Sleep and activity measurement in search and rescue aircraft crews using novel sensing technologies.

Helicopter search and rescue crews (SARC) remain on 24 hour alert. This requires the SARC to remain in a state of readiness and maximise sleep opportunities. When on duty, depending on their proximity to the SAR base, crew members may either sleep on-base or at home. These factors may lead to possible variations in the level of physical activity (PA), sleep duration (Sdur) and sleep efficiency (Sef). Purpose: To investigate the levels of PA, Sdur, and Sef of members of the SARC during a 24 hour on-call shift using several novel sensing technologies. Method: Ten members of the Dublin SARC (mean ± SD: age 40 ± 5 years; height 1.76 ± 0.06m; mass 89.2 ± 14 kg; 5 on-base, 5 off-base) were instrumented with 2 tri-axial accelerometers (XL) and a Sensewear armband (SW) with an internal accelerometer (SWXL). The XL were placed on the right ankle and right hip with the SW placed on the left triceps. Data was recorded for a 26 hour period during which the subjects kept a written record of their activity. Total estimated energy expenditure (tEEE), Seff and Sdur were calculated for each sensor during the 24 hour period. Sleep periods were verified for each subject using a written activity log. Results: Group: Based on the placement location of the sensors (ankle; waist; triceps) significant differences were observed for tEEE (1093.9kcal ± 329.8kcal; 502kcal ± 211.5 kcal; 2371.1kcal ± 838.2kcal , p<0.01). Sleep indices calculated from the SW were seen to be significantly different to the XL data, but not between the XL units themselves (triceps vs. ankle; waist): Sef (72.8% ± 18.5% vs. 96.3% ± 2.6%; 97.3% ± 1.9%, p<0.01) and Sdur ( 257.9mins ± 80.1mins vs. 371.3mins ± 49.0mins; 379.6mins ± 53.9mins, p<0.01). Home vs Base: Significant differences were seen for tEEE for the SW (1907.0kcal ± 397.3kcal vs. 2835.2kcal ± 940.4kcal, p<0.01) and SWXL (193.8kcal ± 63.2kcal vs. 893.2kcal ± 564.2kcal, p<0.01). Similarly a significant difference was observed for Seff (231.4mins ± 82.1mins; vs. 284.4mins ± 77mins, p<0.01) on the SW. Conclusion: The location of the sensor utilised to measure PA and sleep indices in SARC members appears to play a vital role in determining the accuracy of measurement. The SW recorded significant differences in PA levels and Sdur between SARC on-base and off-base. Further research is required to determine if this holds true for a larger sample size

Assessment of Physical Activity in Search and Rescue Operations Using Accelerometer Based Technologies.

Helicopter search and rescue crews (SARC) operate on a 24 hour shift with crew members either sleeping on-base or at home depending on their proximity to the base. This may lead to possible variations in the level of physical activity (PA) that occurs between members of the crew. Aim: To investigate the levels of PA of members of the SARC during a 24 hour on-call shift using several novel sensing technologies. Method: Ten members of the Dublin SARC were instrumented with 2 tri-axial accelerometers (GT3X+), a Sensewear armband (SW) with an internal accelerometer (SWXL) and a Sensecam with an internal accelerometer. Data was recorded for a 26 hour period during which the subjects kept a written record of their activity. Sensors were kept on during all operations, the Sensecam was the only sensor removed while sleeping. Results: Within Group: Significant differences were observed for PA (p<0.01) due to the location of the sensors on the subject. Between Home and Base: Significant differences were seen for PA on the SW and SWXL (p<0.01). Conclusion: The location and type of sensor utilised in SARC operations appears to play a role in measurement of PA. The SW recorded significant differences in PA between SARC on-base and off-base, however the GT3X+ and SWXL were no different. Further research is required to align data from the Sensecam with the sensors used in this study to determine if it is possible to measure PA in this population with the Sensecam accelerometer data while also adding visual contextual data

A comparison of the aerobic energy demands of two commercially available cycle ergometers in trained cyclists

The purpose of this study was to compare the energy demands of two cycling ergometers, (Velotron Dynafit Pro and Monark 834E) commonly used in the physiological monitoring of elite athletes. Eight trained male cyclists with a minimum 2 years training and racing experience participated in the study. Each subject completed an exercise trial involving a maximal incremental test. Testing was performed in a random order on either the Velotron or Monark cycle ergometer at the same time of day with no more than 14 days between each testing session. Subjects were requested to maintain their normal training and nutritional practices during the course of the study but to refrain from any intensive training 48 hours prior to each testing session. During the incremental testing significant differences for power output (PO), heart rate (HR), and oxygen uptake (VO2) were found at both at fixed blood lactate (BL) reference points of; 2.5mmol l-1 (REF2.5mM) and at 4mmol.l-1 (REF4mM). Overall the Velotron appeared to provide a more specific measure of cycling performance with significantly lower energy demands at fixed submaximal exercise intensities being observed as well as a significantly greater peak power output and time to exhaustion being attained, which may reflect the specific cycling position adopted. Further research is required to compare the findings of this study with actual cycling performance

Expanding sensor networks to automate knowledge acquisition

The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment

A comparison of the physiological demands of two commercially available cycle ergometers in trained cyclists

Cycling ergometers are routinely used in a laboratory setting to evaluate physiological function and monitor changes in training status. One limitation of many cycle ergometers, in relation to the performance testing, is their inability to replicate the cyclist own specific cycling position thereby bringing the validity of the ergometer used into question. Purpose: The purpose of this study was to compare the aerobic and anaerobic energy demands of two commercially available cycle ergometers in trained cyclists. The first ergometer allowed full adjustment of cycling position and was electromagnetically braked (EB). The second ergometer allowed for saddle height adjustment only and was resistance braked (RB). Methods: Ten trained male cyclists were tested on 2 separate occasions within a 14 day period under the same conditions. Subjects performed a 30 second Wingate maximal sprint test followed 60 minutes later by a continuous maximal incremental step test on either the EB or RB cycle ergometer, in a random order. The Wingate test was performed at 9% of body mass and for 30 seconds with a 5 second speed up period. The incremental test started at 100W and increased in resistance by 50W every 3 minutes until volitional exhaustion. Heart rate, VO2, power output and blood lactate were measured during the maximal incremental test. Results: The results showed a significant difference (p<0.01) for the Wingate test between the RB and EB both in terms of peak power output (POmax) and mean power output (POmean) with subjects generating greater power outputs on the EB. During the maximal incremental test, significant differences (p<0.01) were found between EB and RB for submaximal power output, heart rate, and VO2 at both lactate threshold 1 (1mmol.l-1 rise above baseline, LT1) and onset of blood lactate accumulation (4mmol.l-1 blood lactate reference point, OBLA), as well as peak power output at VO2max (PVO2max). Conclusions: Overall it was shown that significant differences in physiological demands were present between the two ergometers under both anaerobic and aerobic conditions. This is may in part be explained by the different positions that the cyclists adopted on either ergometer. Further research is required to compare the findings of the current study with actual cycling performance

Investigating the Impact of Green Exercise on Population Health and Well-being in a Small Community in Ireland:a Novel Approach Using a Natural Laboratory Ecosystem.

Green exercise is defined as undertaking physical activity whilst being directly exposed to nature (Pretty et al., 2005; 2007). Pretty et al. (2003) were among the first wave of researchers to investigate the synergistic benefits of incorporating physical activity and exposure to the natural environment to produce positive psychological affect. Over the past decade, investigations into the possible additive effects on well-being of green exercise and how it can be used as an influential tool to help combat the rising rate of both physical inactivity and non –communicable disease has gained prominence in scientific literature. However, there is still a need to investigate the mechanisms behind observed health benefits of the natural environment and to gain a deeper understanding of the benefits of environmental components and how this has potential to improve wellbeing and increase autonomous motivation in physical activity in a community setting. The research project GoGreenEx (Going Outdoors: Gathering Research Evidence on ENvironment and Exercise) aims to build engagement between expert researchers across interdisciplinary perspectives (psychology, physiology, biomechanics, environmental sciences and physical activity) and societal groups, both from the charity sector (Mental Health Ireland-a charity that promotes positive mental health) and the sporting domain (Local Sport Partnerships and commercial entities-e.g., Clarisford Park). This novel research in the field of public health will use the natural laboratory of Clarisford Park to study the impacts and underlying processes that surround green exercise and further add to our understanding of its potential effects on population health and well-being

Salivary biomarkers and training load during training and competition in paralympic swimmers

CONTEXT: Stress responses in athletes can be attributed to training and competition, where increased physiological and psychological stress may negatively affect performance and recovery. PURPOSE: To examine the relationship between training load (TL) and salivary biomarkers immunoglobulin A (IgA), alpha-amylase (AA), and cortisol across a 16-wk preparation phase and 10-d competition phase in Paralympic swimmers. METHODS: Four Paralympic swimmers provided biweekly saliva samples during 3 training phases-(1) normal training, (2) intensified training, and (3) taper-as well as daily saliva samples in the 10-d Paralympic competition (2016 Paralympic Games). TL was measured using session rating of perceived exertion. RESULTS: Multilevel analysis identified a significant increase in salivary immunoglobulin A (sIgA: 94.98 [27.69] μg·mL-1), salivary alpha-amylase (sAA: 45.78 [19.07] μg·mL-1), and salivary cortisol (7.92 [2.17] nM) during intensified training concurrent with a 38.3% increase in TL. During the taper phase, a 49.5% decrease in TL from the intensified training phase resulted in a decrease in sIgA, sAA, and salivary cortisol; however, all 3 remained higher than baseline levels. A further significant increase was observed during competition in sIgA (168.69 [24.19] μg·mL-1), sAA (35.86 [16.67] μg·mL-1), and salivary cortisol (10.49 [1.89] nM) despite a continued decrease (77.8%) in TL from the taper phase. CONCLUSIONS: Results demonstrate that performance in major competition such as Paralympic games, despite a noticeable reduction in TL, induces a stress response in athletes. Because of the elevated stress response observed, modifications to individual postrace recovery protocols may be required to enable athletes to maximize performance across all 10 d of competition

Physiological and metabolic characteristics of elite tug of war athletes

Objective—To determine the aerobic power ([Image: see text] O(2)MAX), body composition, strength, muscular power, flexibility, and biochemical profile of an elite international squad of tug of war athletes. Methods—Sixteen male competitors (mean (SEM) age 34 (2) years) were evaluated in a laboratory. For comparative purposes, data were analysed relative to normative data for our centre and to a group of 20 rugby forwards from the Irish international squad. Results—The tug of war participants were lighter (83.6 (3.0) v 104.4 (1.8) kg, p<0.0001) and had less lean body mass (69.4 (2.1) v 86.2 (1.2) kg) than the rugby players and had lower than normal body fat (16.7 (0.9)%); all values are mean (SEM). Aerobic power measured during a treadmill test was 55.8 (1.6) ml/kg/min for the tug of war participants compared with 51.1 (1.4) ml/kg/min for the rugby forwards (p<0.03). A composite measure of strength derived from (sum of dominant and non-dominant grip strength and back strength)/lean body mass yielded a strength/mass ratio that was 32% greater (p<0.0001) for the tug of war group than the rugby group. Dynamic leg power was lower for the tug of war group than the rugby forwards (4659.8 (151.6) v 6198.2 (105) W respectively; p<0.0001). Leg flexibility was 25.4 (2.0) cm for the tug of war group. Back flexibility was 28.6 (1.4) cm which was lower (p<0.02) than the rugby forwards 34.2 (1.5) cm. Whereas blood chemistry and haematology were normal, packed cell volume, haemoglobin concentration, and erythrocyte volume were lower in the tug of war group than in the rugby players (p<0.05). All three haematological measures correlated with muscle mass (packed cell volume, r(2) = 0.37, p<0.0001; haemoglobin concentration, r(2) = 0.13, p<0.05; erythrocyte volume, r(2) = 0.21, p<0.01). Conclusions—The data indicate that international level tug of war participants have excellent strength and above average endurance relative to body size, but have relatively low explosive leg power and back flexibility. The data provide reference standards for the sport and may be useful for monitoring and evaluating current and future participants. Key Words: tug of war; body composition; [Image: see text] O(2)MAX; strength; power; flexibilit

Physiological, haematological and performance characteristics of ultra endurance cyclists competing in the inaugural race around Ireland

Ultra-endurance events are a growing area within the sport of cycling. The Race Around Ireland (RAI) is a non-stop event where cyclists must complete the 2,170km route in under 96 hours. Purpose: The purpose of this study was to investigate the physiological, haematological and performance characteristics of members of a 4 man team before, during, and after the RAI. Methods: Four trained male cyclists were tested on 2 separate occasions within a 14 day period, with the second bout of testing performed within 7 days of the start of the race, to determine baseline values. Each cyclist completed a maximal incremental test on an electromagnetically braked cycle ergometer, commencing at 100W and increasing in intensity by 50W every 3 minutes until volitional exhaustion. Heart rate, VO2, power output and blood lactate were measured during the test. Following a standardized recovery period, each cyclist then completed a 20 minute maximal performance test (MPT) designed to mimic the demands of the RAI. Baseline blood samples were taken prior to each testing session to facilitate a detailed haematological analysis. Blood samples were also taken before the start of the race, at set intervals during the race, as well as on the race completion. Subjects were also weighed and urine samples collected at the same time points in order to assess hydration status using urine specific gravity (Usg). Further testing was carried out 7 days (haematology), and 14 days (haematology and MPT) post race. Results: No significant differences were found between the MPT results pre and post race. Significant differences were found for white blood cells (WBC) and granulocyte count (p<0.01), haematocrit, haemoglobin, lymphocytes, and red blood cells (p<0.05). No significant difference was observed for changes in body mass or Usg. Conclusions: Variations in WBC and other immune function markers showed initial decrease followed by a gradual elevation during the race. However this did not seem have an impact on the post race MPT. Although there appears to be a significant change in immune function during ultra endurance cycling, this may not lead to a subsequent performance decrement. However, analysis may be complicated by the specific race tactics adopted by the team during the race and the time course of post race assessment

Wearable chemical sensing – sensor design and sampling techniques for real-time sweat analysis

Wearable chemical sensors have the potential to provide new methods of non-invasive physiological measurement. The nature of chemical sensors involves an active surface where a chemical reaction must occur to elicit a response. This adds complexity to a wearable system which creates challenges in the design of a reliable long-term working system. This work presents the design of a real-time sweat sensing platform to analyse sweat loss and composition. Sampling methods have an impact on composition therefore skin encapsulation needs to be avoided so as not to disrupt normal sweating patterns. Sensors ideally need to be placed close to the sampling site which may be subject to motion artefacts [1]. The design of this device takes into account sample collection and delivery, sensor placement and associated electronics. The overall design is ergonomic to interface with the contours of the body. Results of lab-based simulations and real-time exercise trials are presented. This device can offer valuable information regarding hydration status and electrolyte balance which may be especially important for optimised rehydration during or after sports activities. [1] Curto, V. F. S. Coyle, R. Byrne, N. Angelov, D. Diamond, F. Benito-Lopez., Sens. Actuators, B, 2012, 175, 263-270