Comparison of weekly training load and acute:chronic workload ratio methods to estimate change in training load in running.

CONTEXT: Before examining the impact of training load on injury risk in runners, it is important to gain insight in the differences between methods that are used to measure change in training load. OBJECTIVE: To investigate differences between four methods to calculate change in training load: (1) weekly training load; (2) acute:chronic workload ratio (ACWR), coupled rolling average (RA); (3) ACWR, uncoupled RA; (4) ACWR, exponentially weighted moving averages (EWMA). DESIGN: Descriptive epidemiology study. SETTING: This study is part of a randomized-controlled trial on running injury prevention among recreational runners. Runners received a baseline questionnaire and a request to share GPS training data. PARTICIPANTS: Runners who registered for running events (distances 10-42.195 kilometers) in the Netherlands. MAIN OUTCOME MEASURES: Primary outcome measure was the predefined significant increase in training load (weekly training loads ≥30% progression and ACWRs ≥1.5), based on training distance. Proportional Venn diagrams visualized the differences between the methods. RESULTS: 430 participants (73.3% men; age 44.3 years) shared their GPS training data with in total 22,839 training sessions. For the weekly training load, coupled RA, uncoupled RA, and EWMA method, respectively 33.4% (95% CI 32.8-34.0), 16.2% (95% CI 15.7-16.6), 25.8% (95% CI 25.3-26.4), and 18.9% (95% CI 18.4-19.4) of the training sessions were classified as significant increase in training load. Of the training sessions with significant increase in training load, 43.0% expressed in the weekly training load method showed a difference with the coupled RA and EWMA method. Training sessions with significant increase in training load based on the coupled RA method showed 100% overlap with the uncoupled RA and EWMA method. CONCLUSIONS: The difference in change in training load measured by weekly training load and ACWR methods was high. To validate an appropriate measure of change in training load in runners, future research on the association between training loads and RRI risk is needed.

Feasibility and usability of GPS data in exploring associations between training load and running-related knee injuries in recreational runners.

BACKGROUND: The purpose of the present study was to explore the feasibility of collecting GPS data and the usability of GPS data to evaluate associations between the training load and onset of running-related knee injuries (RRKIs). METHODS: Participants of the INSPIRE-trial, a randomized-controlled trial on running injury prevention, were asked to participate in this study. At baseline, demographic variables were collected. Follow-up questionnaires assessed information on RRKIs. Participants with a new reported RRKI and uninjured participants were sent a GPS export request. Weekly GPS-based training distances were used to calculate Acute:Chronic Workload Ratios (ACWRs). RESULTS: A total of 240 participants (62.7%) tracked their running training sessions with the use of a GPS-enabled device or platform and were willing to share their GPS data. From the participants (N = 144) who received a GPS export request, 50.0% successfully shared their data. The majority (69.4%) of the shared GPS data were usable for analyses (N = 50). GPS data were used to present weekly ACWRs of participants with and without an RRKI eight weeks prior to RRKI onset or running event. CONCLUSIONS: It seems feasible to collect GPS data from GPS-enabled devices and platforms used by recreational runners. The results indicate that GPS data is usable to calculate weekly ACWRs to evaluate associations between training load and onset of RRKIs in recreational runners. Therefore, GPS-based ACWR measures can be used for future studies to evaluate associations between training load and onset of RRIs.

The biomechanics of running and running styles: a synthesis.

Running movements are parametrised using a wide variety of devices. Misleading interpretations can be avoided if the interdependencies and redundancies between biomechanical parameters are taken into account. In this synthetic review, commonly measured running parameters are discussed in relation to each other, culminating in a concise, yet comprehensive description of the full spectrum of running styles. Since the goal of running movements is to transport the body centre of mass (BCoM), and the BCoM trajectory can be derived from spatiotemporal parameters, we anticipate that different running styles are reflected in those spatiotemporal parameters. To this end, this review focuses on spatiotemporal parameters and their relationships with speed, ground reaction force and whole-body kinematics. Based on this evaluation, we submit that the full spectrum of running styles can be described by only two parameters, namely the step frequency and the duty factor (the ratio of stance time and stride time) as assessed at a given speed. These key parameters led to the conceptualisation of a so-called Dual-axis framework. This framework allows categorisation of distinctive running styles (coined 'Stick', 'Bounce', 'Push', 'Hop', and 'Sit') and provides a practical overview to guide future measurement and interpretation of running biomechanics.

Optimal stride frequencies in running at different speeds.

During running at a constant speed, the optimal stride frequency (SF) can be derived from the u-shaped relationship between SF and heart rate (HR). Changing SF towards the optimum of this relationship is beneficial for energy expenditure and may positively change biomechanics of running. In the current study, the effects of speed on the optimal SF and the nature of the u-shaped relation were empirically tested using Generalized Estimating Equations. To this end, HR was recorded from twelve healthy (4 males, 8 females) inexperienced runners, who completed runs at three speeds. The three speeds were 90%, 100% and 110% of self-selected speed. A self-selected SF (SFself) was determined for each of the speeds prior to the speed series. The speed series started with a free-chosen SF condition, followed by five imposed SF conditions (SFself, 70, 80, 90, 100 strides·min-1) assigned in random order. The conditions lasted 3 minutes with 2.5 minutes of walking in between. SFself increased significantly (p<0.05) with speed with averages of 77, 79, 80 strides·min-1 at 2.4, 2.6, 2.9 m·s-1, respectively). As expected, the relation between SF and HR could be described by a parabolic curve for all speeds. Speed did not significantly affect the curvature, nor did it affect optimal SF. We conclude that over the speed range tested, inexperienced runners may not need to adapt their SF to running speed. However, since SFself were lower than the SFopt of 83 strides·min-1, the runners could reduce HR by increasing their SFself.

Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running.

The number of validation studies of commercially available foot pods that provide estimates of running speed is limited and these studies have been conducted under laboratory conditions. Moreover, internal data handling and algorithms used to derive speed from these pods are proprietary and thereby unclear. The present study investigates the use of foot contact time (CT) for running speed estimations, which potentially can be used in addition to the global positioning system (GPS) in situations where GPS performance is limited. CT was measured with tri axial inertial sensors attached to the feet of 14 runners, during natural over ground outdoor running, under optimized conditions for GPS. The individual relationships between running speed and CT were established during short runs at different speeds on two days. These relations were subsequently used to predict instantaneous speed during a straight line 4 km run with a single turning point halfway. Stopwatch derived speed, measured for each of 32 consecutive 125m intervals during the 4 km runs, was used as reference. Individual speed-CT relations were strong (r2 >0.96 for all trials) and consistent between days. During the 4km runs, median error (ranges) in predicted speed from CT 2.5% (5.2) was higher (P<0.05) than for GPS 1.6% (0.8). However, around the turning point and during the first and last 125m interval, error for GPS-speed increased to 5.0% (4.5) and became greater (P<0.05) than the error predicted from CT: 2.7% (4.4). Small speed fluctuations during 4km runs were adequately monitored with both methods: CT and GPS respectively explained 85% and 73% of the total speed variance during 4km runs. In conclusion, running speed estimates bases on speed-CT relations, have acceptable accuracy and could serve to backup or substitute for GPS during tarmac running on flat terrain whenever GPS performance is limited.