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Epilepsy patients commonly exercise less than the general population. Animal studies indicate beneficial effects of physical activity in established epilepsy, while its effect on the development is currently less known. Here, we investigated the incidence of epilepsy during 20 years in a cohort of participants from the long-distance Swedish cross-country ski race Vasaloppet (n = 197,685) and compared it to the incidence of non-participating-matched controls included in the Swedish population register (n = 197,684). Individuals diagnosed with diseases such as stroke and epilepsy before entering the race were excluded from both groups. Experimentally, we also determined how physical activity could affect the development of epilepsy in epilepsy-prone synapsin II knockout mice (SynIIKO), with and without free access to a running wheel. We identified up to 40–50% lower incidence of epilepsy in the Vasaloppet participants of all ages before retirement. A lower incidence of epilepsy in Vasaloppet participants was seen regardless of gender, education and occupation level compared to controls. The participants included both elite and recreational skiers, and in a previous survey, they have reported a higher exercise rate than the general Swedish population. Sub-analyses revealed a significantly lower incidence of epilepsy in participants with a faster compared to slower finishing time. Dividing participants according to specified epilepsy diagnoses revealed 40–50% decrease in focal and unspecified epilepsy, respectively, but no differences in generalized epilepsy. Voluntary exercise in seizure-prone SynIIKO mice for 1 month before predicted epilepsy development decreased seizure manifestation from > 70 to 40%. Brain tissue analyses following 1 month of exercise showed increased hippocampal neurogenesis (DCX-positive cells), while microglial (Iba1) and astrocytic activation (GFAP), neuronal Map2, brain-derived neurotrophic factor and its receptor tyrosine receptor kinase B intensity were unaltered. Continued exercise for additionally 2 months after predicted seizure onset in SynIIKO mice resulted in a 5-fold reduction in seizure manifestation (from 90 to 20%), while 2 months of exercise initiated at the time of predicted seizure development gave no seizure relief, suggesting exercise-induced anti-epileptogenic rather than anti-convulsive effect. The clinical study and the experimental findings in mice indicate that physical activity may prevent or delay the development of epilepsy.

The current recommendations for resistance training (RT) frequency range from 2 to 5 days per week (days week− 1) depending on the subjects’ training status. However, the relationship between RT frequency and muscular strength remains controversial with reported variances existing across different population groups. We conducted a meta-analysis that (1) quantified the effects of low (LF; 1 day week− 1), medium (MF; 2 days week− 1), or high (HF; ≥ 3 days week− 1) RT frequency on muscular strength per exercise; (2) examined the effects of different RT frequency on one repetition maximum (1RM) strength gain profiles (multi-joint exercises and single joint exercises); (3) examined the effects of different RT frequency on 1RM strength gain when RT volume is equated; and (4) examined the effects of different RT frequency on 1RM strength gains on upper and lower body. Computerised searches were performed using the terms ‘strength training frequency’, ‘resistance training frequency’, ‘training frequency’, and ‘weekly training frequency’. After review, 12 studies were deemed suitable according to pre-set eligibility criteria. Primary data were pooled using a random-effects model. Outcomes analysed for main effects were pre- to post strength change with volume-equated studies that combined multi-joint and isolation exercise; isolation-only exercise and untrained subjects only. Heterogeneity between studies was assessed using I2 and Cochran’s Q statistics with funnel plots used to assess publication bias and sensitivity analyses calculated for subgroups. Pre- versus post-training strength analysis comprised of 74 treatment groups from 12 studies. For combined multi-joint and isolation exercises, there was a trend towards higher RT frequency compared with lower frequency [mean effect size (ES) 0.09 (95% CI − 0.06–0.24)] however not significant (p = 0.25). Volume-equated pre- to post-intervention strength gain was similar when LF was compared to HF [mean ES 0.03 (95% CI − 0.20–0.27); p = 0.78]. Upper body pre- to post-intervention strength gain was greater when HF was compared with LF [mean ES 0.48 (95% CI 0.20–0.76)] with significant differences between frequencies (p < 0.01). Upper body pre- to post-intervention strength gain was similar when MF was compared with LF (ES 0.12; 95% CI − 0.22–0.47); p = 0.48]. There was no significant difference in lower body mean ES between HF and LF [mean ES 0.21(95% CI − 0.55–0.13); p = 0.22]. There was a trend towards a difference in mean ES between MF and HF [mean ES 0.41(95% CI − 0.26–1.09); however, the effect was not significant (p = 0.23). The existing data does not provide a strong correlation between increased weekly training frequency (HF) and maximal strength gain in upper and lower body resistance exercises for a mixed population group. When RT is volume-equated for combined multi-joint and isolation exercises, there is no significant effect of RT frequency on muscular strength gain. More investigations are required to explore the effects of varying weekly training frequencies adequately.

There is evidence for considerable heterogeneity in the responsiveness to regular physical activity (PA) which might reflect the influence of genetic factors. The aim of this systematic review was to assess whether the response to a PA intervention for measures of body composition and cardiorespiratory fitness is (i) correlated within twin pairs and/or families and (ii) more correlated in monozygotic twins (MZ) compared to dizygotic twins (DZ), which would be consistent with genetic effects. We performed electronic database searches, combining key words relating to “physical activity” and “genetics”, in MEDLINE, CINAHL, EMBASE, SPORTS Discuss, AMED, PsycINFO, WEB OF SCIENCE, and SCOPUS from the earliest records to March 2016. Twin and family studies were included if they assessed body composition and/or cardiorespiratory fitness following a PA intervention, and provided a heritability estimate, maximal heritability estimate, or within MZ twin pair correlation (rMZ). Data on heritability (twin studies), maximal heritability (family studies), and the rMZ were extracted from included studies, although heritability estimates were not reported as small sample sizes made them uninformative. After screening 224 full texts, nine twin and five family studies were included in this review. The pooled rMZ in response to PA was significant for body mass index (rMZ = 0.69, n = 58), fat mass (rMZ = 0.58, n = 48), body fat percentage (rMZ = 0.55, n = 72), waist circumference (rMZ = 0.50, n = 27), and VO2max (rMZ = 0.39, n = 48), where “n” represents the total number of twin pairs from all studies. Maximal heritability estimates ranged from 0–21% for measures of body composition, and 22–57% for cardiorespiratory fitness. Twin studies differed in sample age, baseline values, and PA intervention, although the exclusion of any one study did not affect the results. Shared familial factors, including genetics, are likely to be a significant contributor to the response of body composition and cardiorespiratory fitness following PA. Genetic factors may explain individual variation in the response to PA. PROSPERO Registration No CRD42015020056 .

Exercise changes the concentrations of many metabolites, which are small molecules (< 1.5 kDa) metabolized by the reactions of human metabolism. In recent years, especially mass spectrometry-based metabolomics methods have allowed researchers to measure up to hundreds of metabolites in a single sample in a non-biased fashion. To summarize human exercise metabolomics studies to date, we conducted a systematic review that reports the results of experiments that found metabolite concentrations changes after a bout of human endurance or resistance exercise. We carried out a systematic review following PRISMA guidelines and searched for human metabolomics studies that report metabolite concentrations before and within 24 h after endurance or resistance exercise in blood, urine, or sweat. We then displayed metabolites that significantly changed their concentration in at least two experiments. Twenty-seven studies and 57 experiments matched our search criteria and were analyzed. Within these studies, 196 metabolites changed their concentration significantly within 24 h after exercise in at least two experiments. Human biofluids contain mainly unphosphorylated metabolites as the phosphorylation of metabolites such as ATP, glycolytic intermediates, or nucleotides traps these metabolites within cells. Lactate, pyruvate, TCA cycle intermediates, fatty acids, acylcarnitines, and ketone bodies all typically increase after exercise, whereas bile acids decrease. In contrast, the concentrations of proteinogenic and non-proteinogenic amino acids change in different directions. Across different exercise modes and in different subjects, exercise often consistently changes the average concentrations of metabolites that belong to energy metabolism and other branches of metabolism. This dataset is a useful resource for those that wish to study human exercise metabolism.

The Nordic Hamstring Exercise (NHE) is very popular for selective eccentric hamstring strengthening. However, NHE-related research is hindered by insufficient details about implementation and reporting. Available tools to assess study quality (e.g., PEDro or TESTEX scale) are too unspecific to account for the specific demands of NHE. Therefore, this study aimed to introduce two rating scales for Assessing Nordic Hamstring Exercise Quality (ANHEQ) of assessment and intervention studies. Eighteen graduated sports scientists, sports physiotherapists and elite coaches with scientific experience independently evaluated the quality of published NHE studies via ANHEQ scales, each comprising eight items and a maximal 13-point score. Inter-rater agreement was analyzed by using criterion-based reference values, while Krippendorff´s alpha determined inter-rater reliability. Systematic differences of the summated ANHEQ scores were determined using Friedman tests. Inter-rater agreement was 87 ± 5% for NHE assessments and 88 ± 6% for interventions with single items ranging from 71 to 100%. Alpha values for inter-rater reliability ranged from fair (.250) to perfect (1.00) depending on the item. Total ANHEQ scores revealed coefficients of .829 (almost perfect) and .772 (substantial) without significant inter-rater differences (p = .292). The ANHEQ scales are suitable tools to rate NHE execution quality and data presentation. They facilitate a comprehensive review of NHE-related evidence and potentially improve the design and reporting of future NHE studies.

Previous injury in the last 12 months is the main risk factor for future running-related injuries (RRI) during training and competition environments. However, the relationship between a recent versus old previous injury and a new RRI has not been established yet, nor a separate analysis by different types of runners. An online questionnaire was sent to 6000 participants of a running event (10 km, 21 km and 42 km), 10 days following the event. The questionnaire included the following information: the presence and topography of new RRIs during the race, old previous injury (from 12–4 months before the race), recent previous injury (from 3–0 months before the race), running experience, training factors and socio-demographic characteristics. Univariate binomial regression analysis was applied to assess different associated factors, and multivariable binomial backward regression (p < 0.05) was used to analyse the relationship between the new and previous injury. A total of 868 surveys were analysed (10 km, 32.6%; 21 km, 52%; 42 km, 15.4%). The median age was 38 years (IQR 31–46), and 63.5% were males. Previous injury was reported by 30.3% and 27.6% for old and recent, respectively. The majority of runners were categorised into the advanced group (42.9%), having more than 5 years of running experience. During the race, 7.0% reported a RRI, with 36.1% located at the knee. The multivariable analysis showed an association only between new injury and recent injury. The delineation of recent and old previous injuries should be considered in running epidemiological research.

Match analysis has evolved exponentially over the past decades in team sports resulting in a significant number of published systematic reviews and meta-analyses. An umbrella review of the available literature is needed to provide an integrated overview of current knowledge and contribute to more robust theoretical explanations of team performance. The Web of Science (all databases), PubMed, Cochrane Library (Cochrane Database of Systematic Reviews), Scopus, and SPORTDiscus databases were searched for relevant publications prior to 19 February 2021. Appraisal of the methodological quality of included articles was undertaken using the tool for Assessing the Methodological Quality of Systematic Reviews (AMSTAR-2). Twenty-four studies were reviewed that met the following criteria: (1) contained relevant data from match analyses in team ball sports; (2) were defined as systematic reviews or/and meta-analyses; and (3) were written in the English language. The overall methodological quality of the 24 included reviews, obtained through the AMSTAR-2, revealed very low confidence ratings (Critically Low, n = 12) for the results of most systematic reviews of match analyses in team ball sports. Additionally, the results showed that research is focused mainly on four levels of analysis: (1) dyadic (microlevel); (2) individual (molecular level; predominant); (3) group (mesolevel), and (4) team dynamics (macrolevel). These levels of analysis included tactical, technical, physical, and psychosocial variables. Team performance was contextualized at two levels, with reference to: (1) match context (e.g. match status, match location, match period, quality of opposition) and (2) sociodemographic and environmental constraints (sex, age groups, competitive level, altitude, temperature, pitch surface). The evolution of methods for match analysis in team ball sports indicates that: (1) an individual-level performance analysis was predominant; (2) the focus on intermediate levels of analysis, observing performance in dyadic and group interactions, has received less attention from researchers; (3) neglected areas of research include psychosocial aspects of team sports and women’s performance; and (4) analyses of match contexts need greater depth. Registration: The protocol was registered in the International Platform of Registered Systematic Review and Meta-Analysis Protocols with the number 202080067 and the DOI number https://doi.org/10.37766/inplasy2020.8.0067 .

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment—such as response to data imbalance, model fitting, and a lack of multi-season data—limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.