Female RNA concussion (FeRNAC) study: assessing hormone profiles and salivary RNA in females with concussion by emergency departments in New Zealand: a study protocol.

BACKGROUND: Females of reproductive age with concussion report a greater number of symptoms that can be more severe and continue for longer than age matched males. Underlying mechanisms for sex differences are not well understood. Short non-coding Ribonucleic Acids (sncRNAs) are candidate salivary biomarkers for concussion and have been studied primarily in male athletes. Female sex hormones influence expression of these biomarkers, and it remains unclear whether a similar pattern of sncRNA expression would be observed in females following concussion. This study aims to evaluate recovery time, the ratio of salivary sncRNAs and symptom severity across different hormone profiles in females presenting to emergency departments (ED) with concussion and, to investigate the presence of low energy availability (LEA) as a potential modifier of concussion symptoms. METHODS: This prospective cohort study recruits participants from New Zealand EDs who are biologically female, of reproductive age (16-50 years) and with a confirmed diagnosis of concussion from an ED healthcare professional. Participants are excluded by ED healthcare professionals from study recruitment as part of initial routine assessment if they have a pre-diagnosed psychiatric condition, neurological condition (i.e., epilepsy, cerebral palsy) or more than three previously diagnosed concussions. Participants provide a saliva sample for measurement of sncRNA's, and online survey responses relating to hormone profile and symptom recovery at 7-day intervals after injury until they report a full return to work/study. The study is being performed in accordance with ethical standards of the Declaration of Helsinki with ethics approval obtained from the Health and Disability Ethics Committee (HDEC #2021 EXP 11655), Auckland University of Technology Ethics Committee (AUTEC #22/110) and locality consent through Wellington hospital research office. DISCUSSION: If saliva samples confirm presence of sncRNAs in females with concussion, it will provide evidence of the potential of saliva sampling as an objective tool to aid in diagnosis of, and confirmation of recovery from, concussion. Findings will determine whether expression of sncRNAs is influenced by steroid hormones in females and may outline the need for sex specific application and interpretation of sncRNAs as a clinical and/or research tool. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) registration number ACTRN12623001129673.

Predicting daily recovery during long-term endurance training using machine learning analysis.

PURPOSE: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. METHODS: Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. RESULTS: Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5-23.6 and 0.05-0.44 for AM PRS and HRV change, respectively). CONCLUSION: At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.

Sociodemographic differences in 24-hour time-use behaviours in New Zealand children.

BACKGROUND: The time that children spend in physical activity, sedentary behaviour, and sleep each day (i.e., 24-h time-use behaviours), is related to physical and mental health outcomes. Currently, there is no comprehensive evidence on New Zealand school-aged children's 24-h time-use behaviours, adherence to the New Zealand 24-h Movement Guidelines, and how these vary among different sociodemographic groups. METHODS: This study utilises data from the 8-year wave of the Growing Up in New Zealand longitudinal study. Using two Axivity AX3 accelerometers, children's 24-h time-use behaviours were described from two perspectives: activity intensity and activity type. Compositional data analysis techniques were used to explore the differences in 24-h time-use compositions across various sociodemographic groups. RESULTS: Children spent on average, 31.1%, 22.3%, 6.8%, and 39.8% of their time in sedentary, light physical activity, moderate-to-vigorous physical activity, and sleep, respectively. However, the daily distribution of time in different activity types was 33.2% sitting, 10.8% standing, 7.3% walking, 0.4% running, and 48.2% lying. Both the activity intensity and activity type compositions varied across groups of child ethnicity, gender, and household income or deprivation. The proportion of children meeting each of the guidelines was 90% for physical activity, 62.5% for sleep, 16% for screen time, and 10.6% for the combined guidelines. Both gender and residence location (i.e., urban vs. rural) were associated with meeting the physical activity guideline, whereas child ethnicity, mother's education and residence location were associated with meeting the screen time guideline. Child ethnicity and mother's education were also significantly associated with the adherence to the combined 24-h Movement Guidelines. CONCLUSIONS: This study provided comprehensive evidence on how New Zealand children engage in 24-h time-use behaviours, adherence to the New Zealand 24-h Movement Guidelines, and how these behaviours differ across key sociodemographic groups. These findings should be considered in designing future interventions for promoting healthy time-use patterns in New Zealand children.

Peak fat oxidation is positively associated with vastus lateralis CD36 content, fed-state exercise fat oxidation, and endurance performance in trained males.

PURPOSE: Whole-body fat oxidation during exercise can be measured non-invasively during athlete profiling. Gaps in understanding exist in the relationships between fat oxidation during incremental fasted exercise and skeletal muscle parameters, endurance performance, and fat oxidation during prolonged fed-state exercise. METHODS: Seventeen endurance-trained males underwent a (i) fasted, incremental cycling test to assess peak whole-body fat oxidation (PFO), (ii) resting vastus lateralis microbiopsy, and (iii) 30-min maximal-effort cycling time-trial preceded by 2-h of fed-state moderate-intensity cycling to assess endurance performance and fed-state metabolism on separate occasions within one week. RESULTS: PFO (0.58 ± 0.28 g.min-1) was associated with vastus lateralis citrate synthase activity (69.2 ± 26.0 µmol.min-1.g-1 muscle protein, r = 0.84, 95% CI 0.58, 0.95, P < 0.001), CD36 abundance (16.8 ± 12.6 µg.g-1 muscle protein, rs = 0.68, 95% CI 0.31, 1.10, P = 0.01), pre-loaded 30-min time-trial performance (251 ± 51 W, r = 0.76, 95% CI 0.40, 0.91, P = 0.001; 3.2 ± 0.6 W.kg-1, r = 0.62, 95% CI 0.16, 0.86, P = 0.01), and fat oxidation during prolonged fed-state cycling (r = 0.83, 95% CI 0.57, 0.94, P < 0.001). Addition of PFO to a traditional model of endurance (peak oxygen uptake, power at 4 mmol.L-1 blood lactate concentration, and gross efficiency) explained an additional ~ 2.6% of variation in 30-min time-trial performance (adjusted R2 = 0.903 vs. 0.877). CONCLUSION: These associations suggest non-invasive measures of whole-body fat oxidation during exercise may be useful in the physiological profiling of endurance athletes.

Quantifying cricket fast bowling volume, speed and perceived intensity zone using an Apple Watch and machine learning.

This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70-85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.

The relationship between bowling intensity and ground reaction force in cricket pace bowlers.

This study examined the relationship between perceived bowling intensity, ball release speed and ground reaction force (measured by peak force, impulse and loading rate) in male pace bowlers. Twenty participants each bowled 36 deliveries, split evenly across three perceived intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. Peak force and loading rate were significantly different across the three perceived intensity zones in the horizontal and vertical directions (Cohen's d range = 0.14-0.45, p < 0.01). When ball release speed increased, peak force and loading rate also increased in the horizontal and vertical directions (ηp2 = 0.04-0.18, p < 0.01). Lastly, bowling at submaximal intensities (i.e., low - medium) was associated with larger decreases in peak horizontal force (7.9-12.3% decrease), impulse (15.8-21.4%) and loading rate (7.4-12.7%) compared to decreases in ball release speed (5.4-8.3%). This may have implications for bowling strategies implemented during training and matches, particularly for preserving energy and reducing injury risk.

Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket?

This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was attached to the thoracic back of 44 fast bowlers. Each participant bowled 36 deliveries at two different PIZ zones (Zone 1 = 24 deliveries at 70% to 85% of maximum perceived bowling effort; Zone 2 = 12 deliveries at 100% of maximum perceived bowling effort) in a random order. IMU data (sampling rate = 250 Hz) were downsampled to 125 Hz, 50 Hz, and 25 Hz to determine if model accuracy was affected by the sampling frequency. Data were analysed using four machine learning models. A two-way repeated-measures ANOVA was used to compare the mean absolute error (MAE) and accuracy scores (separately) across the four models and four sampling frequencies. Gradient boosting models were shown to be the most consistent at measuring BRS (MAE = 3.61 km/h) and PIZ (F-score = 88%) across all sampling frequencies. This method could be used to measure BRS and PIZ which may contribute to a better understanding of overall bowling load which may help to reduce injuries.

Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?

Injuries in handball are common due to the repetitive demands of overhead throws at high velocities. Monitoring workload is crucial for understanding these demands and improving injury-prevention strategies. However, in handball, it is challenging to monitor throwing workload due to the difficulty of counting the number, intensity, and type of throws during training and competition. The aim of this study was to investigate if an inertial measurement unit (IMU) and machine learning (ML) techniques could be used to detect different types of team handball throws and predict ball velocity. Seventeen players performed several throws with different wind-up (circular and whip-like) and approach types (standing, running, and jumping) while wearing an IMU on their wrist. Ball velocity was measured using a radar gun. ML models predicted peak ball velocity with an error of 1.10 m/s and classified approach type and throw type with 80-87% accuracy. Using IMUs and ML models may offer a practical and automated method for quantifying throw counts and classifying the throw and approach types adopted by handball players.

Smart Watch Versus Classic Receivers: Static Validity of Three GPS Devices in Different Types of Built Environments.

In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies.

Efficacy of a compulsory homework programme for increasing physical activity and improving nutrition in children: a cluster randomised controlled trial.

BACKGROUND: Most physical activity interventions in children focus on the school setting; however, children typically engage in more sedentary activities and spend more time eating when at home. The primary aim of this cluster randomised controlled trial was to investigate the effects of a compulsory, health-related homework programme on physical activity, dietary patterns, and body size in primary school-aged children. METHODS: A total of 675 children aged 7-10 years from 16 New Zealand primary schools participated in the Healthy Homework study. Schools were randomised into intervention and control groups (1:1 allocation). Intervention schools implemented an 8-week applied homework and in-class teaching module designed to increase physical activity and improve dietary patterns. Physical activity was the primary outcome measure, and was assessed using two sealed pedometers that monitored school- and home-based activity separately. Secondary outcome measures included screen-based sedentary time and selected dietary patterns assessed via parental proxy questionnaire. In addition, height, weight, and waist circumference were measured to obtain body mass index (BMI) and waist-to-height ratio (WHtR). All measurements were taken at baseline (T0), immediately post-intervention (T1), and 6-months post-intervention (T2). Changes in outcome measures over time were estimated using generalised linear mixed models (GLMMs) that adjusted for fixed (group, age, sex, group x time) and random (subjects nested within schools) effects. Intervention effects were also quantified using GLMMs adjusted for baseline values. RESULTS: Significant intervention effects were observed for weekday physical activity at home (T1 [P < 0.001] and T2 [P = 0.019]), weekend physical activity (T1 [P < 0.001] and T2 [P < 0.001]), BMI (T2 only [P = 0.020]) and fruit consumption (T1 only [P = 0.036]). Additional analyses revealed that the greatest improvements in physical activity occurred in children from the most socioeconomically deprived schools. No consistent effects on sedentary time, WHtR, or other dietary patterns were observed. CONCLUSIONS: A compulsory health-related homework programme resulted in substantial and consistent increases in children's physical activity - particularly outside of school and on weekends - with limited effects on body size and fruit consumption. Overall, our findings support the integration of compulsory home-focused strategies for improving health behaviours into primary education curricula. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12618000590268 . Registered 17 April 2018.

Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning.

Fast bowlers are at a high risk of overuse injuries. There are specific bowling frequency ranges known to have negative or protective effects on fast bowlers. Inertial measurement units (IMUs) can classify movements in sports, however, some commercial products can be too expensive for the amateur athlete. As a large number of the world's population has access to an IMU (e.g. smartphones), a system that works on a range of different IMUs may increase the accessibility of automated workload monitoring in sport. Seventeen elite fast bowlers in a training setting were used to train and/or validate five machine learning models by bowling and performing fielding drills. The accuracy of machine learning models trained using data from all three bowling phases (pre-delivery, delivery and post-delivery) were compared to those trained using only the delivery phase at a sampling rate of 250 Hz. Next, models were trained using data down-sampled to 125 Hz, 50 Hz, and 25 Hz to mimic results from lower specification sensors. Models trained using only the delivery phase showed similar accuracy (> 95%) to those trained using all three bowling phases. When delivery-phase data were down-sampled, the accuracy was maintained across all models and sampling frequencies (>96%).

Associations of the perceived and objective neighborhood environment with physical activity and sedentary time in New Zealand adolescents.

BACKGROUND: There is accumulating evidence supporting the association between neighborhood built environments and adults' physical activity (PA) and sedentary time (ST); however, few studies have investigated these associations in adolescents. A better understanding of the features of the built environment that encourage PA or ST is therefore of critical importance to promote health and wellbeing in adolescents. The aim of this study was to estimate the associations of GIS-determined and perceived walkability components in individual residential buffer zones with accelerometer-assessed moderate-to-vigorous physical activity (MVPA) and ST in adolescents. METHODS: The Built Environment in Adolescent New Zealanders (BEANZ) study was conducted in two cities (Auckland and Wellington) during the 2013-2014 academic school years. The exposure measures were subjective and objective environmental indices of activity-friendliness using four residential buffers. Road network buffers were calculated around participant's residential addresses using the sausage buffer approach at 250 m, 500 m, 1 km, and 2 km scales. A 25 m radius was used for the buffers. Data were analysed using Generalized Additive Mixed Models in R. RESULTS: Data were analysed from 524 participants (15.78 ± 1.62 years; 45% male). Participants accumulated ~114 min/day of moderate-to-vigorous PA (MVPA) and ~354 min/day of ST during accelerometer wear-time (~828 min/day). The estimated difference in MVPA between participants with the 1st and 3rd quartiles observed values on the composite subjective environmental index of activity-friendliness (perceived land use mix - diversity, street connectivity and aesthetics) was equivalent to ~8 min/day (~56 MVPA min/week) and for the objective environmental index of activity-friendliness (gross residential density and number of parks within 2 km distance from home) was ~6 min of MVPA/day (~45 MVPA min/week). When both indices were entered in a main-effect model, both indices remained significantly correlated with MVPA with sex as a moderator. The predicted difference in sedentary time between those with the minimum and maximum observed values on the subjective index of non-sedentariness was ~20 min/day. CONCLUSIONS: The combined assessment of the main effects of subjective and objective indices of activity-friendliness on NZ adolescents' PA and ST showed positive relationships with MVPA for the subjective index only. The subjective index was a significant correlate of PA in both girls and boys, while the objective index was significant only in boys when sex was entered as a moderator. Further research is warranted to understand the relationships of ST with the built environment.

A novel assessment of adolescent mobility: a pilot study.

BACKGROUND: The accurate measurement of daily mobility and travel to destinations beyond the residential neighbourhood has been identified as an important but almost systematically overlooked factor when investigating the relationship between exposure to the built environment and physical activity. The recent development of VERITAS - a web-based application nested within a computer-assisted personal interview - allows researchers to assess daily mobility, travel to regular destinations, and perceived neighbourhood boundaries using interactive mapping technology. The aims of this pilot study were to (1) demonstrate the feasibility and functionality of using VERITAS in an adolescent sample, and (2) compare urban form characteristics and geometric features of the perceived neighbourhood with traditional neighbourhood delimitations. METHODS: Data were collected and analysed for twenty-eight participants (14 male, 15.9 ± 1.48 years) in 2013. Participants underwent anthropometric assessment before completing a custom-designed VERITAS protocol under the supervision of trained interview technicians. Regularly visited destinations, school travel routes, transportation modes, travel companions, and perceived neighbourhood boundaries were assessed. Data were imported into ArcGIS and street network distances between the home and each geolocated destination were generated. Convex hull activity spaces were derived from destinations. Urban form variables and geometric characteristics were compared between the perceived neighbourhood, existing meshblocks, 1 mile Euclidean buffers, and 1 km network buffers. RESULTS: In total, 529 destinations were geolocated, 58% of which were outside the perceived neighbourhood boundary. Active travel was inversely associated with distance to destinations (r = -.43, p < .05) and traveling with adults (r = -.68, p < .01). Urban form and geometric characteristics of the perceived neighbourhood were different from those in other neighbourhood delimitations. CONCLUSIONS: This study demonstrates the feasibility of using VERITAS to assess mobility within adolescent populations. Our results also illustrate the potential novelty and use of user-defined spaces, and highlight the limitations of relying on restricted definitions of place (i.e., administrative or residential-focused neighbourhoods) when assessing environmental exposure.

Nutrition affects Sertoli cell function but not Sertoli cell numbers in sexually mature male sheep.

We tested whether the reversible effects of nutrition on spermatogenesis in sexually mature sheep were mediated by Sertoli cells. Rams were fed with diets designed to achieve a 10% increase (High), no change (Maintenance) or a 10% decrease (Low) in body mass after 65 days. At the end of treatment, testes were lighter in the Low than the High group (PP<0.05) in the expression of seven Sertoli cell-specific genes. Under-nutrition appeared to reverse cellular differentiation leading to disruption of tight-junction morphology. In conclusion, in sexually mature sheep, reversible reductions in testis mass and spermatogenesis caused by under-nutrition were associated with impairment of basic aspects of Sertoli cell function but not with changes in the number of Sertoli cells.

Menstrual cycle patterns and their relationship with measures of well-being and perceived performance metrics in competitive and recreational resistance-trained athletes.

BACKGROUND: There is mixed evidence on how the menstrual cycle (MC) affects sports performance, with many studies showing variations in performance during different phases of the MC, while other evidence shows that the MC's effects on performance may be trivial. Therefore, this exploratory longitudinal monitoring study was designed to investigate MC characteristics and symptoms in a resistance-trained (RT) population to look for associations between measures of well-being and perceived performance metrics across the MC. METHODS: RT females reported their workout habits, perceived performance metrics, and measures of well-being while tracking their MC with detailed methods via daily check-ins in an app. RESULTS: Most MC characteristics and symptoms in the present RT population aligned with previous research on the general population. However, the frequency of irregular cycles was higher than in previous research on the general population. The amount of individual variation and within-subject cycle-to-cycle variation in MC characteristics and MC symptoms was also high. All measures of well-being were significantly associated with specific days of the MC, demonstrating a change in well-being based on the timing of the MC. Several perceived performance metrics were significantly associated with changes across the MC, while others were not. CONCLUSIONS: Overall, with the current evidence as it stands, a highly individualized approach should be taken for any training or performance considerations surrounding the MC due to the high levels of individual variation.

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains.

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

Acceptability of standing workstations in elementary schools: a pilot study.

OBJECTIVE: To examine the acceptability of introducing standing workstations in elementary-school classrooms; and to quantify changes in children's time spent sitting, standing, and walking; step counts; sit-to-stand transitions; and musculoskeletal discomfort. METHODS: A controlled trial was conducted in two elementary schools in Auckland, New Zealand (March-May 2012). Participants were 30 third and fourth graders (n = 23 intervention, n = 7 control). Intervention classes received standing workstations; control class retained usual sitting desks. Children wore ActivPAL monitors over 7 days at baseline and during the fourth week of the intervention. RESULTS: Children spoke enthusiastically of the standing workstations. School staffs were supportive of the standing workstations because they offered "flexibility in learning". Overall, children in the intervention group sat less (intervention: 8.27 (1.45), mean (SD); control: 9.00 (0.80) h/day), stood longer (3.75 (0.88); 2.85 (0.30) h/day), and engaged in fewer transitions from sitting to standing (93 (17); 98 (26) counts) compared to the control group. Effect size ranged from small-large (-0.49; 95% confidence limits (0.64)%, 0.71; (0.48), -0.96; (0.54)% respectively). Results for time spent stepping and step counts were unclear. CONCLUSION: Standing workstations can be successfully integrated in classroom environments and appear to decrease overall sedentariness.

Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

Factors Influencing Substrate Oxidation During Submaximal Cycling: A Modelling Analysis.

BACKGROUND: Multiple factors influence substrate oxidation during exercise including exercise duration and intensity, sex, and dietary intake before and during exercise. However, the relative influence and interaction between these factors is unclear. OBJECTIVES: Our aim was to investigate factors influencing the respiratory exchange ratio (RER) during continuous exercise and formulate multivariable regression models to determine which factors best explain RER during exercise, as well as their relative influence. METHODS: Data were extracted from 434 studies reporting RER during continuous cycling exercise. General linear mixed-effect models were used to determine relationships between RER and factors purported to influence RER (e.g., exercise duration and intensity, muscle glycogen, dietary intake, age, and sex), and to examine which factors influenced RER, with standardized coefficients used to assess their relative influence. RESULTS: The RER decreases with exercise duration, dietary fat intake, age, VO2max, and percentage of type I muscle fibers, and increases with dietary carbohydrate intake, exercise intensity, male sex, and carbohydrate intake before and during exercise. The modelling could explain up to 59% of the variation in RER, and a model using exclusively easily modified factors (exercise duration and intensity, and dietary intake before and during exercise) could only explain 36% of the variation in RER. Variables with the largest effect on RER were sex, dietary intake, and exercise duration. Among the diet-related factors, daily fat and carbohydrate intake have a larger influence than carbohydrate ingestion during exercise. CONCLUSION: Variability in RER during exercise cannot be fully accounted for by models incorporating a range of participant, diet, exercise, and physiological characteristics. To better understand what influences substrate oxidation during exercise further research is required on older subjects and females, and on other factors that could explain additional variability in RER.

Factors Influencing AMPK Activation During Cycling Exercise: A Pooled Analysis and Meta-Regression.

BACKGROUND: The 5' adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a cellular energy sensor that is activated by increases in the cellular AMP/adenosine diphosphate:adenosine triphosphate (ADP:ATP) ratios and plays a key role in metabolic adaptations to endurance training. The degree of AMPK activation during exercise can be influenced by many factors that impact on cellular energetics, including exercise intensity, exercise duration, muscle glycogen, fitness level, and nutrient availability. However, the relative importance of these factors for inducing AMPK activation remains unclear, and robust relationships between exercise-related variables and indices of AMPK activation have not been established. OBJECTIVES: The purpose of this analysis was to (1) investigate correlations between factors influencing AMPK activation and the magnitude of change in AMPK activity during cycling exercise, (2) investigate correlations between commonly reported measures of AMPK activation (AMPK-α2 activity, phosphorylated (p)-AMPK, and p-acetyl coenzyme A carboxylase (p-ACC), and (3) formulate linear regression models to determine the most important factors for AMPK activation during exercise. METHODS: Data were pooled from 89 studies, including 982 participants (93.8% male, maximal oxygen consumption [[Formula: see text]] 51.9 ± 7.8 mL kg-1 min-1). Pearson's correlation analysis was performed to determine relationships between effect sizes for each of the primary outcome markers (AMPK-α2 activity, p-AMPK, p-ACC) and factors purported to influence AMPK signaling (muscle glycogen, carbohydrate ingestion, exercise duration and intensity, fitness level, and muscle metabolites). General linear mixed-effect models were used to examine which factors influenced AMPK activation. RESULTS: Significant correlations (r = 0.19-0.55, p < .05) with AMPK activity were found between end-exercise muscle glycogen, exercise intensity, and muscle metabolites phosphocreatine, creatine, and free ADP. All markers of AMPK activation were significantly correlated, with the strongest relationship between AMPK-α2 activity and p-AMPK (r = 0.56, p < 0.001). The most important predictors of AMPK activation were the muscle metabolites and exercise intensity. CONCLUSION: Muscle glycogen, fitness level, exercise intensity, and exercise duration each influence AMPK activity during exercise when all other factors are held constant. However, disrupting cellular energy charge is the most influential factor for AMPK activation during endurance exercise.