"To Tech or Not to Tech?" A Critical Decision-Making Framework for Implementing Technology in Sport

Bridging Gaps in Wearable Technology for Exercise and Health Professionals: A Brief Review

The proliferation of wearable devices, especially over the past decade, has been remarkable. Wearable technology is used not only by competitive and recreational athletes but is also becoming an integral part of healthcare and public health settings. However, despite the technological advancements and improved algorithms offering rich opportunities, wearables also face several obstacles. This review aims to highlight these obstacles, including the prerequisites for harnessing wearables to improve performance and health, the need for data accuracy and reproducibility, user engagement and adherence, ethical considerations in data harvesting, and potential future research directions. Researchers, healthcare professionals, coaches, and users should be cognizant of these challenges to unlock the full potential of wearables for public health research, disease surveillance, outbreak prediction, and other important applications. By addressing these challenges, the impact of wearable technology can be significantly enhanced, leading to more precise and personalized health interventions, improved athletic performance, and more robust public health strategies. This paper underscores the transformative potential of wearables and their role in advancing the future of exercise prescription, sports medicine and health.

Intensity Gradients: A Novel Method for Interpreting External Loads in Football

PURPOSE

Global navigation satellite system device-derived metrics are commonly represented by discrete zones with intensity often measured by standardizing volume to per-minute of activity duration. This approach is sensitive to imprecision in duration measurement and can lead to highly variable outcomes-transforming data from zones to a gradient may overcome this problem. The purpose of this study was to critically evaluate this approach for measuring team-sport activity demands.

METHODS

Data were collected from 129 first-team and 73 academy matches from a Scottish Premiership football club. Gradients were calculated for velocity, acceleration, and deceleration zones, along with per-minute values for several commonly used metrics. Means and 95% CIs were calculated for playing level, as well as first-team positional groups. Within-subject coefficients of variation were also calculated for match level, position, and individual groups.

RESULTS

The gradient approach showed consistency with per-minute metrics when measuring playing level and position groups. With coefficients of variation of 10.8% to 26.9%, the gradients demonstrated lower variability than most per-minute variables, which ranged from 10.7% to 84.5%.

CONCLUSIONS

Gradients are a potentially useful way of describing intensity in team sports and compare favorably to existing intensity variables in their ability to distinguish between match types and position groups, providing evidence that gradient variables can be used to monitor match and training intensity in team sports.

A framework for test measurement selection in athlete physical preparation

Preparing athletes for competition requires the diagnosis and monitoring of relevant physical qualities (e.g., strength, power, speed, endurance characteristics). Decisions regarding test selection that attempt to measure these physical attributes are fundamental to the training process yet are complicated by the myriad of tests and measurements available. This article presents an evidenced based process to inform test measurement selection for the physical preparation of athletes. We describe a method for incorporating multiple layers of validity to link test measurement to competition outcome. This is followed by a framework by which to evaluate the suitability of test measurements based on contemporary validity theory that considers technical, decision-making, and organisational factors. Example applications of the framework are described to demonstrate its utility in different settings. The systems presented here will assist in distilling the range of measurements available into those most likely to have the greatest impact on competition performance.

Professional soccer practitioners' perceptions of using performance analysis technology to monitor technical and tactical player characteristics within an academy environment: A category 1 club case study

This study aimed to identify professional soccer practitioners' perceptions of the application of performance analysis technology within a single academy club. Secondary aims were to understand the importance that practitioners place on monitoring technical and tactical player characteristics, current practices, and barriers to implementing wearable technology. Utilising a mixed method design, forty-four professional soccer academy practitioners (Age = 32 ± 5.8; Years of experience = 8.5 ± 6.2) completed an online survey intended to examine present trends, professional practices, and perceptions regarding the monitoring of technical and tactical metrics. Frequency and percentages of responses for individual items were calculated. Subsequently, eleven participants who were directly involved with the monitoring of players were recruited to participate in a semi-structured interview. Interview data was transcribed and analysed using a combination of deductive and inductive approaches to identify key themes. The main findings across both phases of the study were that (1) technical and tactical metrics are monitored more frequently in matches (Technical: 89%; tactical: 91%) than training (Technical: 80%; Tactical 64%), predominantly due to time constraints and staffing numbers. Accordingly, practitioners believe that it would be beneficial to have an automated way of tracking technical (79%) and tactical (71%) metrics and would consider using a foot-mounted IMU to do so (technical (68%) and tactical (57%)). (2) Monitoring technical and tactical metrics is beneficial to assist with player development and to enrich feedback provision (3) Key stake holders, coaches and players should be informed of the relevance and rationale for monitoring. (4) For successful implementation and continued uptake, the information delivered needs to be both meaningful and easy to understand. Findings suggest that although participants appreciate the need to collect technical and tactical metrics, they are keen to ensure that wearable-derived data does not replace experiential and contextual knowledge. Accordingly, practitioners need to work closely with coaches to determine the contexts in which metrics may or may not prove useful. However, as the sample comprised of participants from a single academy, further studies including more practitioners are warranted. Likewise, future research could also extend to include academy soccer players perceptions too.

Development of a sports technology quality framework

Identifying tools and processes to effectively and efficiently evaluate technologies is an area of need for many sport stakeholders. This study aimed to develop a standardised, evidence-based framework to guide the evaluation of sports technologies. In developing the framework, a review of standards, guidelines and research into sports technology was conducted. Following this, 55 experts across the sports industry were presented with a draft framework for feedback. Following a two-round Delphi survey, the final framework consisted of 25 measurable features grouped under five quality pillars. These were 1) Quality Assurance & Measurement (Accuracy, Repeatability, Reproducibility, Specifications), 2) Established Benefit (Construct Validity, Concurrent Validity, Predictive Validity, Functionality), 3) Ethics & Security (Compliance, Privacy, Ownership, Safety, Transparency, Environmental Sustainability), 4) User Experience (Usability, Robustness, Data Representation, Customer Support & Training, Accessibility) & 5) Data Management (Data Standardisation, Interoperability, Maintainability, Scalability). The framework can be used to help design and refine sports technology in order to optimise quality and maintain industry standards, as well as guide purchasing decisions by organisations. It may also serve to create a common language for organisations, manufacturers, investors, and consumers to improve the efficiency of their decision-making relating to sports technology.

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.

Relationships Between Sprint, Acceleration, and Deceleration Metrics with Training Load in Division I Collegiate Women's Soccer Players

Player load is a variable derived from GPS technology that quantifies external load demands. Sprints and change-of-direction movements are high-intensity activities that place stress on the body. Research is needed to determine which sprint metrics may relate to and predict player load during practice sessions in collegiate women's soccer players, as coaches could manipulate the most impactful variables. This study analyzed which sprint metrics related to GPS player load in women's soccer players from one Division I team. Data from 19 practice sessions for 18 field players were analyzed. Players wore GPS sensors during all training sessions, and the variables assessed were player load, sprint count, sprint volume, sprint distance, average top speed, maximum top speed, and the number of accelerations and decelerations in different speed zones (±1, ±2, ±3, ±4, ±5 m/s²). Pearson's correlations (p < 0.05) analyzed relationships between the sprint variables and player load. Stepwise regression analyses (p < 0.05) determined if any metrics predicted player load. The results indicated significant relationships between player load and sprint count, maximum top speed, sprint distance, sprint volume, number of decelerations at -1, -2, and -3 m/s², and accelerations at 1, 2, and 5 m/s²(r = 0.512-0.861, p ≤ 0.025). Sprint distance and decelerations at 1 m/s²predicted player load (p = 0.001, r²= 0.867). Maximal sprinting and decelerations and accelerations at different speeds were significant contributors to player load in collegiate women's soccer players. Sprint distance, decelerations, and accelerations could be targeted in training drills via dimension and movement manipulation to adjust training intensity for collegiate women's soccer players.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

A Comprehensive Analysis of the Validity and Reliability of the Perception Neuron Studio for Upper-Body Motion Capture

The Perception Neuron Studio (PNS) is a cost-effective and widely used inertial motion capture system. However, a comprehensive analysis of its upper-body motion capture accuracy is still lacking, before it is being applied to biomechanical research. Therefore, this study first evaluated the validity and reliability of this system in upper-body capturing and then quantified the system's accuracy for different task complexities and movement speeds. Seven participants performed simple (eight single-DOF upper-body movements) and complex tasks (lifting a 2.5 kg box over the shoulder) at fast and slow speeds with the PNS and OptiTrack (gold-standard optical system) collecting kinematics data simultaneously. Statistical metrics such as CMC, RMSE, Pearson's r, R², and Bland-Altman analysis were utilized to assess the similarity between the two systems. Test-retest reliability included intra- and intersession relations, which were assessed by the intraclass correlation coefficient (ICC) as well as CMC. All upper-body kinematics were highly consistent between the two systems, with CMC values 0.73-0.99, RMSE 1.9-12.5°, Pearson's r 0.84-0.99, R² 0.75-0.99, and Bland-Altman analysis demonstrating a bias of 0.2-27.8° as well as all the points within 95% limits of agreement (LOA). The relative reliability of intra- and intersessions was good to excellent (i.e., ICC and CMC were 0.77-0.99 and 0.75-0.98, respectively). The paired t-test revealed that faster speeds resulted in greater bias, while more complex tasks led to lower consistencies. Our results showed that the PNS could provide accurate enough upper-body kinematics for further biomechanical performance analysis.

Design of an Assistant Decision Support System for Sports Training Based on Association Rules

In order to quantitatively evaluate the effect of sports training, it is necessary to track the dynamic characteristics of sports by using sports training aided decision support system. When the existing sports training assistant decision support system extracts the features of decision association rules, some redundant features will appear when establishing the global association rules, which increases the amount and difficulty of data calculation and affects the effect of assistant decision support. On this basis, the data fusion of assistant decision support information is carried out, and the optimal assistant decision support scheme is obtained according to the fusion results. The experimental results show that the design system is superior to the existing auxiliary decision-making system in motion recognition rate, motion result accuracy rate, and decision-making accuracy rate, which can provide users with auxiliary decision-making support for sports training and has good practical application effect.

Automated Urinal-Based Specific Gravity Measurement Device for Real-Time Hydration Monitoring in Male Athletes

Acute and chronic hydration status is important for athlete safety and performance and is frequently measured by sports scientists and performance staff in team environments via urinalysis. However, the time required for urine collection, staff testing, and reporting often delays immediate reporting and personalized nutrition insight in situations of acute hydration management before training or competition. Furthermore, the burdensome urine collection and testing process often renders chronic hydration monitoring sporadic or non-existent in real-world settings. An automated urinalysis device (InFlow) was developed to measure specific gravity, an index of hydration status, in real-time during urination. The device was strongly correlated to optical refractometry with a mean absolute error of 0.0029 (±0.0021). Our results show this device provides a novel and useful approach for real-time hydration status via urinalysis for male athletes in team environments with high testing frequency demands.

Differential Ratings of Perceived Exertion: Relationships With External Intensity and Load in Elite Men's Football

PURPOSE

To examine the utility of differential ratings of perceived exertion (dRPE) for monitoring internal intensity and load in association football.

METHODS

Data were collected from 2 elite senior male football teams during 1 season (N = 55). External intensity and load data (duration × intensity) were collected during each training and match session using electronic performance and tracking systems. After each session, players rated their perceived breathlessness and leg-muscle exertion. Descriptive statistics were calculated to quantify how often players rated the 2 types of rating of perceived exertion differently (dRPEDIFF). In addition, the association between dRPEDIFF and external intensity and load was examined. First, the associations between single external variables and dRPEDIFF were analyzed using a mixed-effects logistic regression model. Second, the link between dRPEDIFF and session types with distinctive external profiles was examined using the Pearson chi-square test of independence.

RESULTS

On average, players rated their session perceived breathlessness and leg-muscle exertion differently in 22% of the sessions (range: 0%-64%). Confidence limits for the effect of single external variables on dRPEDIFF spanned across largely positive and negative values for all variables, indicating no conclusive findings. The analysis based on session type indicated that players differentiated more often in matches and intense training sessions, but there was no pattern in the direction of differentiation.

CONCLUSIONS

The findings of this study provide no evidence supporting the utility of dRPE for monitoring internal intensity and load in football.

Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

Elite sport practitioners increasingly use data to support training process decisions related to athletes' health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

Monitoring work and training load in military settings - what's in the toolbox?

Military personnel are required to complete physically demanding tasks when performing work and training, which may be quantified through the physical stress imposed (external load) or the resultant physiological strain (internal load). The aim of this narrative review is to provide an overview of the techniques used to monitor work and training load in military settings, summarise key findings, and discuss important practical, analytical, and conceptual considerations. Most investigations have focused upon measuring external and internal load in military training environments; however, limited data exist in operational settings. Accelerometry has been the primary tool used to estimate external load, with heart rate commonly used to quantify internal load. Supplemental to heart rate, psychophysiological and biochemical measures have also been investigated to elucidate aspects of internal load. Broadly, investigations have revealed that military training requires personnel to perform relatively large volumes of physical activity (e.g. averaging ∼15,000 steps·day^-1) of typically low-moderate intensity activity (<6 MET), although considerable temporal and inter-individual variability is observed from these gross mean estimates. There are limitations associated with these measures and, at best, estimates of external and internal load can only be inferred. These limitations are particularly pertinent for military tasks such as load carriage and manual material handling, which often involve complex activities performed individually or in teams, in a range of operational environments, with multiple layers of protection, over a protracted duration. Comprehensively quantifying external and internal loads during these functional activities poses substantial practical and analytical challenges.

Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload

Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.