A Heart Rate Based Algorithm to Estimate Core Temperature Responses in Elite Athletes Exercising in the Heat.

Purpose: Non-invasive non-obtrusive continuous and real-time monitoring of core temperature (Tc) may enhance pacing strategies, the efficacy of heat mitigation measures, and early identification of athletes at risk for heat-related disorders. The Estimated Core Temperature (ECTemp™) algorithm uses sequential heart rate (HR) values to predict Tc. We examined the validity of ECTemp™ among elite athletes exercising in the heat. Methods: 101 elite athletes performed an exercise test in simulated hot and humid environmental conditions (ambient temperature: 31.6 ± 1.0°C, relative humidity: 74 ± 5%). Tc was continuously measured using a validated ingestible telemetric temperature capsule system. In addition, HR was continuously measured and used to compute the estimated core temperature (Tc-est) using the ECTemp™ algorithm. Results: Athletes exercised for 44 ± 10 min and n = 5,025 readouts of Tc (range: 35.8-40.4°C), HR (range: 45-207 bpm), and Tc-est (range: 36.7-39.9°C) were collected. Tc-est demonstrated a small yet significant bias of 0.15 ± 0.29°C (p < 0.001) compared to Tc, with a limit of agreement of ±0.45°C and a root mean square error of 0.35 ± 0.18°C. Utilizing the ECTemp™ algorithm as a diagnostic test resulted in a fair to excellent sensitivity (73-96%) and specificity (72-93%) for Tc-est thresholds between 37.75 and 38.75°C, but a low to very-low sensitivity (50-0%) for Tc-est thresholds >39.0°C, due to a high prevalence of false-negative observations. Conclusion: ECTemp™ provides a valuable and representative indication of thermal strain in the low- to mid-range of Tc values observed during exercise in the heat. It may, therefore, be a useful non-invasive and non-obtrusive tool to inform athletes and coaches about the estimated core temperature during controlled hyperthermia heat acclimation protocols. However, the ECTemp™ algorithm, in its current form, should not solely be used to identify athletes at risk for heat-related disorders due to low sensitivity and high false-negative rate in the upper end of the Tc spectrum.

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.

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-1to -1.7 (±1.8) kcal.min-1for 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-1to 0.8 (±3.0) b.min-1for 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 <30%, but all investigated devices reported overall MAPE <1.4% in the HR assessment.Conclusions. The present study demonstrated poor to moderate validity in terms of PAEE estimation, but excellent validity in all investigated devices in terms of HR assessment. Overall, the Everion performed among the best in both parameters and with a device placement on the upper arm, the Everion system is particularly useful during military service, as it does not interfere with other relevant equipment.

An evaluation of measurement systems estimating gait speed during a loaded military march over graded terrain.

This study aimed to evaluate the accuracy of three measurement systems estimating gait speed during a loaded military march over graded terrain. Systems developed by the Swiss and Netherlands Armed Forces and a commercial wrist-based device were evaluated in comparison to a Global Positioning System. The first part of the paper focuses on the development of the Dutch system, where speed is estimated from a chest worn accelerometer and body measurements. For this validation study 36 subjects were walking or running 13 laps of 200 m at different speeds. Results showed that walking and running speed can be estimated with a R2adj of 0.968 and 0.740, respectively. In the second part of this paper, data from 64 soldiers performing a 35 km march were used to evaluate the accuracy of three measurement systems in estimating speed. Data showed that estimating gait speed with a single accelerometer can be accurate for military activity, even without prior individual calibration measurements. However, predictions should be corrected for confounders such as body size and shoe type to be accurate. Both, downhill and uphill walking led to changes in gait characteristics and to an overestimation of speed by up to 10%. Correcting for slope or gradient using altimetry in future algorithms/experiments could improve the estimation of gait speed.

Examining the Relationship Between Mental, Physical, and Organizational Factors Associated With Attrition During Maritime Forces Training.

For infantry units of the Dutch Ministry of Defence, high attrition rates (varying from 42 to 68%) during initial training are a persisting problem. The reasons for this attrition are diverse. Having better insight into the causes of attrition is a prerequisite for implementing preventive measures. To achieve this, a monitoring assessment system was developed that integrated the effects of physical, mental, and organizational determinants on operational readiness. The aim of this study was to implement the monitoring tools and to establish the set of determinants that best predicted attrition during infantry training of new recruits. Eighty-five recruits were monitored over a 24-week infantry training course. Before the training, recruits were screened for medical, psychological, and physical wellness. During the monitoring phase, mental, physiological, and organizational indicants were obtained using an array of tools such as questionnaires, chest belt monitors (for heart rate, acceleration, and skin temperature measurements), and computerized tests (e.g., vigilance, long-term memory). Survival analyses were used to tease out the determinants of individual and grouped predictors of attrition. Nearly half the recruits (47%) failed the training. Attrition was predicted by both physiological and mental determinants. However, the organizational determinant "trainers' judgment" on the "recruits' military quality" dominated the physiological and mental determinants. It was concluded that the monitoring system was successfully implemented during infantry training, and that the survival analysis method emphasized on single effects and interactions between the different determinants. Based on the current findings, we recommend several steps to successfully implement a monitoring method in settings with high demands.

Ultrasound characteristics of the lateral retinaculum in 10 patients with patellofemoral pain syndrome compared to healthy controls.

BACKGROUND: Histopathologic changes of the lateral retinaculum are described in patients with patellofemoral pain syndrome (PFPS). No information is available on the presence of structural changes of the lateral retinaculum on ultrasound examination in patients with PFPS. PURPOSE: To describe ultrasound characteristics and colour Doppler findings in patients with unilateral PFPS and in healthy controls. METHODS: 10 patients with unilateral PFPS and 10 healthy control subjects underwent ultrasound and colour Doppler examination of the lateral retinaculum of both knees. Thickness of the lateral retinaculum was measured at three predefined locations. In addition presence of neovascularisation was assessed. RESULTS: Thickness of the lateral retinaculum of both affected (mean [SD] of three locations 4.0 [1.4] mm, 95%CI: 1.2-6.8) and asymptomatic (3.7 [0.8] mm, 95%CI: 2.1-5.3) knees was increased in the patient group compared to the control subjects (3.0 [0.1] mm, 95%CI: 2.8-3.2), although not reaching statistical significance. Positive colour Doppler signals of the lateral retinaculum were found in 4 patients and in none of the control subjects (4/10 versus 0/10; 2 × 2 Fisher's exact test 1-tailed p = 0.0433; 2-tailed p = 0.0866; mid p value = 0.0217). CONCLUSIONS: The results of these measurements indicate a trend towards a larger thickness of the lateral retinaculum and showed neovascularisation measured by ultrasound and colour Doppler examination in patients with PFPS. The larger thickness of the lateral retinaculum on ultrasound examination was found in both affected and in asymptomatic knees of the patients, supporting the concept that PFPS is a bilateral rather than a unilateral disorder. Further research is needed to unravel the role of the lateral retinaculum in pathogenesis of PFPS and to clarify the role of the lateral retinaculum as a target for therapy in patients with PFPS.

Sleep monitoring: a comparison between three wearable instruments.

During military operations soldiers often encounter extreme environmental circumstances like heat, cold, prolonged physical exercise, and disturbed sleep, which hamper their performance. Monitoring changes in physiological parameters may assist with adequate interventions to prevent the negative consequences and support recovery. The current study was employed to reduce the number of measurement instruments to monitor physiological variables, especially with respect to adequate sleep prediction. We compared three instruments with respect to their effectiveness in predicting sleep; the Equivital, Sensewear, and Actiwatch. Additionally, we investigated the added value of cardio-respiratory to accelerometer signals to estimate sleep duration. The Equivital model (based on acceleration data) and Sensewear predict sleep and wake as accurate as the commonly used Actiwatch model, and the cardio-respiratory Equivital data further improve accuracy and specificity. In sum, the current study provides an indication that the Equivital system (or any other chestband that measures 3-dimensional acceleration plus other physiological variables) might be interchanged with an Actiwatch for sleep prediction.

Predicting marching capacity while carrying extremely heavy loads.

The objective of this study was to establish the best prediction for endurance time of combat soldiers marching with extremely heavy loads. It was hypothesized that loads relative to individual characteristics (% maximal load carry capacity [MLCC], % body mass, % lean body mass) would better predict endurance time than load itself. Twenty-three male combat soldiers participated. MLCC was determined by increasing the load by 7.5 kg every 4 minutes until exhaustion. The marching velocity and gradient were 3 km.h(-1) and 5%, respectively. Endurance time was determined carrying 70, 80, and 90% of MLCC. MLCC was on average 102.6 kg +/- 11.6. Load expressed as % MLCC was the best predictor for endurance time (R2 = 0.45). Load expressed as % body mass, as % lean body mass, and absolute load predicted endurance time less well (R2 = 0.30, R2 = 0.24, and R2 = 0.23, respectively). On the basis of these results, it is recommended to assess the MLCC of individual combat soldiers.