Abstract
arable resistance training is widely applied to enhance running performance, but how different placements of wearable resistance across various body parts influence running efficiency remains unclear. This study aimed to explore the impacts of wearable resistance placement on running efficiency by comparing five running conditions: no load, and an additional 10% load of individual body mass on the trunk, forearms, lower legs, and a combination of these areas. Running efficiency was assessed through biomechanical (spatiotemporal, kinematic, and kinetic) variables using acceleration-based wearable sensors placed on the shoes of 15 recreational male runners (20.3±1.23 years) during treadmill running in a randomized order. The main findings indicate distinct effects of different load distributions on specific spatiotemporal variables (contact time, flight time, and flight ratio,p≤0.001) and kinematic variables (footstrike type,p< 0.001). Specifically, adding loads to the lower legs produces effects similar to running with no load: shorter contact time, longer flight time, and a higher flight ratio compared to other load conditions. Moreover, lower leg loads result in a forefoot strike, unlike the midfoot strike seen in other conditions. These findings suggest that lower leg loads enhance running efficiency more than loads on other parts of the body.
produces effects similar to running with no load: shorter contact time, longer flight time, and a higher flight ratio compared to other load conditions. Moreover, lower leg loads result in a forefoot strike, unlike the midfoot strike seen in other conditions. These findings suggest that lower leg loads enhance running efficiency more than loads on other parts of the body. Keywords:wearable resistance training; weight vest; running performance; running gait; RunScribe™ 1. Introduction Running is a fundamental form of exercise, physical activity, and sport, with ongoing efforts to develop innovative training methods aimed at improving running performance. One such method gaining prominence is wearable resistance training, which involves strategically attaching additional weight or resistance, such as weighted vests or specialized cuffs, to various parts of the body [1]. Unlike traditional gym-based workouts, wearable resistance training enables athletes to perform sport-specific exercises with added weight, potentially leading to a better transfer of improvements to actual performance [2]. This approach has been extensively used to enhance athletes’ muscular strength, endurance, and overall performance during warm-up routines [3], running [2,4,5], and activities like netball that require a change in direction [6], as an integral component of regular training programs [7]. Research has extensively explored the potential benefits of wearable resistance train- ing, particularly its impact on running efficiency, biomechanics, and performance [2,5,8–10]. Previous studies reported that runners utilize various load-bearing strategies, e.g., weighted vests [4,11] or cuffs [5], to distribute the loads, potentially enhancing force generation [4] or increasing muscular activity depending on the placement [9,12]. External loading attached directly to the trunk or limbs is thought to provide a vertical load, possibly increasing braking forces and overloading the stretch-shortening cycle [13]. Wearable resistance has also been reported to increase force production, improve sprint performance [2,8], modify stride length and frequency during loaded sprinting, and increase contact time and ground Sensors2024,24, 4399.
Sensors2024,24, 4399 2 of 14 reaction forces [2]. Significant kinematic changes have been observed with different place- ments of wearable resistance, such as on the calves versus the thighs [2]. Understanding how various placements affect running biomechanics reflecting neuromuscular control [9] is crucial for integrating wearable resistance into training and rehabilitation programs. A recent systematic review comparing wearable resistance and weighted vests for sprint performance found distinct differences in their impact on kinematics [14]. Acute studies in- dicated that wearable resistance reduced step frequency while weighted vests reduced step length, both increasing sprint times and ground contact times. Long-term adaptations fa- vored wearable resistance for improving sprint times, suggesting varying benefits between equipment types and durations [14]. Despite extensive research on wearable resistance training effects on biomechanics and performance, gaps remain regarding how different placements of wearable resistance across various body parts (e.g., forearms, lower legs, trunk, and combined segments) influence running efficiency. Addressing these gaps could inform more targeted training and rehabilitation programs tailored to optimize running efficiency across diverse popula-tions and performance levels. The interaction of muscle actions and external forces, e.g., friction, air resistance, ground reaction forces, and gravity, collectively modulate the body’s acceleration during movements [15–17]. For running, this underscores the neuromuscular control ability to control motion [17,18], which can be indirectly assessed by focusing on running biome- chanics [19]. Recent advancements in wearable sensor technology have enabled continuous monitoring and analysis of running efficiency through various biomechanical variables in different environments used by clinicians, researchers, and athletes [20–22]. Acceleration- based wearable sensor systems, incorporating a triaxial accelerometer and gyroscope affixed to the runner’s shoe, have gained considerable recognition for measuring running efficiency variables related to gait dynamics [23–29]. Systems like RunScribe™used by several studies [23,24,30–34] provide detailed data on spatiotemporal metrics (step rate, length, contact time, flight time), kinematics (footstrike type, pronation), and kinetics (brak- ing G-forces), offering insights into rhythm, timing, foot motion, and running form [35]. Previous research has shown robust correlations (r> 0.9) for these measures and moderate to strong correlations (r= 0.4–0.8) for kinematic measures compared with gold standard methods [36,37].
provide detailed data on spatiotemporal metrics (step rate, length, contact time, flight time), kinematics (footstrike type, pronation), and kinetics (brak- ing G-forces), offering insights into rhythm, timing, foot motion, and running form [35]. Previous research has shown robust correlations (r> 0.9) for these measures and moderate to strong correlations (r= 0.4–0.8) for kinematic measures compared with gold standard methods [36,37]. High agreement with standard accelerometry measurement systems has been reported for pronation excursion and pronation velocity, with ICC values ranging from 0.5 to 0.6 [38]. Kinetic variables, e.g., braking G-forces, reflect the forces exerted on the body during each stride, demonstrating good concurrent validity with ICC values ranging from 0.8 to 0.9 [39]. Overall, spatiotemporal metrics, kinematics, and kinetics variables derived from this device have acceptable reliability [40–42] and validity [37,43] compared to traditional gold standard devices. In addition to advancements in running efficiency analysis, exploring the physiolog- ical effects of running with added load is paramount for comprehensive understanding. Basic physiological measures, such as heart rate, blood pressure, and oxygen saturation, offer insights into the cardiovascular and metabolic demands imposed by running with added resistance [44]. Monitoring these physiological responses helps evaluate the body’s adaptation to the increased workload and guides the optimization of training protocols tailored to individual athletes’ needs and goals [45]. Moreover, monitoring perceived exertion can provide valuable information for training since it is a recognized marker of intensity and homeostatic disturbance during exercise [46,47]. Examining the biomechan- ical and physiological aspects provides a holistic understanding of wearable resistance effects on running performance, maximizing athlete potential while minimizing injury risks or overexertion. In summary, the current study aimed to explore the effects of applying external loads on running efficiency assessed by acceleration-based wearable sensors. Additionally, al- tered physiological responses according to running with and without loads were also measured. We hypothesized that variations in running efficiency (spatiotemporal, kine- matic, and kinetic variables) and physiological responses (heart rate, respiratory rate, blood
tered physiological responses according to running with and without loads were also measured. We hypothesized that variations in running efficiency (spatiotemporal, kine- matic, and kinetic variables) and physiological responses (heart rate, respiratory rate, blood
Sensors2024,24, 4399 3 of 14 pressure, blood oxygen saturation, and perceived exertion) would be evident between running without any load and running with added loads distributed across different body parts. The findings may provide valuable insights for integrating wearable resistance into training routines, empowering individuals to enhance performance effectively while minimizing potential drawbacks. 2. Materials and Methods 2.1. Participants Fifteen regular, recreational male runners with treadmill running experience were recruited for the study. Each participant confirmed their absence of musculoskeletal or neurological issues within the past six months, did not have medical conditions, e.g., hyper- tension or cardiovascular problems, and had no prior use of wearable resistance equipment during training. Eligible participants underwent a health screening and received essential information from researchers on appropriate attire and footwear, ensuring adequate rest (6–8 h), consumption of a meal 2–3 h before testing, abstaining from consuming energy or alcoholic drinks, and refraining from vigorous activity for at least 24 h. The sample size was determined through a priori power analysis using G*Power software version 3.1.9.7 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany), based on findings from a preliminary study that assessed the effects of different wearable resistance placements using acceleration data. The preliminary study indicated an average effect size of 0.42 [9], withα= 0.05 and a desired power of 0.95, suggesting a minimum sample size ofN= 14. Fifteen young, regular recreational runners volunteered, slightly exceeding the calculated requirement. Male recreational runners were specifically chosen due to the known performance differences between sexes (i.e., sex gap) in recreational settings [48,49], attributed to biological disparities, (e.g., skeletal muscle mass, hormonal factors, and oxidative capacities), which have been accepted as the primary cause [50,51]. Moreover, the diversity in race among amateur runners introduces potential limitations, as outcomes may vary based on training backgrounds and experience levels [52]. This study is, therefore, regarded as an initial exploration due to its emphasis on young male recreational runners. Ethical guidelines outlined in the Declaration of Helsinki were followed, and approval was obtained from the Institutional Review Board of the University of Phayao, Thailand (HREC-UP-HSST 1.3/038/66, Approval Date: 20 August 2023). Written informed
based on training backgrounds and experience levels [52]. This study is, therefore, regarded as an initial exploration due to its emphasis on young male recreational runners. Ethical guidelines outlined in the Declaration of Helsinki were followed, and approval was obtained from the Institutional Review Board of the University of Phayao, Thailand (HREC-UP-HSST 1.3/038/66, Approval Date: 20 August 2023). Written informed consent was obtained from all participants before their involvement. Table participants’ demographic characteristics and baseline physiological data collected prior to the experiments. Table 1.Characteristics of participants (mean±SD). Mean Range Age (years) 20.3 ±1.0 22.0–19.0 Mass (kg) 64.7 ±6.5 77.0–53.0 Height (cm) 170.3 ±5.6 180.0–163.0 Body mass index (kg/m 2 ) 22.3±2.1 26.6–19.2 Weekly mileage (km) 20.1 ±11.1 40.0–5.0 Heart rate (bpm) 72.1 ±13.1 52–97 Respiratory rate (bpm) 16.1 ±1.9 12–19 Systolic blood pressure (mmHg) 131.5 ±10.7 114–141 Dyastolic blood pressure (mmHg) 76.1 ±9.0 65–96 Blood oxygen saturation (SpO2 (%)) 97.3 ±0.9 96–99 Borg Rating of Perceived Exertion (RPE) scale 6.6 ±1.5 6–8 2.2. Experimental Procedure Each participant began with a warm-up regimen, starting with a 5 min brisk walk (5.5 km/h)on a treadmill (Brightway TT-X10, Shandong Brightway Fitness Equipment Co., Ltd., Jinan, China), followed by a 5-min whole-body stretching routine [10]. Afterward,
Sensors2024,24, 4399 4 of 14 a 5-min rest period preceded the start of the initial experiment. Participants completed five treadmill running trials in a randomized sequence: one trial without additional load and four trials with a 10% body weight. This load was achieved by inserting detachable metal plates (FigureA) into cuffs and vests placed around the forearm, lower leg, and trunk areas, respectively. The selection of a 10% body weight load was based on its documented effectiveness in enhancing running performance [1], while minimizing the risk of injury or overexertion compared to heavier loads and enhancing power output without significantly altering movement mechanics [5]. For the forearm, lower leg, and combined segment conditions, the load was evenly distributed to ensure equal weight allocation to each segment. Metal plates were inserted into the sockets of the cuffs, covering the circumference of the forearms and lower legs. Symmetrical weight distribution was maintained on both the front and back sides of the vest, ensuring the load was evenly distributed across the front and back of the trunk.Sensors 2024, 24, x FOR PEER REVIEW 4 of 15 2.2. Experimental Procedure Each participant began with a warm-up regimen, starting with a 5 min brisk walk (5.5 km/h) on a treadmill (Brightway TT-X10, Shandong Brightway Fitness Equipment Co., Ltd., Jinan, China), followed by a 5-minute whole-body stretching routine [10]. Afterward, a 5-minute rest period preceded the start of the initial experiment. Participants completed five treadmill running trials in a randomized sequence: one trial without additional load and four trials with a 10% body weight. This load was achieved by inserting detachable metal plates (Figure 1A) into cuffs and vests placed around the forearm, lower leg, and trunk areas, respectively. The selection of a 10% body weight load was based on its docu- mented effectiveness in enhancing running performance [1], while minimizing the risk of injury or overexertion compared to heavier loads and enhancing power output without significantly altering movement mechanics [5]. For the forearm, lower leg, and combined segment conditions, the load was evenly distributed to ensure equal weight allocation to each segment. Metal plates were
load was based on its docu- mented effectiveness in enhancing running performance [1], while minimizing the risk of injury or overexertion compared to heavier loads and enhancing power output without significantly altering movement mechanics [5]. For the forearm, lower leg, and combined segment conditions, the load was evenly distributed to ensure equal weight allocation to each segment. Metal plates were inserted into the sockets of the cuffs, covering the cir- cumference of the forearms and lower legs. Symmetrical weight distribution was main- tained on both the front and back sides of the vest, ensuring the load was evenly distrib- uted across the front and back of the trunk. Figure 1. Illustration of (A) wearable equipment utilized in the study ((a) weighted vest, (b) forearm cuffs, (c) lower leg cuffs, and (d) detachable metal plates); (B) RunScribe™ wearable sensors; and (C) the experimental protocol. Figure 1B depicts the experimental procedure, where treadmill velocity progres- sively increased from 0 to 10 km/h over one minute, remained constant for 3 minutes, and then gradually decreased from 10 to 0 km/h within another minute for each running sce- nario. The chosen speed of 10 km/h aligns closely with the preferred pace of recreational runners [53]. Moreover, before commencing each running trial and after every run, par- ticipants were granted a 5-minute break, during which they assessed their readiness; if Figure 1.Illustration of (A) wearable equipment utilized in the study ((a) weighted vest, (b) forearm cuffs, (c) lower leg cuffs, and (d) detachable metal plates); (B) RunScribe™wearable sensors; and (C) the experimental protocol. FigureB depicts the experimental procedure, where treadmill velocity progressively increased from 0 to 10 km/h over one minute, remained constant for 3 min, and then gradually decreased from 10 to 0 km/h within another minute for each running scenario. The chosen speed of 10 km/h aligns closely with the preferred pace of recreational run- ners [53]. Moreover, before commencing each running trial and after every run, participants
within another minute for each running scenario. The chosen speed of 10 km/h aligns closely with the preferred pace of recreational run- ners [53]. Moreover, before commencing each running trial and after every run, participants
Sensors2024,24, 4399 5 of 14 were granted a 5-min break, during which they assessed their readiness; if this interval was insufficient, they could extend their rest. Any abnormal symptoms experienced by participants during testing, e.g., dizziness, nausea, vomiting, pain, feelings of insecurity, accidents, or simply a desire to withdraw from participation, constituted grounds for their withdrawal from the study. 2.3. Measuring Running Efficiency A pair of acceleration-based wearable sensors, the RunScribe™system (Figure was placed on the shoelaces. These devices provide raw acceleration data to be processed on-board through the proprietary RunScribe™software version 3.4.0 (470) (Scribe Labs Inc., San Francisco, CA, USA) to derive specific biomechanical variables through the manufacturer’s online dashboard, facilitating the acquisition of three types of biomechanical outcome measures (spatiotemporal, kinematic, and kinetic measures) for analysis [35]. All biomechanical measures were computed for the middle 2-min run of each condition to omit the accelerate and decelerate phases of the running (FigureC). Spatiotemporal variables encompass footstrike type, pronation excursion, and max- imum pronation velocity [35]. Briefly, step rate denotes the frequency of steps taken per minute by a runner. Step length, conversely, measures the linear distance between the heel of one foot and the heel of the same foot in the subsequent step. Contact time represents the duration from the initiation of the heel strike until the conclusion of the toe-off within the same step. Flight time indicates the duration of a running stride when both feet are off the ground and the body is airborne. The flight ratio quantifies the proportion of time during a gait cycle when both feet are off the ground, calculated by dividing the duration of the flight phase by the total duration of the gait cycle. A higher flight ratio is frequently associated with a more efficient running or walking style, as noted by RunScribe™, which attributes this to a combination of shorter contact time, longer flight time, and a higher step rate [35]. When compared to the gold standard technique, prior studies have shown moderate to strong correlations (r= 0.4–0.8) for kinematic measurements and robust correlations(r> 0.9) for spatiotemporal measures [36,37]. Kinematic
with a more efficient running or walking style, as noted by RunScribe™, which attributes this to a combination of shorter contact time, longer flight time, and a higher step rate [35]. When compared to the gold standard technique, prior studies have shown moderate to strong correlations (r= 0.4–0.8) for kinematic measurements and robust correlations(r> 0.9) for spatiotemporal measures [36,37]. Kinematic variables encompass parameters, e.g., footstrike type, pronation excur-sion, and maximum pronation velocity [35]. In essence, RunScribe™assigns numerical values to foot strikes, with values ranging from 0 to 6 indicating a heel or rear foot strike, 6 to 10 representing a midfoot strike, and 10 to 16 denoting a forefoot strike. Pronation excursion refers to the total angular movement range between the initial foot strike and the point of maximal pronation, serving as a measure of foot roll, a typical pronation metric [25]. RunScribe™provides two figures for pronation: from foot strike to maximum pronation (−2 to−20 degrees) and from maximum pronation to toe-off (−10 to 15 degrees). Negative values signify pronation, while positive values indicate supination or outward rolling. Furthermore, maximum pronation velocity refers to the peak angular velocity at which the foot pronates between the initial foot strike and maximal pronation, signifying the speed of foot pronation in degrees per second [25]. RunScribe™reports a range from 200 to over 1000 degrees per second. It has been reported that there is high agreement with standard accelerometry measurement systems for pronation excursion and pronation velocity, with ICC values ranging from 0.5 to 0.6 [38]. Kinetic variables encompass impact Gs and braking Gs [35]. Impact Gs represents the vertical component of peak Gs, which correlates with the ground impact force experienced at foot strike. According to RunScribe™, braking Gs typically range from 4 to 13 Gs, with lower values considered more favorable. Braking Gs denotes the horizontal component of Peak Gs, indicating the braking forces experienced at foot strike. Additionally, the system’s kinetic measures, especially acceleration data, demonstrated good concurrent validity, with ICC values ranging from 0.8 to 0.9 [39].
Description
This study explores the impacts of wearable resistance placement on running efficiency.