Call to increase statistical collaboration in sports science, sport and exercise medicine and sports physiotherapy

Chronic Effects of Static Stretching Exercises on Skeletal Muscle Hypertrophy in Healthy Individuals: A Systematic Review and Multilevel Meta-Analysis

BACKGROUND

The chronic effect of static stretching (SS) on muscle hypertrophy is still unclear. This study aimed to examine the chronic effects of SS exercises on skeletal muscle hypertrophy in healthy individuals.

METHODS

A systematic literature search was conducted in the PubMed, Web of Science, Cochrane Library, and SPORTDiscus databases up to July 2023. Included studies examined chronic effects of SS exercise compared to an active/passive control group or the contralateral leg (i.e., utilizing between- or within-study designs, respectively) and assessed at least one outcome of skeletal muscle hypertrophy in healthy individuals with no age restriction.

RESULTS

Twenty-five studies met the inclusion criteria. Overall, findings indicated an unclear effect of chronic SS exercises on skeletal muscle hypertrophy with a trivial point estimate (standardised mean difference [SMD] = 0.118 [95% prediction interval [95% PI] = - 0.233 to 0.469; p = 0.017]) and low heterogeneity (I2 = 24%). Subgroup analyses revealed that trained individuals (β = 0.424; 95% PI = 0.095 to 0.753) displayed larger effects compared to recreationally trained (β = 0.115; 95% PI = - 0.195 to 0.425) and sedentary individuals (β = - 0.081; 95% PI = - 0.399 to 0.236). Subanalysis suggested the potential for greater skeletal muscle hypertrophy in samples with higher percentages of females (β = 0.003, [95% confidence interval [95% CI] = - 0.000 to 0.005]). However, the practical significance of this finding is questionable. Furthermore, a greater variety of stretching exercises elicited larger increases in muscle hypertrophy (β = 0.069, [95% CI = 0.041 to 0.097]). Longer durations of single stretching exercises (β = 0.006, [95% CI = 0.002 to 0.010]), time under stretching per session (β = 0.006, [95% CI = 0.003 to 0.009]), per week (β = 0.001, [95% CI = 0.000 to 0.001]) and in total (β = 0.008, [95% CI = 0.003 to 0.013]) induced larger muscle hypertrophy. Regarding joint range of motion, there was a clear positive effect with a moderate point estimate (β = 0.698; 95% PI = 0.147 to 1.249; p < 0.001) and moderate heterogeneity (I2 = 43%). Moreover, findings indicated no significant association between the gains in joint range of motion and the increase in muscle hypertrophy (β = 0.036, [95% CI = - 0.123 to 0.196]; p = 0.638).

CONCLUSIONS

This study revealed an overall unclear chronic effect of SS on skeletal muscle hypertrophy, although interpretation across the range of PI suggests a potential modest beneficial effect. Subgroup analysis indicated larger stretching-induced muscle gains in trained individuals, a more varied selection of SS exercises, longer mean duration of single stretching exercise, increased time under SS per session, week, and in total, and possibly in samples with a higher proportion of females. From a practical perspective, it appears that SS exercises may not be highly effective in promoting skeletal muscle hypertrophy unless a higher duration of training is utilized. PROSPERO registration number: CRD42022331762.

Poor statistical reporting: do we have a reason for concern? A narrative review and recommendations

PURPOSE OF REVIEW

The aim of the review conducted was to present recent articles indicating the need to implement statistical recommendations in the daily work of biomedical journals.

RECENT FINDINGS

The most recent literature shows an unchanged percentage of journals using specialized statistical review over 20 years. The problems of finding statistical reviewers, the impractical way in which biostatistics is taught and the nonimplementation of published statistical recommendations contribute to the fact that a small percentage of accepted manuscripts contain correctly performed analysis. The statistical recommendations published for authors and editorial board members in recent years contain important advice, but more emphasis should be placed on their practical and rigorous implementation. If this is not the case, we will additionally continue to experience low reproducibility of the research.

SUMMARY

There is a low level of statistical reporting these days. Recommendations related to the statistical review of submitted manuscripts should be followed more rigorously.

Meta-analysis prediction intervals are under reported in sport and exercise medicine

AIM

Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval.

METHODS

We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval.

RESULTS

Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals.

CONCLUSION

Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research

OBJECTIVE: To investigate open science practices in research published in the top 5 sports medicine journals from May 1, 2022, and October 1, 2022. DESIGN: A meta-research systematic review. LITERATURE SEARCH: Open science practices were searched in MEDLINE. STUDY SELECTION CRITERIA: We included original scientific research published in one of the identified top 5 sports medicine journals in 2022 as ranked by Clarivate: (1) British Journal of Sports Medicine, (2) Journal of Sport and Health Science, (3) American Journal of Sports Medicine, (4) Medicine and Science in Sports and Exercise, and (5) Sports Medicine-Open. Studies were excluded if they were systematic reviews, qualitative research, gray literature, or animal or cadaver models. DATA SYNTHESIS: Open science practices were extracted in accordance with the Transparency and Openness Promotion guidelines and patient and public involvement. RESULTS: Two hundred forty-three studies were included. The median number of open science practices in each study was 2, out of a maximum of 12 (range: 0-8; interquartile range: 2). Two hundred thirty-four studies (96%, 95% confidence interval [CI]: 94%-99%) provided an author conflict-of-interest statement and 163 (67%, 95% CI: 62%-73%) reported funding. Twenty-one studies (9%, 95% CI: 5%-12%) provided open-access data. Fifty-four studies (22%, 95% CI: 17%-27%) included a data availability statement and 3 (1%, 95% CI: 0%-3%) made code available. Seventy-six studies (32%, 95% CI: 25%-37%) had transparent materials and 30 (12%, 95% CI: 8%-16%) used a reporting guideline. Twenty-eight studies (12%, 95% CI: 8%-16%) were preregistered. Six studies (3%, 95% CI: 1%-4%) published a protocol. Four studies (2%, 95% CI: 0%-3%) reported an analysis plan a priori. Seven studies (3%, 95% CI: 1%-5%) reported patient and public involvement. CONCLUSION: Open science practices in the sports medicine field are extremely limited. The least followed practices were sharing code, data, and analysis plans. J Orthop Sports Phys Ther 2023;53(12):1-13. Epub 20 October 2023. doi:10.2519/jospt.2023.12016.

Assessing the Evidential Value of Mental Fatigue and Exercise Research

It has often been reported that mental exertion, presumably leading to mental fatigue, can negatively affect exercise performance; however, recent findings have questioned the strength of the effect. To further complicate this issue, an overlooked problem might be the presence of publication bias in studies using underpowered designs, which is known to inflate false positive report probability and effect size estimates. Altogether, the presence of bias is likely to reduce the evidential value of the published literature on this topic, although it is unknown to what extent. The purpose of the current work was to assess the evidential value of studies published to date on the effect of mental exertion on exercise performance by assessing the presence of publication bias and the observed statistical power achieved by these studies. A traditional meta-analysis revealed a Cohen's dz effect size of - 0.54, 95% CI [- 0.68, - 0.40], p < .001. However, when we applied methods for estimating and correcting for publication bias (based on funnel plot asymmetry and observed p-values), we found that the bias-corrected effect size became negligible with most of publication-bias methods and decreased to - 0.36 in the more optimistic of all the scenarios. A robust Bayesian meta-analysis found strong evidence in favor of publication bias, BFpb > 1000, and inconclusive evidence in favor of the effect, adjusted dz = 0.01, 95% CrI [- 0.46, 0.37], BF10 = 0.90. Furthermore, the median observed statistical power assuming the unadjusted meta-analytic effect size (i.e., - 0.54) as the true effect size was 39% (min = 19%, max = 96%), indicating that, on average, these studies only had a 39% chance of observing a significant result if the true effect was Cohen's dz = - 0.54. If the more optimistic adjusted effect size (- 0.36) was assumed as the true effect, the median statistical power was just 20%. We conclude that the current literature is a useful case study for illustrating the dangers of conducting underpowered studies to detect the effect size of interest.

Extraordinary Claims in the Literature on High-Intensity Interval Training (HIIT): I. Bonafide Scientific Revolution or a Looming Crisis of Replication and Credibility?

The literature on high-intensity interval training (HIIT) contains claims that, if true, could revolutionize the science and practice of exercise. This critical analysis examines two varieties of claims: (i) HIIT is effective in improving various indices of fitness and health, and (ii) HIIT is as effective as more time-consuming moderate-intensity continuous exercise. Using data from two recent systematic reviews as working examples, we show that studies in both categories exhibit considerable weaknesses when judged through the prism of fundamental statistical principles. Predominantly, small-to-medium effects are investigated in severely underpowered studies, thus greatly increasing the risk of both type I and type II errors of statistical inference. Studies in the first category combine the volatility of estimates associated with small samples with numerous dependent variables analyzed without consideration of the inflation of the type I error rate. Studies in the second category inappropriately use the p > 0.05 criterion from small studies to support claims of 'similar' or 'comparable' effects. It is concluded that the situation in the HIIT literature is reminiscent of the research climate that led to the replication crisis in psychology. As in psychology, this could be an opportunity to reform statistical practices in exercise science.

The bias for statistical significance in sport and exercise medicine

OBJECTIVES

We aimed to examine the bias for statistical significance using published confidence intervals in sport and exercise medicine research.

DESIGN

Observational study.

METHODS

The abstracts of 48,390 articles, published in 18 sports and exercise medicine journals between 2002 and 2022, were searched using a validated text-mining algorithm that identified and extracted ratio confidence intervals (odds, hazard, and risk ratios). The algorithm identified 1744 abstracts that included ratio confidence intervals, from which 4484 intervals were extracted. After excluding ineligible intervals, the analysis used 3819 intervals, reported as 95 % confidence intervals, from 1599 articles. The cumulative distributions of lower and upper confidence limits were plotted to identify any abnormal patterns, particularly around a ratio of 1 (the null hypothesis). The distributions were compared to those from unbiased reference data, which was not subjected to p-hacking or publication bias. A bias for statistical significance was further investigated using a histogram plot of z-values calculated from the extracted 95 % confidence intervals.

RESULTS

There was a marked change in the cumulative distribution of lower and upper bound intervals just over and just under a ratio of 1. The bias for statistical significance was also clear in a stark under-representation of z-values between -1.96 and +1.96, corresponding to p-values above 0.05.

CONCLUSIONS

There was an excess of published research with statistically significant results just below the standard significance threshold of 0.05, which is indicative of publication bias. Transparent research practices, including the use of registered reports, are needed to reduce the bias in published research.

Predictive models for musculoskeletal injury risk: why statistical approach makes all the difference

OBJECTIVE

Compare performance between an injury prediction model categorising predictors and one that did not and compare a selection of predictors based on univariate significance versus assessing non-linear relationships.

METHODS

Validation and replication of a previously developed injury prediction model in a cohort of 1466 service members followed for 1 year after physical performance, medical history and sociodemographic variables were collected. The original model dichotomised 11 predictors. The second model (M2) kept predictors continuous but assumed linearity and the third model (M3) conducted non-linear transformations. The fourth model (M4) chose predictors the proper way (clinical reasoning and supporting evidence). Model performance was assessed with R², calibration in the large, calibration slope and discrimination. Decision curve analyses were performed with risk thresholds from 0.25 to 0.50.

RESULTS

478 personnel sustained an injury. The original model demonstrated poorer R² (original:0.07; M2:0.63; M3:0.64; M4:0.08), calibration in the large (original:-0.11 (95% CI -0.22 to 0.00); M2: -0.02 (95% CI -0.17 to 0.13); M3:0.03 (95% CI -0.13 to 0.19); M4: -0.13 (95% CI -0.25 to -0.01)), calibration slope (original:0.84 (95% CI 0.61 to 1.07); M2:0.97 (95% CI 0.86 to 1.08); M3:0.90 (95% CI 0.75 to 1.05); M4: 081 (95% CI 0.59 to 1.03) and discrimination (original:0.63 (95% CI 0.60 to 0.66); M2:0.90 (95% CI 0.88 to 0.92); M3:0.90 (95% CI 0.88 to 0.92); M4: 0.63 (95% CI 0.60 to 0.66)). At 0.25 injury risk, M2 and M3 demonstrated a 0.43 net benefit improvement. At 0.50 injury risk, M2 and M3 demonstrated a 0.33 net benefit improvement compared with the original model.

CONCLUSION

Model performance was substantially worse in the models with dichotomised variables. This highlights the need to follow established recommendations when developing prediction models.

All MCIDs Are Wrong, But Some May be Useful

OBJECTIVE

To demonstrate how to apply a baseline-adjusted receiver operator characteristic curve (AROC) analysis for minimum clinically important differences (MCIDs) in an empirical data set and discuss new insights relating to MCIDs.

DESIGN

Retrospective study.

METHODS

This study includes data from 999 active-duty military service patients enrolled in the United States Military Health System's Military Orthopedics Tracking Injuries and Outcomes Network. Anchored MCIDs were calculated using the standard receiver operator characteristic analysis and the AROC analysis for the Patient-Reported Outcome Measure Information System (PROMIS) Pain Interference and Defense and Veterans Pain Rating Scale (DVPRS). Point estimates where confidence intervals (CIs) crossed the 0.5 identity line on the area-under-the-curve (AUC) analysis were considered statistically invalid. MCID estimates where CIs crossed 0 were considered theoretically invalid.

RESULTS

In applying an AROC analysis, the region of AUC and MCID validity for the PROMIS Pain Interference score exists when the baseline score is greater than 61.0 but less than 72.3. For DVPRS, the region of MCID validity is when the baseline score is greater than 5.9 but less than 7.9.

CONCLUSION

Baseline values influence not only the MCID but also the accuracy of the MCID. MCIDs are statistically and theoretically valid for only a discrete range of baseline scores. Our findings suggest that the MCID may be too flawed a construct to accurately benchmark treatment outcomes. J Orthop Sports Phys Ther 2022;52(6):401-407. doi:10.2519/jospt.2022.11193.

Assessing the cumulative effect of long-term training load on the risk of injury in team sports

Objectives

Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports.

Methods

First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice.

Results

DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure.

Conclusion

DLNM can be used to assess the cumulative effect of training load on injury risk.

What Is the Numerical Nature of Pain Relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) or using percentage units (multiplicative). However, additive and multiplicative scales have different assumptions and are incompatible with one another. In this work, we describe the assumptions and corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical and clinical perspectives. First, we explain the math underlying each model and illustrate these points using simulations, for which readers are assumed to have an understanding of linear regression. Next, we connect this math to clinical interpretations, stressing the importance of statistical models that accurately represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain intensity as a measurement construct, including its philosophical limitations and how clinical pain differs from acute pain measured during psychophysics experiments. This work has broad implications for clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.

Research in football: evolving and lessons we can learn from our mistakes

Background:Football is evolving in many ways, including technical and physical demands as well as the scientific research underpinning and providing many recommendations to practitioners on how to optimise performance of players and by default, team performance. Evolution is a natural process and necessary to grow and develop and research into football is no different. Researchers are by nature, curious and inquisitive and trying to push the boundaries of knowledge; however, researchers are also humans and humans are open to making errors. The important point is that researchers learn from both their own and others' mistakes, evolving, growing and developing in response. By doing so will maximise the impact that research can have on the field. Purpose:With this commentary, I discuss lessons that can be learned from some common mistakes I and others have made in football (and sports) related research and some insights to evolve our profession for the better.

Bayesian prediction of winning times for elite swimming events

To develop a statistical model of winning times for international swimming events with the aim of predicting winning time distributions and the probability of winning for the 2020 and 2024 Olympic Games. The data set included first and third place times from all individual swimming events from the Olympics and World Championships from 1990 to 2019. We compared different model formulations fitted with Bayesian inference to obtain predictive distributions; comparisons were based on mean percentage error in out-of-sample predictions of Olympics and World Championships winning swim times from 2011 to 2019. The Bayesian time series regression model, comprising auto-regressive and moving average terms and other predictors, had the smallest mean prediction error of 0.57% (CI 0.46-0.74%). For context, using the respective previous Olympics or World Championships winning time resulted in a mean prediction error of 0.70% (CI 0.59-0.82%). The Olympics were on average 0.5% (CI 0.3-0.7%) faster than World Championships over the study period. The model computes the posterior predictive distribution, which allows coaches and athletes to evaluate the probability of winning given an individual's swim time, and the probability of being faster or slower than the previous winning time or even the world record.

A Systems Analysis Critique of Sport-Science Research

PURPOSE

The broad aim of sport-science research is to enhance the performance of coaches and athletes. Despite decades of such research, it is well documented that sport-science research lacks empirical evidence, and critics have questioned its scientific methods. Moreover, many have pointed to a research-practice gap, whereby the work undertaken by researchers is not readily applied by practitioners. The aim of this study was to use a systems thinking analysis method, causal loop diagrams, to understand the systemic issues that interact to influence the quality of sport-science research.

METHODS

A group model-building process was utilized to develop the causal loop diagram based on data obtained from relevant peer-reviewed literature and subject-matter experts.

RESULTS

The findings demonstrate the panoply of systemic influences associated with sport-science research, including the existence of silos, a focus on quantitative research, archaic practices, and an academic system that is incongruous with what it actually purports to achieve.

CONCLUSIONS

The emergent outcome of the interacting components is the creation of an underperforming sport-science research system, as indicated by a lack of ecological validity, translation to practice, and, ultimately, a research-practice gap.

Causes and Consequences of Interindividual Response Variability: A Call to Apply a More Rigorous Research Design in Acute Exercise-Cognition Studies

The different responses of humans to an apparently equivalent stimulus are called interindividual response variability. This phenomenon has gained more and more attention in research in recent years. The research field of exercise-cognition has also taken up this topic, as shown by a growing number of studies published in the past decade. In this perspective article, we aim to prompt the progress of this research field by (i) discussing the causes and consequences of interindividual variability, (ii) critically examining published studies that have investigated interindividual variability of neurocognitive outcome parameters in response to acute physical exercises, and (iii) providing recommendations for future studies, based on our critical examination. The provided recommendations, which advocate for a more rigorous study design, are intended to help researchers in the field to design studies allowing them to draw robust conclusions. This, in turn, is very likely to foster the development of this research field and the practical application of the findings.

Telemedicine as a Therapeutic Option in Sports Medicine: Results of a Nationwide Cross-Sectional Study among Physicians and Patients in Germany

Background: Worldwide, the number of treatments in the field of sports medicine is increasing. However, the COVID-19 pandemic has changed everyday life. Many consultations had to be cancelled, postponed, or converted to a virtual format. Telemedicine in sports medicine could support physicians. This study analyzes the use and perception of telemedicine applications among physicians and patients in the field of sports medicine in Germany. Methods: This prospective cross-sectional study was based on a survey of sports medicine physicians and patients in Germany during the COVID-19 pandemic. Descriptive statistics were calculated. Results: We analyzed the responses of 729 patients and 702 sports medicine physicians. Most believed that telemedicine is useful. Both physicians and patients rated their knowledge of telemedicine as unsatisfactory. The majority of respondents said they do not currently use telemedicine but would like to do so. Patients and physicians reported that their attitude had changed positively towards telemedicine and that their usage had increased due to COVID-19. The majority in both groups agreed on implementing virtual visits in stable disease conditions. Telemedicine was considered helpful for follow-up monitoring and prevention by both groups. Conclusion: Telemedicine in sports medicine has seen limited use but is highly accepted among physicians and patients alike. The absence of a structured framework is an obstacle to effective implementation. Training courses should be introduced to improve the limited knowledge regarding the use of telemedicine. More research in telemedicine in sports medicine is needed. This includes large-scale randomized controlled trials, economic analyses and explorations of user preferences.

Movement Patterns and Match Statistics in the National Rugby League Women's (NRLW) Premiership

As women's rugby league grows, the need for understanding the movement patterns of the sport is essential for coaches and sports scientists. The aims of the present study were to quantify the position-specific demographics, technical match statistics, and movement patterns of the National Rugby League Women's (NRLW) Premiership and to identify whether there was a change in the intensity of play as a function of game time played. A retrospective observational study was conducted utilizing global positioning system, demographic, and match statistics collected from 117 players from all NRLW clubs across the full 2018 and 2019 seasons and were compared between the ten positions using generalized linear mixed models. The GPS data were separated into absolute (i.e., total distance, high-speed running distance, and acceleration load) and relative movement patterns (i.e., mean speed, mean high speed (> 12 km·h^-1), and mean acceleration). For absolute external outputs, fullbacks covered the greatest distance (5,504 m), greatest high-speed distance (1,081 m), and most ball-carry meters (97 m), while five-eighths recorded the greatest acceleration load (1,697 m·s^-2). For relative external outputs, there were no significant differences in mean speed and mean high speed between positions, while mean acceleration only significantly differed between wingers and interchanges. Only interchange players significantly decreased in mean speed as their number of minutes played increased. By understanding the load of NRLW matches, coaches, high-performance staff, and players can better prepare as the NRLW Premiership expands. These movement patterns and match statistics of NRLW matches can lay the foundation for future research as women's rugby league expands. Similarly, coaches, high-performance staff, and players can also refine conditioning practices with a greater understanding of the external output of NRLW players.