Purpose: To compare the impact of two different training intensity distributions in terms of conditional and performance parameters and spent time to training in recreational athletes. Methods: Two different training intensity distribution model were performed for 8 weeks by 38 recreational runners. Runners recruited were randomly assigned to 2 different training models based on HR intensity detected with maximal test. The percentage distribution splitted in zone 1, 2, and 3 were by 77/3/20 and 40/50/10 in polarized endurance training group (PET) and focused endurance training (FOC) group, respectively. Programs were balanced for total training impulse (TRIMP). To evaluate effects of training, before and after treatment were performed a maximal exercise test to determine Maximal Oxygen Uptake (V'O_2max), Ventilatory Threshold (VT), respiratory-compensation point (RCT) Running Economy (RE), and 2 Km performance. To investigate the effects of training on muscular performance were performed one repetition maximum (1 RM), squat jump (SJ), and counter movement jump (CMJ). Results: Both groups significantly improved their velocity at V'O_2max (3.2 and 4.0%), at VT (4.0 and 3.2%), RCT (5.7 and 3.4%), the average velocity in 2 Km performance (3.5 and 3.0%) and RE (−5.3 and −8.7%) for PET and FOC, respectively for each variable. No differences were found between the groups on any parameter investigated except about the total training time (PET = 29.9 ± 3.1 h and FOC = 24.8 ± 2.0 h). Conclusion: Focused Endurance Training obtains similar improvements than Polarized Endurance Training saving 17% of training time in recreational runners.

Festa, L, Tarperi, C, Skroce, K, Boccia, G, Lippi, G, La Torre, A, and Schena, F. Effects of flywheel strength training on the running economy of recreational endurance runners. J Strength Cond Res 33(3): 684–690, 2019—Running economy (RE) has been defined as the most important determining factor in endurance performance in both elite and recreational runners. The purpose of this study was to evaluate the effect of flywheel strength training (FST) and high-intensity training (HIT) protocols on RE and strength parameters in a group of recreational runners. Twenty-nine recreational runners were recruited to take part in the study and were randomly assigned to FST (n = 9; 44.5 ± 6.0 years; V̇o₂max 48.8 ± 5.2 ml·min⁻¹·kg⁻¹), HIT (n = 9; 42.2 ± 8.6 years; V̇o₂max 50.3 ± 3.7 ml·min⁻¹·kg⁻¹), or low-intensity training (LIT) (n = 11; 45.4 ± 8.0 years; V̇o₂max 50.2 ± 6.8 ml min⁻¹ kg⁻¹) groups. Before and after 8 weeks of an experimental period, maximal oxygen uptake (V̇o₂max), ventilatory thresholds (VTs), maximal dynamic force (1 repetition maximum [1RM]), and anthropometric data were evaluated. The FST group showed significant increases (p < 0.05) in 1RM and RE. No differences were found in the other groups. Significative changes are found for all groups on average speed on 2 and 10 km (p < 0.05). Anthropometric data were unchanged after the training period. The results of this study indicate that in recreational runners, FST seems able to obtain improvements in RE and neuromuscular adaptation.

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m²; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h—men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70–140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Nowadays, it is widely acknowledged that low physical activity levels are associated with an increase in terms of both disease recurrence and mortality in cancer survivors. In this light, deciphering those factors able to hamper or facilitate an active lifestyle is crucial in order to increase patients’ adherence to physical activity. The purpose of this study was to explore barriers and motivations in a sample of female oncological patients, practising running using the ecological model and compare them with healthy controls. Focus group interviews were conducted at Verona University. Participants were 12 female cancer survivors and 7 matched healthy controls who had participated at “Run for Science” project. The interviews were transcribed verbatim and analyzed using content analysis. Transcripts were categorized according to the ecological model, identifying barriers and motivations as themes. About motivations, three sub-themes were included: personal, interpersonal and environmental/organizational factors. Regarding barriers, another sub-theme was recognized: community/policy factors. Compared to healthy controls, survivors expressed motivations and barriers specifically related to their oncological disease. Running was a challenge with their cancer and a hope to give to other patients. Main barriers were represented by treatment-related side effects, inexperienced trainers and external factors, e.g. delivery of incorrect information. Running programs dedicated to oncological patients should consider intrinsic obstacles, related to cancer and its treatment. The interventions should offer a personalized program performed by qualified trainers, together with a motivational approach able to improve participants’ adherence to an active lifestyle.

Background: Calcitonin gene-related peptide (CGRP) is a powerful neuropeptide that is strongly involved in headache pain pathogenesis by triggering vasodilation, mast cell degranulation and neurogenic inflammation. This evidence has prompted us to investigate the acute influence of endurance exercise on CGRP concentration in blood. Methods: The study population consisted of 48 male amateur runners, who ran a half-marathon distance at 75%–85% of maximal oxygen uptake. Blood was drawn before the run (pre-run) and immediately after each runner ended his trial (post-run). The serum concentration of CGRP was measured with a commercial enzyme-linked immunosorbent assay (ELISA) technique. Results: Overall, 22/48 subjects (45.8%) reported suffering from headache, three of whom (6.2%) had an exertional headache, whilst 26/48 (54.2%) subjects did not report at least one headache episode during the previous 6 months (i.e. headache-free). All 48 athletes successfully covered the 21.1 km distance. Serum concentration of CGRP significantly increased by 1.5-fold in the entire group, as well as in the headache-positive and headache-free cohorts. Univariate Spearman’s correlation revealed that post-run variation of serum CGRP was significantly and inversely associated with running time (r = −0.30; p = 0.036). Conclusions: The serum concentration of CGRP is significantly enhanced by medium-distance endurance exercise and the post-exercise increase is dependent on running intensity. Accordingly, high-exercise intensity might be directly related to triggering both exertional headache and/or migraine episodes.

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre‐exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self‐reported 48,799 pre‐exercise food ingestion events and logged minute‐by‐minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non‐linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with 20% of their events. The majority of the reactive hypoglycemia events were found with pre‐exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F‐score (0.75 vs 0.59) of the non‐linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30‐to‐90 min pre‐exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.