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Does a shorter ground contact time improve running economy?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Biomechanical Factors Associated with Intraindividual Differences in Running Economy Across Advanced Footwear Technology Models in Long-Distance Runners
Fohrmann et al. (2026) Sports Medicine - Open (click here to open the original paper)
Are findings of this study useful for runners and coaches?
◦ This paper is useful because it nudges coaches and runners away from the lazy question, “Which super shoe is best?” and toward the better one: “Which shoe works best for this runner?”. Although the study found no overall winner between the Asics Metaspeed Sky+, Nike Alphafly Next% 2, and Puma Fast-R Nitro Elite v1, it did find that shoes associated with shorter ground contact times tended to improve running economy within the same athlete. That makes the practical message fairly clear: test the runner before believing the marketing hype. Is ground contact time the key to shoe choice? Only partly: the paper also shows that ground contact time explained only part of the story. Based on 1 small and short study in male and female runners, we cannot be sure about the effects, and I’d be a little uneasy about overconfident shoe prescriptions based on treadmill data alone.
What was my Rating of Perceived scientific Enjoyment (RPsE)?
6 out of 10 → I experienced moderate scientific enjoyment because the paper clearly described the protocol, eligibility criteria, outcomes, and missing data, and it even included 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.. However, there was no clear 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., no allocation concealmentAllocation concealment is the step that hides the next treatment assignment before a patient enters a trial. It prevents staff from guessing or peeking, so they can’t steer patients to one group or another. It happens at enrollment, before blinding, and guards against selection bias., and no 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., and the final analysis drifted away from the original analysis plan, which is a bit of a red flag. The study is also small, which makes the signal interesting but kinda fragile.
Remember: Don’t make any major changes to your training habits based on the findings of one study, especially if the study is small and/or provides 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.. Do other trials on this topic confirm the findings of this study? If there is a meta-analysis, what is 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. and quality 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.?
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 crossoverCrossover means that all subjects completed all interventions (control and treatment) usually with a wash-out period in between..
What was the hypothesis or research question?
◦ The researchers wanted to find out which biomechanical changes were associated with better running economy when the same runner used different advanced footwear technology models. In plain English, they were trying to explain why 1 runner may benefit more from 1 shoe than another runner does.
How did the researchers test the hypothesis or answer the research question?
◦ The researchers ran a small, single-visit treadmill study in 22 trained long-distance runners to see whether changes in shoe-related running mechanics were associated with changes in how much energy each runner used. The group included 11 women and 11 men, and the average age was 33 years. Each runner did several 5-minute runs at their own season-best marathon pace while wearing 3 different advanced racing shoes, with the shoe order assigned by 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..
◦ During the runs, the authors measured how much oxygen and energy the runners used, plus several movement measures such as step rate, ground contact timeGround contact time is the amount of time your foot stays on the ground during each running step. Shorter ground contact time often reflects a quicker, springier stride, while longer ground contact time can reflect more braking or less stiffness., pelvis motion, and foot and ankle angles. They also recorded heart rate, blood lactate, effort rating, and shoe comfort. The main outcome was 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.. The authors then compared the 3 shoes and also checked which movement changes were most closely associated with better energy use within the same runner.
What did the study find?
◦ The main result was simple and interesting: when a shoe reduced contact time for a given runner, running economy tended to improve. In the final model, about 4 milliseconds less contact time was associated with about a 1% improvement in energetic cost of transport. The effect was consistent in sensitivity analysesA sensitivity analysis tests whether a study’s results stay the same when the researchers change some of their methods, assumptions, or data inputs. E.g., they might reanalyse the data without studies that had a high risk of bias. Sensitivity analyses help show how robust the findings are and whether small changes could lead to different conclusions., which gives it some backbone.
◦ At the group level, though, the 3 shoes did not differ clearly in running economy. The average energetic cost values were very close across the Asics, Nike, and Puma models, and comfort scores were also similar. So the paper does not support the idea that 1 of these shoes is the universal king of the nerd pile.
◦ The paper also showed that some biomechanics differed between shoes, including contact time, pelvis motion, and foot and ankle angles. But most of those variables did not consistently predict better economy across runners. That matters because it hints that shoe response is individual and probably multi-factorial rather than driven by 1 magic variable alone.
◦ The authors concluded that advanced shoe models that reduce contact time for an individual runner may improve running economy, but no single model was best for everyone.
What were the strengths of the study?
◦ The study has several strengths. It used a within-subject design, so each runner served as his or her own comparison. It included both male and female runners, reported clear eligibility criteria, used detailed physiological and biomechanical measurements, handled missing data transparently, and checked robustness of the findings with a sensitivity analysisA sensitivity analysis tests whether a study’s results stay the same when the researchers change some of their methods, assumptions, or data inputs. E.g., they might reanalyse the study without high risk of bias studies. Sensitivity analyses help show how robust the findings are and whether small changes could lead to different conclusions. (a “leave-1-participant-out” analysis). The statistical approach was also thoughtful, using model averaging and penalised modelling rather than pretending 1 model choice was sacred. Nerdy but important.
What were the limitations of the study?
◦ The big limitation is the small 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. of 22 runners. The testing also happened in a single lab visit, with only 1 main trial per condition, on a treadmill whose surface stiffness was not quantified. The authors also note that their original power calculation for repeated-measures analysis no longer directly matched the revised mixed-model approach. There was also no 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. or 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., which increases 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.. These issues do not kill the study, but they do reduce confidence in how broadly the result will extrapolate to road racing in the wild.
Who funded the study, and were there any conflicts of interestA conflict of interest happens when a person or group has a personal, financial, or professional interest that could influence their judgment. It does not always mean they did something wrong. But it can create bias or make others question whether the decision or result is fully fair and trustworthy.?
◦ The study was funded by the German Federal Institute for Sports Science, and the shoes were obtained at a discounted rate through Deutsche Leichtathletik Marketing, ASICS Germany, or Hamburger Laufladen. The authors also reported some relevant conflicts: 1 author serves in honorary or team physician roles associated with sporting bodies that receive sponsorship from Nike and adidas, and another author’s group had previous and ongoing financial support from adidas. The paper states that these relationships did not influence the study, but they are still worth knowing about.
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