Abstract
ndurance running exposure alone may not be sufficient to slow the age-related decline in plantarflexor function, which is also thought to contribute to the decline in running economy. Strength training has been shown to improve running performance, but specific programs have not been evaluated for their assistance in maintaining plantarflexor func- tion and “youthful” metabolic costs in aging runners. The purpose of this study was to assess the relative influence of three types of resistance training interventions on running economy (RE), plantarflexor function, and Achilles tendon (AT) stiffness in middle-aged runners.Methods:Twenty-six middle-aged runners (51±5 yrs) participated in one of three 10-week resistance training interventions: (1) heavy resistance training, (2) heavy resistancetraining + plyometrics, and (3) endurance resistance training + plyometrics. Lab- oratory testing for RE, biomechanical variables, peak plantarflexor torque, and AT stiffness during isometric contractions occurred before and after the interventions. A mixed-design repeated measures ANOVA was used to address our research question, while paired and independentt-tests were used to compare time and group effects, respectively.Results: Relative (to . VO2max ) RE (−2.4%,p= 0.016), AT stiffness (+26.1%,p= 0.002), and peak isometric plantarflexor torque (+26.4%,p= 0.001) improved with resistance training, with no interaction or group effects. No significant interaction, time, or group effects were observed for . VO2max and peak plantarflexor torque, peak positive
while paired and independentt-tests were used to compare time and group effects, respectively.Results: Relative (to . VO2max ) RE (−2.4%,p= 0.016), AT stiffness (+26.1%,p= 0.002), and peak isometric plantarflexor torque (+26.4%,p= 0.001) improved with resistance training, with no interaction or group effects. No significant interaction, time, or group effects were observed for . VO2max and peak plantarflexor torque, peak positive ankle power, or positive and negative ankle work while running.Conclusions:We present novel but exploratory findings that resistance training, regardless of modality, may moderately improve RE and Achilles tendon stiffness in middle-aged recreational runners. However, sagittal plane lower joint kinematics, extensor torques, powers, and work were unaffected by resistance training in middle-aged runners. Keywords:runners; age; resistance training; economy; Achilles tendon 1. Introduction Exercise helps slow the decline in function due to advancing age and decreases the risk of developing cardiovascular disease, cancer, and neurological conditions [1]. Running has a low barrier to entry and participation in adults over 35 years has risen dramatically in the past 20 years [2]. Among many health benefits, runners tend to have lower resting heart rates and higher . VO2max than non-runners. Research on Masters athletes (over 40 years) suggest vigorous exercisers, like marathon competitors, exhibit different rates of biological Biomechanics2026,6, 18 https://doi.org/10.3390/biomechanics6010018
Biomechanics2026,6, 18 2 of 18 aging than sedentary adults [3]. Age-related factors may contribute to higher relative metabolic costs, resulting in higher perceived running effort and less enjoyment, but it has been well-established that running alone improves running economy, regardless of age [4,5]. Dedicated runners are constantly searching for new options that can help them improve and maintain their running performance. Relatedly, resistance training is a popular modality employed by many runners to supplement their training programs. Resistance training could play a role in mitigating age-related changes in running biomechanics, muscle and tendon characteristics, and joint kinetics that contribute to declining performance with age [5–9]. Biomechanical hallmarks of middle-aged and older runners are considered to be a reduction in ankle plantarflexor function including smaller peak plantarflexor torque, less positive work, and less peak positive power than young runners [6,8,10]. These biomechanical changes are thought to result from some combination of an age-related decrease in muscle mass [11], plantarflexor strength [12], and Achilles tendon (AT) stiffness [10,13]. Lower AT stiffness may require more plantarflexor force (increasing energetic demands) and a higher rate of muscle force production to power the muscle–tendon unit during propulsion. These age-related factors may contribute to higher relative metabolic costs, resulting in higher perceived running effort and less enjoyment, thus increasing the likelihood that athletes seek additional training modalities, such as resistance training, to help reduce these metabolic costs and improve running enjoyment. Resistance training interventions, including complex multi-joint movements with free weights and heavier loads, may provide greater improvements in running economy (RE) in young distance runners [14]. Heavy resistance training (HRT) generally consists of loads greater than 75% of the one-repetition maximum (1RM) [15–17], while endurance resistance training typically employs loads between 40 and 70% of the 1RM [16,17]. HRT alone and in combination with plyometrics exercises are effective training strategies to improve performance and RE in young, trained runners [16,18], perhaps due to improved maximal strength, explosiveness, and velocity at . VO2max [18]. Furthermore, HRT increases tendon stiffness [19], likely related to greater plantarflexor strength tending to be concomitant with increased AT stiffness [20]. Although
the 1RM [16,17]. HRT alone and in combination with plyometrics exercises are effective training strategies to improve performance and RE in young, trained runners [16,18], perhaps due to improved maximal strength, explosiveness, and velocity at . VO2max [18]. Furthermore, HRT increases tendon stiffness [19], likely related to greater plantarflexor strength tending to be concomitant with increased AT stiffness [20]. Although plyometric training may not improve the plantarflexor morphology (increased plantarflexor muscle cross-sectional area and pennation angle or longer muscle fascicle length) associated with maximal plantarflexor force production, it can increase AT stiffness in young adults [21]. To date, the effects of resistance training on running performance and tendon stiffness in middle-aged runners have not been fully elucidated. Endurance resistance training is less effective than HRT for improving muscle strength [17,22] and may only lead to small increases in tendon stiffness. Thus, HRT in combination with plyometric training may be most effective to mitigate age-related decreases in plantarflexor strength [12] and AT stiffness [10,13]. These changes in the muscle–tendon unit function could ultimately contribute to improved RE in middle-aged runners, but the effects of focused resistance training on RE and the underlying mechanisms have not been well-established in middle-aged adults. It is important to better understand the training effect magnitudes of different types of resistance training on running economy to (1) better design future randomized control trials on this topic and (2) better design resistance training programs for aging runners. The primary purpose of this exploratory study was to assess the relative influence of three types of resistance training interventions on RE, plantarflexor function, and AT stiffness in middle-aged runners. We hypothesized that heavy resistance with plyometrics would yield the largest improvements in economy, ankle plantarflexor function, and AT stiffness, followed by heavy resistance and endurance resistance with plyometrics training. The secondary purpose was to determine if changes in ankle plantarflexor function and https://doi.org/10.3390/biomechanics6010018
Biomechanics2026,6, 18 3 of 18 AT stiffness as a result of resistance training were associated with changes in RE. We hypothesized that improvements in ankle plantarflexor function and AT stiffness would be positively associated with improved RE in middle-aged runners. 2. Materials and Methods 2.1. Participants Thirty-three runners (45–60 years) were recruited, and twenty-six completed both testing sessions and at least 80% of the resistance training sessions. Runners were able to participate if they had been running at least three times per week for an average of at least 75 min per week over the previous year and had not engaged in any planned lower limb resistance training more than once per week over the previous year. Participants were excluded if they had lower extremity surgery in the previous two years or had suffered a running-related injury requiring them to stop training for more than a week in the previous 6 months. Each participant was informed of all procedures, potential risks, and benefits associated with the study in both verbal and written forms in accordance with the procedures approved by the University Institutional Review Board for Human Participants Research. 2.2. Experimental Design Participants attended a testing session during which training information and biomet- ric data were collected (Week 1). During this session, the pre-training RE, plantarflexor strength, AT stiffness, and spatiotemporal, ankle joint kinematic and kinetic variables during running were assessed. Participants were then assigned to one of three resistance training groups. Groups were stratified first by age, then gender, weekly training duration, and lastly, AT stiffness to ensure group homogeneity before the 10-week intervention (Table). Participants took part in the 10-week intervention (Weeks 2–11) in one of three re- sistance training interventions: (1) heavy resistance training (HRT), (2) heavy resistance and plyometrics training (HRPT), and (3) endurance resistance and plyometrics training (ERPT). Following the 10th week of the resistance training intervention (Week 12), participants returned for the post-training testing session. Table 1.Pre-training participant characteristics and training details and 10-week intervention training details for the heavy resistance and plyometric (HRPT) group, heavy resistance (HRT) group, and the endurance resistance and
resistance and plyometrics training (HRPT), and (3) endurance resistance and plyometrics training (ERPT). Following the 10th week of the resistance training intervention (Week 12), participants returned for the post-training testing session. Table 1.Pre-training participant characteristics and training details and 10-week intervention training details for the heavy resistance and plyometric (HRPT) group, heavy resistance (HRT) group, and the endurance resistance and plyometric (ERPT) group (mean±SD). Training Group HRT HRPT ERPT p-Value Sample Size (women/men N) * 9 (5W/4M) 9 (5W/4M) 8 (5W/3M) - Age (years) 50 ±5 52 ±4 51 ±5 0.65 Mass (kg) 75.8 ±20.6 70.7 ±10.7 72.2 ±14.9 0.78 Height (m) 1.71 ±0.11 1.70 ±0.09 1.69 ±0.1 0.98 Running Experience (years) 16 ±8 14 ±7 13 ±10 0.73 Preferred Speed (m·s −1 ) 2.7±0.3 2.7 ±0.3 2.5 ±0.4 0.47 Pre-Training Weekly Running Duration (min) 207 ±98 214 ±100 183 ±85 0.78 Intervention Period Weekly Running Duration (min)171±70 211 ±102 177 ±97 0.59 Intervention Adherence (% attended) 92.2 ±7.1 91.1 ±6.5 88.8 ±8.3 0.62 Notes:N: sample size; W: women; M: men; *: sample size varies for running economy (see Results section for details), AT stiffness, and running biomechanics variables; and Bold:p-value≤0.05. 2.3. Experimental Procedures Both testing sessions followed identical testing procedures under the same laboratory conditions. Participants performed all testing procedures wearing standardized footwear https://doi.org/10.3390/biomechanics6010018
Biomechanics2026,6, 18 4 of 18 (NB1080, New Balance, Boston, MA, USA). A 10-camera, three-dimensional (3D) motion capture system (240 Hz, Qualysis AB, Goteburg, Sweden) and instrumented force treadmill (1200 Hz, Bertec, Columbus, OH, USA) were used to simultaneously collect kinematic and GRF data during running trials, respectively. Additionally, a metabolic system (TrueOne 2400; ParvoMedics, Murray, UT, USA) was used to collect expired gases while running. Before testing, clusters of reflective markers mounted non-collinearly on thermoplastic shells were secured on the right thigh, shank, and posterior rearfoot (Figure). Participants then performed a five-minute running warm-up on the treadmill at their preferred easy-run pace (Table). Anatomical reflective markers were placed on the right femoral epicondyles, malleoli, and head of the first and fifth metatarsals and bilaterally on the greater trochanter of the femur and the iliac crest to define the right leg and right foot, as described in previous work [23]. A standing calibration trial was taken with all reflective markers to establish segment dimensions and local coordinate systems. Participants completed two 2 min running bouts at (1) their preferred speed (PS) and (2) preferred speed plus 5% (PS5). The 5% change increased the running demands while also keeping testing safe for this population. Participants were given two-minute rest breaks between each bout to limit potential fatigue effects. During running bouts, 3D kinematic and 3D GRF data were collected for 15 s starting at 90 s. Figure 1.Participant setup with anatomical and tracking clusters while metabolic data is collected. Participants were then fitted with a rubber facemask connected to the metabolic cart via a plastic breathing tube. Participants completed a . VO2max test on the testing treadmill using two-minute stages with increasing speed, beginning with participants’ preferred speed and increasing by 5% every two minutes until volitional failure, confirmed with a plateau in . VO2max for two consecutive stages. . VO2 was collected continuously during the entire test. Following at least ten minutes of recovery, we assessed maximal plantarflexor isomet- ric torque and AT characteristics. To measure maximum plantarflexor isometric torque (strength), participants lay prone in an isokinetic dynamometer with the knee
every two minutes until volitional failure, confirmed with a plateau in . VO2max for two consecutive stages. . VO2 was collected continuously during the entire test. Following at least ten minutes of recovery, we assessed maximal plantarflexor isomet- ric torque and AT characteristics. To measure maximum plantarflexor isometric torque (strength), participants lay prone in an isokinetic dynamometer with the knee slightly flexed and ankle at 90 ◦ . The foot was strapped to the pedal to minimize movement at the ankle https://doi.org/10.3390/biomechanics6010018
Biomechanics2026,6, 18 5 of 18 joint. A diagnostic ultrasound probe (MSK probe, L12-4MHz Philips, Lumify, frame rate 24 Hz, depth 3.5 cm, and width 3.5 cm), supported by a rigid custom 3D-printed orthotic secured around the shank, was centered over the gastrocnemius medialis muscle–tendon junction (MTJ). Heel displacement was not measured but assumed to be negligible based on visual confirmation. Following standardized familiarization trials [13], participants completed three trials of maximal voluntary isometric contraction (MVIC) for three sec- onds, separated by one-minute rest periods [13,24,25]. During each MVIC, longitudinal displacement of the most distal point of the gastrocnemius medialis MTJ was tracked using the diagnostic ultrasound probe. 2.4. Home-Based Resistance Training Interventions The focus of all three home-based resistance training programs was on lower body movements involving the plantarflexors (Appendix). All interventions included two vir- tual (Zoom) sessions per week with an ACSM certified personal trainer, with participants’ weight provided by the researchers. The load (combination of sets and repetitions for each exercise) of each training group was matched based on AT loading index, a summation of scaled and normalized peak loading, loading impulse, and loading rate [26]. For heavy re- sistance exercises, participants performed 4 sets with 5–8 repetitions and were instructed to “choose a weight that would make it challenging to achieve the goal number of repetitions”. At least one-minute rest periods were allowed between sets [15–17], and participants could adjust their weight to ensure reaching the goal number of repetitions would be challenging. For plyometric exercises, participants performed 1–2 sets with 4–8 repetitions, with rest periods of at least one-minute between sets. Finally, for endurance exercises, participants performed 1–2 sets with 10–20 repetitions and were instructed to choose a weight for which failure was expected before achieving the goal number of repetitions, with rest periods of at least one-minute between sets [16,17]. All training interventions began with two weeks of training on techniques and movement skills in preparation for more challenging demands of heavy resistance and plyometrics training in the following eight weeks. The eight weeks consisted of two four-week training cycles, three weeks of progressive loading,
the goal number of repetitions, with rest periods of at least one-minute between sets [16,17]. All training interventions began with two weeks of training on techniques and movement skills in preparation for more challenging demands of heavy resistance and plyometrics training in the following eight weeks. The eight weeks consisted of two four-week training cycles, three weeks of progressive loading, and one week of reduced load, reducing the previous week’s lifting load by approximately 50% [18]. Participants were instructed to maintain their typical running training during the intervention, and researchers monitored running duration. 2.5. Data Analyses Visual3D software (v2023.05.3, C-Motion, Germantown, MD, USA) was used to pro- cess and analyze all kinematic and kinetic data. Kinematic data were interpolated using a least-squared fit of a 3rd order polynomial, with three-data point fitting and maximum gap of 10 frames. Kinematic and GRF data were filtered using a fourth-order Butterworth low-pass filer with cut-off frequencies of 8 and 40 Hz. A right-hand rule with a Cardan rotational sequence (x-y-z) was used for 3D angular computations, where x represented the sagittal plane, y represented the frontal plane, and z represented the transverse plane. A vertical GRF threshold of 20 N defined the start and end of stance phase while running. Primary dependent variables included RE, peak plantarflexor torque, peak positive ankle power, positive ankle mechanical work, plantarflexor strength, and AT stiffness. Relative running economy was calculated as the average . VO2 (mL·kg −1 · min −1 ) dur- ing the last 30 s of each running bout when steady-state at each speed was confirmed [27,28] as a percentage of . VO2max at each speed. The peak torque generated across the three plan- tarflexor MVICs was used to assess plantarflexor strength [12,13,25,29]. Ankle joint angular kinematic and kinetic variables were expressed in the shank coordinate system. Newto- nian inverse dynamics were used to calculate net internal joint moments normalized to https://doi.org/10.3390/biomechanics6010018
Biomechanics2026,6, 18 6 of 18 body mass (Nm·kg −1 ) during the stance phase. Joint angular powers were computed as the scalar product of joint angular velocities and joint moments during the stance phase (W·kg −1 ). Stance phase negative and positive joint work (J·kg −1 ) was calculated using trapezoidal integration of angular power with respect to time using a custom program in MATLAB (R2018a, Mathworks, Natick, MA, USA). The distal-to-proximal shift in joint kinetics (biomechanical plasticity) was quantified as the ratio of hip-to-ankle positive joint work (larger magnitude signifies more reliance on hip joints). AT force was calculated by multiplying the plantarflexor MVIC torque with the exter- nally measured lever arm of the AT [13,29]. The lever arm was measured as the distance from the right medial malleolus to the posterior aspect of the AT. The MTJ displacement was used to assess tendon elongation using an open source software (version 2.2, DeepLabCut). This software allows for the training of a deep neural network that was used in the current study to recognize the MTJ and track its movements throughout the contraction using 2764 labeled frames from 26 participants. We used a MobileNetV2-1-based neural network pre-trained on ImageNet with default parameters for 500,000 training iterations [30]. These procedures have been described in detail in previous work [31] and are validated as a reliable method to track the AT MTJ [32]. AT stiffness was calculated as the slope of the AT force and the AT elongation between 10% and 80% of MVIC torque output [13]. The average of ten steps for each running variable, the average and peak of three trials for plantarflexor MVIC torque, the AT stiffness of one ramped MVIC trial, and RE during the pre- and post-testing sessions were used in statistical analyses. 2.6. Statistical Analyses A 2×3 mixed design ANOVA (SPSS 24.0, IBM) was used to address the primary purpose. Time served as the within-subject factor, while intervention group served as the between-subject factor. Data normality was assessed using Kolmogorov–Smirnov tests. If data were not normally distributed, a Mann–Whitney non-parametric test was used to compare group
sessions were used in statistical analyses. 2.6. Statistical Analyses A 2×3 mixed design ANOVA (SPSS 24.0, IBM) was used to address the primary purpose. Time served as the within-subject factor, while intervention group served as the between-subject factor. Data normality was assessed using Kolmogorov–Smirnov tests. If data were not normally distributed, a Mann–Whitney non-parametric test was used to compare group mean differences. A one-way ANOVA was used to compare the pre- and post-intervention difference in body mass. To decipher interaction effects, paired t-tests were used to compare time points and independentt-tests to compare groups. The 95% confidence intervals for mean differences were reported and Cohen’s d effect sizes calculated to assess effect magnitudes using the interpretation of Hopkins (small: d < 0.6, moderate: 0.6≥d < 1.2, and large: d≥1.2) [33]. Pearson’s correlation coefficient was used to assess the secondary purpose to assess the correlation between plantarflexion function with RE (% . VO2max). Significance level was set at an alpha level of≤0.05. 3. Results 3.1. Participant Characteristics, Intervention Adherence, and Running Training Participant and training characteristics are shown in Table. Twenty-six participants completed both testing sessions and attended at least 80% of the resistance training sessions. Due to instrumentation malfunction or dropouts, the sample size varied among dependent variables. All details regarding participant dropouts are provided in Appendix. There were no significant group differences for physical or training characteristics during the intervention (Table). The pre–post difference in body mass was not different among HRT (+0.6±1.0 kg), HRPT (−0.5±1.8 kg), and ERPT (−0.4±1.8 kg) groups (p= 0.25). Participants maintained their pre-intervention weekly running duration (p= 0.30) during the intervention. The intervention’s weekly running duration compared to pre-intervention was slightly lower for HRT (−17.8%, d = 0.5) but unchanged for the HRPT (−1.4%,d = 0.1) and ERPT (−3.2%, d = 0.0). Weekly running duration during the intervention was not https://doi.org/10.3390/biomechanics6010018
Description
This study assesses the influence of resistance training on running economy and plantarflexor function in middle-aged runners.