Publications

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This study aims to examine the effects of acute whole-body photobiomodulation (wbPBM), applied pre-exercise, on bouts of anaerobic cycling (Wingate) performances. Forty-eight healthy, active males and females participated in this single-blind, randomized, crossover study. Participants visited the laboratory three times to complete repeat (4 ×) Wingate testing, with one week between each visit. All participants completed baseline testing during their first visit and randomly received either the wbPBM or placebo condition before testing on the second visit, followed by the opposite condition on the third visit. There were no significant condition × time interactions for any variable (peak power, average power, power decrement, lactate, heart rate, ratings of perceived exertion, heart rate variability (HRV), root-mean square of differences between R-R intervals (rMSSD), power in the high-frequency range (HF) average, power in the low-frequency range (LF) average, total power, LF/HF, or power in the very-low-frequency range average). A main condition effect was only noted for heart rate, where peak heart rate was significantly higher for wbPBM (145, 141–148 bpm) than placebo (143, 139–146 bpm; p = 0.006) and baseline testing (143, 140–146; p = 0.049) throughout the entire testing session (i.e., collapsed across all timepoints). Furthermore, HRV (rMSSD) the following morning after testing was significantly higher for the wbPBM session compared to placebo (p = 0.043). There were no differences in perceived recovery (p = 0.713) or stress (p = 0.978) scores between wbPBM and placebo. Implementing 20 min of wbPBM immediately prior to maximal bouts of anaerobic cycling did not improve performance (i.e., power output) or physiological responses (e.g., lactate). However, wbPBM elicited the ability to work at a higher heart rate throughout testing and seemed to enhance recovery through improved HRV the following morning.

Heart rate samples (n = 4500–8000) from wearables were compared to electrocardiography during a steady-state ruck (Ruck-S), maximal effort ruck (Ruck-M), submaximal cycle (Cycle), and Tabata Circuit. One device was worn at each location (wrist: Polar Grit-X, Garmin Fenix 6; chest-straps: Polar H10, Garmin HRM-Pro; armband: Polar Verity). Comparisons were made via percent error (MAPE) ≤5%, Concordance Correlation Coefficient (CCC), and ordinary least product regressions (OLP). H10 demonstrated strong agreement for all movements (MAPE = 1.28–3.40%, CCC = 0.93–0.99). During Ruck-S, Ruck-M, and Cycle, HRM-Pro (MAPE = 1.96–3.73%, CCC = 0.95–0.99) and Verity (MAPE = 1.84–5.36%, CCC = 0.98–0.99) demonstrated strong agreement. Fenix-6 demonstrated low MAPE (4.23–5.44%) and moderate to strong CCC (0.76–0.96) for Ruck-S, Ruck-M, and Cycle, while Grit-X had poor agreement (MAPE = 8.49–16.45%; CCC = 0.24–0.78). Tabata Circuit had the worst disagreement for all devices. Overall, chest straps and armbands demonstrated the strongest agreement, and should be worn when precise heart rate training is necessary.

The dynamic work environments of tactical athletes are difficult to replicate in a laboratory. Accelerometers and inertial measurement units provide a way to characterize movement in the field. This systematic review identified how accelerometers and inertial measurement units are currently being used to quantify movement patterns of tactical athletes. Seven research and military databases were searched, producing 26,228 potential articles with 78 articles included in this review. The articles studied military personnel (73.1%), firefighters (19.2%), paramedics (3.8%), and law enforcement officers (3.8%). Accelerometers were the most used type of sensor, and physical activity was the primarily reported outcome variable. Seventy of the studies had fair or poor quality. Research on firefighters, emergency medical services, and law enforcement officers was limited. Future research should strive to make quantified movement data more accessible and user-friendly for non-research personnel, thereby prompting increased use in tactical athlete groups, especially first responder agencies.

Background: Mindfulness in Motion (MIM) is a workplace resilience-building intervention that has shown reductions in perceived stress and burnout, as well as increased resilience and work engagement in health care workers. Objective: To evaluate effects of MIM delivered in a synchronous virtual format on self-reported respiratory rates (RR), as well as perceived stress and resiliency of health care workers. Methods: Breath counts were self-reported by 275 participants before and after 8 weekly MIM sessions. MIM was delivered virtually in a group format as a structured, evidence-based workplace intervention including a variety of mindfulness, relaxation, and resilience-building techniques. Participants counted their breaths for 30 seconds, which was then multiplied by 2 to report RR. Additionally, participants completed Perceived Stress Scale and Connor–Davidson Resiliency Scale.

Introduction: The aim was to determine reliability and validity of bioelectrical impedance analysis (BIA) compared to dual energy x-ray absorptiometry (DEXA).

Abstract Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA Division I American football athletes: use of principal component analysis. J Strength Cond Res 36(2): 411–419, 2022—This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures ( p \textless 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSI MOD ). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSI MOD , concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metric s applicability and reliability.

Merrigan, JJ, Stone, JD, Galster, SM, and Hagen, JA. Analyzing force-time curves: Comparison of commercially available automated software and custom MATLAB analyses. J Strength Cond Res XX(X): 000–000, 2022—With the growing prevalence of commercial force plate solutions providing automated force-time curve analysis, it is critical to understand the level of agreement across techniques. Thus, this study directly compared commercial and custom software analyses across force-time curves. Twenty-four male and female subjects completed 6 trials of countermovement, squat, and drop jumps, and isometric mid-thigh pulls on the same force plate. Vertical ground reaction forces were analyzed by automated software from Vald Performance, Hawkin Dynamics, and custom MATLAB scripts. Trials were visually assessed to verify proper landmark identifications. Systematic and proportional bias among analyses were compared via least products regressions, Bland-Altman plots, and percent error. Hawkin Dynamics had subtle differences in analysis procedures and demonstrated low percent errors across all tests (,3% error), despite demonstrating systematic and proportional bias for several metrics. ForceDecks demonstrated larger percent differences and greater biases for several metrics. These errors likely result from different identification of movement initiation, system weight, and integration techniques, which causes error to subsequent landmark identifications (e.g., braking/propulsive phases) and respective force-time metrics. Many metrics were in agreement between devices, such as isometric mid-thigh pull peak force consistently within 1 N across analyses, but some metrics are difficult and incomparable across software analyses (i.e., rate of force development). Overall, many metrics were in agreement across each commercial software and custom MATLAB analyses after visually confirming landmarks. However, because of inconsistencies, it is important to only compare metrics that are in agreement across software analyses when absolutely necessary.

Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.

Human performance optimization of tactical personnel requires accurate, meticulous, and effective monitoring of biological adaptations and systemic recovery. Due to an increased understanding of its importance and the commercial availability of assessment tools, the use of heart rate variability (HRV) to address this need is becoming more common in the tactical community. Measuring HRV is a non-invasive, practical method for objectively assessing a performer’s readiness, workload, and recovery status; when combined with additional data sources and practitioner input, it provides an affordable and scalable solution for gaining actionable information to support the facilitation and maintenance of operational performance. This narrative review discusses the nonclinical use of HRV for assessing, monitoring, and interpreting autonomic nervous system resource availability, modulation, effectiveness, and efficiency in tactical populations. Broadly, HRV metrics represent a complex series of interactions resulting from internal and external stimuli; therefore, a general overview of HRV applications in tactical personnel is discussed, including the influence of occupational specific demands, interactions between cognitive and physical domains, and recommendations on implementing HRV for training and recovery insights into critical health and performance outcomes.