Does carbohydrate periodization boost running performance?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Periodized carbohydrate intake influences metabolic flexibility and indices of running economy during endurance training in recreationally active males
Kripp et al. (2026) Front Nutr (click here to open the original paper)
How can you apply this paper's findings to your training or coaching practice?
◦ If you coach (or are an) endurance runner, the practical takeaway for performance is pretty much “nothing to see here, move along, move along”. Periodizing carbohydrates might help you swing fuel use towards fat oxidationFat oxidation is the process where your body breaks down fat and uses it for energy, again usually with oxygen. It’s a key way your body fuels longer, steadier efforts and daily living. during base training, then swing it back when sessions get spicy, and it may even nudge 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 kilometre travelled. A runner with a lower energy cost per kilometre has a higher economy than a runner with a higher energy cost. in that second phase, but carbohydrate periodisation did not translate into a clear performance edge. So should you go low-carb for 4 weeks, then reload? For guaranteed faster running, not based on this trial. I’m always worried about how often athletes chase “better fat burning” with low carb or ketones like it’s a cheat code while forgetting that the finish line rewards pace, not biochemistry. Biochemistry says that low carb can’t fuel fast running as efficiently as high carb, and the evidence shows that low carb doesn’t improve performance over high carb.
What is my Rating of Perceived scientific Enjoyment (RPsE)?
7 out of 10 → I experienced moderate scientific enjoyment because the study nails the basics (randomisationRandomization means assigning people to different parts of a study (e.g., groups in a randomised controlled trial) by chance, not by choice. This helps make the groups similar at the start and reduces bias, so any differences you see are more likely due to the treatment, not background differences. In a crossover study, randomization usually decides the order in which each person gets the treatments (for example, Treatment A first then B, or B first then A). This way, order effects—like learning, fatigue, or simple time passing—are less likely to skew the results., clear eligibility criteria, protocol pre-registrationPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings., sample size (N)N is how many participants or observations are analyzed. A bigger N usually means more precise estimates and more power (ability to detect a true effect). A smaller N results in a study that is less likely to detect a true effect (false negative/type II error) and is more likely to report false positives (type I error). Of course, a badly designed study is still bad even if it has a big N. planning with a power calculationA power calculation is a way to figure out how many people or data points you need in a study so you can reliably spot a real effect if it exists. It balances four things: the size of the effect you care about, how much random variation there is, how strict you are about false alarms, and how likely you want to be to detect the effect. In plain terms: it helps you avoid running a study that’s too small to be useful or so big that it wastes time and money., solid measurement methods, standiardised reporting, etc.), but it loses points for not blindingBlinding is when people in a study don’t know which treatment they’re getting. It stops expectations or beliefs (from patients or researchers) from skewing the results. “Single-blind” means participants don’t know; “double-blind” means participants and researchers don’t know; “triple-blind” means that the participants, researchers, and data analysts are kept in the dark. The goal is simple: fair tests and trustworthy findings. the participants and for participant attrition that leaves the analysis short of the full randomized sample — which matters when performance effects are subtle and statistical powerStatistical power is the probability that a statistical test will correctly detect a true positive, i.e., detect a real effect if there is one (and correctly reject an incorrect null hypothesis). Higher statistical 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 at least 80% (or 0.8). is low.
Remember: 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 if the study is poor quality (e.g., has a high risk of biasRisk of bias in meta-analysis refers to the potential for systematic errors in the studies included in the analysis, which can lead to misleading or invalid results. Assessing this risk is crucial to ensure the conclusions drawn from the combined data are reliable. or a low quality of evidenceA low quality of evidence means that, in general, studies in this field have several limitations. This could be due to inconsistency in effects between studies, a large range of effect sizes between studies, and/or a high risk of bias (caused by inappropriate controls, a small number of studies, small numbers of participants, poor/absent randomization processes, missing data, inappropriate methods/statistics). When the quality of evidence is low, there is more doubt and less confidence in the overall effect of an intervention, and future studies could easily change overall conclusions. The best way to improve the quality of evidence is for scientists to conduct large, well-controlled, high-quality randomized controlled trials.). Always check what other trials in this field (link opens a new tab) have shown. Do they confirm the findings of this study? If there is a high-quality meta-analysis (link opens a new tab) evaluating the entirety of the evidence in this field, what does it say about the effect sizeA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients., the risk of biasRisk of bias in a meta-analysis refers to the potential for systematic errors in the studies included in the analysis. Such errors can lead to misleading/invalid results and unreliable conclusions. This can arise because of issues with the way participants are selected (randomisation), how data is collected and analysed, and how the results are reported., and the quality/certainty of evidenceCertainty of evidence tells us how confident we are that the results reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, and precision. Low certainty means more doubt and less confidence, and that future studies could easily change the conclusions. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions.? I’ve also written a deep-dive article on this topic; so, check it out at veohtu.com/carbohydrateperiodisation.
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 exposure to the treatment and control..
What was the authors’ hypothesis or research question?
◦ The authors aimed to test whether periodizing carbohydrate intake across 8 weeks of endurance training improves performance, fuel use, and body composition compared with staying high-carbohydrate or staying low-carbohydrate.
What did the authors do to test the hypothesis or answer the research question?
◦ The researchers recruited 30 recreationally active males aged 18 to 40, then used randomisationRandomization means assigning people to different parts of a study (e.g., groups in a randomised controlled trial) by chance, not by choice. This helps make the groups similar at the start and reduces bias, so any differences you see are more likely due to the treatment, not background differences. to allocate them to 1 of 3 diets: a periodized group that ate very low carbohydrate for 4 weeks then switched to high carbohydrate for 4 weeks, a low-carbohydrate high-fat group that stayed very low carbohydrate for 8 weeks, or a high-carbohydrate group that stayed high carbohydrate for 8 weeks. Everyone did the same 8-week running plan, with a base phase followed by a harder interval-focused phase.
◦ The main testing was a treadmill graded exercise test at baseline, week 4, and week 8. The researchers measured peak running speed, time to exhaustion, oxygen use, blood lactate, substrate oxidation estimated from breathing (this is indirect calorimetryIndirect calorimetry is a way to measure how much energy your body is using by analyzing your breathing. It tracks how much oxygen you breathe in and how much carbon dioxide you breathe out. From that, it estimates how many calories you burn and how much of that energy comes from fat or carbohydrate.), and 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 kilometre travelled. A runner with a lower energy cost per kilometre has a higher economy than a runner with a higher energy cost.. They anchored several measures to the lactate threshold (a.k.a. LT1)Lactate threshold is the exercise intensity where blood lactate starts to gradually rise faster than your body can clear it, but you can still keep going for a while. It marks the shift from “this is hard but manageable” to “this is going to catch up with me soon” if you hold it. (basically, the intensity where lactate starts climbing in a sustained way).
What did the authors find?
◦ Peak running speed rose by about 0.8 kilometres per hour from baseline to week 8, and time to exhaustion increased by about 97 seconds from baseline to week 8, across the whole sample. But the effects were not different between diet groups.
◦ During the first 4 weeks, both low-carbohydrate arms (periodized and low-carbohydrate high-fat) shifted toward higher fat oxidationFat oxidation is the process where your body breaks down fat and uses it for energy, again usually with oxygen. It’s a key way your body fuels longer, steadier efforts and daily living. and lower carbohydrate oxidationCarbohydrate oxidation is the process where your body breaks down carbs and uses them for energy, usually with oxygen. It’s what happens when you burn glucose or stored glycogen to fuel activity. at speeds around the lactate threshold (a.k.a. LT1)Lactate threshold is the exercise intensity where blood lactate starts to gradually rise faster than your body can clear it, but you can still keep going for a while. It marks the shift from “this is hard but manageable” to “this is going to catch up with me soon” if you hold it.. Then the periodized group flipped back in the second 4 weeks when carbohydrates returned, with fat oxidation dropping and carbohydrate oxidation rising again—basically a rebound that the authors interpret as restored “metabolic flexibility.” The low-carbohydrate high-fat group did not show that rebound because they never reintroduced carbohydrates.
◦ 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 kilometre travelled. A runner with a lower energy cost per kilometre has a higher economy than a runner with a higher energy cost. at the lactate threshold showed a clear diet-by-time pattern: only the periodized group improved across the 8 weeks. The paper frames this as a potential efficiency benefit during the higher-intensity training phase after carbohydrate reintroduction. Is running economy at the lactate threshold the right hill to die on? Maybe, but it’s also a measure that can get tangled up with shifting thresholds over time, and the authors themselves flag that concern.
◦ Body weight and fat mass dropped in the periodized and low-carbohydrate high-fat groups during the first 4 weeks. In the periodized group, weight fell by about 2.1 kilograms (about 4.6 pounds) and fat mass by about 1.5 kilograms (about 3.3 pounds) by week 4, with little further change after carbohydrates came back. In the low-carbohydrate high-fat group, weight and fat mass kept falling through week 8. Despite these within-group changes, the groups were not statistically different from each other at the measured timepoints. What explains these changes? It is likely that low-carb and periodised groups were hypocaloricProviding fewer calories than are needed to maintain current body weight, which often leads to weight loss — total daily energy intake is less than total daily energy expenditure., i.e., their total energy intake was insufficient to meet the daily energy requirements.
◦ The authors concluded that carbohydrate periodization mainly changes metabolism (fuel use) and some physiological markers (like running economy), but it did not clearly improve performance in this treadmill test.
What were the strengths?
◦ The study did several things right: it used randomisationRandomization means assigning people to different parts of a study (e.g., groups in a randomised controlled trial) by chance, not by choice. This helps make the groups similar at the start and reduces bias, so any differences you see are more likely due to the treatment, not background differences., kept the training program consistent across groups, and repeated the same lab test at 3 timepoints. Diet support was hands-on (dietitians plus food logging), and ketosis checks helped confirm compliance during the low-carbohydrate phases. The trial was also pre-registeredPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings., which is a quiet green flag for transparency.
What were the limitations?
◦ The biggest issue is the sample size (N)N is how many participants or observations are analyzed. A bigger N usually means more precise estimates and more power (ability to detect a true effect). A smaller N results in a study that is less likely to detect a true effect (false negative/type II error) and is more likely to report false positives (type I error). Of course, a badly designed study is still bad even if it has a big N.: only 24 out of 30 participants finished, which is small for a 3-arm trial and makes false negative (type II error)When a statistical test fails to detect an effect or difference when there actually is one. I.e, “a missed detection”. Studies with a small sample size (N, number of participants) are more likely to produce false negative results. findings more likely — especially since the authors’ own statistical powerStatistical power is the probability that a statistical test will correctly detect a true positive, i.e., detect a real effect if there is one (and correctly reject an incorrect null hypothesis). Higher statistical 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 at least 80% (or 0.8). was low for some performance outcomes. The trial was non-blindedBlinding is when people in a study don’t know which treatment they’re getting. It stops expectations or beliefs (from patients or researchers) from skewing the results. “Single-blind” means participants don’t know; “double-blind” means participants and researchers don’t know; “triple-blind” means that the participants, researchers, and data analysts are kept in the dark. The goal is simple: fair tests and trustworthy findings., and the performance test was a graded treadmill test rather than a race-like time trial, so “real-world performance” changes remains an open question.
How was the study funded, and are there any conflicts of interest that may influence the findings?
◦ The authors reported financial support for publication, with open-access funding provided by the University of Vienna, and they declared no commercial or financial conflicts of interest.
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Other papers I reviewed this month:
Does slow tempo strength training help runners race faster?
Does carbohydrate periodization boost running performance?
Does heavy lifting make cyclists faster?
Should athletes use antioxidant supplements for performance?
Niche Peach Connoisseur 5000 (from Sudden Death Brewing)
Additional papers I read that you might find interesting:
Whey protein intakes up to 0.4g/kg body mass are well tolerated before a 10km run at 85% of race pace: a clinical trial. Shaw et al. (2026) J Int Soc Sports Nutr.
Lower limb neuromotor control during perturbed and unperturbed gait conditions in male runners with Achilles tendinopathy: an exploratory analysis. Quarmby et al. (2026) Int Biomech.
National-Standard Middle-Distance Runners Maintain 1500 m Time Trial Running Performance on Successive Days. Birdsey et al. (2026) Eur J Sport Sci.
Dual-task effects on spatiotemporal, kinematic, and kinetic parameters and their variability during running. Teng et al. (2026) Gait Posture.
Morphological Changes and MRI Characteristics of the Achilles Tendon in Amateur Marathon Runners With Different Running Experience. Yao et al. (2026) J Foot Ankle Res.
Association between bone mineral density and ground reaction force in male and female runners. Smith et al. (2026) Gait Posture.
Effect of gait retraining in minimalist footwear or barefoot on running footstrike and cadence: a systematic review. DesRochers et al. (2026) Res Sports Med.
Does preferred technique influence how kinematics change during a run to exhaustion?-A cluster based approach. Rivadulla et al. (2026) PeerJ.
Biomechanical insights into Achilles tendinopathy risk and protection in runners: a large prospective study 4HAIE. Jandacka et al. (2026) Br J Sports Med.
Sprint running mechanics are associated with hamstring strain injury: a 6-month prospective cohort study of 126 elite male footballers. Bramah et al. (2026) Br J Sports Med.
Effect of high time under tension strength training on different muscular actions in the performance of runners: A randomized controlled trial. Martins et al. (2026) PLoS One.
The effect of a familiarization critical speed testing session on critical speed determination during treadmill running. Micheli et al. (2026) PLoS One.
Baseline Inflammatory Markers as Predictors of Running-Related Injuries: A One-Year Prospective 4HAIE Cohort Study. Cipryan et al. (2026) Scand J Med Sci Sports.
Stride-to-Stride Fluctuations and Temporal Patterns of Muscle Activity Exhibit a Stronger Relationship in Running-Induced Fatigue. Chalitsios et al. (2026) Scand J Med Sci Sports.
Examining attention- deficit/ hyperactivity disorder in endurance and ultra-endurance runners. Scheer et al. (2026) Acta Psychol (Amst).
"Running in circles": Breastfeeding experiences in women who have had bariatric surgery before pregnancy: A qualitative study. Mokhlesi et al. (2026) Women Birth.
Acute effects of dynamic stretching on knee joint position sense and dynamic balance in recreational runners: A randomized controlled trial. Simões et al. (2026) Gait Posture.
Carryover effects of treadmill-based footstrike modification gait retraining on overground running biomechanics. Chan et al. (2026) J Sports Sci.
Agreement and Reliability Between Urine Reagent Strips and Refractometry for Field Assessment of Hydration in Ultra-Trail Runners. Rojas-Valverde et al. (2026) Nutrients.
Effects of Marathon Running on Skin and Plasma Carotenoids in Endurance Runners. Joyner et al. (2026) Nutrients.
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