Determination, measurement, and validation of maximal aerobic speed

Household food insecurity among child and adolescent athletics practitioners: A cross-sectional, descriptive, and exploratory study

OBJECTIVE

The aim of the study was to explore the prevalence of food insecurity among child and adolescent athletics practitioners and to investigate factors associated with exercise performance, dietary habits, body composition, sleep, and socioeconomic status based on food security status.

METHODS

This was a cross-sectional, descriptive, and exploratory study conducted in Campinas, São Paulo, Brazil, between June and July 2023. The convenience sample included children and adolescents (7-17 y old) of both sexes. We evaluated exercise performance, household food insecurity (HFI), dietary-related parameters, and other body composition, lifestyle, and social-related variables. Exercise performance was assessed using the counter movement jump (CMJ), squat jump (SJ), horizontal long jump (HLJ), 50-m sprint test (50-m ST) and throwing strength test (TST). The assessment of HFI was conducted using the food insecurity experience scale (FIES). Comparisons and associations were investigated based on food security status.

RESULTS

The total sample size was comprised of 138 children (n = 42; 30.4%) and adolescents (n = 96; 69.6%). We found an association between food security status and sex (X2(138,1) = 4.42; P = 0.036). SJ was higher in the food security group than in the HFI group (t(117) = 2.112; P = 0.037; ES = 0.39). Sleep- and dietary-related factors did not differ between the groups.

CONCLUSIONS

In summary, the prevalence of HFI among child and adolescent athletics participants was approximately 40%. Regarding exercise performance, SJ was better in the food security group than in the HFI group. Concerning dietary-related data, the HFI group had a lower number of meals per day than the food security group, and other dietary data did not differ between the groups. Body composition and sleep-related parameters were similar between the groups.

Wearable device for continuous sweat lactate monitoring in sports: a narrative review

In sports science, the use of wearable technology has facilitated the development of new approaches for tracking and assessing athletes' performance. This narrative review rigorously explores the evolution and contemporary state of wearable devices specifically engineered for continuously monitoring lactate levels in sweat, an essential biomarker for appraising endurance performance. Lactate threshold tests have traditionally been integral in tailoring training intensity for athletes, but these tests have relied on invasive blood tests that are impractical outside a laboratory setting. The transition to noninvasive, real-time monitoring through wearable technology introduces an innovative approach, facilitating continuous assessment without the constraints inherent in traditional methodologies. We selected 34 products from a pool of 246 articles found through a meticulous search of articles published up to January 2024 in renowned databases: PubMed, Web of Science, and ScienceDirect. We used keywords such as "sweat lactate monitoring," "continuous lactate monitoring," and "wearable devices." The findings underscore the capabilities of noninvasive sweat lactate monitoring technologies to conduct long-term assessments over a broad range of 0-100 mM, providing a safer alternative with minimal infection risks. By enabling real-time evaluations of the lactate threshold (LT) and maximal lactate steady state (MLSS), these technologies offer athletes various device options tailored to their specific sports and preferences. This review explores the mechanisms of currently available lactate monitoring technologies, focusing on electrochemical sensors that have undergone extensive research and show promise for commercialization. These sensors employ amperometric reactions to quantify lactate levels and detect changes resulting from enzymatic activities. In contrast, colorimetric sensors offer a more straightforward and user-friendly approach by displaying lactate concentrations through color alterations. Despite significant advancements, the relationship between sweat lactate and blood lactate levels remains intricate owing to various factors such as environmental conditions and the lag between exercise initiation and sweating. Furthermore, there is a marked gap in research on sweat lactate compared to blood lactate across various sports disciplines. This review highlights the need for further research to address these shortcomings and substantiate the performance of lactate sweat monitoring technologies in a broader spectrum of sports environments. The tremendous potential of these technologies to supplant invasive blood lactate tests and pioneer new avenues for athlete management and performance optimization in real-world settings heralds a promising future for integrating sports science and wearable technology.

Running Energy Reserve Index (RERI) as a new model for assessment and prediction of world, elite, sub-elite, and collegiate running performances

The purpose of this study was to utilize the Running Energy Reserve Index (RERI) model and two-trial procedure to predict all-out athletic performances. Twenty-nine trained athletes tested for differences between RERI_E and RERI_spd (hypothesis 1). Six sprint trained (ST), six middle distance (MD), and six endurance trained (ET) athletes were selected to test for differences in the value of the constant. The prediction of all-out run performances using the RERI model (hypothesis 2) and two treadmill trials procedure (hypothesis 3) were tested on eighteen trained athletes. Lastly, three trained athletes were utilized to predict all-out running performances utilizing two track trials equation (hypothesis 3). RERI_E and RERI_spd were significantly different between ST, MD, and ET athletes. The RERI_E model with a fixed c_E value of 0.0185 s^-1 predicted all-out running performances to within an average of 2.39 ± 2.04% (R² = 0.99, n_T = 252) for all athletes, with treadmill trials to within an average of 2.26 ± 1.89% (R² = 0.99, n_T = 203) and track trials to within an average of 2.95 ± 2.51% (R² = 0.99, n_T = 49). The two trials equations predicted all-out track performances to within errors of 2.43%. The RERI model may be accurate in determining running performances of 200 m and 5000 m, and treadmill performances ranging between 5 and 1340 s with a high level of accuracy. In addition, the two-trial procedure can be used to determine short and middle distance running performances of athletes and world-class runners.