Do pelvic compression garments help runners after pregnancy?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Pelvic compression garments alter running biomechanics, perceived support, and fear of symptoms in postpartum women with pelvic floor dysfunction: preliminary observations from an exploratory, randomised, repeated-measures crossover design
Donnelly et al. (2026) Front Sports Act Living (click here to open the original paper)
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 authors’ hypothesis or research question?
◦ The authors aimed to test whether a pelvic compression garment changes running biomechanics and perceptions (support, fear, symptoms) in postpartum runners with pelvic floor dysfunction, and whether levator hiatus distensibility predicts those changes.
What did the authors do to test the hypothesis or answer the research question?
◦ The researchers recruited 13 postpartum women (mean age 38 years) who could run and had self-reported pelvic floor dysfunction symptoms. Each participant completed 2 self-paced treadmill runs lasting 7 minutes, in randomised order: wearing either their own shorts (control) or a pelvic compression garment. The team measured pelvic floor function and pelvic support at baseline, collected biomechanics (motion capture, force data, and accelerometers), and then asked participants to rate perceived pelvic floor support, core support, fear of symptoms, and symptom experience after each run.
What did the authors find?
◦ All 13 participants completed both conditions with no adverse events. For the main biomechanics outcomes (pelvic loading and shock attenuation), the garment produced 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. improvements that were mostly large in magnitude: peak pelvic “jerk” (how abruptly pelvic acceleration changes) decreased, low-frequency pelvic shock attenuation shifted in a direction consistent with smoother damping at the pelvis, and the area under the peak pelvic acceleration curve decreased.
◦ Kinetic and spatiotemporal measures (loading rates, stride metrics, ground contact time, and similar basics) did not clearly change. In the kinematics, the statistical parametric mapping analysis found statistically significant late-stance differences on the left side: less pelvic rotation excursion and more axial trunk-to-pelvis rotation, interpreted by the authors as a trunk–pelvis coordination pattern that looks a bit more like “healthy runner” mechanics.
◦ On the perception side, the effects were big and statistically significant: perceived pelvic floor support and perceived core support increased by about 5 and 4 points (on a 0 to 10 scale), and fear of experiencing pelvic floor symptoms dropped by about 2 points. Despite those perceptual wins, self-reported symptom experience immediately after the runs did not improve.
◦ Finally, levator hiatus distensibility (i.e., how easily/widely the pelvic-floor opening can widen when it’s under pressure, like during pushing, pregnancy, childbirth, heavy lifting, or straining) did not meaningfully interact with the biomechanics or perceptual responses in the regression models (so it did not help predict “who benefits”).
◦ The authors concluded that pelvic compression garments can alter running biomechanics and improve perceived support and fear of symptoms in postpartum runners with pelvic floor dysfunction, but levator hiatus distensibility did not seem to explain the responses, and symptom experience did not change in this short lab test.
What were the strengths?
◦ The design is tidy for an early, exploratory paper: each person served as their own control (so a lot of between-person noise gets cancelled), the order was 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., and the outcomes included objective biomechanics alongside real-world-relevant perceptions like support and fear. The baseline pelvic floor assessment was done carefully (including chaperoned, private-room exams), and the paper reports the key descriptive data and effect estimates clearly enough to sanity-check what’s going on.
What were the limitations?
◦ The big one 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. (13 women), which raises the risk of both missed effects and flukey “wins,” especially when many variables are tested. Relatedly, the authors say the study was powered to detect only large effects, so smaller but still meaningful changes could have been missed (which means that false negativesWhen 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. are possible).
◦ The participants also had mild symptom severity overall, which makes it harder to see symptom improvements over just 7 minutes of running. And because participants obviously knew when they were wearing a different garment, a placebo effect could have inflated the perceptual outcomes.
◦ So, the big support and fear shifts are intriguing, but we should treat them as “promising, not proven”. Finally, a short treadmill run in a lab is not the same thing as a week of outdoor training, fatigue, and real-life symptom triggers, so generalisabilityGeneralisability 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. is kinda limited.
How was the study funded, and are there any conflicts of interest that may influence the findings?
◦ The study received grant and institutional support: Donnelly was funded by a 2023 Pelvic, Obstetric and Gynaecological Physiotherapy Jo Laycock research grant; Coltman received support from the University of Canberra’s Outside Studies Program; and the University of Canberra Research Institute for Sport and Exercise funded equipment. The authors declared no commercial or financial conflicts of interest.
How can you apply these findings to your training or coaching practice?
◦ For coaches and clinicians helping runners after pregnancy, this paper basically says: a pelvic compression garment might make running feel safer and more supported, and may slightly “smooth” pelvic mechanics — even if symptoms don’t instantly disappear after a short run. Is lowering a person’s fear of symptoms enough to change behaviour and keep them running consistently? Maybe… but this study can’t fully answer that, and it doesn’t prove longer-term symptom relief. Still, for some athletes, it might be useful — at least psychologically.
What is my Rating of Perceived scientific Enjoyment (RPsE)?
6 out of 10 → I experienced moderate scientific enjoyment because the design is sensible and the reporting is pretty transparent, but the lack of 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 pre-planned sample size (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.), and the tiny 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. keeps the certainty 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. on a short leash.
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 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 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.). What do other trials in this field show? (opens in new tab) 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 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. or 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. across the included studies?
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