Gait asymmetry during 400- to 1000-m high-intensity track running in relation to injury history

Purpose: To quantify gait asymmetry in well-trained runners with and without previous injuries during interval training sessions incorporating different distances.Methods: Twelve well-trained runners participated in 8 high-intensity interval-training sessions on a synthetic track over a 4-wk period. The training consisted of 10 × 400, 8 × 600, 7 × 800, and 6 × 1000-m running. Using an inertial measurement unit, the ground- contact time (GCT) of every step was recorded. To determine gait asymmetry, the GCTs between the left and right foot were compared.Results: Overall, gait asymmetry was 3.3% ± 1.4%, and over the course of a training session, the gait asymmetry did not change (F1,33 = 1.673, P = .205). The gait asymmetry of the athletes with a previous history of injury was significantly greater than that of the athletes without a previous injury. However, this injury-related enlarged asymmetry was detectable only at short (400 m), but not at longer, distances (600–1000 m).Conclusion: The gait asymmetry of well-trained athletes differed, depending on their history of injury and the running distance. To detect gait asymmetries, high-intensity runs over relatively short distances are recommended

Eyeblink Detection in the Field: A Proof of Concept Study of Two Mobile Optical Eye-Trackers.

High physical and cognitive strain, high pressure, and sleep deficit are part of daily life for military professionals and civilians working in physiologically demanding environments. As a result, cognitive and physical capacities decline and the risk of illness, injury, or accidents increases. Such unfortunate outcomes could be prevented by tracking real-time physiological information, revealing individuals' objective fatigue levels. Oculometrics, and especially eyeblinks, have been shown to be promising biomarkers that reflect fatigue development. Head-mounted optical eye-trackers are a common method to monitor these oculometrics. However, studies measuring eyeblink detection in real-life settings have been lacking in the literature. Therefore, this study aims to validate two current mobile optical eye-trackers in an unrestrained military training environment.; Three male participants (age 20.0 ± 1.0) of the Swiss Armed Forces participated in this study by wearing three optical eye-trackers, two VPS16s (Viewpointsystem GmbH, Vienna, Austria) and one Pupil Core (Pupil Labs GmbH, Berlin, Germany), during four military training events: Healthcare education, orienteering, shooting, and military marching. Software outputs were analyzed against a visual inspection (VI) of the video recordings of participants' eyes via the respective software. Absolute and relative blink numbers were provided. Each blink detected by the software was classified as a "true blink" (TB) when it occurred in the software output and the VI at the same time, as a "false blink" (FB) when it occurred in the software but not in the VI, and as a "missed blink" (MB) when the software failed to detect a blink that occurred in the VI. The FBs were further examined for causes of the incorrect recordings, and they were divided into four categories: "sunlight," "movements," "lost pupil," and "double-counted". Blink frequency (i.e., blinks per minute) was also analyzed.; Overall, 49.3% and 72.5% of registered eyeblinks were classified as TBs for the VPS16 and Pupil Core, respectively. The VPS16 recorded 50.7% of FBs and accounted for 8.5% of MBs, while the Pupil Core recorded 27.5% of FBs and accounted for 55.5% of MBs. The majority of FBs-45.5% and 73.9% for the VPS16 and Pupil Core, respectively-were erroneously recorded due to participants' eye movements while looking up, down, or to one side. For blink frequency analysis, systematic biases (±limits of agreement) stood at 23.3 (±43.5) and -4.87 (±14.1) blinks per minute for the VPS16 and Pupil Core, respectively. Significant differences in systematic bias between devices and the respective VIs were found for nearly all activities (P < .05).; An objective physiological monitoring of fatigue is necessary for soldiers as well as civil professionals who are exposed to higher risks when their cognitive or physical capacities weaken. However, optical eye-trackers' accuracy has not been specified under field conditions-especially not in monitoring fatigue. The significant overestimation and underestimation of the VPS16 and Pupil Core, respectively, demonstrate the general difficulty of blink detection in the field

Detecting Soldiers' Fatigue Using Eye-Tracking Glasses: Practical Field Applications and Research Opportunities.

Objectively determining soldiers' fatigue levels could help prevent injuries or accidents resulting from inattention or decreased alertness. Eye-tracking technologies, such as optical eye tracking (OET) and electrooculography (EOG), are often used to monitor fatigue. Eyeblinks-especially blink frequency and blink duration-are known as easily observable and valid biomarkers of fatigue. Currently, various eye trackers (i.e., eye-tracking glasses) are available on the market using either OET or EOG technologies. These wearable eye trackers offer several advantages, including unobtrusive functionality, practicality, and low costs. However, several challenges and limitations must be considered when implementing these technologies in the field to monitor fatigue levels. This review investigates the feasibility of eye tracking in the field focusing on the practical applications in military operational environments.; This paper summarizes the existing literature about eyeblink dynamics and available wearable eye-tracking technologies, exposing challenges and limitations, as well as discussing practical recommendations on how to improve the feasibility of eye tracking in the field.; So far, no eye-tracking glasses can be recommended for use in a demanding work environment. First, eyeblink dynamics are influenced by multiple factors; therefore, environments, situations, and individual behavior must be taken into account. Second, the glasses' placement, sunlight, facial or body movements, vibrations, and sweat can drastically decrease measurement accuracy. The placement of the eye cameras for the OET and the placement of the electrodes for the EOG must be chosen consciously, the sampling rate must be minimal 200 Hz, and software and hardware must be robust to resist any factors influencing eye tracking.; Monitoring physiological and psychological readiness of soldiers, as well as other civil professionals that face higher risks when their attention is impaired or reduced, is necessary. However, improvements to eye-tracking devices' hardware, calibration method, sampling rate, and algorithm are needed in order to accurately monitor fatigue levels in the field

Influence of Soldiers' Cardiorespiratory Fitness on Physiological Responses and Dropouts During a Loaded Long-distance March.

Introduction: In military service, marching is an important, common, and physically demanding task. Minimizing dropouts, maintaining operational readiness during the march, and achieving a fast recovery are desirable because the soldiers have to be ready for duty, sometimes shortly after an exhausting task. The present field study investigated the influence of the soldiers' cardiorespiratory fitness on physiological responses during a long-lasting and challenging 34 km march. Materials and methods: Heart rate (HR), body core temperature (BCT), total energy expenditure (TEE), energy intake, motivation, and pain sensation were investigated in 44 soldiers (20.3 ± 1.3 years, 178.5 ± 7.0 cm, 74.8 ± 9.8 kg, body mass index: 23.4 ± 2.7 kg × m-2, peak oxygen uptake ($\dot{\rm{V}}$O2peak): 54.2 ± 7.9 mL × kg-1 × min-1) during almost 8 hours of marching. All soldiers were equipped with a portable electrocardiogram to record HR and an accelerometer on the hip, all swallowed a telemetry pill to record BCT, and all filled out a pre- and post-march questionnaire. The influence of aerobic capacity on the physiological responses during the march was examined by dividing the soldiers into three fitness groups according to their $\dot{\rm{V}}$O2peak. Results: The group with the lowest aerobic capacity ($\dot{\rm{V}}$O2peak: 44.9 ± 4.8 mL × kg-1 × min-1) compared to the group with the highest aerobic capacity ($\dot{\rm{V}}$O2peak: 61.7 ± 2.2 mL × kg-1 × min-1) showed a significantly higher (P < .05) mean HR (133 ± 9 bpm and 125 ± 8 bpm, respectively) as well as peak BCT (38.6 ± 0.3 and 38.4 ± 0.2 °C, respectively) during the march. In terms of recovery ability during the break, no significant differences could be identified between the three groups in either HR or BCT. The energy deficit during the march was remarkably high, as the soldiers could only replace 22%, 26%, and 36% of the total energy expenditure in the lower, middle, and higher fitness group, respectively. The cardiorespiratory fittest soldiers showed a significantly higher motivation to perform when compared to the least cardiorespiratory fit soldiers (P = .002; scale from 1 [not at all] to 10 [extremely]; scale difference of 2.3). A total of nine soldiers (16%) had to end marching early: four soldiers (21%) in the group with the lowest aerobic capacity, five (28%) in the middle group, and none in the highest group. Conclusion: Soldiers with a high $\dot{\rm{V}}$O2peak showed a lower mean HR and peak BCT throughout the long-distance march, as well as higher performance motivation, no dropouts, and lower energy deficit. All soldiers showed an enormous energy deficit; therefore, corresponding nutritional strategies are recommended

Evaluation of pulse rate measurement with a wrist worn device during different tasks and physical activity

The purpose of this study was to evaluate the wrist-worn device Mio FUSE, which estimates heart rate (HR) based on photo-plethysmography, 1) in a large study group during a standardised activity, 2) in a small group during a variety of activities and 3) to investigate factors affecting HR accuracy in a real-world setting. First, 53 male participants (20 ±1 years; 1.79 ±0.07 m; 76.1 ±10.5 kg) completed a 35-km march wearing the Equivital EQ-02 as a criterion measure. Second, 5 participants (whereof 3 female; 29 ±5 years; 1.74 ±0.07 m; 67.8 ±11.1 kg) independently performed 25 activities, categorised as sitting passive, sitting active, standing, cyclic and anti-cyclic activities with the Polar H7 as a criterion device. Equivalence testing and Bland-and-Altman analyses were undertaken to assess the accuracy to the criterion devices. Third, confounders affecting HR accuracy were investigated using multiple backwards regression analyses. The Mio FUSE was equivalent to the respective criterion measures with only small systematic biases of -3.5 bpm (-2.6%) and -1.7 bpm (-1.3%) with limits of agreements of ±10.1 bpm and ±10.8 bpm during the 35-km march and during different activities, respectively. Confounding factors negatively affecting the accuracy of the Mio FUSE were found to include larger wrist size and intensified arm and/or wrist movement. The wrist-worn Mio FUSE can be recommended to estimate overall HR accurately for different types of activities in healthy adults. However, during sporting activities involving intensified arm and/or wrist movement or for detailed continuous analysis, a chest strap is preferred to the Mio FUSE to optimise HR estimation accuracy

Validation of ambulatory monitoring devices to measure energy expenditure and heart rate in a military setting

Objectives.; To investigate the validity of different devices and algorithms used in military organizations worldwide to assess physical activity energy expenditure (PAEE) and heart rate (HR) among soldiers.; Design.; Device validation study.; Methods; . Twenty-three male participants serving their mandatory military service accomplished, firstly, nine different military specific activities indoors, and secondly, a normal military routine outdoors. Participants wore simultaneously an ActiHeart, Everion, MetaMax 3B, Garmin Fenix 3, Hidalgo EQ02, and PADIS 2.0 system. The PAEE and HR data of each system were compared to the criterion measures MetaMax 3B and Hidalgo EQ02, respectively.; Results; . Overall, the recorded systematic errors in PAEE estimation ranged from 0.1 (±1.8) kcal.min; -1; to -1.7 (±1.8) kcal.min; -1; for the systems PADIS 2.0 and Hidalgo EQ02 running the Royal Dutch Army algorithm, respectively, and in the HR assessment ranged from -0.1 (±2.1) b.min; -1; to 0.8 (±3.0) b.min; -1; for the PADIS 2.0 and ActiHeart systems, respectively. The mean absolute percentage error (MAPE) in PAEE estimation ranged from 29.9% to 75.1%, with only the Everion system showing an overall MAP

An evaluation of the physiological strain index during a prolonged submaximal exercise with sleep deprivation

INTRODUCTION: The physiological strain index (PSI) is a prevention tool to detect incidences of heat-related illnesses at an individual level. The PSI is based on core temperature (Tc) and heart rate (HR) to assess the strain on the cardiovascular and thermoregulatory systems on a scale of 0 – 10 (Moran et al., 1998). Fatigue is known to induce physiological and psychological perturbations, hence, impairs (un)conscious behaviors for thermoregulation (Westwood et al., 2021). During prolonged exercise and sleep deprivation, fatigue can weaken the ability to maintain thermal homeostasis, and therefore, acts as risk factor for heat-related illnesses. This study aimed to evaluate whether the PSI reflects increasing and cumulative fatigue during prolonged submaximal exercise with sleep deprivation. METHODS: Twenty one soldiers (1 female; 21.0 ± 1.1 years; 180.6 ± 8.5cm; 78.9 ± 11.9kg) of the Swiss Armed Forces performed a submaximal 100km march (total duration 24h11 – 25h38). HR and Tc were measured using the chest belt Open Body Area Network (USARIEM & MIT, USA) in 1-min epochs. At five time points (at km 0, 30, 55, 72, and 93) four fatigue indicators were assessed: rate of perceived physical and mental exertion (RPEp; RPEm) using two CR100 scales, physical fatigue (PF) measuring the absolute acceleration during a counter movement jump and mental fatigue (MF) using a questionnaire. The PSI was calculated as PSI = 5•[(Tc(t)–Tc(0))•(39.5–Tc(0))-1]+5•[(HR(t)–HR(0))•(180–HR(0))-1] and compared to the fatigue indicators at the five time points; Tc(t1-5) and HR(t1-5) were calculated as average Tc and HR values of one hour prior every time point, and Tc(0) and HR(0) as average Tc and HR values of the first hour after the start. RESULTS: PSI ranged from 2.56 – 3.09 with a peak of 3.58 at km 55 and the repeated measures ANOVA revealed no significant differences compared to km 0 (p > 0.05). However, all fatigue indicators differed significantly from almost each time point to the other (p 0.05). CONCLUSION: The four fatigue indicators showed that physical and mental fatigue had increased significantly during the march. In contrast, the PSI remained steady at a low level of physiological strain and showed no association with fatigue. When shorter or longer breaks are taken, PSI may lack to identify cumulative fatigue. Whether the PSI would miss a heat- or exertion-related occurrence cannot be conclusively assessed, as no subject experienced such conditions. REFERENCES: 1. Moran, D. S., Shitzer, A., & Pandolf, K. B. (1998). A physiological strain index to evaluate heat stress. Am J Physiol, 275(1), R129-134. 2. Westwood, C. S., Fallowfield, J. L., Delves, S. K., Nunns, M., Ogden, H. B., & Layden, J. D. (2021). Individual risk factors associated with exertional heat illness: A systematic review. Exp Physiol, 106(1), 191-199

Werden körpertragbare Sensoren zur Energieverbrauchsabschätzung im militärischen Alltag als störend empfunden?

Einleitung: Es sind heute bereits unterschiedliche körpertragbare Sensoren zur Energieverbrauchsab­schätzung bei Soldaten im Einsatz (Burrell, Love & Stergiopoulos, 2016). Bislang ist jedoch nicht bekannt, ob und falls ja weshalb diese Geräte ihre Träger im Armeealltag stören. Ziel dieser Studie war es daher, zu untersuchen, welche im militärischen Umfeld eingesetzten Sensoren aus welchen Gründen als störend empfunden werden. Methode: Dreiundzwanzig freiwillige, männliche Probanden trugen während vordefinierten, militärspe­zifischen Aktivitäten sowie während des normalen militärischen Alltags je rund 90 Minuten gleichzeitig Sensoren an unterschiedlichen Körperstellen: ActiHeart (CamNTech, Cambridge, England), Axiamote (Axiamo, Biel, Schweiz), Blue Thunder (IMeasureU, Auck­land, Neuseeland), Biovotion (Biovotion, Zürich, Schweiz), GENEActive (Activinsights, Kim­bolton, England), Hidalgo EQ02 (Equivital, New York, USA), fenix 3 (Garmin, Olathe, USA) und TICKR X (Wahoo Fitness, Atlanta, USA). Im Anschluss an beide Erhebungen wurde den Probanden zu jedem Sensor die Frage gestellt: "Hat Sie dieser Sensor während der Messung gestört?" (Ja/Nein) Bei Bejahung wurden die Probanden gebeten, genauer zu be­schreiben, aus welchen Gründen der Sensor gestört hat. Ergebnisse: Von insgesamt 43 Erhebungen wurden ActiHeart auf der Haut, Axiamote an der Hüfte, Blue Thunder auf dem Schuh, Biovotion am Oberarm sowie GENEActive und fenix 3 am Hand­gelenk sehr selten (in 4.3 ± 3.4% der Fälle) als störend empfunden. Hingegen wurden die um den Brustkorb getragenen Geräte Hidalgo EQ02 sowie TICKR X nach je rund einem Drittel der Messungen Oe 32.6%) als störend beschrieben. Als häufigste Gründe für einge­schränkten Tragekomfort wurden Druckstellen und Verrutschen der Sensoren aufgrund des gleichzeitigen Tragens militärischer Ausrüstung genannt. Diskussion: Die Ergebnisse zeigen, dass die meisten körpertragbaren Sensoren zur Energieverbrauchs­abschätzung während militärischen Aktivitäten kaum als störend empfunden werden. Davon ausgenommen sind Geräte, die um die Brust angebracht werden. Bei solchen Geräten können Inkompatibilitäten mit der militärischen Ausrüstung zu einem eingeschränkten Trage­komfort führen

Energy expenditure estimation from respiration variables

Abstract The aim of this study was to develop and cross-validate two models to estimate total energy expenditure (TEE) based on respiration variables in healthy subjects during daily physical activities. Ninety-nine male and female subjects systematically varying in age (18–60 years) and body mass index (BMI; 17–36 kg*m−2) completed eleven aerobic activities with a portable spirometer as the criterion measure. Two models were developed using linear regression analyses with the data from 67 randomly selected subjects (50.0% female, 39.9 ± 11.8 years, 25.1 ± 5.2 kg*m−2). The models were cross-validated with the other 32 subjects (49% female, 40.4 ± 10.7 years, 24.7 ± 4.6 kg*m−2) by applying equivalence testing and Bland-and-Altman analyses. Model 1, estimating TEE based solely on respiratory volume, respiratory rate, and age, was significantly equivalent to the measured TEE with a systematic bias of 0.06 kJ*min−1 (0.22%) and limits of agreement of ±6.83 kJ*min−1. Model 1 was as accurate in estimating TEE as Model 2, which incorporated further information on activity categories, heart rate, sex, and BMI. The results demonstrated that respiration variables and age can be used to accurately determine daily TEE for different types of aerobic activities in healthy adults across a broad range of ages and body sizes