Enhancing recovery between training? Investigating the benefits of compression stockings in runners

Abstract

Purpose

To determine whether wearing compression stockings (CS) exclusively during the 24-h recovery between consecutive high-intensity running sessions improves next-day countermovement jump (CMJ) performance, gait biomechanics, running economy (RE), perceived outcomes of rating of perceived exertion (RPE) and delayed onset muscle soreness (DOMS) in recreational runners.

Methods

Twelve endurance runners participated in a randomized, counterbalanced within-subjects crossover with two consecutive test days per block. After an individualised high-intensity running protocol, participants recovered for 24 h wearing either medical thigh-high CS (23–32 mmHg) or not to investigate the effects on the above outcomes.

Results

No significant differences were found in CMJ, gait variables, and RE. For DOMS, Friedman tests showed time effect in both conditions (CON χ²(3) = 21.10, p < .001; CGs χ²(3) = 24.68, p < .001). Specifically, DOMS declined from Day 1 Post to Day 2 Pre in CGs (Z = −2.904, p = 0.004) but not in CON (Z = −1.179, p = 0.239), indicating improved recovery with compression garments. A significant Condition × Time interaction was found for RPE (f(1,11) = 5.26, p = 0.043), increasing across days in CON but not in CGs.

Conclusions

These findings suggest that CS do not enhance the objective markers of recovery, in gait variables, RE, or jump performance for training recovery, but may accelerate the decline of DOMS during the overnight recovery phase and reduce RPE across consecutive training days, thereby supporting perceived recovery in runners.

Introduction

Training recovery is an important period that takes place after each training session. Bishop et al. defined training recovery as “the recovery phase between consecutive workouts or competitions”. ¹ Hence, most training recovery occurs between 8 and 24 h after the initial exercise. Maximizing the recovery effect of this phase is beneficial in accelerating the human body's regenerative and recovery processes. ² While exhausting training stimuli are part of the training regime, if not managed properly, they commonly lead to fatigue, measurable, for example, via delayed onset muscle soreness (DOMS), transient reductions in muscle blood flow, and loss of motivation in aerobic sports.^3,4 The solution for this is either to reduce training volume or to improve the quality of the (training) recovery period. ¹ To monitor the balance between training induced fatigue and recovery, there are several options for training recovery. Training recovery can be assessed using subjective measures such as DOMS and perceived fatigue, as well as objective measures like creatine kinase, countermovement jump (CMJ), and running economy (RE). Among these, RE is particularly relevant for runners as it directly reflects endurance performance and readiness to resume training for runners.

Compression garments (CGs) are often attractive to athletes because of they are time-efficient, portable and easy to integrate into training routines.^5–9 The evidence base for CGs as a recovery tool is still growing, with more consistent benefits on perceived recovery than on objective outcomes; importantly, no detrimental effects on recovery are typically reported. ⁸

To establish the rationale for CGs in recovery, we note that running imposes high impact forces and substantial eccentric muscle load, both of which delay neuromuscular recovery.^10–12 During intense or prolonged running, skeletal muscles release large amounts of cytokines and accumulate metabolic byproducts. For example, 60 min at 85% V˙O₂max of running increases plasma interleukin-6 (IL-6) levels by 17 times, indicating that exercise intensity rather than muscle damage itself mainly drives the acute inflammatory response. Excessive loading can prolong this acute phase response. ¹¹ Complementing these biochemical changes, ultrasound images show increased rectus femoris thickness within 24 h after a 50 km run and a concomitant immediate drop in peak knee-extensor force that remains incomplete at 24 h. ¹⁰ Collectively, metabolic accumulation, inflammation, and oedema, which together stimulate muscle damage, delay the recovery of strength and neuromuscular function and constitute the core mechanism of fatigue after endurance running.

To counter these responses, several studies suggested the use of CGs. Physiologically, running studies report accelerated blood lactate removal, reduced DOMS, higher resting muscle oxygen saturation, and improved ventilatory efficiency with CGs.^13–18 However, in terms of objective performance, findings are mixed. A running-specific review indicates that CGs can potentially improve running performance based on the beneficial effects on RE, biomechanical variables, and a significant reduction in perceived exertion for muscle soreness and fatigue. ¹⁹ However, in subsequent studies, contradicting results are found. For example, CGs showed improved 5 km time-trial performance without affecting VO₂ or blood lactate levels, perceived fatigue, or perception of recovery, ¹⁷ while others found perceived improvements in muscle soreness, performance and muscle stiffness at similar distances. ¹⁸ More recently, a running-specific review by Telles et al. (2025) reported no meaningful effects of CGs worn during running on physiological, performance, or perceptual outcomes, indicating that acute in-run benefits of CGs are likely small. ²⁰ These discrepancies align with broader evidence of heterogeneity and contradictory results. ²¹

In summary, several research gaps remain for the application of CGs in training recovery for runners. First, most CGs studies have not been extensively tested for training recovery in runners.^22–25 By now, there are indications that post-exercise wear may be most effective in exercise that increases femoral venous flow, lowers muscle soreness, and raises total quality of recovery scores, demonstrating a genuine acceleration of functional recovery rather than a placebo effect. ²⁶ However, no experimental study investigated the influence of post-exercise wear on subsequent performance. Second, although some studies have examined wearing CGs for approximately 24 h after running, ^{18 27–29} the large heterogeneity in wear timing, duration, pressure, and running distance, together with the absence of a standardised next-day performance test, makes it difficult to draw firm conclusions. Within this limited and heterogeneous evidence base, RE has been used as a performance proxy only in studies in which runners wore CGs during running,^24,25,30 whereas RE measured during the training recovery period while wearing CGs has been examined in only two studies.^31,32 Consistently, the systematic review by Engel et al. (2016) also highlighted similar discrepancies, reporting trivial overall effects on running performance but small-to-large positive or even negative effects across recovery markers such as RE, CMJ, and muscle soreness. ¹⁹ Therefore, the current evidence remains insufficient to demonstrate that prolonged CGs use after running has a meaningful impact on training recovery and subsequent performance.

To address these gaps, our study examines whether wearing CGs during recovery improves runners’ performance in a subsequent identical training session. Prior work has largely assessed single recovery markers, rather than subsequent-session performance under a standardized repetition of the pre-recovery protocol. Since current evidence mainly supports improvements in perceived recovery, while objective markers remain equivocal, we aimed to test whether this subjective benefit translates into better subsequent performance. Consequently, this study aimed to investigate whether using CGs during the first 12 h of the training recovery period 1) enhances perceived recovery before the subsequent training session, and 2) leads to lower performance declines 24 h later in CMJ height, sub-maximal performance for gait variables and the running economy within 24 h.

Methods

The study used a randomized crossover design to investigate whether the use of CGs influences training recovery regarding biomechanical, performance-related, and psychological factors. Measurements were performed in the laboratory spread across five measurement days, with the first visit being an intake day and the other four lab visits being similar measurement days (see Figure 1). These four visits were divided into two blocks of two consecutive days, with the same training protocol assigned with or without CGs utilized in the training recovery period. In the control condition (CON), participants wore their usual non-compressive clothing. All behavioral restrictions and testing schedules matched the CGs condition, and participants were instructed to maintain identical diet, activity, and recovery habits in both conditions. There were at least seven days of washout periods between each block.

Figure 1.

Experimental crossover design. Twelve participants completed both conditions, compression garments (CGs) and control (CON), in randomized order with at least seven days in between. Each block consisted of two consecutive test days: Day 1 (Pre-HIT measurements, a standardized high-intensity interval training (HIT) session, Post-HIT measurements) and Day 2 (identical assessments). Pre-HIT measurements: DOMS, Warm up + RE, 3x CMJ. Post-HIT measurements: Belief on day 2 for CGs group, RPE, DOMS, 3x CMJ, Re. CGs: compression garments; CMJ: countermovement jump; DOMS: delayed onset muscle soreness; HIT: high-intensity interval training; RE: running economy; RPE: rate of perceived exertion; WU: warm-up.

All testing took place in an indoor, climate-controlled Motek Medical Gait Analysis Interactive Lab (GRAIL) and was conducted under stable conditions with the laboratory door closed and no directed fans. To control for circadian effects, each participant completed the two consecutive test days within a block at the same clock time, ensuring a recovery interval of 24 h between all measurement days; start times varied between participants.

Participants were allocated in the first block by coin toss for the first participant (stockings vs no stockings), with subsequent participants assigned alternately. This pseudo-random allocation ensured balanced exposure to both conditions while maintaining the crossover design, in which each participant served as their own control.

The methodology was reported according to the CONsolidated Standards Of Reporting Trials (CONSORT) ³³ and Consensus on Exercise Reporting Template (CERT) ³⁴ statements. Participants provided written informed consent in accordance with the Declaration of Helsinki and the ethical approval was given by the Medical Ethics Review Board of the University Medical Center Groningen under case no.153426. All participants read the informed consent form sent by email beforehand and signed it upon arrival at the laboratory.

The estimated sample size of participants was calculated with G*Power (version 3.1.9.7). ³⁵ The a priori power analysis was based on the within-subject repeated-measures design with a Condition × Time interaction across four time points in each of two conditions (before and after the intervention condition, and before and after the control condition). The anticipated effect size (Cohen's f = 0.35, moderate effect) was set with reference to Mizuno et al. (2016), who observed significant improvement in CMJ height 24 h after running when CGs were worn during recovery. ²⁹ Using α = 0.05, 1−β = 0.80, r = 0.60, ε = 1.00, G*Power indicated a total sample size of n = 11; we recruited 17 participants to allow for attrition, yielding a final sample of 12 participants for final statistical analysis.

Subjects

Participants were recruited from the Department of Human Movement Sciences at the University of Groningen between November 2023 and April 2024 through announcements in courses and via word of mouth in local gyms. Eligible participants (18–65 years old) should have been in a normal training phase, physically active and had experience in running, as well as classified as recreational runners if they reported running at least twice per week for ≥ 6 months or ≥ 10 km per week for ≥ 6 months. They were also required to avoid competitions just before or after measurements, and to have no current lower-limb injury or known cardiovascular disease. Seventeen participants (ten males and seven females) participated in this study. Five participants dropped out of the study before completing one block of subsequent exercises, and therefore their data were not used in this study. Reasons for dropping out included conflicts with personal schedules, sickness, or injuries. The included participants did not report any injuries during the experiment. Once participants decided to join the study, they were asked to maintain the same diet and exercise regimen as the week before intake day until the end of the data collection.

Procedures

Compression garments

The CGs used in the experiment were commercially available thigh-high compression stockings (STOX ^® Medical Thigh High Stocking Unisex, Amsterdam, Netherlands, colour: black and beige, open toe). They were made of 75% Nylon and 25% Lycra ^® with estimated interface and pressures of 23–32 mmHg as reported by the manufacturer, with the highest compression at the ankle, gradually decreasing towards the thigh. No “in vivo” pressure measurements were conducted. The garment size was determined strictly according to the manufacturer's instructions, by measuring the circumference at the narrowest part of the ankle, the widest part of the calf, and the upper thigh just below the groin, as shown in the product sizing chart.

Compression stockings (CS) were worn only during the 24-h recovery period between consecutive HIT sessions, excluding sleep or bathing, and not during any running tests or exercises. This wear-time was chosen based on prior studies reporting recovery benefits within this period^18,29,36 and because our experimental design required two consecutive days of testing, with CS use limited to the initial 12-h recovery window. All participants confirmed adherence. Participants recorded the time they put on the CS after showering in the lab facilities and their wake-up time. The researchers documented the duration participants wore the CS in the laboratory immediately after the exercise, as well as the time at which the garments were removed. The total wear duration was calculated afterwards.

Intake measurements

Participants were first asked to complete the preliminary questions regarding their dietary habits (caffeine intake on the day, food consumed in the last 2 h, alcohol intake before the day, and regular supplement intake), sleep quality, physical recovery, mental recovery, and beliefs about garments. Sleep quality, physical recovery and mental recovery were each captured with a single item numerical rating scale from 1 to 10 that was developed for this study based on commonly used wellness items in athlete monitoring practice. These single item scores have not undergone formal validation and were therefore treated as exploratory subjective outcomes. Belief in the effectiveness of garments was assessed using a 10 cm Visual Analog Scale (VAS) ³⁷ with the indicators ‘no belief’ and ‘biggest possible belief’. The distance from the beginning of the VAS to the drawn line (mm) was used as the outcome measure. Participants were also familiarized with the DOMS VAS scale and the Borg 6–20 rate of perceived exertion (RPE) scale, ³⁸ which were used in the subsequent measurements. Furthermore, the CMJ technique was demonstrated, and the participants were asked to perform it a few times to ensure proper execution. After that, a VO_2max test was conducted. Finally, participants were instructed to wear stockings according to their garment size, adjust as needed, and ensure proper fit.

Perceptual measures

Participants were required to draw a line on a 10 cm VAS scale ³⁷ from ‘no soreness’ to ‘worst possible soreness’ before and after the running protocol to indicate their perceived muscle soreness. RPE was only asked once immediately after the high-intensity training (HIT). Participants were instructed to rate their exertion orally, from number 6, ‘No exertion’ to 20, ‘Maximal exertion’.

Countermovement jumps

The procedure for countermovement jump height (CMJh) was the same as used by Dias et al. ³⁹ Data was captured from two force plates using VICON (version 2.12) at 2000 Hz and analyzed using the Motion Lab Systems C3Dserver (version 1.203) and Python (version 3.9). First, data from both force plates were summed. Then, a 25 N threshold for take-off and landing was set and needed to be met for at least 40 m/s for the event to be recognized. After that, flight time was calculated by subtracting the point of take-off from the point of landing, and jump height was calculated using equation (1). The average of all three jumps was used as outcome measurement. Additionally, take-off velocity (VTO) was calculated using equation (2) from vertical ground reaction forces using the impulse-momentum method, where body mass was estimated from quiet standing.^40,41

$$h={\displaystyle \frac{t_f^2*g}{8}}$$

(1)

where h = jump height, $t_f$  = flight time (s), g = 9.81 m·s²

$$v_{to}={\displaystyle \frac{1}{m}\underset{t_0}{\overset{t_{to}}{\int }}[F_z(t)-mg]dt}$$

(2)

where $v_{to}$  = take-off velocity (m·s⁻¹), m = body mass (kg), $t_0$  = start of the propulsive phase, $t_{to}$  = instant of take-off, $F_z(t)$  = vertical ground reaction force at time t (N), and $g$  = gravitational acceleration (9.81 m·s⁻¹)

VO_2max test

The VO_2max test was conducted to individualize the running protocol and evaluate subsequent RE performance at the measurement days. The COSMED K5 Portable Gas Exchange System in breath-by-breath (BxB) mode was used to collect expiratory gases and has been proven to be valid and reliable. ⁴² Participants wore a dead-space mask with a turbine to allow sampling of respiratory gases. Before each test, the K5 was calibrated with gases of a known concentration (16% O₂ / 5% CO₂), a syringe with a known volume (3L), and a scrubber to zero the CO₂ analyzer. All tests were performed on a motor-driven GRAIL treadmill (Motek Medical, Amsterdam, The Netherlands) with dual belts and embedded force plates. The treadmill allowed speeds up to 18 km/h⁻¹ and did not permit changes in gradient. Participants wore a safety harness attached to an overhead rail and were instructed how to signal the researcher to stop the treadmill if needed. Participants also wore a heart rate monitor (Garmin HR dual), and their heart rate was observed during the test. The specific VO_2max protocol (adjusted protocol by LourenÇo et al.) ⁴³ included a warm-up (3 min at 8 km/h⁻¹ steady running) followed by incremental running, starting at 9 km/h⁻¹ and increasing 0.3 km/h⁻¹ per 25 s until the participant reached their maximum speed (vVO_2max). The cool-down procedure involved a 5-min run that started at 60% of vVO_2max and decreased every minute by 5% to 40% of vVO_2max. This was a mandatory part of the intake measurement to help participants recover afterwards. The last completed stage was determined as the last stage that took the full 25 s. VO_2max (mL O₂/kg/min) was calculated as the highest average completed stage VO₂, and vVO_2max (km/h⁻¹) was determined as the speed during the last completed stage. For participants who reached the treadmill speed limit of 18 km/h⁻¹, this speed was used as vVO_2max. To increase confidence that these participants were exercising close to their maximal capacity, we inspected the VO₂ response over the final stages for a clear flattening despite increasing speed and checked that their ventilatory equivalents showed a pattern consistent with exercising above the ventilatory threshold. These criteria were met by all participants who reached the treadmill speed limit.

Measurement days procedure

Pre-exercise assessments included perceptual measures of DOMS, followed by a 4-min warm-up at 60% of vVO_2max. After the warm-up, participants directly transitioned to 70% of vVO_2max for 6 min during which RE was measured.^43,44 CMJ was performed right after the RE assessment. The HIT running protocol ⁴⁵ consisted of seven 2.5-min stages running at 90% of vVO_2max, defined as the high-intensity stages, with 2.5 min of active recovery at 60% of vVO_2max in between, defined as the low-intensity stages, resulting a total duration of 35 min. This protocol was selected as it induces high fatigue while minimizing injury risk, aligns with typical runner training practices, and allows implementation of repeated pre- or post-24 h recovery testing within a feasible timeframe for participants. During the exercise, the VICON system was used to capture force plate data. The biomechanical parameters of contact time, flight time, step time, step length, and step frequency were calculated afterwards. RPE was measured immediately following the HIT exercise. Finally, the CMJ and DOMS measurements were completed.

Statistical analyses

Data are presented as mean ± SD and 95% confidence intervals (CI) for each measurement. For DOMS, CMJh, and RE, the first measurement of each block was used as a baseline, and percentage changes were calculated relative to this baseline. Biomechanical parameters were averaged separately for high-intensity and low-intensity stages. If the normality of residuals and sphericity assumptions were met, a two-way repeated-measures ANOVA with within-subject factors Condition (Control (CON) vs CS) and Time (Day 1 Pre HIT, Day 1 Post HIT, Day 2 Pre HIT, Day 2 Post HIT) was used to test main effects and the Condition × Time interaction. Because the within-subject factor of time included four levels, the sphericity assumption was evaluated using Mauchly's test; the two-level factor Condition does not require sphericity. When sphericity was violated, Greenhouse-Geisser-corrected degrees of freedom were reported. Post-hoc pairwise comparisons were conducted only if a main effect or the Condition × Time interaction reached significance.

In addition to the two-way repeated-measures ANOVA, paired t-tests were conducted as complementary simple-effect analyses for RPE and the belief in CS efficiency. When data failed to meet the normality assumption, a non-parametric Friedman test was used to analyze within-condition time effects (for multi-timepoint variables), and Wilcoxon signed-rank tests were used for two-level comparisons (e.g., belief before- vs after-use of CS, or between conditions at a single time point). ⁴⁶ In all post-hoc and pairwise analyses, p-values were adjusted using the Bonferroni method where applicable. ⁴⁷

Statistically significant difference was set at p < 0.05 for all tests. Effect sizes were calculated to assess the magnitude of differences. For ANOVA, partial eta squared (ηp²) was used. For paired t-tests, Cohen's d was calculated as the mean of the paired differences divided by the standard deviation of those differences. ηp² values of 0.01, 0.06, and 0.14 were considered small, medium, and large effects, ⁴⁸ respectively, while Cohen's d values of 0.2, 0.5, and 0.8 indicated small, medium, and large effects. ⁴⁹ Visualization results were plotted as mean and SD range. All statistical analyses were performed in IBM SPSS Statistics 29.0.0.0 (and Python/StatsModels-SciPy for confirmatory checks, if applicable).

Results

The general characteristics of the twelve included participants are summarized in Table 1. Twelve recreational runners (range 21–30 years) were included. Four out of 12 participants completed all HIT stages.

Table 1.

Subject characteristics (Mean ± SD).

	Female (n = 6)	Male (n = 6)	Total (n = 12)
Age (year)	27 ± 2	26 ± 3	26 ± 3
Weight (kg)	59.3 ± 5.5	82.9 ± 11	71.1 ± 14.7
Height (cm)	164.1 ± 4.7	183.5 ± 8.9	173.8 ± 12.1
Stage completed	6 ± 2	6 ± 2	6 ± 2
vVO2max (km/h)	13.8 ± 2.3	16.5 ± 1.4	15.2 ± 2.3
VO2max (ml/min/kg)	48.1 ± 9.7	56.1 ± 7.8	52.1 ± 9.6
Belief VAS before (mm)	39 ± 27	41 ± 17	40 ± 22
Belief VAS after (mm)	56 ± 11	50 ± 16	53 ± 14
Total time worn CS (h,min)	11 h 58 min ± 46 min	13 h 16 min ± 43 min	12 h 37 min ± 59 min
Best 5 km time (min)a	35  (n = 2)	21 ± 2  (n = 1)	26 ± 7  (n = 3)
Best 10 km time (min)b	65 ± 11 (n = 4)	48 ± 6  (n = 3)	57 ± 12  (n = 7)
Training/week (min)	373 ± 193	415 ± 153	394 ± 175

CS: compression stockings

Note: ^a,b 5 km and 10 km times were self-reported and only available for a subset of participants, as indicated by the n values in parentheses. The remaining participants did not provide these times.

Performance and performance proxy variables

Repeated-measures ANOVA (Condition × Time) were performed separately for each dependent variable (RE and CMJh) (see Table 2). Mauchly's test indicated no violations of the sphericity assumption for either the RE or CMJh. The average time-course changes from baseline for RE are presented in Figure 2. The analysis for RE revealed no significant main effect of Time (f(3, 33) = 0.370, p = 0.775, ηp² = 0.033), Condition (f(1, 11) = 1.960, p = 0.189, ηp² = 0.151), or the Condition × Time interaction effect (f(3, 33) = 0.186, p = 0.905, ηp² = 0.017), indicating that there were no significant differences in RE across time points or between conditions. Similarly, CMJh analysis revealed no significant main effect of Time (f(3, 33) = 1.685, p = 0.189, ηp² = 0.133), Condition (f(1, 11) = 0.265, p = 0.617, ηp² = 0.024), or the Condition × Time interaction effect (f(3, 33) = 0.484, p = 0.696, ηp² = 0.042), indicating that there were no significant differences in CMJh across time points or between conditions. The individual time course changes in CMJh are presented in Supplementary Figure S1.

Figure 2.

Time course changes in running economy (RE) from baseline (day 1 pre) across conditions (Δ mean ± SD). Repeated measures ANOVA revealed no significant effects of Time, Condition, or Condition × Time interaction (all p > 0.18). CS: compression stockings group; CON: control group; Pre: before the high intensity running session; Post: after the high intensity running session; RE: running economy.

Figure 3.

Time-course changes in delayed onset muscle soreness (DOMS) from baseline (day 1 pre) across conditions (Δ mean ± SD). Friedman tests revealed significant time effects within both conditions, followed by Wilcoxon signed-rank tests for pairwise comparisons. In CON, no significant difference was observed between Day 1 Post and Day 2 Pre (p = 0.239). In CS, DOMS was significantly lower at Day 2 Pre compared with Day 1 Post (p = 0.004). Asterisks denote significant pairwise differences (**p < 0.01). CS: compression stockings group; CON: control group; Pre: before the high intensity running session; Post: after the high intensity running session; DOMS: delayed onset muscle soreness.

Table 2.

Descriptive statistics for the performance-related variables running economy (RE), countermovement jump height (CMJh) and take-off velocity (VTO) (mean ± SD [95% confidence intervals]).

Variable	Timepoint	CS	% Change (CS)	CON	% Change (CON)	Group comparison (p, η²)
RE (mL O2/min/kg)	Day 1 Pre	42.1 ± 5.4 [38.6–45.5]	0.0% ± 0.0%	40.6 ± 7.6 [35.7–45.4]	0.0% ± 0.0%	Condition × Time interaction: p = 0.905, η2 = 0.017
	Day 1 Post	41.6 ± 6.6 [37.4–45.7]	−1.2% ± 8.6%	40.8 ± 7.8 [35.9–45.8]	0.9% ± 7.0%
	Day 2 Pre	41.0 ± 7.1 [36.5–45.5]	−0.3% ± 0.7%	40.5 ± 6.6 [36.3–44.7]	0.6% ± 0.9%
	Day 2 Post	41.5 ± 6.2 [37.5–45.5]	−1.3% ± 8.6%	40.2 ± 7.0 [35.8–44.6]	−0.4% ± 0.9%
CMJh (cm)	Day 1 Pre	29.8 ± 7.6 [25.0–34.6]	0.0% ± 0.0%	29.2 ± 7.6 [24.4–34.1]	0.0% ± 0.0%	Condition × Time interaction: p = 0.696, η2 = 0.042
	Day 1 Post	28.7 ± 7.9 [23.7–33.7]	−3.6% ± 7.8%	28.9 ± 8.0 [23.9–34.0]	−1.5% ± 6.3%
	Day 2 Pre	28.9 ± 7.3 [24.3–33.6]	−2.6% ± 5.7%	28.7 ± 7.2 [24.1–33.3]	−1.5% ± 5.9%
	Day 2 Post	28.5 ± 7.5 [23.7–33.2]	−4.1% ± 9.4%	28.3 ± 7.8 [23.3–33.2]	−3.4% ± 6.8%
VTO (m/s)	Day 1 Pre	2.62 ± 0.37 [2.41–2.83]	0.00% ± 0.00%	2.58 ± 0.32 [2.40–2.76]	0.00% ± 0.00%	Time effect: p ≥ 0.05 (both conditions), Kendall's W = 0.05–0.08a
	Day 1 Post	2.56 ± 0.44 [2.31–2.81]	−2.19% ± 8.90%	2.54 ± 0.35 [2.34–2.74]	−1.71% ± 6.90%
	Day 2 Pre	2.48 ± 0.39 [2.26–2.70]	−5.22% ± 8.70%	2.51 ± 0.35 [2.31–2.71]	−2.79% ± 7.00%
	Day 2 Post	2.48 ± 0.30 [2.31–2.66]	−5.16% ± 5.20%	2.48 ± 0.45 [2.23–2.73]	−3.75% ± 6.90%

CS: Compression stockings group; CON: Control group; HIT: High-intensity training; Pre: before the high intensity running session; Post: after the high intensity running session; RE: running economy; CMJh: countermovement jump height; VTO: take-off velocity

Note: ^aa Time effects were tested with Friedman tests. No between-condition comparisons were performed for VTO, as both conditions showed non-significant time effects.

Whereas RE and CMJh showed no significant changes, we further examined VTO to explore potential differences (see Table 2). Because the residuals did not meet the normality assumption (CGs Day 1 Post HIT: W = 0.856, p = 0.0044 and CON Day 2 Post HIT: W = 0.857, p = 0.045, Shapiro-Wilk), non-parametric tests were applied. Friedman tests revealed no significant main effect of time for either condition (CON: χ²(3) = 1.80, p = 0.615, Kendall's W = 0.05; CGs: χ²(3) = 2.80, p = 0.423, Kendall's W = 0.08). These findings indicate that VTO also remained stable across time points and between conditions.

None of the gait variables showed a significant Condition × Time interaction at either intensity. The descriptions of the gait variables are presented in Table 3. Overall, contact time and step time were higher during low intensity stages compared to high intensity of the HIT exercise across both groups and days. The step time was longer during the LI stages than during the HI stages across both groups and days. Conversely, flight time, step length, and step frequency were lower in the LI stages compared to HI. These trends were consistent across both groups and days, with no clear differences observed between the CGs and CON groups or between Day 1 and Day 2.

Table 3.

Descriptive statistics for the gait variables (mean ± SD [95% confidence intervals]).

		Day 1		Day 2		Group comparison (p, η²)
		HI	LI	HI	LI
CT (ms)	CS	211.45 ± 26.05[196.71–226.19]	265.62 ± 31.60[247.74–283.50]	211.17 ± 27.61[195.55–226.79]	264.92 ± 31.73[246.97–282.87]	all p > .05
	CON	211.13 ± 27.39[195.63–226.63]	265.29 ± 32.35[246.99–283.59]	211.61 ± 28.49[195.49–227.73]	266.39 ± 34.61[246.81–285.97]
FT (ms)	CS	125.67 ± 23.72[112.25–139.09]	97.28 ± 23.76[83.84–110.72]	123.67 ± 25.37[109.32–138.02]	94.71 ± 24.98[80.58–108.84]
	CON	124.84 ± 22.51[112.10–137.58]	93.14 ± 21.73[80.85–105.43]	124.80 ± 23.01[111.78–137.82]	93.24 ± 24.24[79.53–106.95]
ST (ms)	CS	337.32 ± 16.82[327.80–346.84]	362.76 ± 12.58[355.64–369.88]	335.04 ± 18.31[324.68–345.40]	359.53 ± 11.67[352.93–366.13]
	CON	336.02 ± 17.95[325.86–346.18]	358.53 ± 14.55[350.30–366.76]	336.57 ± 17.36[326.75–346.39]	359.56 ± 13.95[351.67–367.45]
SL (m)	CS	1.30 ± 0.17[1.20–1.40]	0.95 ± 0.12[0.88–1.02]	1.29 ± 0.17[1.19–1.39]	0.94 ± 0.13[0.87–1.01]
	CON	1.29 ± 0.15[1.21–1.37]	0.94 ± 0.12[0.87–1.01]	1.29 ± 0.15[1.21–1.37]	0.94 ± 0.12[0.87–1.01]
SF (min−1)	CS	178.38 ± 9.19[173.18–183.58]	166.15 ± 5.72[162.91–169.39]	179.79 ± 10.26[173.98–185.60]	167.14 ± 5.43[164.07–170.21]
	CON	179.09 ± 9.35[173.80–184.38]	167.85 ± 6.73[164.04–171.66]	178.87 ± 8.95[173.81–183.93]	167.12 ± 6.41[163.49–170.75]

CS: Compression stockings group; CON: Control group; HI: high intensity running session; LI: low intensity running session; CT: contact time; FT: flight time; ST: step time; SF: step frequency; SL: step length

Table 4.

Descriptive statistics for the delayed onset muscle soreness (DOMS) and rate of perceived exertion (RPE) (Mean ± SD [ 95% confidence intervals]).

Variable	Timepoint	CS	CON	Group comparison (p, η²)
DOMS (mm)	Day 1 Pre	19.92 ± 15.83[9.86–29.97]	22.33 ± 21.41[8.73–35.93]	Time effect: p < 0.001 (both conditions), Kendall's W = 0.59–0.68
	Day 1 Post*	58.17 ± 26.01[41.64–74.69]	51.00 ± 20.60[37.91–64.09]
	Day 2 Pre*	38.83 ± 21.00[25.49–52.17]	46.25 ± 25.06[30.33–62.17]
	Day 2 Post	61.67 ± 25.27[45.61–77.73]	64.50 ± 24.83[48.72–80.28]
RPE (6–20)	Day 1 Pre	N/A	N/A	Condition × Time interaction: p = 0.043, ηp²=0.323
	Day 1 Post	17.33 ± 2.19[16.09–18.57]	16.25 ± 2.01[15.11–17.39]
	Day 2 Pre	N/A	N/A
	Day 2 Post	16.50 ± 2.24[15.23–17.77]	17.00 ± 1.81[15.98–18.02]

CS: compression stockings group; CON: control group; HIT: high-intensity training; Pre: before the high intensity running session; Post: after the high intensity running session; DOMS: delayed onset muscle soreness; RPE: rate of perceived exertion

Note: ^aa Time effects were tested with Friedman tests. Between-condition comparisons were assessed with Wilcoxon signed-rank tests and Bonferroni adjustment. *indicate significant within-condition pairwise differences across time points (p < 0.01).

Subjective variables

The average time-course changes from baseline for DOMS are presented in Figure 3, and descriptive statistics with condition by time comparisons for DOMS and RPE are shown in Table 4. No significant difference (Wilcoxon test: Z = 1.96, p = 0.050) was observed between belief scores before (40.17 ± 22.41 mm) and after (52.75 ± 13.82 mm) using CS (see Table 1). In general, participants indicated a rather low belief in CS efficiency with values below or around 50 on the 0 to 100 VAS scale.

For DOMS, due to violations of the normality assumption (CON Day 1 Pre: W = 0.772, p = 0.005, Shapiro-Wilk), a non-parametric Friedman test was used to analyze the time course changes of DOMS. Friedman tests revealed a significant time effect within both conditions (CON: χ²(3) = 21.10, p < 0.001, Kendall's W = 0.59; CS: χ² (3) = 24.68, p < 0.001, Kendall's W = 0.68). Follow-up Wilcoxon signed-rank tests confirmed significant increases in DOMS from Day 1 Pre to Day 1 Post in both conditions (CON: p = 0.003; CS: p = 0.004). From Day 1 Post to Day 2 Pre, DOMS remained elevated in CON (p = 0.239) but decreased significantly in CS (p = 0.004), indicating better across-day recovery with compression. Additional comparisons showed that DOMS remained higher at Day 2 Pre than Day 1 Pre in CS (p = 0.015) and increased further to Day 2 Post in both conditions (all p ≤ 0.007).

For RPE, a significant Condition × Time interaction was observed for post-HIT RPE (f(1,11) = 5.26, p = 0.043, ηp² = 0.323). The mean and SD of RPE are presented in Supplementary Figure S2. Specifically, RPE increased from Day 1 to Day 2 in CON but tended to decrease in CS. Planned paired t-tests comparisons confirmed an increase within CON (Day 1 Post vs. Day 2 Post (t = −2.283, p = 0.043, d = 0.41), indicating a moderate effect. No significant differences were observed between CS (t = 1.650, p = 0.127, d = 0.393). Between-condition comparisons at matched time points were not significant (Day 1 Post: t = −0.692, p = 0.504, d = -0.173; Day 2 Post: t = 1.318, p = 0.213, d = 0.257), all showing small effects.

Discussion

This study aimed to investigate the effects of CGs on performance, gait, and subjective variables during recovery of consecutive exercise sessions. Our results revealed no significant improvements in RE, CMJ, or gait variables when using CGs compared with the control condition. However, wearing CGs showed potential benefits in reducing self-reported measures, such as DOMS and RPE, particularly during training recovery.

Performance and performance proxy variables

Not all participants were able to complete the full HIT protocol, despite exercise intensity being individualized based on aerobic capacity. Seven participants terminated early due to complete exhaustion and one due to stomach discomfort. Interestingly, RPE scores did not differ meaningfully between those who completed and those who did not complete the full protocol, except for the participant who stopped due to stomach discomfort (RPE 13–15). These RPE scores and the effect of time in DOMS indicate that the protocol induced an appropriate amount of (cardiovascular) fatigue.

Running economy

Our findings revealed no significant differences in RE across consecutive running sessions when wearing CS compared to the CON. Current research investigating whether CGs can improve RE has primarily focused on measuring RE during exercise.^24,2550–52 To the best of our knowledge, Mizuno et al. (2016) is the only study that has examined RE following a recovery period with CGs use, reporting no differences at 24 h across 70, 80, and 90% V˙O₂max after both level and downhill running. ²⁹ Consistent with this, our protocol assessed RE at 70% vV˙O₂max (below the ventilatory threshold), and we likewise observed no significant effects of time or condition.

Given that RE is multifactorial and closely linked to gait mechanics, ⁵³ the absence of changes in our spatiotemporal variables offers a plausible explanation. Moreover, single biomechanical variables account for only a small fraction of RE variance, whereas within-runner kinematics can dominate intra-individual variability.^53,54 The day-to-day RE variation typical of recreational runners is about 1–5% at 12–18 km·h⁻¹) ⁵⁵ may further obscure small effects at the group level. Taken together, current evidence suggests that, under a level-running HIT stimulus, in the recovery period of 24 h, CS use does not improve submaximal RE in recreational runners, and RE may be relatively insensitive to such an intervention at this time scale.

Countermovement jumps

No significant differences in CMJh were observed across conditions or time points. Interestingly, Ehrström et al. reported greater muscle damage when employing −8.5 degree downhill running, where the substantial damage induced by the protocol helped detect positive recovery effects with CGs. ⁵² Similar results were found by Mizuno et al. (2016), which found a positive effect of CGs post-exercise for downhill running, but not for level running. These results might indicate that CGs might only facilitate recovery of CMJh if severe muscle damage is induced, for example, via downhill running. In turn, our results may reflect the lack of such a benefit in a normal training or competition, as earlier studies in trail running also did not find a positive effect on CMJ height. ¹⁶

Besides CMJh, VTO is expected to decline with neuromuscular fatigue according the exercise intensity ⁵⁶ ; its stability across time points and conditions indicates that the fatigue elicited by the present high-intensity protocol and recovery-only compression exposure was insufficient to depress propulsive output. This convergence with unchanged CMJh suggests any compression-related effect on fatigue or recovery was small under these conditions. Future work should use protocols that impose greater eccentric stress and include fatigue-sensitive CMJ parameters, for example, phase-specific impulse, eccentric rate of force development, and reactive strength indices. ⁵⁷ These findings suggest that wear duration alone is unlikely to be the sole explanation for CMJ; rather, the combination of limited muscle damage, recovery-only timing, and a single 12 h wear period under our loading conditions may have limited the effects within a typical post-HIT recovery.

Gait variables

Our results showed that there were no significant differences in gait variables between wearing and not wearing CS. Gait variables, including flight time, step length, and step frequency, decreased during the low-intensity phase, whereas contact time and step time increased. These changes reflect natural adjustments in gait patterns associated with the differences in running speed between high- and low-intensity stages. Although the DOMS results suggest that muscle damage may have occurred, athletes may have employed self-regulatory mechanisms (e.g., gait adjustments or altered movement strategies) to minimize the impact of muscle fatigue or damage, thus preventing significant deviations in gait variables during the recovery period. This indicates that even with increase muscle soreness, gait changes may not be immediately apparent, particularly when the recovery period is short. Importantly, our findings showed that wearing CS did not have any negative effects on gait. While extensive research has highlighted the effects of CGs on biomechanics during exercise, ²¹ but their influence during the recovery period remained underexplored. Future research could explore gait changes across different recovery periods, and exercise intensities, or in populations that may be more sensitive to the potential benefits of CGs, such as individuals with no prior experience using CGs.

Subjective variables

Delayed onset muscle soreness

Summing up the previous review ⁵⁸ and studies,^{6,7,3159–61} together with our findings, we support the benefits of CS on perceived muscle soreness during the recovery period between sessions. However, the positive effects are affected by the duration of wearing time, pressure, and the type of exercise. Broatch et al. found that during long-haul air travel, wearing the compression socks (Ankle: between 19 and 22 (+−8) mmHg, Calf: 23+−11 mmHg) can significantly improve muscle soreness at 6.5 and 9 h, but such effect does not exist at 12 h. ⁶ This may be explained by atmospheric pressures during flight and the physiological response of the athletes. Other studies have shown similar results regarding shared or even longer wearing time. One explanation is that the effect has resulted in a delay, or the pressure plus the wearing time differs from ours. Research by Jakeman et al. showed that after plyometric exercise, wearing compression tights (Calf: 17.3 mmHg, Thigh: 14.9 mmHg) for 12 h can decrease muscle soreness at 1, 24, 72, and 96 h. ⁶⁰ Similarly, wearing low-pressure CGs for 15 h (overnight) can also improve DOMS in basketball athletes. ³¹ A running related study likewise reported that wearing lower limb CGs for 72 h after a marathon improved perceived muscle soreness, although objective markers of recovery did not change. ⁵⁹ However, the long period of wearing time made athletes feel uncomfortable. ⁶¹

From a mechanistic perspective, the small reductions in DOMS observed in our study are consistent with proposed physiological effects of lower limb compression. Graduated compression can increase venous return and improve local blood flow and muscle oxygenation, and may limit exercise induced swelling, which in turn may reduce pain associated with movement.^{13,14,26,58,62,63} CGs have also been suggested to attenuate soft tissue oscillations during dynamic activities, which is thought to reduce mechanical loading and subsequent muscle damage.^21,22,58 These peripheral effects may help to make symptoms of muscle damage less noticeable between sessions, even when neuromuscular performance measures remain unchanged.

Nevertheless, compared to other recovery strategies, CGs might not be the most optimal strategy for minimizing DOMS. A previous study reported that 12 h of wearing high-pressure CGs (Calf: 20 mmHg, Ankle: 40 mmHg) can significantly decrease the DOMS from 24 h. Still, the effect is less than that of neuromuscular electrical stimulation. ⁶⁴ In another study, CGs and contrast bathing demonstrated positive effects compared to contrast bathing and passive rest. ⁶⁵ However, it is important to consider that prolonged use of CGs may potentially impact an athlete's daily life. ⁶⁵ On the other hand, CGs are relatively more accessible and cost-effective than most other recovery strategies, making them a practical option for many athletes. Moreover, combining CGs with other recovery strategies, such as massage, ⁶⁶ can maximize the advantage.

Interestingly, beliefs about CGs may also have significant effects. Brophy-Williams et al. (2017) found that athletes who wore CGs and believed in their effectiveness showed significantly less muscle soreness after running. ¹⁷ We also observed a trend of consistent associations between beliefs. By the end of the experiment, athletes showed a tendency to be more confident about the effectiveness of CS. Collectively, our evidence supports the effectiveness of CS in reducing muscle soreness during post-exercise recovery. However, given the subjective nature of the perceived measurement, whether athletes can accept the discomfort associated with wearing CGs for extended periods remains a factor to be weighed.

Rating of perceived exertion

RPE showed a significant Condition × Time interaction. The only significant pairwise difference occurred in CON from Day 1 Post HIT to Day 2 Post HIT, and we observed a trend where RPE increased in the control group compared to the first session, while it decreased in the CS group. This suggests that wearing CS allows individuals to perceive a reduction in exercise intensity. Consistent with the findings on DOMS, this aligns with the observed effect of CS on muscle soreness before the next day's HIT session; the CS exhibited a larger overnight reduction in DOMS. These results imply that CS not only aid in muscle recovery but may also reduce the perception of training fatigue.

To our knowledge, studies on the effect of CGs on RPE have yielded inconsistent results. A review not specific to CGs during HIIT found that the majority of studies (40 studies) reported no change in RPE with CGs use, while a few (8 studies) indicated positive effects. ²¹ Interestingly, these findings contrast with a review by Da Silva et al., who analyzed 11 studies and concluded that wearing CGs during HIIT (>16) had no effect on RPE, regardless of sex, athletic status, lower limb CGs type, or exercise intensity. ⁵⁰ Similarly, De Glanville and Hamlin found no significant differences in RPE between groups in a 40 km time trial. ³² This discrepancy may be due to differences in exercise type and intensity, as the muscle loading and fatigue mechanisms in HIIT may differ from those in prolonged endurance exercise, potentially influencing the effects of CS. Additionally, the belief effect may partially explain the association between reduced subjective fatigue and DOMS relief, emphasizing the importance of the psychological dimension in CGs’ role in recovery. In combination with the peripheral mechanisms proposed for DOMS reduction, such as enhanced venous and lymphatic return and reduced swelling, these perceptual and expectancy related influences provide a plausible explanation for why RPE decreased in the CGs condition despite unchanged objective markers of recovery.

From a pressure perspective, the findings of the present study show some inconsistencies with prior research. Hill et al. demonstrated that high-pressure CGs (Thigh: 14.8 ± 2.2 mmHg, Calf: 24.3 ± 3.7 mmHg) worn for 24 h post-exercise resulted in better recovery effects compared to lower-pressure garments (Thigh: 8.1 ± 1.3 mmHg, Calf: 14.8 ± 2.1 mmHg). ⁶⁷ However, Zinner et al. found no differences in jump performance across different compression levels (0, 10, and 25 mm Hg). ⁶⁸ Compared with the CS we used (23–32 mmHg), these findings suggest that pressure alone may not fully explain the recovery effects, and wearing duration plays a critical role. Further studies should investigate the interplay between compression levels and wearing duration to optimize recovery outcomes.

The findings from this current study suggest that CS could be used as a motivational tool for daily training, as it can reduce the perceived burden of consecutive training sessions. Thereby, it could be helpful to help athletes through the “daily grind” of training. Still, athletes and coaches should monitor training load carefully, as CS did not yield any objective recovery benefits in our study. Lower RPE and DOMS may make accumulating fatigue more difficult to detect and, over the long term, could in theory modify some of the desired training adaptations. Our short-term study cannot address this directly.

Although CGs showed no effect on performance during consecutive days of training, they can be an effective tool for enhancing the recovery experience when included as part of a regular recovery protocol. By considering factors such as wear duration, pressure, exercise type, and conditions, athletes and coaches can optimize the recovery of athletes. However, the present short-term study cannot determine whether regular use of CGs influences long-term performance or adaptation. It therefore seems prudent to use CGs in a strategic way, for example during congested training or competition periods, while continuing to priorities appropriate load management, sleep and nutrition.

Strengths

Accumulating evidence indicates that wearing lower limb CGs during HIT, regardless of sex, athletic status, types of CGs, or test intensity, does not improve aerobic performance (oxygen uptake, maximal and submaximal), vertical jump height, or RPE. ⁵⁰ However, the evidence regarding the effects of these metrics during post-exercise recovery in HIT remains limited. This study explored the role of CS in the training recovery phase until the effect of consecutive exercise performance, specifically making observations of RE, which remains a largely unexplored area during this phase. Our study also incorporates differences in individual responses. ²¹ By focusing on this, we were able to reveal that CS may have a more significant effect on some subjects, particularly in terms of improved recovery of RPE and DOMS. Such an analysis not only complements the results of the group data but also provides new insights into the individualized application of CGs.

In addition, another highlight of our study was the introduction of the analysis of belief factors. Although no significant changes in belief scores were observed, belief factors are nonetheless considered important in shaping perceptions of recovery. Weakley et al. (2022) emphasized that prior belief in CGs may influence subjective outcomes. ²¹ Thus, even though our participants’ belief levels remained generally low, the psychological component could still interact with physiological effects and should be considered in interpreting recovery responses.

Limitations

First, the pressure exerted by the CS was not recorded during the study. Although garment size was matched to the manufacturer's chart and new stockings were used for each participant to minimize loss of elasticity, continuous wear may still lead to changes in pressure distribution, which may significantly influence the effectiveness of the garments. Variations in pressure levels, particularly in different regions of the garment, could mask the potential recovery benefits. Future research should include dynamic monitoring of garment pressure by using a wearable pressure device at different time points during the recovery period to better understand the relationship between pressure levels and recovery outcomes.

Second, regarding functional performance, while this study only focused on CMJh, future studies should consider evaluating additional aspects of performance, such as dynamic balance and muscle endurance. These measures could provide a more comprehensive understanding of how CGs influence recovery across various domains, thereby offering more robust evidence regarding their sustained effects. We used RE as a proxy for running performance rather than a direct performance outcome; future trials should incorporate race-like or task-specific performance tests alongside RE to more precisely detect performance effects. In addition, although we inspected the VO₂ response and ventilatory equivalents in participants who reached the treadmill speed limit, the present study did not include blood lactate sampling, explicit secondary criteria or a verification bout, which limits certainty that all participants reached their true VO₂max.

Third, sleep quality, physical recovery and mental recovery were assessed with single item athlete reported outcome measures that were developed for this study and have not undergone formal psychometric validation. These scores were therefore treated as exploratory indicators of perceived recovery, and conclusions based on them should be interpreted with caution. ⁶⁹

Fourth, full dietary intake was not recorded, so variations between sessions cannot be ruled out. The same applies to the experiment environment control, as we did not measure ambient temperature or humidity.

Fifth, a sham-compression control was not used, which limits participant blinding; future studies should include a visually indistinguishable low-pressure/sham garment alongside real compression, ⁶³ verify interface pressures, and equalize expectancy using placebo-controlled designs. ⁷⁰ Implementing a credible sham CGs is challenging but important for minimizing expectancy bias. Future trials should therefore employ a low-compression or placebo garment with identical appearance and controlled garment pressure to better isolate physiological from psychological effects.

Finally, belief was only assessed in the CS block, which prevented a complete between-condition comparison. Moreover, belief scores did not differ significantly before and after wearing CS, suggesting limited variability in this measure. This aligns with recommendations by Weakley et al. (2022) that belief should be systematically assessed and ideally controlled (e.g., via placebo/sham conditions) to better isolate true physiological effects. ²¹ Therefore, the role of belief as a covariate might not be fully examined in the present study.

Perspective

• This is the first study to examine the effect of wearing compression stockings (CS) for 12 h solely during recovery on subsequent running economy, showing no measurable impact in runners.

• Coaches and athletes may consider the use of CS during training recovery to alleviate perceived muscle soreness and exertion, even though no improvements were observed in objective performance or gait measures.

• Future studies should explore extended wear time, pressure monitoring, and broader performance parameters to clarify individualized recovery benefits and the long-term effects of compression garments.

Conclusions

Wearing compression stockings may help reduce perceived muscle soreness and exertion during consecutive exercise sessions despite no measurable improvements in objective outcomes such as running economy, countermovement jumps or gait variables.

Supplemental Material

sj-docx-1-spo-10.1177_17479541251407841 - Supplemental material for Enhancing recovery between training? Investigating the benefits of compression stockings in runners

Supplemental material, sj-docx-1-spo-10.1177_17479541251407841 for Enhancing recovery between training? Investigating the benefits of compression stockings in runners by Shuting Li, Quint van der Leeuw, Koen APM Lemmink and Matthias Kempe in International Journal of Sports Science & Coaching