Long-range correlations and stride pattern variability in recreational and elite distance runners during a prolonged run

Brahms, Clemens Markus; Zhao, Yong; Gerhard, David; Barden, John M.

doi:10.1016/j.gaitpost.2020.08.107

Cited by 13 publications

(21 citation statements)

References 40 publications

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“…Most of the reviewed studies ( n = 82) included both male and female participants, with eight examining differences between male and female participants [ 31 – 38 ] and three of these studies finding significant differences between sexes [ 32 , 36 , 37 ]. Thirty-nine studies had male participants only, while only three studies solely examined female participants [ 39 – 41 ], seven studies did not report the sex of participants [ 21 , 42 – 47 ] and two studies did not provide a breakdown of the sexes [ 26 , 48 ]. The primary group of interest was healthy young adults who were recreationally active (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Sixty-two articles stated that they used IMUs; however, 14 of these studies only used the accelerometer capabilities within the IMU [ 23 , 24 , 38 , 52 , 56 , 57 , 62 , 72 – 78 ] and 20 studies stated they used the accelerometer and gyroscope components for the data analysis [ 26 , 33 , 43 , 49 , 50 , 54 , 59 , 61 , 79 – 90 ]. The remaining 27 studies either did not comment on components used [ 70 , 91 – 98 ] or implied they used all accelerometer, gyroscope and magnetometer components for the data analysis [ 19 – 21 , 28 , 29 , 36 , 42 , 99 – 109 ]. One study used an IMU and a separate one-dimensional accelerometer [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most of the reviewed studies (n = 82) included both male and female participants, with eight examining differences between male and female participants [31][32][33][34][35][36][37][38] and three of these studies finding significant differences between sexes [32,36,37]. Thirty-nine studies had male participants only, while only three studies solely examined female participants [39][40][41], seven studies did not report the sex of participants [21,[42][43][44][45][46][47] and two studies did not provide a breakdown of the sexes [26,48]. The primary group of interest was healthy Wearable technology "Wearable*" OR "Wearable Technology" OR "Wearable Devices" OR "Wearable Sensors" OR "IMU" OR "Inertial Sensor" OR "Inertial Measurement Unit" OR "Gyroscope" OR "Magnetometer" OR Acceleromet* OR "Force Plate" OR "Pressure Plate" OR "Pressure Sensor" TITLE-ABS-KEY Running gait "Running" OR "Jogging" OR "Run" OR "Jog" OR "Sprint" OR "Sprinting" OR "Sprints" OR "Runners" OR "Joggers" OR "Athletics" TITLE-ABS-KEY young adults who were recreationally active (Fig.…”

Section: Participant Characteristicsmentioning

confidence: 99%

“…Seventy-seven studies used controlled speeds (58 of these controlled at a set pace), with a range from 2 m/s [85,121] to 26 km/h [117]. Nine studies controlled speed based on individual performance; four of these studies used personal bests as the benchmark [42,48,87,108] and two studies controlled speed based on the participants' preferred speed (e.g. 85 and 115% preferred speed) [21,98].…”

Section: Speed/pacementioning

confidence: 99%

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Wearables for Running Gait Analysis: A Systematic Review

et al. 2022

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Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance. Objectives We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults. Methods A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings. Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards. Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes. Clinical Trial Registration CRD42021235527.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Participant Characteristicsmentioning

confidence: 99%

Section: Speed/pacementioning

confidence: 99%

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Wearables for Running Gait Analysis: A Systematic Review

et al. 2022

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“…In our earlier studies we found higher stride-to-stride variability in novices compared to experts and only slight changes due to fatigue using an UCM approach (Möhler et al, 2019(Möhler et al, , 2020. Brahms et al (2020) analyzed the coefficient of variation (CV) of several spatiotemporal parameters (stride time and length, contact time) and peak acceleration during an overground run with constant speed. They found no effects of fatigue, which is interpreted as a confirmation for the insensitivity of linear variability measures.…”

Section: Introductionmentioning

confidence: 99%

Stride-to-Stride Variability of the Center of Mass in Male Trained Runners After an Exhaustive Run: A Three Dimensional Movement Variability Analysis With a Subject-Specific Anthropometric Model

Möhler

Stetter

Müller

et al. 2021

Front. Sports Act. Living

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The motion of the human body can be described by the motion of its center of mass (CoM). Since the trajectory of the CoM is a crucial variable during running, one can assume that trained runners would try to keep their CoM trajectory constant from stride to stride. However, when exposed to fatigue, runners might have to adapt certain biomechanical parameters. The Uncontrolled Manifold approach (UCM) and the Tolerance, Noise, and Covariation (TNC) approach are used to analyze changes in movement variability while considering the overall task of keeping a certain task relevant variable constant. The purpose of this study was to investigate if and how runners adjust their CoM trajectory during a run to fatigue at a constant speed on a treadmill and how fatigue affects the variability of the CoM trajectory. Additionally, the results obtained with the TNC approach were compared to the results obtained with the UCM analysis in an earlier study on the same dataset. Therefore, two TNC analyses were conducted to assess effects of fatigue on the CoM trajectory from two viewpoints: one analyzing the CoM with respect to a lab coordinate system (PVlab) and another one analyzing the CoM with respect to the right foot (PVfoot). Full body kinematics of 13 healthy young athletes were captured in a rested and in a fatigued state and an anthropometric model was used to calculate the CoM based on the joint angles. Variability was quantified by the coefficient of variation of the length of the position vector of the CoM and by the components Tolerance, Noise, and Covariation which were analyzed both in 3D and the projections in the vertical, anterior-posterior and medio-lateral coordinate axes. Concerning PVlab we found that runners increased their stride-to-stride variability in medio-lateral direction (1%). Concerning PVfoot we found that runners lowered their CoM (4 mm) and increased their stride-to-stride variability in the absorption phase in both 3D and in the vertical direction. Although we identified statistically relevant differences between the two running states, we have to point out that the effects were small (CV ≤ 1%) and must be interpreted cautiously.

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