Super shoes improved running economy, but responses varied

Super shoes
Running economy
Running shoes

Thomas Solomon, PhD

7 September 2026

Super shoes can improve running economy, but the average response does not tell you exactly what will happen to you. This study explored whether simple characteristics of the runner might help explain who benefits more.

Reference: Hebert-Losier et al.. Exploring Factors to Explain Interindividual Responses to Running Economy in Advanced Footwear Technology Shoes: A Randomised Crossover Laboratory Trial with an Explanatory Predictor Analysis. Sports Medicine - Open (2026) DOI: 10.1186/s40798-026-01091-0.

Study snapshot

A quick, practical summary for runners and coaches.

Quick answer

This randomisedRandomization 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. crossoverCrossover means that all subjects completed all interventions (control and treatment) usually with a wash-out period in between. laboratory study involved 64 runners and 2 laboratory visits. The advanced shoe improved running economy on average, but the size of the benefit varied considerably between runners. The characteristics associated with bigger improvements were exploratory and aren't ready to guide shoe choice.

Key takeaways

The advanced running shoe improved running economy for most participants.
The size of the benefit varied substantially, and most of that variation remained unexplained.
Simple measurements of your calf or foot aren't yet a reliable way to choose a super shoe.

How confident should we be?

Evidence confidence: Moderate

The randomised crossover design, repeated running-economy measurements and balanced male and female sample strengthen the shoe comparison. Confidence is lower for the predictor findings because the analyses were exploratory and need independent replication.

Bottom line

Advanced footwear technology can improve running economy, but the average effect doesn't tell you exactly what will happen to you. This study gives us a few possible clues about individual response, not a biomechanical shopping list.

Read the deep dive below to get a practical interpretation, the wider evidence, some actionable decisions, my thoughts, my rating of perceived scientific enjoyment, and the full study details (research question, study design, participants, methods, data integrity, results, and the strengths & limitations).

The deep dive

The details behind the headline result, including the practical meaning, full findings, limitations, and my interpretation.

Practical meaning

What does this research mean for runners and coaches?

The clearest practical finding is that runners did not all respond equally to the same advanced shoe. The study also shows why we should be careful about labelling somebody a permanent “responder” or “non-responder” from 1 test, 1 speed and 1 shoe model. The advice below is practical interpretation rather than something directly tested by the study.

For runners

The advanced shoe reduced the oxygen cost of running by about 4.1% on average, but individual responses ranged from a 2.6% worsening to an 11.0% improvement. So the average result is useful for understanding the technology, but less useful for predicting your personal result.

A poor response to 1 super shoe doesn't mean super shoes simply “don't work” for you. The authors specifically caution that a below-threshold response to 1 model, at 1 speed, after limited familiarisation shouldn't be treated as a fixed physiological trait.

If small performance differences matter to you, trying more than 1 racing shoe under repeatable conditions is a reasonable practical approach. The study did not test whether this strategy improves race performance, though.

Trail and ultra runners should be especially cautious. These were short, level treadmill runs, not technical trails, steep climbs, descents or several hours of running on tired legs.

For coaches

Treat advanced footwear as an individual performance variable rather than assuming every athlete receives the average benefit.

The study doesn't support measuring calf stiffness, navicular drop or ankle geometry and then prescribing a shoe from those numbers. Those measurements produced some interesting associations, but the prediction model left most of the person-to-person variation unexplained.

If laboratory running-economy testing is available, repeated within-athlete comparisons can help quantify whether a shoe produces a meaningful physiological response. Field testing may be a pragmatic alternative, but this study did not establish how accurately ordinary field tests can detect small differences between shoes.

Evidence in context

What does the wider evidence say?

Previous studies generally show that advanced footwear technology improves running economy on average (Stephen et al. 2025), often by around 4%, but individual responses can vary substantially. Research cited in this paper has reported changes ranging from worse running economy in some runners to improvements above 10%, and recent work has struggled to identify consistent physical or biomechanical predictors of these differences.

How this study fits: This study agrees with that broader picture. It confirms a useful average benefit while adding exploratory evidence that greater calf-muscle stiffness and greater navicular drop were associated with larger improvements. Crucially, those characteristics explained only part of the variation. This is confirmation plus a possible clue, not a finished method for matching runners to shoes.

Practical decision

Should runners change anything?

Maybe. The study supports treating shoe response as individual, but it does not support selecting shoes from calf stiffness, foot shape or a single laboratory test.

Consider this if

You are choosing between several racing shoes and small performance differences matter to you.
You've assumed that the shoe with the best average laboratory result must also be the best shoe for you.

Do not overreact if

Your foot shape or calf characteristics resemble the apparent “better responder” profile.
1 test suggests that you respond poorly to 1 super shoe.

A sensible next step

Choose racing shoes using a combination of fit, comfort, race relevance and, where practical, repeated individual testing. Don't start measuring navicular drop in the kitchen and ordering carbon plates accordingly. We're not there yet.

TIP: Never make any major changes to your training or lifestyle habits based on the findings of one study, especially if the study is small or provides low-quality 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 randomisation 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 randomised controlled trials.. Check whether other trials confirm the findings. If there is a meta-analysisA meta-analysis quantifies the overall effect size of a treatment by compiling effect sizes from all known studies of that treatment. on the topic, look at 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 variability between studies, and the quality of evidenceCertainty of evidence tells us how confident we are that the published results accurately reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, precision, and publication bias. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. Whereas, low certainty means more doubt and less confidence, and that future studies could easily change current conclusions..

Expert interpretation

My thoughts

Running science from Thomas Solomon at Veohtu

I like this study because it tackles the awkward question sitting behind most super-shoe research. An average improvement is lovely, but runners aren't averages.

The advanced shoe produced a convincing improvement in running economy under the laboratory conditions. More interestingly, the runners responded quite differently. Greater calf-muscle stiffness and navicular drop were associated with larger benefits, but those findings came from exploratory analyses involving many candidate variables. They need replication before anyone starts prodding calves in a running shop and announcing, “The Force is strong with this one.”

The most useful message is therefore rather less glamorous. Individual response matters, and we're still not very good at predicting it.

I wouldn't change shoe-selection practice because of the calf or foot findings. I would keep testing race shoes sensibly, particularly if a 1% or 2% difference genuinely matters to you, and remember that performance on a treadmill for 6 minutes is not the same thing as performance late in a marathon or ultra.

The interesting next step is whether these possible predictors survive replication across different shoes, speeds and running populations.

My Rating of Perceived scientific Enjoyment

RPsE: 7/10

I experienced moderate scientific enjoyment because the randomised crossover design, preregistrationPre-registration 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., repeated running-economy measurements and detailed reporting made this a useful study, but the exploratory predictors and limited real-world testing keep the conclusions nicely earthbound.

Read on for further details about the methods, results, data integrity, strengths, and limitations.

Research question

What did the researchers ask?

The authors aimed to identify characteristics associated with differences in how strongly runners responded to advanced footwear technology.

They examined whether sex, running ability, body dimensions, previous super-shoe use, foot structure, calf and ankle strength, muscle and tendon stiffness, running mechanics and perceptions of the shoes were associated with the change in running economy.

Study design

What type of study was this?

This was a randomised crossover laboratory trialA study in which a group of people is randomised to receive BOTH the treatment and the no-treatment control, and the outcome of interest is measured before and after both. The “crossover” means that all participants complete all interventions (the control and the treatment), usually with a washout period in between. with an exploratory predictor analysis.

Each participant completed both shoe conditions, so the researchers could compare each runner with themselves. That is a strong design for testing whether the 2 shoes caused different short-term running-economy responses under these laboratory conditions.

The predictor analysis is different. The researchers didn't manipulate calf stiffness, foot mobility or the other runner characteristics. Those analyses can identify associations, but they cannot show that a particular characteristic caused somebody to benefit more.

Participants

Who took part?

The study included 64 runners: 32 males and 32 females. They ranged from 14.6 to 72.2 years old, with an average age of 33.5 years. Most were long-distance runners; 33 were classified as recreational runners, 27 as experienced runners and 4 as national-level athletes.

The participants had to be in good general health, injury-free and running for at least 30 minutes once per week during the previous 6 months. The researchers excluded runners with a lower-extremity or lower-back injury within the previous 3 months.

Methods

What did the researchers do?

Who? 64 runners

What? Advanced shoe versus control shoe

How long? 2 laboratory visits

The participants attended 2 laboratory sessions. The visits occurred 2 to 30 days apart, with an average gap of 5.7 days. The original registration planned for the sessions to occur within 7 days, but scheduling difficulties extended this for some participants.

During the first visit, the researchers measured a fairly heroic collection of possible predictors. These included body dimensions, foot structure, calf and ankle strength, calf-muscle stiffness, Achilles tendon stiffness, plantar-fascia stiffness, hopping stiffness and peak oxygen uptakeVO₂peak 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..

The participants also completed a maximal treadmill test.

During the second visit, each participant ran in 2 shoes: the Salomon S/Lab Phantasm 2 advanced shoe and the Salomon Aero Glide 2 control shoe. Both had a reported 37 mm stack height, but they differed in several other properties. The advanced shoe contained a carbon plate, different foam materials, greater measured resilience and greater bending stiffness. This means the experiment compared 2 complete shoe designs; it cannot tell us whether any 1 component caused the difference.

Each runner completed two 6-minute trials in each shoe at 70% of their individual speed at peak oxygen uptake. The shoe sequence was randomised.

The researchers measured oxygen consumption, blood lactate, perceived exertion and basic running mechanics. They averaged the 2 trials in each shoe for the main running-economy analysis.

The participants weren't 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. to the footwear. The researchers explained that previous work had not shown a meaningful placeboA dummy treatment that looks like the real one but has no active ingredient, or no active effect for the outcome being studied, and is used for fair comparison. effect on their primary running-economy outcome.

Recruitment also stopped at 64 participants because of resource constraints. The researchers had originally aimed for 85 to improve their ability to detect moderate associations between runner characteristics and shoe response.

Data integrity check

Do the numbers add up?

Minor integrity concern; closer inspection may be warranted

I didn’t spot any major numerical red flags. Most of the results agree across the text, tables, and figures, and the reported effects look biologically plausible. One detail deserves a closer look: the same running-economy result is reported with slightly different standard deviations (1.6%, 1.95%, and 1.97%) in different parts of the paper, and the control oxygen consumption average value also differs slightly between sections (36.8 and 36.5 mL per kg per minute). These are most likely reporting or transcription mistakes, not evidence of misconduct.

Main findings

What did the study find?

Running economy Improved by about 4.1% on average

Individual response Varied from worse to much better

Prediction model Explained about 27% of the variation

The advanced shoe reduced the oxygen required to run at the same treadmill speed. Across all 64 runners, oxygen-consumption-based running economy improved by about 4.1% compared with the control shoe. The difference was statistically significantEvidence that a result is unlikely to be due to chance under a “no effect” model (or null hypothesis). Statistical significance is often judged by a p-value below 0.05 to flag that “something” is going on, but not how big or important that “something” is. One statistically significant result doesn’t mean proof; replication is needed. And, a statistically significant result doesn’t necessarily indicate clinical significance. and, on average, exceeded the researchers' prespecified 2.5% threshold for a meaningful response.

But that average hid substantial individual variation.

Responses ranged from a 2.62% worsening to an 11.01% improvement. Using the prespecified 2.5% threshold, 53 of the 64 runners were classified as above-threshold responders and 11 were below the threshold.

That division wasn't perfectly clean. The typical measurement error was about 2%, and 17 runners fell within the error range surrounding the 2.5% cut-off. Their responder classification was therefore potentially vulnerable to ordinary measurement noise.

The researchers found little evidence that several obvious characteristics predicted response. Age, sex, weekly mileage, previous super-shoe use, peak oxygen uptake and running speed were not significantly associated with the size of the benefit in these analyses.

The clearest physical association involved the gastrocnemius medialis, 1 of the major calf muscles. Greater passive calf-muscle stiffness was moderately associated with a larger running-economy improvement, and the difference between the above-threshold and below-threshold runners was large.

Greater navicular drop was also moderately associated with a larger benefit. Navicular drop measures how much the height of part of the foot's medial arch changes from sitting to standing.

The researchers then combined several candidate predictors in a regression model. Calf-muscle stiffness and navicular drop remained statistically significant, but the whole model explained only about 27% of the variation in running-economy response. In other words, about 73% of the statistical variation remained unexplained by those measured predictors.

Ankle gear ratio and plantar-flexor strength showed weaker associations in simpler analyses but were not statistically significant in the combined model.

The authors concluded that advanced footwear improved running economy overall and that some muscle, tendon and foot characteristics may be associated with individual differences in the size of the response. They also stressed that much of the variation remains unexplained and that the findings need confirmation.

What helps my confidence in the findings?

The strengths

The randomised crossover design allowed every runner to serve as their own comparison.
The researchers measured running economy twice in each shoe and averaged the results, reducing the influence of normal trial-to-trial variation.
The sample included equal numbers of male and female participants, helping address the underrepresentation of female runners in previous advanced-footwear research.
The trial was prospectively registered, and the researchers clearly reported deviations from the planned protocol.
The paper reports detailed statistical methods, confidence intervals, effect sizes and measurement reliability.

What limits my confidence in the findings?

The limitations

The researchers tested only 1 advanced shoe, 1 control shoe and 1 relative running speed. The findings may not generaliseGeneralisability is about how far you can confidently stretch a study’s findings beyond the specific people, place, and conditions that were tested. In simple terms, it asks: “If this result is true here, how likely is it to also be true in other groups or real-world settings?” It’s closely linked to external validity, which is the overall strength of those broader conclusions. to other super shoes, race speeds or running conditions.
The predictor analyses were exploratory, involved many comparisons and did not formally correct for multiple testing. Some statistically significant associations could therefore be chance findings.
Only 11 runners fell below the 2.5% response threshold, weakening comparisons between the response categories. Recruitment also stopped at 64 rather than the planned target of 85.
The experiment used short treadmill runs rather than outdoor races. It didn't test technical terrain, prolonged fatigue or actual race performance. Anatomical measurements were also collected from the right leg while the running-kinematic measurements came from the left leg.
The paper contains small reporting inconsistencies in the standard deviation reported for the primary response and in the control oxygen-consumption mean. These look like reporting errors rather than evidence of a wider numerical problem, but they should ideally be clarified.

Funding and conflicts

Who funded the study?

The authors reported no additional external research funding.

Salomon supplied the shoes. The authors state that the company did not participate in data collection, data processing or the formal statistical analyses.

Marlene Giandolini was both an author and an employee of Salomon SAS. She contributed to the study concept, methods, resources and manuscript writing and editing. Kim Hebert-Losier and Anh Phong Nguyen also reported serving as speakers for The Running Clinic.

These disclosures don't invalidate the results. Still, an employee of the shoe manufacturer contributing to the design and writing is a relevant conflict of interest when interpreting research involving that manufacturer's product.

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FAQ

Do super shoes improve running economy?

On average, yes. In this study, the advanced shoe reduced the oxygen cost of running compared with the control shoe, broadly matching earlier advanced-footwear research.

Do super shoes work for every runner?

Not equally. Most runners benefited in this study, but the size of the response varied substantially between individuals.

Can foot shape predict which super shoe is best?

Not reliably yet. Greater navicular drop was associated with a larger benefit in this study, but the finding was exploratory and needs replication.

Do stronger calves make super shoes work better?

This study didn't show that. Greater passive calf-muscle stiffness was associated with a larger improvement, while plantar-flexor strength was not a significant predictor in the combined model.

Should runners test different carbon-plated shoes?

It can be a reasonable approach when small performance differences matter, but that is practical interpretation rather than a strategy directly tested in this study.

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