Differences in Spatiotemporal Parameters Between Trained Runners and Untrained Participants

Gómez-Molina, Josué; Ogueta-Alday, Ana; Stickley, Christopher D.; Cámara, Jesús; Cabrejas-Ugartondo, Jon; García-López, Juan

doi:10.1519/jsc.0000000000001679

Cited by 29 publications

(30 citation statements)

References 33 publications

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“…submaximal text, Table 3 ), there were no differences among groups in step rate and length, in concordance with previous findings [ 13 , 20 ]. On the contrary, a recent study performed in collaboration with our research group and following similar experimental procedures showed differences in both step rate and length, but not in contact time when trained and untrained runners were compared [ 15 ]. Trained runners showed higher step rate and shorter step length at the same running speeds than untrained ones.…”

Section: Discussionmentioning

confidence: 99%

“…These discrepancies could be due to the dependence of contact time on both running speed and foot strike pattern [ 13 ]. In regards to step rate and length, some studies observed a higher step rate in highly-trained runners compared to well-trained and non-trained ones [ 14 , 15 ]. This seems to be a natural adaptation to obtain an energetically more optimal step rate [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Similarities and differences among half-marathon runners according to their performance level

et al. 2018

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This study aimed to identify the similarities and differences among half-marathon runners in relation to their performance level. Forty-eight male runners were classified into 4 groups according to their performance level in a half-marathon (min): Group 1 (n = 11, < 70 min), Group 2 (n = 13, < 80 min), Group 3 (n = 13, < 90 min), Group 4 (n = 11, < 105 min). In two separate sessions, training-related, anthropometric, physiological, foot strike pattern and spatio-temporal variables were recorded. Significant differences (p<0.05) between groups (ES = 0.55–3.16) and correlations with performance were obtained (r = 0.34–0.92) in training-related (experience and running distance per week), anthropometric (mass, body mass index and sum of 6 skinfolds), physiological (VO2max, RCT and running economy), foot strike pattern and spatio-temporal variables (contact time, step rate and length). At standardized submaximal speeds (11, 13 and 15 km·h-1), no significant differences between groups were observed in step rate and length, neither in contact time when foot strike pattern was taken into account. In conclusion, apart from training-related, anthropometric and physiological variables, foot strike pattern and step length were the only biomechanical variables sensitive to half-marathon performance, which are essential to achieve high running speeds. However, when foot strike pattern and running speeds were controlled (submaximal test), the spatio-temporal variables were similar. This indicates that foot strike pattern and running speed are responsible for spatio-temporal differences among runners of different performance level.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Similarities and differences among half-marathon runners according to their performance level

et al. 2018

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“…No significant associations between SF and experience, injury incidence or performance measures were found. Previous studies showed that, at a given speed, novice (untrained) runners have a lower SF compared with experienced runners (Gómez-Molina et al, 2017). The lower SF in novice runners seems to be energetically suboptimal (de Ruiter et al, 2013;Van Oeveren et al, 2017), while experienced runners appear to run near optimal SL/SF ratios (Cavanagh & Williams, 1982;de Ruiter et al, 2013;Hunter & Smith, 2007;Moore, Jones, & Dixon, 2012).…”

Section: Training Characteristics As Determinants Of Sfmentioning

confidence: 93%

Inter-individual differences in stride frequencies during running obtained from wearable data

Oeveren

Ruiter

Hoozemans

et al. 2019

Journal of Sports Sciences

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The purpose of the present study was to identify factors that underlie differences among runners in stride frequency (SF) as a function of running speed. Participants (N = 256; 85.5% males and 14.5% females; 44.1 ± 9.8 years; 181.4 ± 8.4 cm; 75.3 ± 10.6 kg; mean ± SD) shared their wearable data (Garmin Inc). Individual datasets were filtered to obtain representative relationships between stride frequency (SF) and speed per individual, representing in total 16.128 h of data. The group relationship between SF (72.82 to 94.73 strides • min −1) and running speed (V) (from 1.64 to 4.68 m • s −1) was best described with SF = 75.01 + 3.006 V. A generalised linear model with random effects was used to determine variables associated with SF. Variables and their interaction with speed were entered in a stepwise forward procedure. SF was negatively associated with leg length and body mass and an interaction of speed and age indicated that older runners use higher SF at higher speed. Furthermore, run frequency and run duration were positively related to SF. No associations were found with injury incidence, athlete experience or performance. Leg length, body mass, age, run frequency and duration were associated with SFs at given speeds. KEY POINTS • On a group level, stride frequency can be described as a linear function of speed: SF (strides • min −1) = 75.01+ 3.006•speed (m • s −1) within the range of 1.64 to 4.68 m • s −1. • On an individual level, the SF-speed relation is best described with a second order polynomial. • Leg length and body mass were positively related to stride frequency while age was negatively related to stride frequency. • Run frequency and run duration were positively related to stride frequency, while running experience, performance and injury incidence were unrelated.

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“…However, the biomechanical load will be distributed differently and hence load other tissues that may not be adapted to this load, thereby increasing injury risk. Changing from a heel strike to a forefoot strike, for example, increases plantar flexors and Achilles tendon forces, which may lead to plantar flexor strains and Achilles tendinopathy (Barnes et al, 2014;Folland et al, 2017;Gomez-Molina et al, 2017;Pizzuto et al, 2019;Santos-Concejero et al, 2013;Santos-Concejero et al, 2015;Santos-Concejero et al, 2017;Santos-Concejero et al, 2014b;Støren et al, 2011;Tam et al, 2018;Tartaruga et al, 2012;Tartaruga et al, 2009;Williams & Cavanagh, 1987)…”

Section: How To Modify Running Technique?mentioning

confidence: 99%

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

Hooren

Goudsmit

Restrepo

et al. 2019

Journal of Sports Sciences

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Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise realtime feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

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Differences in Spatiotemporal Parameters Between Trained Runners and Untrained Participants

Cited by 29 publications

References 33 publications

Similarities and differences among half-marathon runners according to their performance level

Similarities and differences among half-marathon runners according to their performance level

Inter-individual differences in stride frequencies during running obtained from wearable data

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

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