Running research nerd alert.
May 2020.
by Thomas Solomon PhD and Matt Laye PhD.

Each month we compile a short-list of recently-published, Pubmed-indexed, peer-reviewed journal articles that have caught our eye in the world of running science. We then break them into bite-sized chunks so you can digest them as food for thought during your training sessions... Welcome to this month's instalment of our "nerd alert". You can click the title of each article to see the review. Enjoy!
Reading time ~20-mins (4000-words)
or listen to the Podcast version here.
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Full article available at https://pubmed.ncbi.nlm.nih.gov/31823338/.
What was the hypothesis or research question? The study was not hypothesis-driven. Using a systematic review and meta-analysis approach, the authors’ aimed to examine whether rearfoot strike and non-rearfoot strike running influenced injury risk, biomechanics, and running economy.
What did they do to test the hypothesis or answer the research question? The authors conducted a systematic review and meta-analysis using standard PRISMA approach. All major databases were searched using a set of search terms (see paper). The inclusion criteria for studies they included in the analysis were: (1) Studies comparing rearfoot strike and non-rearfoot strike pattern whilst running, regardless of surface, whether on treadmill (excepting curved treadmills) or overground; (2) Studies comparing habitual and imposed foot strike patterns; and, (3) Studies of participants running barefoot, in their own shoes, or in standardised shoes, as long as comparisons between different strike patterns are made in the same footwear condition. They identified 3109 studies, 53 of which met their inclusion and exclusion criteria. Data quality was assessed using standard procedures for systematic reviews and meta-analyses. Data from all identified studies were pooled, and means and standard deviations were used to calculate the standardised mean difference (an indication of the size of the effect on changes or differences in mean values).
What did they find? Just 1 high-quality retrospective study indicated limited evidence that runners with a habitual non-rearfoot strike pattern had a significantly lower rate of previous repetitive stress injuries compared to runners with a habitual rearfoot strike, per 10,000 miles of running. Studies provided conflicting evidence of differences in running economy between habitual rearfoot vs. non-rearfoot strikers at slow (10.8–11.0 km/h), medium (12.6–13.5 km/h) and fast (14.0–15.0 km/h) speeds. When habitual rearfoot strikers transitioned to non-rearfoot strike, moderate evidence indicated a decrease in running economy at slow and medium speeds but no change at a fast speed. Furthermore, transition from rearfoot to non-rearfoot strike showed no difference in running economy at the end of a long run. When habitual non-rearfoot strikers transitioned to a rearfoot strike, again no change in running economy was found. When comparing biomechanical variables, it was found that rearfoot strikers had a lower ground contact time, greater peak vertical ground reaction force, and lower peak vertical loading rate, when compared to non-rearfoot strikers, with no difference in cadence or stride length. When rearfoot strikers were transitioned to a non-rearfoot strike, an increase in peak vertical ground reaction force was found along with a decrease in peak vertical loading rate, while no changes in gait (ground contact time, cadence, stride length, etc) were found.
Heaps of biomechanical data were analysed. The best evidence was found at the knee, where moderate-strong evidence identified that non-rearfoot strike running was associated with a lower knee flexion range of motion for both habitual and imposed comparisons. Non-rearfoot striking was generally associated with reduced loading at the knee, including lower patellofemoral joint stress and contact force when habitual strike patterns were compared, and reduced peak knee extension moment and patellofemoral joint stress time integral when non-rearfoot strike running was imposed on habitual rearfoot strike runners. Additionally, imposing non-rearfoot strike running on habitual rearfoot strikers appears to reduce peak knee flexion and quadriceps muscle force. These differences provide possible mechanistic explanations for the previously reported benefits of transitioning from rearfoot to non-rearfoot striking in runners with patellofemoral pain.
What were the strengths? A comprehensive systematic review and meta-analysis of the literature. Large sample of 53 studies. A comprehensive evaluation of injury risk, biomechanics, and running economy, a variable closely related to running performance especially in endurance events.
What were the weaknesses? The study excluded other gait retraining strategies (e.g., increased step rate) or interventions (e.g., change in footwear) and is specific to foot strike pattern. This is a minor issue, however, and is also a strength of the design to isolate the rearfoot vs. non-rearfoot question. The main weakness of the study is actually due to the lack of prospective studies available in the literature. It is also important to note that a history of an injury may influence habitual strike patterns. But again, prospective studies in injury-prone vs. injury-free athletes are lacking. Many of the prospective studies that do exist are rather short-term. For example, it remains to be examined whether running economy returns to normal or improves in the longer-term in response to adaptation to a non-rearfoot strike pattern. Lastly, most of the studies examined in relation to biomechanics included subjects who were asymptomatic and injury-free at the time of assessment.
Are the findings useful in application to training/coaching practice? Yes. The study finds that although limited evidence indicates that a non-rearfoot strike pattern may be retrospectively associated with lower rates of previous injury, a causal relationship of strike pattern with injury risk cannot be determined. No prospective evidence supported one strike pattern being associated with a reduced likelihood of future injury development. This is important because it means that claims people make about non-rearfoot striking runners being less likely to be injured than rearfoot strikers has not been adequately tested in research studies. Furthermore, any recommendations to transition from rearfoot to non-rearfoot striking to improve running economy also lack supporting evidence. In relation to coaching practice, it is very important not to make clinical guidance without evidence. If an athlete is injured consult a physician and a sports physiotherapist (physical therapist). Muscular weakness and imbalances are often causes of injury, so coupling clinical guidance with strength and conditioning is always sensible.

Full article available at https://pubmed.ncbi.nlm.nih.gov/32412884/.
What was the hypothesis or research question? The authors did not propose a hypothesis but aimed to assess the influence of energy drink ingestion on heart rate variability during the recovery period following a single bout of aerobic exercise. By energy drink, the authors mean a drink containing 45 calories, 11.2 g carbohydrate, 80mg sodium, 32mg caffeine, Taurine 400 mg, Niacin 4.6 mg, Pantothenic Acid 2 mg, Vitamin B6 0.5 mg, Vitamin B12 0.4 mg, Glucuronolactone 240 mg, inositol 20 mg.
What did they do to test the hypothesis or answer the research question? The authors used a randomised, controlled, cross-over design and recruited 35 young (19 to 29 y), healthy, physically active, male volunteers. Subjects completed two experimental trials, a control (water ingestion) and an energy drink trial. During each trial, subjects drank 500 mL of water 2-hours prior to exercise to ensure hydration and completed 30-minutes of treadmill running (5-minutes at 5.0 km/h followed by 25-minutes at 60% of their velocity measured at VO2max, measured on a separated lab visit). The two trials were identical, except that 15-mins prior to exercise subjects drank either 200mL water or an unknown volume of energy drink containing 45 calories, 11.2 g carbohydrate, 80mg sodium, 32mg caffeine, Taurine 400 mg, Niacin 4.6 mg, Pantothenic Acid 2 mg, Vitamin B6 0.5 mg, Vitamin B12 0.4 mg, Glucuronolactone 240 mg, inositol 20 mg. Blood pressure and Heart rate were monitored regularly during and after exercise. Heart rate responses were measured using a Polar RS800CX and heart rate variability was quantified using Kubios heart rate variability software.
What did they find? “Energy drink” consumption did not influence the autonomic control of heart rate following exercise. I.e. the change in heart rate variability following a single 30-minute bout of moderate-intensity exercise was unaffected by the random blend of metabolites/nutrients provided in the energy drink trial.
What were the strengths? Sample size selection was justified using power calculations from previous data. A randomised, cross-over, counterbalanced design was used. All testing took place at the same time of day to account for circadian variation. Normality of the data was tested. Appropriate statistics were used to compare mean values between groups.
What were the weaknesses? Only young men were studied who were physically active but their running history was unknown. Hydration status was not measured, just assumed. The “energy drink” was a proprietary blend of metabolites (similar, but not identical, to a can of Redbull) so interaction effects between metabolites cannot be known. Heart rate variability following exercise is just one small piece of the post-exercise recovery phase and is not a comprehensive evaluation of recovery (readiness to go again). Furthermore, there are several variables that can be derived and analysed from heart rate variability data, and the relevance of all such variables in the context of exercise is not known. The authors chose to present 9 such variables of heart rate variability (see Tables 1 and 2) but do not describe the relevance of them in the context of exercise.
Are the findings useful in application to training/coaching practice? No. Since there was no hypothesis nor an applicable context for the study, the effect (or lack thereof) of a proprietary blend of metabolites on post-exercise HRV is not informative. Understanding the effects of metabolites/nutrients on post-exercise recovery cannot be advanced when several such metabolites/nutrients are administered all at once. For this reason, we cannot know whether the effects (or lack of effect) is due to carbohydrate, caffeine, taurine, etc, etc. Furthermore, 60% of vVO2max is below the first ventilatory threshold (aka aerobic threshold) in the majority of people and is, therefore, a speed approximately equal to a runner's “easy” pace. So, the findings are not relevant to high-intensity sessions when “energy” products may be useful. In the bigger picture, current knowledge in the field does not allow us to fully understand the relevance of HRV to training responses and recovery since HRV can be influenced by so many factors and, as stated above, autonomic heart rate control is just one small piece of the training load-recovery puzzle.

Full article available at https://link.springer.com/article/10.1007%2Fs00421-020-04386-6.
What was the hypothesis or research question? The authors hypothesized that improvement in sleep would be achieved when exercise was performed near to habitual bedtime.
What did they do to test the hypothesis or answer the research question? Eighteen young, healthy, men and women were recruited into a randomised, controlled, cross-over trial design. All subjects were intermediate chronotype (avoiding late sleep onset and/or difficulty waking in the morning). Subjects completed three 24-hour sessions on three consecutive weeks: a no-exercise control trial, and two exercise trials in which 60-minutes of running at 60% of HRmax was completed at 6:30 pm or 8:30 pm (4 and 2-hours prior to habitual sleep). The intensity should be lower than the first ventilatory threshold (aka aerobic threshold) in the majority of people and is, therefore, a speed approximately equal to a runner's “easy” pace, BUT maximum heart rate was estimated as 220-age, so they were simply “pinning the tail on the donkey”. Physical activity and sleep were monitored by accelerometry (SenseWear Pro 3 Armbands) for 24-h in each trial, starting at 8 am on the morning of the trial day. Sleep quality was further assessed using the Pittsburgh sleep quality index and the Epworth sleepiness scale questionnaire. Breakfast and lunch were standardised prior to exercise but dinner (945pm) and breakfast (8 am) after exercise was ad libitum (free consumption, “as they desired”, until they were comfortably full). Subjects were allowed to go home between dinner and breakfast. Energy intake at dinner and breakfast was analysed using Nutrisoft software. Tympanic membrane temperature (to estimate core temperature) was measured just prior to sleep.
What did they find? Running for 1-hour at 60% of HRmax in the evening did not disrupt sleep or alter evening food intake in healthy young adults, i.e. neither exercise trial worsened sleep variables compared to the no-exercise control trial. Furthermore, energy intake at dinner or breakfast was not different between trials. That said, there was a statistically significant difference in sleep quality with a moderate effect size between exercise trials (Table 3) where the 6:30 pm exercise trial had a lower incidence of waking after sleep onset, a lower number of awakenings longer 3 min after sleep onset, and better sleep efficiency (% of time in bed asleep), when compared to the 830pm exercise trial.
What were the strengths? Appropriate statistical tests were used to compare means, normality of the data was tested, and effect sizes were reported. Both men and women were studied but it is not made clear how menstrual cycles were controlled for.
What were the weaknesses? The sample size was not justified using power calculations. Only subjects of intermediate chronotype were included. Using 220-age to estimate HRmax is a massive faux pas in exercise physiology studies and introduces much error. It is, therefore, impossible to know in which of the three biological intensity domains the subjects were working during exercise. Using accelerometry to quantify sleep quantity and quality also introduces an error of measurement. The gold standard tool would be polysomnography. The breakfast and lunch were standardised according to the RDA value. Again, this introduces a huge error on the individual level and is a rather poor way to approach the control of energy intake. A simple exercise test with resting and during-exercise indirect calorimetry and heart rate would have provided more accurate values for prescribing the diet and exercise intensity.
Are the findings useful in application to training/coaching practice? Possibly. The data may be relevant to athletes with intermediate chronotypes, in whom, evening exercise may be better performed earlier in the evening rather than closer to bedtime (i.e. 630pm rather than 830pm) in order to optimise sleep hygiene. Although sleep is arguably our most powerful exercise recovery and adaptation tool, a single night intervention using only moderate-intensity exercise is not particularly informative for long-term training adaptations. And, the findings cannot be used to inform practise in older-aged athletes or in athletes with different chronotypes.

Full article available at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231628.
What was the hypothesis or research question? To see if there is a link between incidence of injuries and psychological traits such as resilience, internal motivation, and external motivation in amateur runners. The hypothesis was that higher resilience would be associated with less injuries. A second hypothesis was that internal motivation would be more associated with resilience than external motivation.
What did they do to test the hypothesis or answer the research question? They used three questionnaires administered to 1725 amateur runners (1260 males) of a local road race, the San Sebastian half marathon. These questionnaires were to assess exercise behaviour, resilience, and injury history. Each of these questionnaires are validated and used in previous studies. Their analysis looked both at correlations between different aspects of the questionnaire as well as so-called mediating factors which are factors that may link two different things which are correlated. Specifically, they looked at whether internal or external motivation as a mediating factor could explain the relationship between injuries and resilience.
What did they find? They found that males amateur runners were significantly less internally motivated and more injured than female runners, but the overall difference was fairly small (effect size of 0.18, 0.23). The number of injuries was positively associated with internal motivation when resilience was considered an indirect mediating factor. However, the number of injuries was not significantly linked to external motivation when resilience was used as a mediating factor. Negative correlations between internal and external motivation and the number of injuries were found and a positive correlation between resilience and external motivation was also found. Their general conclusion from the various models was that internal motivation is associated with higher rates of injury and resilience.
What were the strengths? The authors used validated questionnaires and had a fairly large sample size.
What were the weaknesses? All of the associations are fairly weak even though they are statistically significant. All data is self-reported. Training load or history was not captured (are they truly runners or did they run a race?). It was a cross-sectional study design and thus unable to determine cause and effect. The definition of injury was fuzzy and only considered something that caused you to be away from training for a period of time.
Are the findings useful in application to training/coaching practice? Yes, many of our athletes are highly internally motivated and that may lead them to push physically and be more prone to injury. In addition, finding ways to help athletes increase their resilience may offset some of the internally motivation linked injury likelihood.

Full article available at http://www.ncbi.nlm.nih.gov/pubmed/29023328.
What was the hypothesis or research question? To identify what proportion of distance runners participate in strength and conditioning and what types of activities they perform. A secondary aim was to link strength and conditioning workouts with reported injury rate.
What did they do to test the hypothesis or answer the research question? They used a 16 question survey administered to competitive runners who competed between 800 meters and the marathon. This was a convenience sample. The authors designed the survey with the help of coaches. They used a variety of statistical approaches to try to tease out the various relationships.
What did they find? 1883 surveys were completed, of which only 667 were from competitive runners and a majority of those competed in distances at or above 5,000 meters. Runners reported using strength and conditioning to improve performance (53.8%) and reduce injury rates (63.1%). However, the most commonly used strength and conditioning workouts were related to stretching (86.2%) and core workouts (70.2%) with 62.5% doing resistance training, 60.4% doing bodyweight exercises, and 35.1% performed plyometrics. Plyometrics were performed by younger and higher ability runners more frequently than older runners and club level runners. Similarly, national and international competitive runners were 3.37 times more likely to perform resistance training compared to club level runners. Middle distance runners were much more likely to perform resistance training and plyometrics compared to distance runners. If you combine resistance training, bodyweight training, stretching, core, and foam rolling into a model you could predict 20% of the injury incidence. However, no single strength and conditioning activity was able to predict injury incidence.
What were the strengths? Captured data on many different types of strength and conditioning activities. Non-competitive runners were excluded.
What were the weaknesses? Cross-sectional study and thus no causality can be inferred. Convenience sampling may introduce bias. The data was not evenly distributed and lacked certain demographics. The barriers to completing strength and conditioning workouts were not considered. Non-competitive runners were excluded.
Are the findings useful in application to training/coaching practice? Yes, plyometrics is not commonly used among recreational runners despite the strong evidence indicating that it improves performance. Similarly, the longer distance specialists are less likely to engage in resistance training and plyometrics and thus encouragement of this group to participate in these types of strength and conditioning may be beneficial. The more serious a runner, the more likely they are to participate in non-running activities to improve performance.

Full article available at https://www.tandfonline.com/doi/full/10.1080/03091902.2020.1753836.
What was the hypothesis or research question? Wrist-based heart rate monitors use photoplethysmography to assess heart rate and several studies have shown that they underestimate heart rate values and are inaccurate in numerous conditions. Previous research suggests that the inaccuracy that might be related to the latency, or delayed response, of these devices. The authors compared two wrist-based monitors using different exercise types and ] exercise intensities to determine whether the latency of the response is responsible for the inaccuracies of the wrist-based measurements.
What did they do to test the hypothesis or answer the research question? They recruited 30 individuals to complete standardized exercise protocols that included cycling, walking, and weightlifting activities. Each activity started at an easy intensity interval for a 3.5 minute stage with 3 minutes of rest after. Two additional intervals, each slightly harder than the previous were also completed. The 3.5 minute stage allowed a consistent heart rate to be obtained. During the activity, each participant was wearing two wrist-based HR monitors (Apple Watch 2 and Garmin Forerunner 235) to compare against the chest based HR monitor (Polar H10) which was used as the “gold standard”. Latency was determined by determining how many seconds after the chest strap it took for the wrist-based monitors to reach steady-state heart rates.
What did they find? The average latency varied between different devices and different types of exercises. In general, the Apple watch was more accurate and had a shorter latency (1-4.3 seconds) versus the Garmin (7.4 -29.9 seconds). During exercise, the Apple and Garmin watch underestimated heart rate and during rest, they overestimated heart rate. During cycling the Garmin was up to 30% lower than the Polar chest strap and took about 30 seconds to reach steady state, while the Apple watch was very accurate (1-2%) and took only about 1-2 seconds to reach steady state. During running the Garmin was more accurate with an error rate of 5-10% and latency of 13 - 18 seconds, but the Apple watch still performed better with error rates of about 3% and a latency of 2 - 5 seconds.
What were the strengths? The study used different exercise types and different exercise intensities. A decent sample size was used.
What were the weaknesses? Validity of these findings to the outside of the lab is limited and the subject population was pretty homogenous - young, relatively normal BMI. The algorithms which the Apple and Garmin watch use to calculate heart rate from the wrist are proprietary and thus further elucidation of why there are differences is not known.
Are the findings useful in application to training/coaching practice? Yes, don’t trust wrist-based heart rates if you can help it. The chest straps are still the gold standard.


That is all for this month's nerd alert. If you are interested in the entire list of papers we perused, you can download the text file here. We hope to have succeeded in helping you learn a little more about the developments in the world of running science. Until next month, keep active, stay nerdy, and train smart.

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Dr Thomas Solomon and Dr Matt Laye. Running science nerd alert.
About the authors:
Matt and Thomas are both passionate about making science accessible and helping folks meet their fitness and performance goals. They both have PhDs in exercise science, are widely published, have had their own athletic careers, and are both performance coaches alongside their day jobs. Originally from different sides of the Atlantic, their paths first crossed in Copenhagen in 2010 where they were both research scientists at the Centre for Inflammation and Metabolism at Rigshospitalet (Copenhagen University Hospital). After discussing lots of science during many-a-mile pounding the trails and frequent micro brew pub drinking sessions, they became firm friends. Thomas even got a "buy one get one free" deal out of the friendship, marrying one of Matt's best friends from home after a chance encounter during a training weekend for the CCC in Schwartzwald. Although they are once again separated by the Atlantic, Matt and Thomas meet up about once a year and have weekly video chats about science, running, and beer. This "nerd alert" was created as an outlet for some of the hundreds of scientific papers they read each month.

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