Balsalobre-Fernández, C, Santos-Concejero, J, and Grivas, GV. Effects of strength training on running economy in highly trained runners: a systematic review with meta-analysis of controlled trials. J Strength Cond Res 30(8): 2361–2368, 2016—The purpose of this study was to perform a systematic review and meta-analysis of controlled trials to determine the effect of strength training programs on the running economy (RE) of high-level middle- and long-distance runners. Four electronic databases were searched in September 2015 (PubMed, SPORTDiscus, MEDLINE, and CINAHL) for original research articles. After analyzing 699 resultant original articles, studies were included if the following criteria were met: (a) participants were competitive middle- or long-distance runners; (b) participants had a V̇o ₂max >60 ml·kg⁻¹·min⁻¹; (c) studies were controlled trials published in peer-reviewed journals; (d) studies analyzed the effects of strength training programs with a duration greater than 4 weeks; and (e) RE was measured before and after the strength training intervention. Five studies met the inclusion criteria, resulting in a total sample size of 93 competitive, high-level middle- and long-distance runners. Four of the 5 included studies used low to moderate training intensities (40–70% one repetition maximum), and all of them used low to moderate training volume (2–4 resistance lower-body exercises plus up to 200 jumps and 5–10 short sprints) 2–3 times per week for 8–12 weeks. The meta-analyzed effect of strength training programs on RE in high-level middle- and long-distance runners showed a large, beneficial effect (standardized mean difference [95% confidence interval] = −1.42 [−2.23 to −0.60]). In conclusion, a strength training program including low to high intensity resistance exercises and plyometric exercises performed 2–3 times per week for 8–12 weeks is an appropriate strategy to improve RE in highly trained middle- and long-distance runners.

A growing number of companies are developing or using wearable sensor technologies that can monitor, analyse and transmit data from humans in real time that can be used by the sporting, biomedical and media industries. To explore this phenomenon, we describe and review two high-profile sporting events where innovations in wearable technologies were trialled: the Tokyo 2020 Summer Olympic Games (Tokyo 2020, Japan) and the 2022 adidas Road to Records (Germany). These two major sporting events were the first time academic and industry partners came together to implement real-time wearable solutions during major competition, to protect the health of athletes competing in hot and humid environments, as well as to better understand how these metrics can be used moving forwards. Despite the undoubted benefits of such wearables, there are well-founded concerns regarding their use including: (1) limited evidence quantifying the potential beneficial effects of analysing specific parameters, (2) the quality of hardware and provided data, (3) information overload, (4) data security and (5) exaggerated marketing claims. Employment and sporting rules and regulations also need to evolve to facilitate the use of wearable devices. There is also the potential to obtain real-time data that will oblige medical personnel to make crucial decisions around whether their athletes should continue competing or withdraw for health reasons. To protect athletes, the urgent need is to overcome these ethical/data protection concerns and develop wearable technologies that are backed by quality science. The fields of sport and exercise science and medicine provide an excellent platform to understand the impact of wearable sensors on performance, wellness, health, and disease.

Previous studies from our group have shown that "pure" eccentric exercise performed on an isokinetic dynamometer can induce health-promoting effects that may improve quality of life. In order to investigate whether the benefits of "pure" eccentric exercise can be transferred to daily activities, a new and friendlier way to perform eccentric exercise had to be invented. To this end, we have proceeded to the design and construction of an automatic escalator, offering both stair descending (eccentric-biased) and stair ascending (concentric-biased) exercise.

Outdoor recreational activities offer critical benefits to youth development, yet their impacts have been insufficiently synthesized. This systematic review and meta-analysis evaluated the effects of outdoor recreation on children and adolescents aged 6 to 18 years. Significant improvements were observed in psychological well-being, social connectivity, and environmental awareness, emphasizing the multidimensional benefits of such activities. Challenges such as urbanization and reduced access to green spaces highlighted the need for prioritizing outdoor engagement to counteract the growing detachment from nature. This study followed PRISMA guidelines and included 21 studies published between 2014 and 2024. A random-effects meta-analysis revealed positive effects on mood, anxiety reduction, interpersonal relationships, and environmental responsibility. However, significant heterogeneity reflected variability in study designs and contexts. The GRADE framework assessed evidence certainty, rating psychological benefits as moderate, social connectivity as high, and environmental awareness as low. Limitations included reliance on self-reported data and exclusion of pandemic-era studies. These findings emphasized the role of outdoor activities in addressing modern challenges such as urbanization and climate change by fostering holistic youth development. Policymakers and educators should be encouraged to integrate outdoor programs into curricula and community initiatives to promote mental health, social cohesion, and environmental stewardship.

Twenty males ran either on a level treadmill (nonmuscle-damaging condition) or on a downhill treadmill (muscle-damaging condition). Blood and urine samples were collected before and after exercise (immediately after, 1h, 4h, 24h, 48h, and 96h). The following assays were performed: F₂-isoprostanes in urine, protein carbonyls in plasma, glutathione, superoxide dismutase, glutathione peroxidase, and catalase in erythrocytes. The main finding was that monophasic redox responses were detected after nonmuscle-damaging exercise compared to the biphasic responses detected after muscle-damaging exercise. Based on these findings, muscle-damaging exercise may be a more appropriate experimental model to induce physiological oxidative stress.

Background: Artificial Intelligence (AI) is increasingly applied in endurance sports to optimize performance, enhance recovery, and personalize nutrition and supplementation. This review synthesizes current knowledge on AI applications in endurance sports, emphasizing implications for metabolic health, nutritional strategies, and recovery optimization, while also addressing ethical considerations and future directions. Methods: A narrative review was conducted using targeted searches of PubMed, Scopus, and Web of Science with cross-referencing. Extracted items included sport/context, data sources, AI methods including machine learning (ML), validation type (internal vs. external/field), performance metrics, comparators, and key limitations to support a structured synthesis; no formal risk-of-bias assessment or meta-analysis was undertaken due to heterogeneity. Results: AI systems effectively integrate multimodal physiological, environmental, and behavioral data to enhance metabolic health monitoring, predict recovery states, and personalize nutrition. Continuous glucose monitoring combined with AI algorithms allows precise carbohydrate management during prolonged events, improving performance outcomes. AI-driven supplementation strategies, informed by genetic polymorphisms and individual metabolic responses, have demonstrated enhanced ergogenic effectiveness. However, significant challenges persist, including measurement validity and reliability of sensor-derived signals and overall dataset quality (e.g., noise, missingness, labeling error), model performance and generalizability, algorithmic transparency, and equitable access. Furthermore, limited generalizability due to homogenous training datasets restricts widespread applicability across diverse athletic populations. Conclusions: The integration of AI in endurance sports offers substantial promise for improving performance, recovery, and nutritional strategies through personalized approaches. Realizing this potential requires addressing existing limitations in model performance and generalizability, ethical transparency, and equitable accessibility. Future research should prioritize diverse, representative, multi-site data collection across sex/gender, age, and race/ethnicity. Coverage should include performance level (elite to recreational), sport discipline, environmental conditions (e.g., heat, altitude), and device platforms (multi-vendor/multi-sensor). Equally important are rigorous external and field validation, transparent and explainable deployment with appropriate governance, and equitable access to ensure scientifically robust, ethically sound, and practically relevant AI solutions.