Background Single-item athlete self-report measures consist of a single question to assess a dimension of wellbeing. These methods are recommended and frequently used for athlete monitoring, yet their uniformity has not been well assessed, and we have a limited understanding of their relationship with measures of training load. Objective To investigate the applications and designs of single-item self-report measures used in monitoring team-sport athletes and present the relationship between these measures and measures of training load. Data Sources PubMed, Scopus, and SPORTDiscus were searched between inception and March 2019. Study Selection Articles were included if they concerned adult athletes from field- or court-sport domains, if athlete well-being was measured using a single-item self-report, and if the relationship with a measure of modifiable training load was investigated over at least 7 days. Data Extraction Data related to participant characteristics, self-report measures, training load measures, and statistical analysis and outcomes were extracted by 2 authors (C.D. and C.D.). Data Synthesis A total of 21 studies were included in the analysis. A narrative synthesis was conducted. The measures used most frequently were muscle soreness, fatigue, sleep quality, stress, and mood. All measures presented various relationships with metrics of training load from no association to a very large association, and the associations were predominantly trivial to moderate in the studies with the largest numbers of observations. Relationships were largely negative associations. Conclusions The implications of this review should be considered by users in the application and clinical utility of single-item self-report measures in athlete monitoring. Great emphasis has been placed on examining the relationship between subjective and objective measures of training load. Although the relationship is still unclear, such an association may not be expected or useful. Researchers should consider the measurement properties of single-item self-report measures and seek to establish their relationship with clinically meaningful outcomes. As such, further study is required to inform practitioners on the appropriate objective application of data from single-item self-report measures.

Background: Concussion is one of the most common sports-related injuries, with little understood about the modifiable and non-modifiable risk factors. Researchers have yet to evaluate the association between modifiable sensorimotor function variables and concussive injury. Purpose: Investigate the association between dynamic balance performance, a discrete measure of sensorimotor function, and concussive injuries. Study Design: Prospective Cohort Study Methods: One-hundred and nine elite male Rugby Union players were baseline tested in dynamic balance performance while wearing an inertial sensor, and prospectively followed during the 2016/2017 Rugby Union season. The sample entropy of the inertial sensor gyroscope magnitude signal was derived to provide a discrete measure of dynamic balance performance. Logistic regression modelling was then used to investigate the association between the novel digital biomarker of balance performance, known risk factors of concussion (concussion history, age and playing position) and subsequent concussive injury. Results: Participant demographic data (mean ± SD) was as follows: age: 22.6±3.6 years; height: 185±6.5 cm; weight: 98.9±12.5 Kg; BMI: 28.9±2.9 kg/m 2 ; leg length: 98.8±5.5 cm. Of the 109 players, 44 (40.3%) had a previous history of concussion, while 21 (19.3%) sustained a concussion during the follow-up period. The receiver operatic curve analysis for the anterior sample entropy demonstrated a statistically significant area under the curve (0.64; 95%CI = 0.52 to 0.76; p < 0.05), with the cutoff score of anterior sample entropy ≥ 1.2, that maximized the sensitivity (76.2%) and specificity (53.4%) for identifying individuals who subsequently sustained a concussion. Players with sub-optimal balance performance at baseline were at a 2.81 greater odds (95% CI = 1.02-7.74) of sustaining a concussion during the Rugby Union season than those with optimal balance performance, even when controlling for concussion history. Conclusion: Rugby Union players who possess poorer dynamic balance performance as measured by a wearable inertial sensor during the Y Balance Test have a three-times higher relative risk of sustaining 3 a sports-related concussion, even when controlling for previous history of concussion. These findings have important implications for future research and clinical practice, as it identifies a potential modifiable risk-factor. Further research is required to investigate this association in a large cohort, consisting of males and females, across a range of sports. Clinical Relevance: This study has identified a modifiable risk-factor for concussion in Rugby Union players, suggesting movement control and balance training interventions may help reduce the incidence of concussion in this population.

No abstract

Background Consumer wearables can provide a practical and accessible method of data collection in actigraphy research. However, as this area continues to grow, it is becoming increasingly important for researchers to be aware of the many challenges facing the capture of quality data using consumer wearables. Objective This study aimed to (1) present the challenges encountered by a research team in actigraphy data collection using a consumer wearable and (2) present considerations for researchers to apply in the pursuit of robust data using this approach. Methods The Nokia Go was deployed to 33 elite Gaelic footballers from a single team for a planned period of 14 weeks. A bring-your-own-device model was employed for this study where the Health Mate app was downloaded on participants’ personal mobile phones and connected to the Nokia Go via Bluetooth. Retrospective evaluation of the researcher and participant experience was conducted through transactional data such as study logs and email correspondence. The participant experience of the data collection process was further explored through the design of a 34-question survey utilizing aspects of the Technology Acceptance Model. Results Researcher challenges included device disconnection, logistics and monitoring, and rectifying of technical issues. Participant challenges included device syncing, loss of the device, and wear issues, particularly during contact sport. Following disconnection issues, the data collection period was defined as 87 days for which there were 18 remaining participants. Average wear time was 79 out of 87 days (90%) and 20.8 hours per day. The participant survey found mainly positive results regarding device comfort, perceived ease of use, and perceived usefulness. Conclusions Although this study did not encounter some of the common published barriers to wearable data collection, our experience was impacted by technical issues such as disconnection and syncing challenges, practical considerations such as loss of the device, issues with personal mobile phones in the bring-your-own-device model, and the logistics and resources required to ensure a smooth data collection with an active cohort. Recommendations for achieving high-quality data are made for readers to consider in the deployment of consumer wearables in research.

Athlete Self-Report Measures (ASRM) are used in research and practice as an accurate, practical and accessible method of athlete monitoring. Mobile adaptations of constructs from validated ASRM have increasingly been employed for athlete monitoring in various sports settings, however, insights on the user experience and perceived value of these systems in the applied team sport setting has been limited. This study aimed to portray the experiences of stakeholders using a pre-existing mobile ASRM (M-ASRM) in elite Gaelic Games. Twentyone stakeholders in elite Gaelic Games were recruited for this study (players n = 10, coaches and support staff n = 11). Participants completed a semi-structured interview with the lead researcher regarding their experience of using an M-ASRM in practice. Thematic analysis of the transcripts was conducted using NVivo 12 software. Results were defined under the themes of positive and negative user experience. Positive user experience was portrayed through M-ASRM uses and perceived value: communication and information disclosure, remote player monitoring, decision making and advanced planning, and player education and self-management. Negative user experience was portrayed through M-ASRM challenges: player adherence, player dishonesty, coach time and expertise requirements and sociotechnical and system factors. Results outline the major uses of M-ASRM in elite Gaelic Games and importantly, highlight the key challenges experienced by stakeholders. These results can be applied by coaches, sports medicine professionals and sports scientists using or intending to use an M-ASRM, providing key considerations to employ for effective use in team sport.