Purpose: The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions. Materials and Methods: Data collection spanned 98 separate nights of ad libitum sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device. Results: Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval); TST: 9.90%, 0.25 (− 0.11, 0.61); TWT: 25.64%, − 0.17 (− 0.28, − 0.06); SE: 3.49%, 0.65 (− 0.82, 2.12)] and Oura smart ring [TST: 7.39%, 0.19 (0.04, 0.35); TWT: 36.29%, − 0.18 (− 0.31, − 0.04); SE: 5.42%, 1.66 (0.17, 3.15)], whereas all other devices demonstrated a propensity to over or underestimate at least one if not all of the aforementioned sleep metrics. No commercial sleep technology appeared to accurately quantify sleep stages. Conclusion: Generally speaking, commercial sleep technologies displayed lower error and bias values when quantifying sleep/wake states as compared to sleep staging durations. Still, these findings revealed that there is a remarkably high degree of variability in the accuracy of commercial sleep technologies, which further emphasizes that continuous evaluations of newly developed sleep technologies are vital. End-users may then be able to determine more accurately which sleep device is most suited for their desired application(s).

Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to “measure” sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success.

Force plate assessments, such as countermovement jumps and isometric mid-thigh pulls, examine performances (e.g., jump height, force, power) and movement strategies (e.g., asymmetries, durations), and are best suited to characterize and monitor physical capabilities, not predict injuries. To begin applying force plate technologies, users must first; (1) develop a data management plan to visualize and capture data over time; (2) select appropriate force plates for their scenario; (3) design appropriate testing protocols to ensure valid and reliable data. Force plate assessments may be added to existing testing, serve as separate testing batteries for annual profile testing to compare individuals and understand initial physical capabilities, or for more frequent testing (i.e., monthly or weekly) to monitor training-related adaptations or neuromuscular fatigue. Although these assessments inform evidence-based program designs, human performance practitioners must understand the considerations for conducting appropriate force plate testing, as well as proper visualizations and management of force plate data. Thus, the aim of this review is to provide evidence-based practices for utilizing force plates in tactical populations (e.g., military, firefighters, police). This includes best practices to implement testing for performance profiling, training adaptations, and monitoring neuromuscular fatigue and force asymmetries. Of note, due to the large amount of force-time metrics to choose from, this article provides general examples of important metrics to monitor and training recommendations based on changes to these force-time metrics, followed by specific examples in three case studies.

This study was conducted to identify whether team-wide or positional differences exist in simple or choice reactivity of collegiate soccer athletes when completed under various loads. Much research exists surrounding the assessment of reaction time in the general population, but given variations in training, little insight exists surrounding how unique and elite populations may differ based upon performance demands and task translatability to training. Reactive performance was assessed using the Dynavision D2 in 24 female soccer players (19.73 ± 1.05 years old) from a team within a power five conference of the National Collegiate Athletic Association. Evaluated loads included two conditions of simple reactivity (no additional load and with a concurrent lower body motor task) and three conditions of choice reactivity (no additional load, with a concurrent lower body motor task, and prolonged durations). Paired t-tests and ANOVAs were used to identify differences in task performance based upon load and positional group. No significant load-based or positional differences existed in measured simple reaction times. Performances in choice reaction tasks across the team were found to be slower when completed across extended durations (p < 0.0001) and faster when completed concurrent with an added balance task (p = 0.0108), as compared to performance under normal conditions. By assessment of positional differences, goalkeepers tended to be slower than other positions in reactivity during choice tasks, despite no differences existing in simple task performance. Given the unique population utilized herein, measured reactivity in different tasks suggests a strong relation to the training demands of soccer, as well as those of goalkeepers as compared to field positions. Findings suggest that sport and positional demands may be substantial contributors to population- and individual-based reactivity performance.

As sports technology has continued to develop, monitoring athlete workloads, performance, and recovery has demonstrated boundless benefits for athlete and team success. Specifically, technologies such as global positioning systems (GPS) and heart rate (HR) monitors have granted the opportunity to delve deeper into performance contributors, and how variations may exist based upon context. A team of NCAA Division I women’s soccer athletes were monitored during games throughout one competitive season. Individual athlete, positional groups, and team external and internal workloads were explored for differences based upon game location, opponent ranking, game result, and the final score differential. Game location and opponent ranking were found to have no effect on team-wide absolute or relative external workloads, whereas game result and score differential did. Internal workloads across the team tended to only vary by game half, independent of game context; however, the HR of defenders was determined to be higher during losses as compared to wins (p = 0.0256). Notably, the games that resulted in losses also represented the games with the fewest number of substitutions. These findings suggest high value in monitoring performance and workloads that are characteristic of varying, often multifaceted, contexts. It is hoped that this information can lead to more informed approaches to vital game-time and coaching decisions.

The ongoing Coronavirus 2 (COVID-19) pandemic abruptly halted athletic competition and standard training practices, consequently generating great confusion surrounding when and how to safely reintroduce sports. Therefore, tangible solutions disseminated to performance staff, coaches, and athletes are warranted to ensure optimal levels of health and physical performance for all personnel during both the current social distancing standards as well as the impending return of competition despite continued risks. In this commentary, we offer strategies for utilizing technology and data tools as components of longitudinal COVID-19 surveillance based on ongoing research efforts as well as current guidance from governing bodies, while also serving the performance needs of the athletes and staff. Recommended data sources include digital symptom and well-being surveys, standardized and routine physical performance testing, sleep and sleep physiology monitoring, cognitive applications, and temperature. This system is flexible to numerous commercially available products and is designed for easy implementation that permits instant feedback provided directly to the athlete as well as their support staff for early intervention, ultimately mitigating COVID-19 risks. We will discuss multiple options, including examples of data, data visualizations and recommendations for data interpretation and communication.