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Introduction 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. Materials and Method 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. Results 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 Conclusion 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.

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 MAPE, Physiological Measurement, 42 (8), ISSN:0967-3334, ISSN:1361-6579

Context: Successful elite sprint to long-distance runners are known to have shorter ground-contact time (GCT) than their less successful counterparts. Purpose: To investigate whether augmented feedback (aF) about GCT can reduce the time on ground (TOG) per minute in long-distance runners and, if so, whether this reduction improves running performance. Methods: Thirty well-trained runners were allocated to 3 groups. The intervention group (IG) received visual aF about their GCT during 8 high-intensity interval sessions in the 4-wk training period and were instructed to minimize GCT. The 1st control group (CG1) trained with the IG but was not given any feedback. The 2nd control group (CG2) followed their own training routine. Data were obtained pre- and postintervention for all 3 groups. The dependent variable was TOG per minute, computed from step frequency and GCT. Results: The IG significantly reduced TOG (P = .043, −1.7%, 90%CL −3.1;−0.3) and improved their mean 10 × 400-m performance time (P Conclusions: The provision of aF about GCT seems to be a promising approach that should be considered during training practice of well-trained runners.

This study was designed to examine the RR interval signal qualities of a Holter device and a heart rate chest belt monitor at rest and during exercise. Ten healthy individuals completed five low- to high-intensity activities while simultaneously using the medilog® AR12plus Holter monitor and the Polar H10 heart rate monitor. The RR interval signal quality was based on the quantification of the missing RR intervals and RR interval detection errors. Therefore, both measurement systems were compared against visual inspection of the raw electrocardiography signal. The missing and wrong R-wave peak detections were counted manually for both measurement systems. RR interval signal quality was defined as the relative number of correctly detected RR intervals. Overall, RR interval signal qualities of 94.6% and 99.6% were demonstrated for the medilog® AR12plus and the Polar H10. During the high-intensity activities, the RR interval signal quality of the medilog® AR12plus dropped to 89.8%, whereas the Polar H10 maintained a signal quality of 99.4%. The correlation between both systems was high (r = 0.997, p > 0.001). The excellent RR interval signal quality during low- to moderate-intensity activities in the medilog® AR12plus and during low- to high-intensity activities in the Polar H10 demonstrates both measurement systems’ validity for the detection of RR intervals throughout a wide range of activities. A simple chest strap such as the Polar H10 might be recommended as the gold standard for RR interval assessments if intense activities with strong body movements are investigated.

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., Current Issues in Sport Science (CISS)

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.

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.