Impact of sport context and support on the use of a self-report measure for athlete monitoring

Athlete self-report measures (ASRM) are a popular method of athlete monitoring in high-performance sports. With increasing recognition and accessibility, ASRM may potentially be utilized by athletes from diverse sport contexts. The purpose of the present study was to improve understanding of ASRM implementation across different sport contexts by observing uptake and compliance of a newly implemented ASRM over 16 weeks, and investigating the perceived roles and factors influencing implementation. Athletes (n=131) completed an electronic survey at baseline and week 16 on their perceptions and experiences with ASRM implementation respectively. Despite initial interest, only 70 athletes attempted to use the ASRM. Of these athletes, team sport athletes who were supported by their coach or sports program to use the ASRM were most compliant (p < 0.001) with a mean compliance of 84 ± 21 %. Compliance for self-directed individual and team sport athletes was 28 ± 40 % and 8 ± 18 % respectively. Self-directed athletes were motivated to monitor themselves, and rated desired content and minimal burden as key factors for initial and ongoing compliance. Supported athletes were primarily motivated to comply for the benefit of their coach or sports program rather than themselves, however rated data output as a key factor for their continued use. Factors of the measure outweighed those of the social environment regardless of sport context, however the influence of social environmental factors should not be discounted. The findings of the present study demonstrate the impact of sport context on the implementation of an ASRM and the need to tailor implementation strategies accordingly. Key pointsAthletes perceive ASRM and the factors influencing implementation differently. Therefore, to encourage compliance, it is important to tailor implementation strategies to the athlete and their sport context to increase appeal and minimize unappealing factors.Athletes using an ASRM on their own accord typically favor a measure which meets their needs and interests, with minimal burden.Athletes using an ASRM under the direction and support of their coach or sports program typically favor feedback and a positive social environment.

Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review

Background Monitoring athlete well-being is essential to guide training and to detect any progression towards negative health outcomes and associated poor performance. Objective (performance, physiological, biochemical) and subjective measures are all options for athlete monitoring. Objective We systematically reviewed objective and subjective measures of athlete well-being. Objective measures, including those taken at rest (eg, blood markers, heart rate) and during exercise (eg, oxygen consumption, heart rate response), were compared against subjective measures (eg, mood, perceived stress). All measures were also evaluated for their response to acute and chronic training load. Methods The databases Academic search complete, MEDLINE, PsycINFO, SPORTDiscus and PubMed were searched in May 2014. Fifty-six original studies reported concurrent subjective and objective measures of athlete well-being. The quality and strength of findings of each study were evaluated to determine overall levels of evidence. Results Subjective and objective measures of athlete well-being generally did not correlate. Subjective measures reflected acute and chronic training loads with superior sensitivity and consistency than objective measures. Subjective well-being was typically impaired with an acute increase in training load, and also with chronic training, while an acute decrease in training load improved subjective well-being. Summary This review provides further support for practitioners to use subjective measures to monitor changes in athlete well-being in response to training. Subjective measures may stand alone, or be incorporated into a mixed methods approach to athlete monitoring, as is current practice in many sport settings.

The role of a self-report measure in athlete preparation

Athlete self-report measures (ASRM) are a common and cost-effective method of athlete monitoring. It is purported that ASRM be used to detect athletes at risk of overtraining, injury or illness, allowing intervention through training modification. However it is not known whether ASRM are actually being used for or are achieving these objectives in the applied sport setting. Therefore the aim of this study was to better understand how ASRM are being used in elite sports and their role in athletic preparation. Semi-structured interviews were conducted one-on-one with athletes, coaches and sports science and medicine staff (n=30) at a national sporting institute. Interview recordings were transcribed and analysed for emergent themes. Twelve day-to-day and seven longer-term practices were identified which contributed to a four-step process of ASRM use (record data, review data, contextualize, act). In addition to the purported uses, ASRM facilitated information disclosure and communication amongst athletes and staff and between staff, and improved the understanding and management of athlete preparation. These roles of ASRM are best achieved through engagement of athletes, coaches and support staff in the systematic, cyclic process.

Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football

Perceptions of wellness are often used by athletes and coaches to assess adaptive responses to training. The purpose of this research was to describe how players were coping with the demands of elite level Australian football over a competitive season using subjective ratings of physical and psychological wellness and to assess the ecological validity of such a monitoring approach. Twenty-seven players completed ratings for 9 items (fatigue, general muscle, hamstring, quadriceps, pain/stiffness, power, sleep quality, stress, well-being). Players subjectively rated each item as they arrived at the training or competition venue on a 1–5 visual analog scale, with 1 representing the positive end of the continuum. A total of 2,583 questionnaires were analyzed from completions on 183 days throughout the season (92 ± 24 per player, 103 ± 20 per week; mean ± SD). Descriptive statistics and multilevel modelling were used to understand how player ratings of wellness varied over the season and during the week leading into game day and whether selected player characteristics moderated these relationships. Results indicated that subjective ratings of physical and psychological wellness were sensitive to weekly training manipulations (i.e., improve steadily throughout the week to a game day low, p < 0.001), to periods of unloading during the season (i.e., a week of no competition, p < 0.05) and to individual player characteristics (e.g., muscle strain after a game was poorer in players with high maximum speed, p < 0.01). It is concluded that self-reported player ratings of wellness provide a useful tool for coaches and practitioners to monitor player responses to the rigorous demands of training, competition, and life as a professional athlete.

Self-report measures in athletic preparation

This thesis provides theoretical and applied support for the use of self-report measures for athlete monitoring. Athlete self-report measures were found to effectively reflect the training response, whilst also providing a means to improve communication, confidence, and regulation of athletic preparation. Context-specific guidelines for implementation were also identified.

Monitoring training loads and athlete wellness: relationships with rowing performance

 This research provides initial evidence that a novel measure of training load, the T2minute method, is accurate for quantifying training in high performance rowing. This work also explored athlete wellness and rowing performance, with findings suggesting that the wellness-performance relationship is complex and changes over time due to individual-specific factors.

Monitoring athletes through self-report: factors influencing implementation

Monitoring athletic preparation facilitates the evaluation and adjustment of practices to optimize performance outcomes. Self-report measures such as questionnaires and diaries are suggested to be a simple and cost-effective approach to monitoring an athlete’s response to training, however their efficacy is dependent on how they are implemented and used. This study sought to identify the perceived factors influencing the implementation of athlete self-report measures (ASRM) in elite sport settings. Semi-structured interviews were conducted with athletes, coaches and sports science and medicine staff at a national sporting institute (n = 30). Interviewees represented 20 different sports programs and had varying experience with ASRM. Purported factors influencing the implementation of ASRM related to the measure itself (e.g., accessibility, timing of completion), and the social environment (e.g., buy-in, reinforcement). Social environmental factors included individual, inter-personal and organizational levels which is consistent with a social ecological framework. An adaptation of this framework was combined with the factors associated with the measure to illustrate the inter-relations and influence upon compliance, data accuracy and athletic outcomes. To improve implementation of ASRM and ultimately athletic outcomes, a multi-factorial and multi-level approach is needed.

Accuracy of SRM and power tap power monitoring systems for bicycling

Purpose: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions. Methods: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21degreesC) and time (1 h at similar to300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride. Results: The mean error scores for SRM and PT factory calibration over a range of 50-1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however. power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT. Conclusions: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.