]. The aim of this study is to explore inter-observer agreement between a range of clinicians on (dis)organisation of TLF in ultrasound images. There are currently no validated methods for the evaluation of USI of TLF. METHODS: Design: an exploratory analysis using a fully crossed design of inter-observer agreement. This study was approved by the University of Kent’s School of Sport and Exercise Sciences Research and Ethics Committee (Prop. 163 – 2013). Participants: Thirty observers consisting of 21 (70%) Medical Doctors, 7 (23%) physiotherapists and 2 (6%) radiologists, with a combined total average of 13 years of clinical experience (± SD 9.4). 57% had no experience in USI, 36% had experience ranging from monthly to daily evaluations of USI, no observers had experience in evaluating USI of TLF. Protocol: A sub-set of thirty ultrasound scans of TLF were randomly selected from a data set of 308 scans of subjects with and without LBP (from a larger study conducted by the first author). All scans were anonymised and displayed on a desktop computer, or projected on a screen. All observers viewed and rated each of the 30 scans independently on a Likert-type scale from 1(very disorganised) to 10 (very organised). Inter-observer agreement was assessed using a two-way mixed, consistency, average measures intra-class correlation (ICC), the Cronbach’s Alpha, to assess consistency among observers. The Krippendorff’s Alpha (Kalpha) [2] reliability estimate was used to assess agreement. RESULTS: The resulting ICC was in the excellent range, ICC = 0.98, indicating that observers had a high degree of consistency, suggesting that (dis)organisation was rated similarly across observers. Observers without USI experience scored an ICC = 0.96, observers with USI experience scored an ICC = 0.95, again both in the excellent range. In this small cohort, experience in USI does not appear to impact on consistency. The Krippendorff’s ordinal alpha ? was .621, indicating a modest degree of agreement. CONCLUSIONS: The high ICC and modest Kalpha suggest that a minimal amount of measurement error was introduced by the independent observers, and therefore statistical power for subsequent analyses is not substantially reduced. This will allow for further analysis of USI images of TLF in terms of morphology and classification. This could ultimately, lead to a meaningful evaluation of treatments of TLF

Gross efficiency and cycling performance: a review.

Efficiency, the ratio of work generated to the total metabolic energy cost, has been suggested to be a key determinant of endurance cycling performance. The purpose of this brief review is to evaluate the influence of gross efficiency on cycling power output and to consider whether or not gross efficiency can be modified. In a re-analysis of data from five separate studies, variation in gross efficiency explained ~30% of the variation in power output during cycling time-trials. Whilst other variables, notably VO2max and lactate threshold, have been shown to explain more of the variance in cycling power output, these results confirm the important influence of gross efficiency. Case study, cross-sectional, longitudinal, and intervention research designs have all been used to demonstrate that exercise training can enhance gross efficiency. Whilst improvements have been seen with a wide range of training types (endurance, strength, altitude), it would appear that high intensity training is the most potent stimulus for changes in gross efficiency. In addition to physiological adaptations, gross efficiency might also be improved through biomechanical adaptations. However, ‘intuitive’ technique and equipment adjustments may not always be effective. For example, whilst ‘pedalling in circles’ allows pedalling to become mechanically more effective, this technique does not result in short term improvements in gross efficiency

Perfectionism and training distress in junior athletes: A longitudinal investigation

Perfectionistic athletes may train harder and for longer than non-perfectionistic athletes, leaving them susceptible to elevated levels of training distress. So far, however, no study has investigated the relationships between perfectionism and training distress, a key indicator of overtraining syndrome. Furthermore, no study has determined psychological predictors of overtraining syndrome. Using a two-wave design, the present study examined perfectionistic strivings, perfectionistic concerns, and training distress in 141 junior athletes (mean age 17.3 years, range 16-19 years) over 3 months of active training. Multiple regression analyses were employed to test cross-sectional and longitudinal relationships between perfectionism and training distress. In all analyses, perfectionism emerged as a significant predictor, but strivings and concerns showed differential relationships. When the cross-sectional relationships were regarded, perfectionistic concerns positively predicted training distress (p .05). The findings suggest that sports scientists who wish to identify athletes at risk of overtraining syndrome may monitor athletes’ perfectionistic concerns as a possible risk factor