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The Endurance Performance Nerd Alert.

Learn to train smart, run fast, and be strong with Thomas Solomon, PhD

June 2025

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Use this Nerd Alert of the latest exercise science and sports nutrition research to improve your running performance or coaching practice.

The research studies are divided into subtopics — training methods, sports nutrition, supplements, athlete health, injuries and rehab, and female athlete physiology — but I’ve also provided a deeper dive into some studies.

And, there’s my beer of the month to wash it all down.

All the interesting papers I found this month are immediately below.
Dig in and evaluate the authors’ findings by clicking on the titles to access the full papers.
Evaluate each paper thoughtfully—be sceptical, not cynical. To guide you, consider using the framework I applied when doing my deep dives. This approach will help you assess the quality of a study while also appreciating the complexity and nuance of scientific research.

General training methods:

Fluctuating Running Speed During 10-km Running Elevates Physiological Strain. Sumi et al. (2025) Int J Sports Physiol Perform.
Threshold estimation in running using dynamical correlations of RR intervals. Kanniainen et al. (2025) Physiol Rep.
Neural networks can accurately identify individual runners from their foot kinematics, but fail to predict their running performance. Mayerhofer et al. (2025) J Biomech.
Anaerobic speed reserve and acute responses to a short-format high-intensity interval session in runners. Thron et al. (2025) J Sci Med Sport.
Methodological and aerobic capacity adaptations of high-intensity interval training at different altitudes in distance runners: A comprehensive meta-analysis. Fentaw et al. (2025) Physiol Rep.
The Effect of 90 and 120 Min of Running on the Determinants of Endurance Performance in Well-Trained Male Marathon Runners. Zanini et al. (2025) Scand J Med Sci Sports.
Influence of Advanced-Footwear-Technology Spikes on Middle- and Long-Distance Running Performance Measures in Trained Runners. Rodrigo-Carranza et al. (2025) Int J Sports Physiol Perform.
Effects of Heat and Hypoxia Training on the Fat Oxidation Capacity of Competitive Athletes. Geng et al. (2025) Eur J Sport Sci.
Training in normobaric hypoxia induces hematological changes that affect iron metabolism and immunity. Nolte et al. (2025) Sci Rep.
The acute effects of simulated hypoxic training at different altitudes on oxidative stress and muscle damage in elite long-distance runners. Sarikaya et al. (2025) PeerJ.

Sports nutrition and hydration:

General and sport-specific nutrition knowledge and behaviors of adolescent athletes. Gibbs et al. (2025) J Int Soc Sports Nutr.
Effect of Exercise Intensity, Duration, and Volume on Protein Oxidation During Endurance Exercise in Humans: A Systematic Review With Meta-Analysis. Clauss and Jensen. (2025) J Int Soc Sports Nutr.

Sports supplements:

Ketone ester ingestion impairs exercise performance without impacting cognitive function or circulating EPO during acute hypoxic exposure. Stalmans et al. (2025) J Appl Physiol (1985).
Physiological effects of spirulina supplementation during lactate threshold exercise at simulated altitude (2,500 m): a randomized controlled trial. Gurney et al. (2025) J Int Soc Sports Nutr.
7 days of L-citrulline supplementation does not improve running performance in the heat whilst in a hypohydrated state. Cable et al. (2025) Eur J Appl Physiol.
The Effect of Probiotic Supplementation on Cytokine Modulation in Athletes After a Bout of Exercise: A Systematic Review and Meta-Analysis. Aparicio-Pascual et al. (2025) Sports Med Open.
The Role of Vitamin D Supplementation in Enhancing Muscle Strength Post-Surgery: A Systemic Review. Wang et al. (2025) Nutrients.
An investigation into how the timing of nutritional supplements affects the recovery from post-exercise fatigue: a systematic review and meta-analysis. Cheng et al. (2025) Front Nutr.

Athlete health:

Invisible Monitoring for Athlete Health and Performance: A Call for a Better Conceptualization and Practical Recommendations. Leduc et al. (2025) Int J Sports Physiol Perform.
Effect of whole-body vibration training on bone mineral density in older adults: a systematic review and meta-analysis. Massini et al. (2025) PeerJ.
Repetitive Feeding-Challenge With Different Nutritional Densities on Markers of Gastrointestinal Function, Substrate Oxidation, and Endurance Exercise Performance. Martinez et al. (2025) Int J Sport Nutr Exerc Metab.

Injury and rehab:

The impact of blood flow restriction training on tendon adaptation and tendon rehabilitation - a scoping review. Öberg et al. (2025) BMC Musculoskelet Disord.
Technologically advanced running shoes reduce biomechanical factors of running related injury risk. Kim et al. (2025) Sci Rep.

Female athlete physiology and sex differences:

The effect of sex on the cardiopulmonary and neuromuscular response to high-intensity interval exercise. Wilson et al. (2025) Am J Physiol Regul Integr Comp Physiol.
Screening for Relative Energy Deficiency in Sport: Detection of Clinical Indicators in Female Endurance Athletes. Wasserfurth et al. (2025) Med Sci Sports Exerc.
Modelling Female Breast Motion During Running: Implications of Breast Support on the Spine. Mills et al. (2025) Eur J Sport Sci.
Sex-specific characteristics of special endurance and performance potential in female runners. Blödorn et al. (2025) BMC Res Notes.

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My deep dives:

Fluctuating Running Speed During 10-km Running Elevates Physiological Strain.

Sumi et al. (2025) Int J Sports Physiol Perform.

What type of study is this?

This study is a randomised controlled trialThe “gold standard” approach for determining whether a treatment has a causal effect on an outcome of interest. In such a study, a sample of people representing the population of interest is randomised to receive the treatment or a no-treatment placebo (control), and the outcome of interest is measured before and after the exposure to treatment/control. with crossover.Crossover means that all subjects completed all interventions (control and treatment) usually with a wash-out period in between.

What was the authors’ hypothesis or research question?

The authors hypothesised that running 10 kilometres while deliberately fluctuating speed would impose greater physiological strain than covering the same distance at a constant speed.

What did the authors do to test the hypothesis or answer the research question?

Ten trained male endurance runners (mean ± SD age 25 ± 8 y, height 170 ± 6 cm, mass 58.6 ± 4.3 kg) completed two 10-km treadmill trials in random order at least one week apart. One trial maintained a constant speed corresponding to 80 % of maximal oxygen uptake (V̇O₂maxYour maximal rate of oxygen consumption; a measure of cardiorespiratory fitness and maximal aerobic power, which contributes to endurance performance.), whereas the other alternated every 600 m between 75 % and 85 % V̇O₂max while keeping overall distance and completion time identical. Oxygen uptake, minute ventilation, heart rate, step cadence and length, blood lactate, rating of perceived exertion, and countermovement-jump height were recorded; blood lactate and jump height were assessed pre- and post-run.

What did the authors find?

10 km completion time did not differ between conditions (both 38.6 ± 5.1 min; n = 10). Fluctuating pace produced higher mean oxygen uptake (48.6 ± 3.5 vs 47.1 ± 3.6 mL kg⁻¹ min⁻¹; P = 0.03; Cohen’s dA measure of effect size that quantifies the difference between two group means relative to their pooled standard deviation. It indicates how substantial the difference is, independent of sample size. Common benchmarks for interpretation are 0.2 (small effect), 0.5 (medium effect), and 0.8 (large effect). = 0.58) and higher ventilation rate (95 ± 14 vs 87 ± 11 L min⁻¹; P = 0.02; d = 0.66). Session-wide perceived exertion was greater after the fluctuating trial (5.8 ± 2.0 vs 4.8 ± 1.3; P = 0.03; d = 0.72). Exercise-induced blood lactate rose more in the fluctuating trial (4.5 ± 1.5 vs 2.9 ± 1.4 mmol L⁻¹; interaction P = 0.006; partial eta-squared (ηp²)A measure of effect size used in ANOVA that represents the proportion of variance in a dependent variable explained by an independent variable, while controlling for (excluding) the effects of other variables. It is commonly used in factorial designs and typically yields larger values than regular eta-squared. Values range from 0 to 1, with higher values indicating a greater proportion of explained variance. = 0.64). Heart rate, stride cadence, stride length, and countermovement-jump height showed no significant differences.
The authors concluded that deliberately varying speed during a 10-km run, even when total time is unchanged, increases cardiopulmonary demand, metabolic stress, and perceived effort compared with an even-paced run.

What were the strengths?

The crossover design meant every participant served as his own control, reducing inter-individual variability. Running speeds were prescribed relative to each runner’s V̇O₂max, ensuring comparable physiological loads. Randomisation of trial order, collection of multiple objective physiological and perceptual outcomes, reporting of effect sizesAn effect size is a quantitative measure of the magnitude of a relationship or difference between groups in a study. Unlike p-values, effect sizes show how large or meaningful that effect is. Common effect size measures include Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients., and use of standard analytic techniques enhance internal validity.

What were the limitations?

Only ten participants were studied, which reduces statistical power and raises the risk of false negative findingsFailing to detect an effect or difference when there actually is one. I.e, “a missed detection.”. Neither participants nor investigators were blinded, allocation concealment was not described, and no pre-study power calculation or trial registration were reported. The study also lacks information on inclusion/exclusion criteria breadth.

How was the study funded, and are there any conflicts of interest that may influence the findings?

No funding information is provided in the paper, and the authors do not provide any information about their potential conflicts of interest.

How can you apply these findings to your training or coaching practice?

For endurance athletes and their coaches, the findings reinforce long-standing practical wisdom that an even pace is metabolically economical. Runners who must cope with tactical surges—common in championship racing—should therefore train specifically for fluctuating efforts (for example, fartlek, surge sessions, or hill-sprint sessions) to build resistance to the added physiological strain such surges impose. Coaches seeking optimal time-trial performance should emphasise steady pacing when tactical considerations are absent.

What is my Rating of Perceived scientific Enjoyment?

RP(s)E = 5 out of 10.
My Rating of Perceived Scientific Enjoyment was low because the paper, while addressing a relevant applied question with a sound crossover design, is limited by a very small sample, absence of blinding, lack of pre-registration or power analysis, and minimal reporting on methodological guidelines or conflicts of interest. These weaknesses temper confidence in the findings, although the study does provide clear, statistically supported evidence that pace fluctuation heightens physiological strain.

Important: Don’t make any major changes to your daily habits based on the findings of one study, especially if the study is small (e.g., less than 30 participants in a randomised controlled trial or less than 5 studies in a meta-analysis) or poor quality (e.g., high risk of bias or low certainty of evidence in a meta-analysis). What do other trials in this field show? Do they confirm the findings of this study or have mixed outcomes? Is there a high-quality systematic review and meta-analysis evaluating the entirety of the evidence in this field? If so, what does the analysis show? What is the risk of bias or certainty of evidence of the included studies?

Threshold estimation in running using dynamical correlations of RR intervals.

Kanniainen et al. (2025) Physiol Rep.

What type of study is this?

This study is an observational validation study.An observational validation study is a type of scientific investigation where researchers observe and measure outcomes without manipulating the environment or assigning interventions. The main purpose of such a study is to validate or assess the accuracy, agreement, or utility of a new method, tool, or measurement technique by comparing it against a known or reference standard.

What was the authors’ hypothesis or research question?

The authors aimed to validate a method for estimating aerobic and anaerobic thresholds in runners using a novel heart rate variability (HRV) analysis technique — called “dynamical detrended fluctuation analysis” (DDFA) — and compare it with conventional lactate-based thresholds and maximal heart rate-derived thresholds.

What did the authors do to test the hypothesis or answer the research question?

The study analyzed data from 58 participants who completed an incremental treadmill running test. The sample included 31 males and 27 females, with a mean age of 33±8 years. Participants had varying backgrounds in recreational and endurance running. Outcomes included the aerobic threshold and anaerobic threshold, measured via blood lactate concentrations and estimated using both traditional heart rate formulas (theoretical and measured maximal HR) and the DDFA method. RR intervals — i.e., the time between two consecutive heartbeats — were recorded and processed to calculate time- and scale-dependent scaling exponents of heart rate variability, from which thresholds (DDFAT1 and DDFAT2) were derived. These estimates were then compared statistically to lactate thresholds (LT1 and LT2) for validation.

What did the authors find?

The DDFA-based thresholds demonstrated stronger agreement with the lactate thresholds than those derived from theoretical or measured maximal heart rates. Correlation coefficients between DDFAT1 and LT1, and DDFAT2 and LT2, were 0.43 and 0.58, respectively, both statistically significant (p < 0.001). Mean differences were smallest for DDFA thresholds (0.92 BPM for DDFAT1 vs LT1; and -3.16 BPM for DDFAT2 vs LT2), indicating close alignment. In contrast, theoretical HR thresholds showed systematic underestimation (mean differences: 13.19 BPM for LT1, 11.24 BPM for LT2), and measured HR thresholds, although somewhat better, also underestimated lactate thresholds (mean differences: 10.65 BPM for LT1, 7.24 BPM for LT2). Bland-Altman analysisA method for assessing agreement between two quantitative measurements by plotting the differences against the averages of the two measures. It identifies bias (mean difference) and limits of agreement (range where most differences fall). confirmed that only DDFA thresholds lacked systematic bias. The error analysis showed that DDFA thresholds had the smallest estimation errors compared to the other methods.
The authors concluded that the DDFA-based method offers a simple, non-invasive, and accurate alternative for estimating aerobic and anaerobic thresholds, potentially enabling real-time exercise intensity monitoring through wearable devices.

What were the strengths?

The study's primary strength is its innovative application and validation of a scalable HRV-based method that could replace costly and invasive lactate threshold testing. The sample size was moderate and demographically balanced, and testing conditions were standardized using a controlled treadmill protocol. The study used robust statistical analyses, including correlation coefficients, t-tests, and Bland-Altman plotsA method for assessing agreement between two quantitative measurements by plotting the differences against the averages of the two measures. It identifies bias (mean difference) and limits of agreement (range where most differences fall)., along with bootstrappedBootstrapping is a way to estimate uncertainty by repeatedly resampling your data (with replacement) to create many simulated samples. A confidence interval is then calculated from the variation across those samples. I.e., it is a data-driven way to measure how sure we are, without relying on strict assumptions." confidence intervalsA measure of uncertainty used in Frequentist statistics. The 95% confidence interval is a plausible range of values within which the true value (e.g., the true treatment effect) would be found 95% of the time if the data was repeatedly collected in different samples of people. If this range of values (the confidence interval) crosses zero, there is little confidence in the effect.. The physiological thresholds were determined by experienced exercise physiologists, enhancing reference standard credibility. The DDFA method builds on prior work and was transparently described, with algorithmic detail that facilitates replication. Furthermore, the use of validated RR interval data from a high-quality sensor (Polar H10) enhances reliability.

What were the limitations?

Despite these strengths, the study has several limitations. First, the exercise protocol was tightly controlled and may not reflect real-world training or non-incremental sessions. Second, the DDFA method has only been validated in cycling and running, so its generalizability to other sports remains uncertain. Third, although the method shows promise for wearable integration, its performance with photoplethysmography-derived heart rate measures — i.e., optical “heart-rate-at-the-wrist”, commonly used in consumer smartwatches — was not tested. Moreover, the threshold identification still relied on visual inspection and some subjective adjustment. Finally, the study did not report a priori sample size calculation or formal statistical power analysis for detecting clinically meaningful differences between threshold estimation methods.

How was the study funded, and are there any conflicts of interest that may influence the findings?

The study was funded by Business Finland, The Kalle Kaihari Heart Research Fund, The Finnish Foundation for Cardiovascular Research, The Finnish Cultural Foundation (Pirkanmaa Regional Fund), and the European Regional Development Fund. Three authors (MK, TP, and ER) are shareholders in MoniCardi Ltd., a company related to cardiac health assessment, and one author (ER) is involved in a pending patent related to this field. However, the study was conducted independently of company influence.

How can you apply these findings to your training or coaching practice?

This study provides valuable insights for endurance athletes and coaches by offering a promising method for threshold estimation that does not require laboratory-based lactate testing. The DDFA approach could enable continuous, real-time monitoring of training zones, allowing athletes to adjust effort based on physiological data rather than fixed heart rate percentages. This is especially relevant given the known individual variability in heart rate thresholds. Its integration into smartwatches or other wearable devices could make threshold-based training more accessible to non-elite athletes.

What is my Rating of Perceived scientific Enjoyment?

RP(s)E = 8 out of 10.
My Rating of Perceived Scientific Enjoyment was high because the paper presented a novel and well-described method for estimating physiological thresholds using RR interval data. The DDFA method showed good agreement with lactate thresholds and avoids the systematic biases seen in common heart rate-based methods. The authors provided clear methodology, robust statistical comparisons, and discussed practical implications for wearable tech use. However, limitations include the lack of generalizability to real-world exercise conditions and non-validated application in consumer photoplethysmography/optical heart rate devices.

Important: Don’t make any major changes to your daily habits based on the findings of one study, especially if the study is small (e.g., less than 30 participants in a randomised controlled trial or less than 5 studies in a meta-analysis) or poor quality (e.g., high risk of bias or low certainty of evidence in a meta-analysis). What do other trials in this field show? (Follow the link to explore those trials.) Do they confirm the findings of this study or have mixed outcomes? Is there a high-quality systematic review and meta-analysis evaluating the entirety of the evidence in this field? If so, what does the analysis show? What is the risk of bias or certainty of evidence of the included studies?

The Effect of 90 and 120 Min of Running on the Determinants of Endurance Performance in Well-Trained Male Marathon Runners.

Zanini et al. (2025) Scand J Med Sci Sports.

What type of study is this?

This study is a randomised controlled trialThe “gold standard” approach for determining whether a treatment has a causal effect on an outcome of interest. In such a study, a sample of people representing the population of interest is randomised to receive the treatment or a no-treatment placebo (control), and the outcome of interest is measured before and after the exposure to treatment/control. with crossover.Crossover means that all subjects completed all interventions (control and treatment) usually with a wash-out period in between.

What was the authors’ hypothesis or research question?

The authors aimed to quantify and compare the changes in running performance determinants — namely maximal oxygen uptake (V̇O₂maxV̇O₂max is the maximal rate of oxygen consumption your body can achieve during exercise. It is a measure of cardiorespiratory fitness and indicates the size of your engine, i.e., your maximal aerobic power, which contributes to endurance performance.), fractional utilizationFractional utilization is the percentage of your VO₂max that you can sustain at a given effort, often measured at the lactate threshold. I.e., it describes how much of your engine’s max power you can use steadily without fatigue. (i.e., the % of VO₂max) at lactate thresholdLactate threshold is the exercise intensity at which lactate starts to accumulate rapidly in the blood, signaling a shift toward more glycolytic (glucose using) and anaerobic energy use. It is typically the intensity at which effort begins to feel hard and fatigue builds faster.”. (VO₂@LT), running economyThe rate of energy expenditure (measured in kiloJoules [KJ], kilocalories [kcal] or oxygen consumption [V̇O₂]) per kilogram bodymass (kg) per unit of distance i.e. per 1 kilometer traveled. A runner with a lower energy cost per kilometer has a higher economy than a runner with a higher energy cost., and speed at lactate threshold (Speed@LT) — following 90 and 120 minutes of prolonged running in well-trained male marathon runners.

What did the authors do to test the hypothesis or answer the research question?

Fourteen well-trained male marathon runners (mean V̇O₂max: 63.1 ± 5.8 mL·kg⁻¹·min⁻¹; average marathon time: 2:46:58) completed three treadmill sessions: one in an unfatigued state, and two following 90 and 120 minutes of running at a fixed submaximal intensity. Respiratory gases were sampled periodically during the prolonged runs to assess running economy, while separate incremental tests before and after the prolonged runs were used to measure V̇O₂max and determine lactate thresholds. Physiological variables assessed included running economy (via oxygen cost), V̇O₂max, VO₂@LT, Speed@LT, and associated metrics such as heart rate, respiratory exchange ratio (RER), blood lactate, and rating of perceived exertion (RPE).

What did the authors find?

The study found that all primary endurance performance determinants significantly changed following prolonged running. V̇O₂peakV̇O₂peak is the highest oxygen uptake measured during a test, even if VO₂max wasn’t fully reached. I.e., it is the best effort recorded, but not always your max. decreased by 3.1% after 90 minutes (p = 0.04) and 7.1% after 120 minutes (p < 0.001). Running economyThe rate of energy expenditure (measured in kiloJoules [KJ], kilocalories [kcal] or oxygen consumption [V̇O₂]) per kilogram body mass (kg) per unit of distance i.e. per 1 kilometer traveled. A runner with a lower energy cost per kilometer has a higher economy than a runner with a higher energy cost. deteriorated linearly over time, increasing by 4.2% after 90 minutes and 5.8% after 120 minutes (both p < 0.001). VO₂@LT increased by 2.8% (p = 0.03) and 4.9% (p = 0.01) at 90 and 120 minutes, respectively, largely due to the drop in VO₂peak. Speed@LT decreased nonlinearly from 14.0 to 13.5 km/h after 90 minutes (−3.0%, p = 0.01) and to 13.0 km/h after 120 minutes (−6.6%, p < 0.001). Fractional utilizationFractional utilization is the percentage of your VO₂max that you can sustain at a given effort, often measured at the lactate threshold. I.e., it describes how much of your engine’s max power you can use steadily without fatigue. (% of VO₂peak) during the run rose from 79% to 91% of VO₂peak from 15 to 120 minutes, indicating increasing physiological strain and reduced sustainability. Effect sizesAn effect size is a quantitative measure of the magnitude of a relationship or difference between groups in a study. Unlike p-values, effect sizes show how large or meaningful that effect is. Common effect size measures include Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. were large for most outcomes (e.g., ηp²A measure of effect size used in ANOVA that represents the proportion of variance in a dependent variable explained by an independent variable, while controlling for (excluding) the effects of other variables. It is commonly used in factorial designs and typically yields larger values than regular eta-squared. Values range from 0 to 1, with higher values indicating a greater proportion of explained variance. = 0.60–0.90). Changes in VO₂peak and running economy negatively impacted performance predictions, while the increase in VO₂@LT was seen as a compensatory effect.
The authors concluded that physiological determinants of endurance performance and Speed@LT deteriorate during prolonged submaximal running, particularly after 120 minutes, which may negatively impact marathon performance due to reduced physiological reserves.

What were the strengths?

This study demonstrated multiple methodological strengths. The crossover design allowed for within-subject comparisons across conditions. The protocol included high-resolution physiological assessments and control of confounding variables such as diet, time of day, and exercise habits. Detailed statistical reporting was provided, including effect sizes and significance values. The investigators used validated testing protocols for running economy, VO₂max, and lactate thresholds. The study also featured frequent within-trial measurements and adhered to standardized laboratory testing methods.

What were the limitations?

The primary limitation is the small sample size (n = 14), which reduces statistical powerStatistical power is the probability that a statistical test will correctly reject a false null hypothesis (i.e., detect an effect if there is one). Higher power reduces the risk of a false negative (failing to detect a true effect; or a Type II error). Power is typically influenced by sample size, effect size, significance level, and variability in the data, with a common target being 80% (or 0.8). and increases the risk of false negativeFailing to detect an effect or difference when there actually is one. I.e, “a missed detection.” findings. The 2-hour duration, while substantial, does not capture the full physiological stress of a complete marathon (or ultra-distance event), limiting real-world applicability. Only male participants were included, so findings may not extrapolate to females. Carbohydrate intake during trials was below recommended race-day guidelines, which may have exaggerated fatigue effects. Additionally, the authors did not assess LT2 post-run due to low lactate values, leaving this aspect of the endurance profile unexamined. Statistical power for individual variables was not reported.

How was the study funded, and are there any conflicts of interest that may influence the findings?

No funding information is provided in the paper. The authors declare no conflicts of interest.

How can you apply these findings to your training or coaching practice?

These findings are directly relevant for endurance athletes and coaches. They underscore the importance of considering fatigue-induced changes in physiological capacity during long-distance events. Training strategies that target durability — such as strength training, nutrition optimization, and fatigue resistance — may help mitigate these changes. Additionally, pacing strategies may need to account for the declining VO₂max and running economy to optimize late-race performance.

What is my Rating of Perceived scientific Enjoyment?

RP(s)E = 6-7 out of 10.
My Rating of Perceived Scientific Enjoyment was moderate because the paper was well-designed, clearly reported, and investigated an important and underexplored area: the temporal deterioration of physiological performance determinants during prolonged running. However, the study had limitations in ecological validity (duration was less than a full marathon, and suboptimal carbohydrate intake), and the relatively small and homogeneous sample (well-trained males only) reduces generalizability. Additionally, some standard clinical trial reporting elements (e.g., power calculation, protocol registration) were absent, which limits its methodological completeness.

Important: Don’t make any major changes to your daily habits based on the findings of one study, especially if the study is small (e.g., less than 30 participants in a randomised controlled trial or less than 5 studies in a meta-analysis) or poor quality (e.g., high risk of bias or low certainty of evidence in a meta-analysis). What do other trials in this field show? Do they confirm the findings of this study or have mixed outcomes? Is there a high-quality systematic review and meta-analysis evaluating the entirety of the evidence in this field? If so, what does the analysis show? What is the risk of bias or certainty of evidence of the included studies?

To help you wash down the latest evidence, here's a snifter from my recent indulgence...

My beer of the month.

Mudcake Bänger.
Brewed by Põhjala (Tallinn, Estonia).
Imperial Stout brewed with chocolate and cacao.
12.5% ABV.
Smooth, mildly chocolatey, and lots of joy. As per usual, Põhjala have ripped yet another stouty Bänger.

RP(be)E(r)
(Rating of Perceived beer Enjoyment)
8 out of 10

Access to education is a right, not a privilege:

Equality in education, health, and sustainability is important to me. I was fortunate to be born into a social welfare system where higher education was free. Sadly, that is no longer true. Consequently, I created Veohtu to provide easy access to high-quality exercise science and sports nutrition education to folks from all walks of life. All the content is free, and always will be. This nerd alert is part of that offering.

Every day is a school day.
Empower yourself to train smart.
Be informed. Stay educated. Think critically.

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Disclaimer: I occasionally mention brands and products but it is important to know that I am not affiliated with, sponsored by, an ambassador for, or receiving advertisement royalties from any brands. I have conducted biomedical research for which I have received research money from publicly-funded national research councils and medical charities, and also from private companies, including Novo Nordisk Foundation, AstraZeneca, Amylin, A.P. Møller Foundation, and Augustinus Foundation. I’ve also consulted for Boost Treadmills and Gu Energy on their research and innovation grant applications and I’ve provided research and science writing services for Examine.com — some of my articles contain links to information on Examine.com but I do not receive any royalties or bonuses from those links. These companies have had no control over the research design, data analysis, or publication outcomes of my work. Any recommendations I make are, and always will be, based on my own views and opinions shaped by the evidence available. My recommendations have never and will never be influenced by affiliations, sponsorships, advertisement royalties, etc. The information I provide is not medical advice. Before making any changes to your habits of daily living based on any information I provide, always ensure it is safe for you to do so and consult your doctor if you are unsure.