Using a wireless consumer accelerometer to measure tibial acceleration during running: agreement with a skin-mounted sensor

Brayne, Leona; Barnes, Andrew; Heller, Ben; Wheat, Jonathan

doi:10.1007/s12283-018-0271-4

Cited by 24 publications

(13 citation statements)

References 11 publications

(21 reference statements)

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“…Of note, no studies have examined the concurrent validity of PW during running estimated from any power meter, finding only two studies [ 12 , 15 ] which examined the reliability of PW during running. The remaining 5 studies tested the validity and reliability of spatiotemporal parameters [ 11 , 14 ], kinematic parameters [ 37 , 38 ], or both variables [ 13 ].…”

Section: Resultsmentioning

confidence: 99%

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Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

Jaén-Carrillo

Roche-Seruendo

Cartón-Llorente

et al. 2020

Sensors

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Mechanical power may act as a key indicator for physiological and mechanical changes during running. In this scoping review, we examine the current evidences about the use of power output (PW) during endurance running and the different commercially available wearable sensors to assess PW. The Boolean phrases endurance OR submaximal NOT sprint AND running OR runner AND power OR power meter, were searched in PubMed, MEDLINE, and SCOPUS. Nineteen studies were finally selected for analysis. The current evidence about critical power and both power-time and power-duration relationships in running allow to provide coaches and practitioners a new promising setting for PW quantification with the use of wearable sensors. Some studies have assessed the validity and reliability of different available wearables for both kinematics parameters and PW when running but running power meters need further research before a definitive conclusion regarding its validity and reliability.

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Section: Resultsmentioning

confidence: 99%

“…GOVSS model estimates power using the runner’s speed, step rate, weight, and height, as well as slope gradient and wind velocity based on linear regression models [ 52 ]. Several studies attempted to determine the reliability and validity of such foot pods for either kinetic or kinematic parameters [ 13 , 14 , 15 , 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

Jaén-Carrillo

Roche-Seruendo

Cartón-Llorente

et al. 2020

Sensors

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“…The RunScribe sensors consist of small foot pods secured bilaterally on runners' shoes that can collect spatiotemporal, kinematic, and kinetic outcomes continuously throughout activity. Previous authors 3,4 have confirmed that the RunScribe sensors are a valid means of assessing spatiotemporal outcomes, including cadence, stride length, cycle time, and foot-contact time (intraclass correlation coefficient [ICC] ¼ 0.86-0.94), and kinematic outcomes, particularly pronation excursion, and maximum pronation velocity (ICCs ¼ 0.57 and 0.74, respectively). Additionally, these outcomes have demonstrated face validity with respect to expected changes in running speed and surface 5 and in response to anklebracing and -taping interventions.…”

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confidence: 94%

Validation of Foot-Strike Assessment Using Wearable Sensors During Running

Lempke

Hertel

2020

Journal of Athletic Training

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Wearable sensors are capable of capturing foot-strike positioning, which lends insight into landing biomechanics during running. The purpose of our study was to assess the relationship between foot-strike categorization and foot-strike angle during running to validate the sensor-derived foot-strike outcome. Twenty collegiate cross-country athletes (12 females, 8 males) ran at 2 speeds on an instrumented treadmill. RunScribe sensors were used to determine foot-strike categorizations (1–5 = rearfoot, 6–10 = midfoot, 11–16 = forefoot), and foot-strike angles were simultaneously assessed with 3-dimensional motion capture bilaterally. We calculated Pearson r correlation coefficients to compare foot-strike categorizations and angles at initial contact over 800 steps as well as sensor foot-strike identification accuracy. A strong, inverse correlation between foot-strike categorizations and foot-strike angles was present (r = −0.86, P < .001). Overall, the sensors demonstrated 78% accuracy (rearfoot = 72.5%, midfoot = 55.3%, forefoot = 95.4%). These results support the concurrent validity of the sensor-derived foot-strike measures.

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“…Each participant was outfitted with 2 RunScribe inertial measurement units (IMUs) (version 3; Scribe Labs Inc, San Francisco, CA). Authors of a previous study 21 demonstrated that these sensors accurately measured peak accelerations at a range of speeds compared with a research-grade accelerometer. Each IMU included a tri-axial accelerometer (range 6 16 g) and a tri-axial gyroscope (range 6 20008/s) and sampled at a rate of 500Hz.…”

Section: Methodsmentioning

confidence: 99%

Session Rating of Perceived Exertion Combined With Training Volume for Estimating Training Responses in Runners

Napier

Ryan

Menon

et al. 2020

Journal of Athletic Training

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Context Historically, methods of monitoring training loads in runners have used simple and convenient metrics, including the duration or distance run. Changes in these values are assessed on a week-to-week basis to induce training adaptations and manage injury risk. To date, whether different measures of external loads, including biomechanical measures, provide better information regarding week-to-week changes in external loads experienced by a runner is unclear. In addition, the importance of combining internal-load measures, such as session rating of perceived exertion (sRPE), with different external-load measures to monitor week-to-week changes in training load in runners is unknown. Objective To compare week-to-week changes in the training loads of recreational runners using different quantification methods. Design Case series. Setting Community based. Patients or Other Participants Recreational runners in Vancouver, British Columbia. Main Outcome Measure(s) Week-to-week changes in running time, steps, and cumulative shock, in addition to the product of each of these variables and the corresponding sRPE scores for each run. Results Sixty-eight participants were included in the final analysis. Differences were present in week-to-week changes for running time compared with timeRPE (d = 0.24), stepsRPE (d = 0.24), and shockRPE (d = 0.31). The differences between week-to-week changes in running time and cumulative shock were also significant at the overall group level (d = 0.10). Conclusions We found that the use of an internal training-load measure (sRPE) in combination with external load (training duration) provided a more individualized estimate of week-to-week changes in overall training stress. A better estimation of training stress has significant implications for monitoring training adaptations, resulting performance, and possibly injury risk reduction. We therefore recommend the regular use of sRPE and training duration to monitor training load in runners. The use of cumulative shock as a measure of external load in some runners may also be more valid than duration alone.

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Using a wireless consumer accelerometer to measure tibial acceleration during running: agreement with a skin-mounted sensor

Cited by 24 publications

References 11 publications

Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

Validation of Foot-Strike Assessment Using Wearable Sensors During Running

Session Rating of Perceived Exertion Combined With Training Volume for Estimating Training Responses in Runners

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