Acute ingestion of dietary nitrate may not represent an effective strategy for reducing the oxygen cost of submaximal exercise or for enhancing endurance exercise performance in highly trained cross-country skiers.

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Swain (1997) employed the mathematical model of Di Prampero et al. (1979) to predict that, for cycling time-trials, the optimal pacing strategy is to vary power in parallel with the changes experienced in gradient and wind speed. We used a more up-to-date mathematical model with validated coefficients (Martin et al., 1998) to quantify the time savings that would result from such optimization of pacing strategy. A hypothetical cyclist (mass = 70 kg) and bicycle (mass = 10 kg) were studied under varying hypothetical wind velocities (-10 to 10 m x s(-1)), gradients (-10 to 10%), and pacing strategies. Mean rider power outputs of 164, 289, and 394 W were chosen to mirror baseline performances studied previously. The three race scenarios were: (i) a 10-km time-trial with alternating 1-km sections of 10% and -10% gradient; (ii) a 40-km time-trial with alternating 5-km sections of 4.4 and -4.4 m x s(-1) wind (Swain, 1997); and (iii) the 40-km time-trial delimited by Jeukendrup and Martin (2001). Varying a mean power of 289 W by +/- 10% during Swain's (1997) hilly and windy courses resulted in time savings of 126 and 51 s, respectively. Time savings for most race scenarios were greater than those suggested by Swain (1997). For a mean power of 289 W over the "standard" 40-km time-trial, a time saving of 26 s was observed with a power variability of 10%. The largest time savings were found for the hypothetical riders with the lowest mean power output who could vary power to the greatest extent. Our findings confirm that time savings are possible in cycling time-trials if the rider varies power in parallel with hill gradient and wind direction. With a more recent mathematical model, we found slightly greater time savings than those reported by Swain (1997). These time savings compared favourably with the predicted benefits of interventions such as altitude training or ingestion of carbohydrate-electrolyte drinks. Nevertheless, the extent to which such power output variations can be tolerated by a cyclist during a time-trial is still unclear.

Our research shows that no single metric will reflect an individual's physical activity adequately because multiple biologically important dimensions are independent and unrelated. We propose that there is an opportunity to exploit this multidimensional characteristic of physical activity to improve personalized feedback and offer physical activity options and choices that are tailored to an individual's needs and preferences.

We aim to summarise the impact and mechanisms of work-rate pacing during individual cycling time trials (TTs). Unlike time-to-exhaustion tests, a TT provides an externally valid model for examining how an initial work rate is chosen and maintained by an athlete during self-selected exercise. The selection and distribution of work rate is one of many factors that influence cycling speed. Mathematical models are available to predict the impact of factors such as gradient and wind velocity on cycling speed, but only a few researchers have examined the inter-relationships between these factors and work-rate distribution within a TT. When environmental conditions are relatively stable (e.g. in a velodrome) and the TT is >10 minutes, then an even distribution of work rate is optimal. For a shorter TT (< or = 10 minutes), work rate should be increased during the starting effort because this proportion of total race time is significant. For a very short TT (< or = 2 minutes), the starting effort should be maximal, since the time saved during the starting phase is predicted to outweigh any time lost during the final metres because of fatigue. A similar 'time saving' rationale underpins the advice that work rate should vary in parallel with any changes in gradient or wind speed during a road TT. Increasing work rate in headwind and uphill sections, and vice versa, decreases the variability in speed and, therefore, the total race time. It seems that even experienced cyclists naturally select a supraoptimal work rate at the start of a longer TT. Whether such a start can be 'blunted' through coaching or the monitoring of psychophysiological variables is unknown. Similarly, the extent to which cyclists can vary and monitor work rate during a TT is unclear. There is evidence that sub-elite cyclists can vary work rate by +/-5% the average for a TT lasting 25-60 minutes, but such variability might be difficult with high-performance cyclists whose average work rate during a TT is already extremely high (>350 watts). During a TT, pacing strategy is regulated in a complex anticipatory system that monitors afferent feedback from various physiological systems, and then regulates the work rate so that potentially limiting changes do not occur before the endpoint of exercise is reached. It is critical that the endpoint of exercise is known by the cyclist so that adjustments to exercise work rate can be made within the context of an estimated finish time. Pacing strategies are thus the consequence of complex regulation and serve a dual role: they are both the result of homeostatic regulation by the brain, as well as being the means by which such regulation is achieved. The pacing strategy 'algorithm' is sited in the brain and would need afferent input from interoceptors, such as heart rate and respiratory rate, as well as exteroceptors providing information on local environmental conditions. Such inputs have been shown to induce activity in the thalamus, hypothalamus and the parietal somatosensory cortex. Knowledge of time, modulated by ...

Measurement of steroid hormones in saliva is increasingly common in elite sport settings. However, this environment may enforce handling and storage practices that introduce error in measurement of hormone concentrations. We assessed the influence of storage temperature and duration on reproducibility of salivary steroid levels. Nine healthy adults provided morning and afternoon saliva samples on two separate occasions. Each sample was divided into identical saliva aliquots which were stored long-term (i.e. 28 and 84 days) at - 80°C or - 20°C (testing day 1), and short-term (i.e. 1, 3, 7 and 14 days) at 4°C or 20°C (testing day 2). Samples were analyzed for cortisol, testosterone and estradiol using ELISA. In non-freezer conditions, there was a decrease from baseline to 7 days in testosterone (- 26 ± 15%) and estradiol (- 58 ± 17%) but not cortisol concentrations (p < 0.001). This decrease was larger in samples stored at room temperature than in the refrigerator (p ≤ 0.01). There were small but significant changes in measured concentrations of all hormones after 28 and/or 84 days of storage in freezer conditions (p ≤ 0.01), but these were generally within 12% of baseline concentrations, and may be partly explained by inter-assay variability. Whole saliva samples to be analyzed for cortisol, testosterone and estradiol should be frozen at - 20°C or below within 24 h of collection, and analyzed within 28 days. Storage of samples for measurement of testosterone and estradiol at temperatures above - 20°C can introduce large error variance to measured concentrations.

This study examined the effects of acute supplementation with L-arginine and nitrate on running economy, endurance and sprint performance in endurance-trained athletes. In a randomised cross-over, double-blinded design we compared the effects of combined supplementation with 6 g L-arginine and 614 mg nitrate against 614 mg nitrate alone and placebo in nine male elite cross-country skiers (age 18 ± 0 years, VO2max 69.3 ± 5.8 ml ⋅ min(-1) ⋅ kg(-1)). After a 48-hour standardisation of nutrition and exercise the athletes were tested for plasma nitrate and nitrite concentrations, blood pressure, submaximal running economy at 10 km ⋅ h(-1) and 14 km ⋅ h(-1) at 1% incline and 180 m as well as 5-km time-trial running performances. Plasma nitrite concentration following L-arginine + nitrate supplementation (319 ± 54 nmol ⋅ L(-1)) did not differ from nitrate alone (328 ± 107 nmol ⋅ L(-1)), and both were higher than placebo (149 ± 64 nmol ⋅ L(-1), p < 0.01). There were no differences in physiological responses during submaximal running or in 5-km performance between treatments. The plasma nitrite concentrations indicate greater nitric oxide availability both following acute supplementation of L-arginine + nitrate and with nitrate alone compared to placebo, but no additional effect was revealed when L-arginine was added to nitrate. Still, there were no effects of supplementation on exercise economy or endurance running performance in endurance-trained cross-country skiers.

Background: Technological progress has enabled the provision of personalised feedback across multiple dimensions of physical activity that are important for health. Whether this multidimensional approach supports physical activity behaviour change has not yet been examined. Our objective was to examine the effectiveness of a novel digital system and app that provided multidimensional physical activity feedback combined with health trainer support in primary care patients identified as at risk of chronic disease. Methods: MIPACT was a parallel-group, randomised controlled trial that recruited patients at medium (≥10 and < 20%) or high (≥20%) risk of cardiovascular disease and/or type II diabetes from six primary care practices in the United Kingdom. Intervention group participants (n = 120) received personal multidimensional physical activity feedback using a customised digital system and web-app for 3 months plus five health trainer-led sessions. All participants received standardised information regarding physical activity. Control group participants (n = 84) received no further intervention. The primary outcome was device-based assessment of physical activity at 12 months. Results: Mean intervention effects were: moderate-vigorous physical activity:-1.1 (95% CI, − 17.9 to 15.7) min/day; moderate-vigorous physical activity in ≥10-min bouts: 0.2 (− 14.2 to 14.6) min/day; Physical Activity Level (PAL): 0.00 (− 0.036 to 0.054); vigorous physical activity: 1.8 (− 0.8 to 4.2) min/day; and sedentary time: 10 (− 19.3 to 39.3) min/ day. For all of these outcomes, the results showed that the groups were practically equivalent and statistically ruled out meaningful positive or negative effects (>minimum clinically important difference, MCID). However, there was profound physical activity multidimensionality, and only a small proportion (5%) of patients had consistently low physical activity across all dimensions.