sensors
Review
Mechanical Power in Endurance Running: A Scoping
Review on Sensors for Power Output Estimation
during Running
Diego Jaén-Carrillo 1
, Luis E. Roche-Seruendo 1
, Antonio Cartón-Llorente 1
,
Rodrigo Ramírez-Campillo 2
and Felipe García-Pinillos 3,4,*
1
Department of Physiotherapy, Universidad San Jorge, Villanueva de Gállego, 30580 Zaragoza, Spain;
djaen@usj.es (D.J.-C.); leroche@usj.es (L.E.R.-S.); acarton@usj.es (A.C.-L.)
2
Department of Physical Activity Sciences, Universidad de Los Lagos, 5290000 Osorno, Chile;
r.ramirez@ulagos.cl
3
Department of Physical Education and Sport, University of Granada, 18071 Granada, Spain
4
Department of Physical Education, Sports and Recreation, Universidad de La Frontera,
4811000 Temuco, Chile
*
Correspondence: fgpinillos@ugr.es; Tel.: +34-660062066
Received: 10 September 2020; Accepted: 10 November 2020; Published: 13 November 2020


Abstract: 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.
Keywords: biomechanics; endurance runners; long-distance athletes; wearable device
1. Introduction
Endurance running events are on the apex of a performance revolution, with the sub-2-h marathon
barrier just broken (i.e., Vienna in 2019). In the same way the power meter changed training and racing
in cycling [1] by providing a fair tool to assess performance with accurate replication, it might also
change the way runners compete and train.
Power, a term originated in classical physics, is deﬁned as the product of force and velocity [2].
Despite training delivers stress on the body, the way runners measure this level of stress has been very
limited. The faster a runner goes, the higher the stress for a certain level of ﬁtness. Training intensity is
the true marker to ﬁtness (i.e., capacity to deal with a particular amount of stress) [3]. The application
of mechanical load (i.e., external training load factors) and psychological and physiological eﬀorts
(i.e., internal training load factors) are aﬀected by training stress [4]. In running, some external load
factors including volume and pace are widely used, while physiological internal load factors consider
perceived exertion scales, heart rate, or blood lactate level [4]. On multiple training days, running
distance alone could overshadow the accumulated training stress and, eventually, misinterpret the
overall training stress [4]. Pace might be as clear as volume but, indeed, it is not easy to assess as
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the running settings (i.e., surface; slope gradient) as well as weather conditions (i.e., wind velocity)
or individual internal factors (i.e., stress, sleep, illness) may aﬀect pace considerably and, therefore,
challenge pace intensity quantiﬁcation. None of these variables provides a fair and repeatable method
to measure training intensity and, when training stress is measured imprecisely, injury risk may be
increased and performance negatively altered. Given that new wearable devices allow to measure
external load metrics apart from both volume and pace, there should be a growing focus on a
combination of both biomechanical external (i.e., power output (PW)) and internal load metrics in the
future of athletes monitoring [4].
Running, as cycling, is cyclical in nature. When running, three dimensional movements are
needed. Normally, the body describes a forward movement, vertical oscillation, and a bilateral rotation
over the running cycle. For such movements, mechanical work is required accounting vertical and
forward movements for most of it. Throughout such movements, a runner acquires both kinetic
energy and potential energy changes. The applied work runners develop over the loading phase
and the subsequent take-oﬀ push to lift their body at every stride to work against environmental
factors (i.e., ground reaction force, gravity force, and surface) refers to the external mechanical work.
Then, the foot absorbs energy when colliding with the ground and produces power when pushing oﬀ.
During running, expensive equipment such as speciﬁc instrumented treadmills [5] have been utilised
to acquire force data. Despite their proved accuracy, most coaches and practitioners are forced to avoid
their use due to economic issues.
Over the last years, inertial measurement units (IMUs) emerged, allowing the quantiﬁcation
of performance, providing coaches and athletes an easy-to-use tool to monitor PW during running
(e.g., Runscribe (Scribe Lab. Inc., Half Moon Bay, CA, USA), Stryd (Stryd Inc. Boulder, CO, USA) or
Myotest (Myotest SA, Sion, Switzerland)). Previous works have demonstrated the direct relationship
between anthropometric measures (e.g., body mass) and spatiotemporal parameters and kinetics
and kinematics [6–8].
Samozino and colleagues [9] attempted to supply an aﬀordable method
to assess force-velocity and power-velocity proﬁles, using anthropometric and spatiotemporal
data along over-ground sprint acceleration.
However, Samozino’s approach is inapplicable to
submaximal velocities.
Currently, an increasing number of systems allow the assessment of running power (new heart rate
monitors by Polar (Polar Electro Ltd., Kempele, Finland) and Garmin (Garmin Ltd., Olathe, KS, USA)).
Nevertheless, there is a lack of scientiﬁc evidence testing either its validity or reliability, as well as
limited insights on the use and interpretation of power in endurance runners, being this reduced
to a few books [3,10], and further information provided by the devices’ manufacturers (e.g., Stryd,
https://blog.stryd.com/tag/validation-white-papers/; Myotest, https://www.myotest.com/technology;
RunScribe, https://runscribe.com/blog/; Stryd, https://blog.stryd.com; Polar: https://www.polar.com/es/
smart-coaching/running-power).
Although the validity and reliability of a wide array of wearable sensors have been shown
for running spatiotemporal parameters measurement and they seem to be related with PW
estimation [11–15], a deeper knowledge on PW in endurance running and a proper understanding on
the use of power meters to quantify workload would be an outstanding step forward towards a new
boundary within running training and performance. There is a need to measure training intensity with
precision and wearable sensors might help monitor the training-induced stress and, although previous
review articles have been focused on power data while running [16,17], none of those concentrated
on validity and reliability of such wearables for running PW analysis. Advances in the knowledge
of endurance running PW would allow the assessment and monitor of power not only in laboratory
settings, but in the ﬁeld as well. Therefore, the aim of this scoping review was to critically examine
the available running power meters and the current evidences about their use and application to
endurance running performance.
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2. Materials and Methods
A review of the literature was conducted following the guidelines of the Cochrane Collaboration
and taking into consideration the guidance provided by previous studies focused on scoping
reviews [18,19]. This design (i.e., scoping review) was selected in order to have a broader approach with
the aim of mapping literature characterized by a variety of study designs. Additionally, ﬁndings were
reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) for scoping reviews [20].
2.1. Eligibility Criteria
Despite the limited evidence on this topic, some a priori inclusion criteria were considered for this
scoping review: (i) only peer-reviewed articles were included; (ii) studies that were not published in
English were not explored; (iii) no restrictions for age or sex of participants were applied.
Additionally, no limitations regarding the study design were established. All manuscripts related
to running with power or power meters were considered, regardless the study design, except literature
reviews (e.g., systematic reviews or metanalysis).
2.2. Information sources
A systematic search was conducted in the electronic databases PubMed, MEDLINE and SCOPUS
for relevant studies until 1 June 2020. Keywords were collected through experts’ opinion, a systematic
literature review, and controlled vocabulary (e.g., Medical Subject Headings: MeSH). Boolean search
syntax using the operators “AND” and “OR” was applied. The words “endurance”, “running”,
“runner”, “power”, and “power meter” were used. Following is an example of a PubMed search:
((((((endurance) OR submaximal) NOT sprint) AND running) OR runner) AND power) OR power
meter; Filters: Publication date from 1 January 2000; Humans; English.
After an initial search, accounts were created in the respective databases. Through these accounts,
the lead investigator received automatically generated emails for updates regarding the search terms
used. These updates were received on a daily basis (if available), and studies were eligible for inclusion
until the initiation of manuscript preparation on 5 June 2020. Following the formal systematic searches,
additional hand-searches were conducted. Grey literature sources (e.g., conference proceedings)
were also considered if a full-text version was available. In addition, the reference lists of included
studies and previous reviews and meta-analyses were examined to detect studies potentially eligible
for inclusion.
2.3. Study Selection
In selecting studies for inclusion, the three-step method was followed [21]. The ﬁrst step, according
to this procedure, was an initial restricted search of the appropriate database collection, followed by an
analysis of the text words included in the title and abstract, and the index terms used to characterize
the document. A second search using all known keywords and index terms was performed through all
included databases. Finally, the reference list of all the selected studies and reports has been checked
for additional studies. The authors included the aforementioned ﬁlters (i.e., the language and the
publication date limitations).
2.4. Methodological Quality in Individual Studies
To analyse the methodological quality in studies, the recommendations by Cochrane Review
Groups were taken into consideration [22]. Since all the studies examined show a cross-sectional
design, quality was assessed using the modiﬁed version of the Quality Index developed by Downs and
Black [23]. The original scale was reported to have good test–retest (r = 0.88) and inter-rater (r = 0.75)
reliability and high internal consistency (Kuder–Richardson Formula 20 (KR-20) = 0.89). The modiﬁed
version of the Downs and Black Quality Index is scored from 1 to 14, with higher scores indicating
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higher-quality studies. Two independent reviewers (DJC-FGP) performed this process and, in the event
of a disagreement about the methodological quality, a third reviewer (LERS) checked the data and took
the ﬁnal decision on it. Agreement between reviewers was assessed using a Kappa correlation for
methodological quality. The agreement rate between reviewers was k = 0.93 which can be interpreted
as almost perfect [24]. It is worth noting that the study by Snyder and colleagues [25] was excluded as
it is a letter to the editor in response to Aubry and colleagues’ [26] work.
3. Results
3.1. Study Selection
Figure 1 provides a graphical schematization of the study selection process. A total of 1281 studies
were initially identiﬁed: 640 from PubMed, 378 from SCOPUS, and 263 from MEDLINE. Additionally,
6 studies were identiﬁed through other resources. From these 1287 studies, 674 after duplicates
removed. The 613 studies excluded after titles and abstracts revisions were essentially based on a lack
of relationship with the research interests of this review. After full-text revision, only 19 studies which
included either validity or reliability of running wearable sensors suppling running PW and/or the
speciﬁc discussion of such wearable sensors were considered for the current work.
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3. Results 
3.1. Study Selection 
Figure 1 provides a graphical schematization of the study selection process. A total of 1281 
studies were initially identified: 640 from PubMed, 378 from SCOPUS, and 263 from MEDLINE. 
Additionally, 6 studies were identified through other resources. From these 1287 studies, 674 after 
duplicates removed. The 613 studies excluded after titles and abstracts revisions were essentially 
based on a lack of relationship with the research interests of this review. After full-text revision, only 
19 studies which included either validity or reliability of running wearable sensors suppling running 
PW and/or the specific discussion of such wearable sensors were considered for the current work.  
 
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow 
diagram. 
3.2. Study Characteristics 
The main characteristics of the studies included in this review (n = 19) are presented in the  
Tables 1 and 2. Table 1 shows a summary of 12 studies using wearable sensors with the capacity of 
measuring power during different running exercises. Whereas three of those studies [11,27,28] 
examine the PW kinetics during different running protocols, the other four studies [15,25,26,29] 
investigate the relationship between PW and physiological parameters such as oxygen consumption 
(VO2) at different intensities. Additionally, two further works [30,31] analyse the application of 
mathematical models, based on power laws, to predict running performance, whereas a recent study [32] 
assesses the agreement level between two mathematical models and five power meter devices
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) ﬂow diagram.
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3.2. Study Characteristics
The main characteristics of the studies included in this review (n = 19) are presented in the
Tables 1 and 2. Table 1 shows a summary of 12 studies using wearable sensors with the capacity of
measuring power during diﬀerent running exercises. Whereas three of those studies [11,27,28] examine
the PW kinetics during diﬀerent running protocols, the other four studies [15,25,26,29] investigate
the relationship between PW and physiological parameters such as oxygen consumption (VO2) at
diﬀerent intensities. Additionally, two further works [30,31] analyse the application of mathematical
models, based on power laws, to predict running performance, whereas a recent study [32] assesses
the agreement level between two mathematical models and ﬁve power meter devices through diﬀerent
running conditions. Other studies examined some parameters provided by the RunScribe power meter
to describe the eﬀects of the fatigue induced over a marathon [33,34] and the inﬂuence of diﬀerent
types of ankle treatments on running biomechanics [35].
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Table 1. Studies (n = 12) involving the use of wearable sensors with the capacity of measuring power during running protocols.
References
Subject Description
Aim
System Used
Protocol
Outcome Measures
Results
Dobrijevic et al.
(2017) [15]
30 physical education
students (15 men and
15 women)
To explore the properties of the
F-V relationship of leg muscles
exerting the maximum pulling F
at a wide range of V on a standard
motorized treadmill
Motorized treadmill using
externally ﬁxed strain gauge
dynamometer (CZL301,
ALL4GYM, Serbia) connected
to the subject wearing a wide
and hard weightlifting belt
Walking and running on a treadmill
at diﬀerent velocities (1.4−3.3 m.s−1),
and maximum pulling F exerted
horizontally were recorded
Leg muscle capacities for
producing maximum F, V,
and power
The F-V relationship of leg
muscles tested through a wide
range of treadmill V could be
strong, linear, and reliable.
Moreover, the two-velocity
method could provide reliable
and ecologically valid indices of F,
V, and P producing capacities of
leg muscles.
García-Pinillos et al.
(2019) [17]
49 endurance runners
To examine how the PW changes
while running at a continuous
comfortable velocity on a
motorized treadmill by
comparing running power
averaged during diﬀerent time
intervals
Stryd system (foot pod)
Runners performed a 3 min running
protocol at comfortable velocity and
P was examined over six recording
intervals within the 3-min recording
period: 0−10 s, 0−20 s, 0−30 s,
0−60 s, 0−120 s and 0−180 s
Running PW
P during running is a stable
metric with negligible diﬀerences,
in practical terms, between
shorter (i.e., 10, 20, 30, 60 or 120 s)
and longer recording intervals
(i.e., 180 s)
Aubry et al.
(2018) [14]
24 male runners
(13 recreational, 11 elite)
To investigate the applicability of
running power (and its
individually calculated run
mechanics) to be a useful
surrogate of metabolic demand
(Vo2), across diﬀerent running
surfaces, within diﬀerent
caliber runners.
- Stryd system (chest strap)
- Gas exchange measures
(Cosmed Quark CPET and
Cosmed K5 systems)
2 diﬀerent test at 3 diﬀerent paces,
while wearing a Stryd on both an
indoor and an outdoor test:
-Treadmill vO2 test: running at
3 speeds for 2 min each
-Outdoor vO2 test (on track):
identical speeds for 4 min
(1 min rest)
- Spatiotemporal
parameters
- Running PW
- vO2
Running power (with Stryd) is not
a great reﬂection of the metabolic
demand of running in a mixed
ability population of runners
Snyder et al.
(2017) [13]
Manuscript clariﬁcation: Request for clariﬁcation to Aubry et al. (2018)
Some major methodological ﬂaws
in the mentioned paper are
detected. The authors concluded
that data analysis and, thereby,
data interpretation are misleading
Austin et al.
(2018) [18]
17 well-trained
distance runners
To measure the correlations
between running economy and P
and form power at LT pace.
- Stryd system (foot pod)
- Gas exchange measures
(Parvo Medics TrueOne 2400)
Participants ran two 4 min trials:
one with a self-selected cadence,
and one with a target cadence
lowered by 10%
- Gas exchange measures
- RPE
- Power
- Form power
- SF
RE is positively correlated with
Stryd’s power and form power
measures yet the footpod may not
be suﬃciently accurate to
estimate diﬀerences in the
running economy of runners
García-Pinillos et al.
(2019) [36]
18 recreationally-trained
male endurance runners
To determine if the P-V
relationship in endurance runners
ﬁts a linear model when running
at submaximal velocities, as well
as to examine the feasibility of the
“two-point method” for
estimating P at diﬀerent velocities
Stryd system (foot pod)
Incremental running protocol on a
treadmill. Initial speed was set at
8 km.h−1, and speed increased by
1 km.h−1 every 3 min
until exhaustion
PW (W)
The two-point method based on
distant velocities was able to
provide P with the same accuracy
than the multiple-point method.
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Table 1. Cont.
References
Subject Description
Aim
System Used
Protocol
Outcome Measures
Results
Vandewalle et al.
(2018) [21]
Data from 6 elite
endurance runners
- To apply the P-law and
logarithmic models and four
asymptotic models to the
individual performances of the
elite runners.
- To compare the accuracy of these
models.
- To compare the predictions of
MAS by interpolation and the
prediction of maximal running
speeds for long distances by
extrapolation
-
The empirical models were
compared from the performance of 6
elite endurance runners who
participated in international
competitions over a large range
of distances
Mathematical models to
predict running
performance
The predictions of long-distance
performances (maximal running
speeds for 30, 60 min and
marathon) by extrapolation of the
logarithmic and power-law
models were more accurate than
the predictions by extrapolation
in all the asymptotic models.
Mulligan et al.
(2018) [20]
Data from various
records for a range
of distances
To develop a novel, minimal and
universal model for human
running performance that
employs a relative metabolic
P scale
-
European and world records
performances for eight distances,
from 1 km to the marathon,
were analyzed
Mathematical models to
predict running
performance
The model presented provides a
quantitative method for extracting
characteristic parameters from
race performances of runners.
This is the to date most accurate
theoretical description of running
performances that does not
require any a priori ﬁxing of
physiological constants
Gregory et al.
(2019) [25]
12 young adults with
history of ankle sprain
RunScribe system (foot pod,
on the heel)
To evaluate the eﬀects of ankle
taping, bracing, and ﬁbular
reposition taping (FRT) on
running biomechanics
Four 400 m runs at self-selected pace
on an outdoor track. Each run was
performed in a diﬀerent condition
(control, taped, braced, FRT)
- Spatiotemporal (CT,
CycleT, SL)
- Kinematic (PR, PRveloc)
- Kinetic (impact G,
braking G)
Ankle taping and bracing were
shown to be comparable in
decreasing ankle kinematics and
kinetics, while FRT caused
minimal changes in running
biomechanics
Leuchanka et al.
(2019a) [23]
15 endurance runners
To examine the changes in
spatiotemporal variables during a
marathon race
RunScribe system (foot pod,
on the lace shoe)
Monitoring spatiotemporal variables
over a marathon race by comparing
3 points (km 5, 26 and 37)
- Spatiotemporal (Pace,
CT, SL and cadence)
Signiﬁcant diﬀerences were found
in pace, SL, and CT when
compared across 3 race points
Leuchanka et al.
(2019b) [24]
15 endurance runners
To measure the kinematic
asymmetry during a
marathon race
RunScribe system (foot pod,
on the lace shoe)
Monitoring kinematic variables over
a marathon race by comparing
3 points (km 5, 26 and 37)
- Kinematic variables for
right and left foot (pace,
strike index, PR, PRveloc)
Changes in asymmetry were not
found to be statistically signiﬁcant
over the marathon.
Cerezuela-Espejo
et al. (2020) [22]
10 endurance runners
To analyse agreement level
between power estimated PW by
ﬁve commercial wearable systems
and two theoretical models in
diﬀerent environments
and conditions
5 systems:
- Stryd App
- Stryd Watch
- RunScribe (foot pod)
- Garmin Running P (watch
and chest strap)
- Polar Vantage (watch)
Three submaximal
running protocols on a treadmill
(indoor) and an athletic track
(outdoor), with changes in
speed, body weight, and slope.
Running PW derived
from the 5 systems and
theoretical PW from two
mathematical models
(TPw1 and TPw2).
The closest agreement of the Stryd
and PolarV technologies with the
TPW1 and TPW2 models suggest
these tools as the most sensitive,
among those analysed, for PW
measurement when changing
environments and
running conditions
CP: critical power; LT: blood lactate thresholds; Vo2max: maximal oxygen uptake; tlim: exhausting time at a given intensity; W´: residual performance capacity; F: force; V: velocity; P: power;
Dlim: exhaustion distance; MTT: Montreal Track Test; MAS: maximal aerobic speed; CT: ground contact time; SL: step length; PR: pronation excursion; PRveloc: pronation velocity; TPw1:
Mathematical model for power output (PW) estimation 1; TPw2: Mathematical model for PW estimation 2; PW: power output.
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The closest agreement of the Stryd and PolarV technologies with the TPW1 and TPW2 models
suggest these tools as the most sensitive, among those analysed, for PW measurement when changing
environments and running conditions
Table 2 summarises the studies (n = 7) focused on the validity and reliability analysis of
kinetic and kinematic parameters for diﬀerent wearable sensors with the capacity to measure power.
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].
Table 3 shows the methodological quality of the studies examined. Once the review studies and
the letter to editors were excluded, 18 studies were assessed with this purpose. Out of a total score
of 14 points, all studies reported from 11 to 14 points. Of note, 16 out of 17 studies reported 0 in the
item 12 (i.e., participants prepared to participate representative of entire population) and 14 out of
17 studies reported 0 in the item 23 (i.e., randomised).
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Table 2. Studies (n = 7) examining the reliability and validity of diﬀerent wearable sensors with the capacity to measure power during running.
References
Subject Description
Tested System
Reference System
Protocol
Outcome Measures
Results
García-Pinillos et al.
(2018) [16]
18 trained
endurance runners
Stryd system (foot pod)
OptoGait system
Incremental running test
(8−20 km·h−1 with 3-min stages)
on a treadmill
- Spatiotemporal parameters
(CT, FT, SL, SF)
Stryd is reliable for measuring
spatiotemporal parameters. It provides
accurate SL and SF measures but
underestimates CT (0.5−8%) and
overestimates FT (3−67%)
Koldenhoven et al.
(2018) [32]
12 recreational runners
RunScribe wearable sensor
3D motion capture system
(Vicon system)
2.4 km running protocol on
treadmill, at self-selected speed
- PR, PRveloc, and CycleT
RunScribe showed good to excellent
concurrent validity for the
outcome measures
Brayne et al.
(2018) [31]
13 runners
Wireless accelerometer
(RunScribe): skin mounted
Uniaxial piezoresistive
accelerometer (model
352C22, PCB Piezotronics):
skin mounted
Participants ran at 3 diﬀerent
speeds on a treadmill
(2.5, 3.5, 4.5 m.s−1) for a total of
40 s (10 s to regulate running
gait and 30 s data collection)
- Peak tibial acceleration (g)
RunScribe accelerometer accurately
measures peak tibial accelerations
when compared to a research
accelerometer, at a range of speeds
Hollis et al.
(2019) [33]
15 recreational runners
RunScribe system
(foot pod, on the heel)
Intra-system comparison
(in diﬀerent
experimental conditions)
Two 1600 m runs (slow: 3−4;
fast: 5−6 on a 0−10 RPE scale) on
two surfaces (track, grass).
Randomized order.
- Spatiotemporal (CT, CycleT, SL)
- Kinematic (PR, PRveloc)
- Kinetic (impact G, braking G)
RunScribe sensor is valid to identify
changes in the outcome
measures when participants ran in
diﬀerent conditions.
Navalta et al.
(2019) [29]
20 young,
healthy individuals
Stryd system (foot pod)
Intra-system reliability
Two 5 min self-paced walks along
a trail, and two 5 min trail runs
(5 min rest period)
- Pace and distance
- Power: average elapsed power,
maximal power, average elapsed
form power
- Stiﬀness: average elapsed leg spring
- Spatiotemporal: CT
- Vertical oscillation
Trail running task returns moderate to
excellent reliability across all measures
García-Pinillos et al.
(2019) [30]
49 amateur
endurance runners
RunScribe system
(foot pod) on 2 locations:
- Heel shoe
- Lace shoe
High-speed video analysis
at 1000 Hz
Treadmill running for 3 min at
self-selected comfortable velocity
- Spatiotemporal gait parameters
(CT, FT, SL, SF)
RunScribe is a valid system to measure
spatiotemporal parameters during
running on a treadmill. The location of
the RunScribe plays an important role
on the accuracy of spatiotemporal
parameters. The lace shoe placement
showed smaller errors for CT, FT and
SL, whereas the heel shoe was more
accurate for SF
Cerezuela-Espejo et
al. (2020) [19]
12 endurance-trained
male athletes
5 systems:
- Stryd App
- Stryd Watch
- RunScribe (foot pod)
- Garmin Running P
(watch and chest strap)
- Polar Vantage (watch)
- Metabolic cart (VO2)
Participants were initially
familiarized with the protocol and
then, two protocols were
performed in two diﬀerent
settings (outdoor vs. indoor):
- Testing 1: Submaximal protocol
with incremental speed
- Testing 2: Submaximal protocol
with incremental body weight
A 3rd testing condition was
performed only indoor, with
increasing slope at
submaximal velocity
- P output during running
The Stryd system is the most repeatable
technology, among the ﬁve analyzed,
for P estimation.
The concurrent validity analysis
indicated that PW estimated by the
Stryd device showed the closest
relationship with the VO2 directly
measured by the metabolic cart.
CT: ground contact time; CycleT: cycle time; SL: step length; PR: pronation excursion; PRveloc: pronation velocity; RPE: rate of perceived exertion; FT: ﬂight time; SF: step frequency; VO2:
oxygen uptake; RE: running economy; PW: power output.
Sensors 2020, 20, 6482
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Table 3. Modiﬁed Downs and Black scale [23].
Study
Item 1
Item 2
Item 3
Item 6
Item 7
Item 10
Item 12
Item 15
Item 16
Item 18
Item 20
Item 22
Item 23
Item 25
Total (out of 14)
Dobrijevic et al. (2017) [15]
1
1
1
1
1
1
1
1
1
1
1
1
0
1
13
Aubry et al. (2018) [14]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Austin et al. (2018) [18]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
García-Pinillos et al. (2019) [36]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
García-Pinillos et al. (2019) [17]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Vandewalle et al. (2018) [21]
1
1
1
1
1
1
0
U
1
1
1
0
0
1
11
Mulligan et al. (2018) [20]
1
1
1
1
1
1
0
U
1
1
1
0
0
1
11
Gregory et al. (2019) [25]
1
1
1
1
1
1
0
1
1
1
1
1
1
1
13
Leuchanka et al. (2019a) [23]
1
1
1
1
1
1
0
1
1
1
1
1
0
0
11
Leuchanka et al. (2019b) [24]
1
1
1
1
1
1
0
1
1
1
1
1
0
0
11
García-Pinillos et al. (2018) [16]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Koldenhoven et al. (2018) [32]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Brayne et al. (2018) [31]
1
1
1
1
1
1
0
1
1
1
1
1
1
1
13
Hollis et al. (2019) [33]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Navalta et al. (2019) [29]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
García-Pinillos et al. (2019) [30]
1
1
1
1
1
1
0
1
1
1
1
1
0
1
12
Cerezuela-Espejo et al. (2020) [19]
1
1
1
1
1
1
0
1
1
1
1
1
1
1
13
Cerezuela-Espejo et al. (2020) [22]
1
1
1
1
1
1
0
1
1
1
1
1
1
1
13
Key: 0 = no; 1 = yes; U = unable to determine. Item 1: clear aim/hypothesis; Item 2: outcome measures clearly described; Item 3: patient characteristics clearly described; Item 6: main
ﬁndings clearly described; Item 7: measures of random variability provided; Item 10: actual probability values reported; Item 12: participants prepared to participate representative of
entire population; Item 15: Blinding of outcome measures; Item 16: analysis completed was planned; Item 18: appropriate statistics; Item 20: valid and reliable outcome measures; Item 22:
participants recruited over same period; Item 23: Randomised; Item 25: adjustment made for confounding variables.
Sensors 2020, 20, 6482
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4. Discussion
This review provides a critical assessment on the existing scientiﬁc literature regarding PW
quantiﬁcation in endurance running as well as the diﬀerent current accessible devices for its estimation.
After the meticulous analysis described above, a few studies aiming at assessing running power in
relation to physiological parameters and power-duration relationship at several running intensities
were found. Eighteen studies included in this review were assessed in order to determine the
methodological quality and high scores were reported according to the modiﬁed Downs and Black
scale [23] (i.e., all studies reported more than 11 points out of a total score of 14). Although no
studies attempting to assess concurrent validity of PW estimation in running using power meters,
their reliability for such estimation was analysed.
The controversy surrounding power estimation in running is rooted in the question of whether it is
indeed power which is being estimated. Unlike cycling, running entails negligible external mechanical
work. It involves positive and negative work; the former, pushing oﬀ with each stride and the latter,
braking on landing [39]. Moreover, elastic energy stored in the Achilles tendon and other tissues
makes a signiﬁcant contribution as up to ﬁfty percent of power required for each step is released
as these tissues stretch upon landing and subsequently recoil to aid pushing oﬀ. The issue when
estimating power in running is that even perfect estimates do not closely correlate to eﬀort required [39].
During cycling, the relationship between mechanical power and total metabolic energy consumption
remains constant when conditions are altered, but this is not so when running [39,40]. Readers need to
be aware that given the recent application of power meters to endurance running, the increasing need
for PW quantiﬁcation, and the consequent novelty of this research interest, the limited information
available might make the discussion of the current study diﬃcult. However, the subsequent sections
seek to provide some insight into how running power quantiﬁcation can help enhance running
performance and its quality.
4.1. Current Evidence on PW during Running
While in cycling PW is measured in reference to both direction and quantity of the force applied
to the crank, as well as its angular velocity, power needs to be calculated in a diﬀerent way while
running. Since forward and vertical movements of the body account for most of the mechanical work,
an accurate calculation of both horizontal and vertical power over the propulsion phase (i.e., a function
of forward force and vertical force, respectively) is required to measure running power eﬀectively.
Mechanical power on ﬂat terrain might be estimated in mechanical terms just as function of
runner anthropometry (height, mass), spatiotemporal parameters (speed, step rate, ground contact
time) and wind speed employing model proposed recently by Jenny and Jenny [41]. In steady running
on ﬂat surface, mechanical power and the rate of mechanical energy dissipated into heat should match.
Considering this assumption and following the mathematical approach mentioned above [41],
mechanical energy in steady ﬂat running compiles the energy dissipated by aerodynamic drag,
dissipation due to both vertical oscillation and braking. The aerodynamic contribution may be
estimated based on air and runner density and running and wind velocity. However, when running on
a treadmill wind speed can be considered zero reducing, thus, the importance of this variable.
On the one hand, dissipation in vertical oscillation can be estimated regarding step rate, ground
contact time, running velocity and a potential energy recovery factor. This factor is variable between
subjects and that might be the main concern with this assumption. The lack of considering this factor
could lead to overestimation in this part of the mechanical power. On the other hand, dissipation
due to braking ground reaction force could be modelled by using the runner’s centre of mass
excursion and spring-mass model assumptions. In that context, the power generated in a horizontal
direction to maintain running velocity could be estimated by anthropometrics, running speed, and the
aforementioned energy recovery factor.
The most controversial part of such a model [41] might be the energy recovery factor. Nevertheless,
when measuring mechanical power calculations employing ‘gold standard’ methods diﬀerent
Sensors 2020, 20, 6482
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assumptions are, done making the assessment of mechanical PW a challenging measure even in
the best testing conditions.
The critical power (CP) in tasks such as swimming, cycling, and running and its relationship
with VO2, blood lactate threshold, and work-exhaustion time was critically reviewed by Vandewalle
and colleagues [42]. Theoretically, CP supposes the existence of a particular work-rate that can be
held before exhaustion [43]. In this review [42], it is determined that CP matches a steady state
during heavy submaximal exercises (i.e., between 6 and 30 min). On the contrary, CP is not a reliable
predictor of exhaustion time considering the hyperbolic nature of power-exhaustion time relation [42].
Another review focused on the existing models for residual performance capacity estimation and
its application for pacing [16]. The authors examined the quantity of work than can be executed
in exercises above CP. Although the review by Vandewalle and colleagues found CP to be a poor
predictor of exhaustion time given the power-exhaustion time relation, Jones and Vanhatalo determined
that within a range of various exercise intensities (e.g., endurance running), this relationship gives a
fundamental basis to proper understand the physiological bases of fatigue development, what may
result in an outstanding eﬀect for monitoring both training and athletic performance [16].
The power-duration relationship was also described over a wide range of power intensities [17].
Three diﬀerent exercise intensities were identiﬁed. First, exercise intensity below aerobic threshold
(i.e., fatigue appears slowly and it mainly has a central origin) was deﬁned as moderate intensity.
Then, intensity over lactate threshold but under CP was referred as heavy intensity (i.e., there is a
depletion of muscle glycogen due to central and peripheral fatigue). Finally, severe intensity was
identiﬁed referring to an intensity above the CP, which relates to gradual muscle metabolic homeostasis
alterations and subsequent peripheral fatigue [17]. Literature shows diﬀerent calculation methods for
power-duration relationship such as power law [44,45] and hyperbolic models [46–48], and exponential
decay operations [49,50]. Seemingly, hyperbolic calculations of power-duration relation suit best for
both reasonable physiological estimations and a proper option to the fundamental data [17] but, the truth
is that all these calculations are operationally weak for coaches and extremely time-consuming. In order
to counteract the models mentioned above and to provide in-ﬁeld application for running biomechanics
monitoring and training loads tracking to clinicians, coaches and practitioners, wearable technologies
were upgraded considerably and made economically aﬀordable. A review study on wearable devices
and their provided metrics (i.e., kinetic and kinematic parameters) in the evaluation and treatment of
runners identiﬁed best practices, applications and potential limitations of such systems [51]. The author
stated that clinicians should assure that the use of wearable sensors should be based on evidence
aiming at running-related injuries prevention and performance enhancement, and the guidelines given
by each sensor’s manufacturer must be followed [51].
Regarding evidence-based use of wearable sensors, the relationship between VO2 as metabolic
demand and running PW measured by ﬁve commercially available technologies was recently
assessed [15]. Twelve endurance-trained male athletes completed 10 submaximal multistage running
tests wearing a portable metabolic computer. On two occasions (test-retest), the athletes performed
three submaximal treadmill running protocols with manipulations in speed, body weight and slope,
and the same protocol was repeated in an athletic track. The Stryd system showed the higher concurrent
validity to the VO2 (r ≥ 0.911) between the ﬁve wearables, and it was also found as the more repeatable
and sensitive in all the conditions studied. Furthermore, the level of agreement between these
5 wearable systems was also analysed against two physics theoretical models for PW estimation [10,52]
in diﬀerent running conditions [32], showing that the Stryd and Polar Vantage systems are the most
sensitive tools for PW estimation in running given their close agreement with both theoretical models
(r > 0.93). The Stryd power meter estimates power production while running separating this metric
into two parts: power and form power. Apparently, power reﬂects the PW associated with changes in
the athlete’s horizontal movement, while form power represents the power production originated by
the combination of the oscillatory up and down movements of the centre of mass and lateral power
as the athlete moves forward. This system utilises mathematical calculations to estimate these two
Sensors 2020, 20, 6482
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parameters from kinematic data collected from the described movements executed by the runner’s
foot [29]. Form power apparently represents the power production originated by the combination
of the oscillatory up and down movements of the centre of mass and lateral power as the athlete
moves forward.
On the other hand, the power-VO2 relationship in elite and recreational runners had been
previously assessed by Aubry and colleagues [26]. To this aim, 13 amateur and 11 elite runners executed
a two-setting protocol (i.e., indoor and outdoor). Indoors, participants developed 3 sequential paces
(i.e., elite: 14, 16, and 18 km·h−1; amateur: 11−16 km·h−1) 2 min each, where VO2 was analysed via
gases expiration system. Outdoors (no precipitations and minimal wind), participants were asked to
run at the same pace that they ran indoors. Participants ran for 4 min each pace while measuring VO2
using a portable metabolic computer. Additionally, Stryd was used to calculate running power in both
settings. Regarding the relationship metabolic demand-running power, the authors found a signiﬁcant
but weak correlation between VO2 and running power (r = 0.29, p = 0.02). Comparing both settings,
metabolic demands were found to be signiﬁcantly higher (i.e., greater VO2) outdoors (i.e., outdoor
track) than when treadmill running. When speed increased, the diﬀerence in VO2 values become
higher amongst treadmill and outdoor running [26]. Then, after assessing metabolic demand-running
mechanics relationship, the authors found moderate strength associations for metabolic demand
and ground contact time, vertical oscillation, and step frequency at treadmill running in recreational
runners [26]. The authors of the aforementioned study concluded that the use of Stryd power meter
should be avoided when assessing running economy as it is unable to distinguish the metabolic
demands of an athlete when running on diﬀerent settings (i.e., outdoors vs. indoor). Of note, the version
used during the study is not mentioned (the latest version is even able to consider air resistance)
limiting, therefore, their ﬁndings. Controversially, Snyder and colleagues clariﬁed several important
methodological mistakes made by Aubry and colleagues [26] which led to confusing conclusions [25].
Regarding surface, VO2 was measured long before steady state for treadmill tests (latest VO2 test
started at 1:30 min), but much later over ground (latest VO2 test started at 3:30). It is well-known,
as stated by Snyder and colleagues, that VO2 needs more than 1:30 min to reach steady state causing,
therefore, great diﬀerences between VO2 when measured at 1:30 and 3:30 min, and, even greater at
faster speeds [25]. The authors claimed that these methodological ﬂaws exclude precise correlation
analysis between VO2 and power measured with Stryd on diﬀerent surfaces [25]. Considering speed,
a speed-normalised power to speed-normalised VO2 correlation was reported in the article [26],
therefore denying VO2 change because of speed [53]. Snyder and colleagues [25] suggested the use of
the accepted physiological term ‘cost of transport’ instead of ‘metabolic demand’, which was used
by the authors and leads to confusion in the readers and it does not vary over speed [54]. The actual
power-VO2 correlation is proposed to address this error [25]. With respect to subjects, Snyder and
colleagues [25] criticise the individual assessment of training metric as they [26] collect data by subject
prior executing the correlation analysis when within-subject correlation between VO2 and further
variables is appropriate for training and racing [55]. For such study [26], data collection should be
developed over diﬀerent within-subject measurements [25].
Furthermore, the Stryd reliability for PW during treadmill running at a self-selected constant
speed with a slope gradient at 0% was proved to be a stable data between short and long intervals
(i.e., 10–120 s and 180 s, respectively) [28]. No signiﬁcant diﬀerences were found in the amount of
power production between the diﬀerent spans of times acquired (p = 0.276, partial ETA2 = 0.155) and an
almost perfect association in the previously mentioned amount of power production recorded over the
intervals (ICC ≥ 0.999). As the authors mentioned, the conditions in which the study was performed
may inﬂuence the stability of running power over time and these ﬁndings should not be taken for
granted when transferred to over-ground running [28]. The ﬁndings reported here seem to be very
advantageous for clinicians and practitioners since, if compared to other physiological parameters such
as heart rate or VO2, PW tend to stabilise over time earlier than others traditionally used. However,
PW is a mechanical parameter which considers work per time. That work exhibits a muscular and
Sensors 2020, 20, 6482
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tendinous component. While muscle work needs oxygen consumption to produce work, tendons store
and release energy without consuming any oxygen. Therefore, work produced while running requires
diﬀerent quantities of oxygen depending upon the amount of work is done by muscles or tendons.
Thus, PW may not be directly related to running metabolic cost. Following the evidence-based use
of wearable sensors, it has been found a linear power-velocity relationship(r = 0.999) at submaximal
speed, and, the consequent used of the two-point method to predict PW in running at diﬀerent speeds
using the Stryd power meter [36]. The authors executed an incremental run-to-exhaustion protocol
on a motorized treadmill at 0% slope gradient. The power-velocity relationship determined from
three two-point methods at proximal (10 and 12 km·h−1), intermediate (10 and 14 km·h−1), and distal
(10 and 17 km·h−1) speeds showed the same precision than the multiple-point method (used also
by the authors to compare PWs through the study) to provide PW estimated by the Stryd power
meter. As stated by the authors of the aforementioned study, since the two-point method can be
developed faster and without developing fatigue in the athletes, it should be used when assessing PW
to acquire accurate power estimations over a range of submaximal running speeds [36]. This might be
an outstanding contribution to the strength and conditioning scene as the power-velocity relationship
could be frequently updated inﬂuencing, therefore, on the quality of both running training and
performance. The lack of evidence regarding the power-biomechanics (i.e., contact time, ﬂight time,
step frequency, step length, surface) relationship as well as the eﬀect of fatigue on PW when running
expose the need of further research on how the running gait parameters and environmental factors
aﬀect PW estimation. Bridging the gap between research and practical use of power in running would
bring the stunning potential of such parameter to light. The insights provided here into the validity and
reliability of the diﬀerent commercially available wearable sensors for spatiotemporal parameters show
the emerging potential of such devices for running PW measurement given their narrow association
considering theoretical approaches previously proposed [6–9].
4.2. Commercially Available Systems to Measure PW during Running
Despite the application of IMUs for estimating PW during running being recent, diﬀerent
commercially systems are available. Two of the most widely used wearable sensors for such purposes
are Stryd and Runscribe.
Stryd system is a pioneer in manufacturing wearable power meters for running. Stryd estimates
running power in watts. This power meter, a foot pod reinforced with carbon ﬁbre (weight: 9.1 g) and
based on an IMU of 6 diﬀerent axis (i.e., 3-axis accelerometer and 3-axis gyroscope) and with a sampling
rate of 1000 Hz, attaches to the runner’s shoe to estimate metrics for performance quantiﬁcation
(i.e., pace and distance, average elapsed power, maximal power, average elapsed form power, average
elapsed leg spring, and average elapsed ground time). Some studies have analysed the reliability of
this sensor for both spatiotemporal and PW parameters [11,12,15]. Of note, the latest version of Stryd
is capable of estimating the energy expenditure of working against air resistance by measuring the air
resistance one faces while running in regards with a white paper located at the manufacturer’s website
and where the trials performed to assess the Stryd’s ability to determine wind speed are meticulously
described (https://storage.googleapis.com/stryd_static_assets/white_papers/wind-white-paper-8-17.
pdf). This sensor employs both kinematic and environmental microelectromechanical sensors together
with user-supplied biometrics and proprietary physical and data-driven algorithms to calculate air
resistance force as follows:
FA = 1
2ρCdAv2
(1)
where ρ stands for air density, Cd for drag coeﬃcient, A for the cross-sectional area that encounters the
air resistance, and v for the vector of the runner’s relative velocity with local air mass surrounding
them. According to the aforementioned white paper, the Stryd system should be centrally located
on the laces and towards the toe of the shoe as this placement reported the lowest error regarding
wind measurement accuracy (i.e., wind technology is able to correctly report relative air speed under
Sensors 2020, 20, 6482
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4 km·h−1). However, no peer-reviewed research has been performed to assess the level of accuracy
of such device when accounting for air resistance arising therefore the need to evaluate it in the
near future.
The use of the Runscribe wearable sensor attached to either the lace or heel of the shoes, based on a
nine-axis (three-axis magnetometer, accelerometer, and gyroscope, respectively) IMU with an accuracy
of 0.002 seconds (sampling rate: 500 Hz), is also widespread around the running world. The way
Runscribe estimates power is based on GOVSS model [52] and various assumptions. 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 [13–15,37,38].
Despite the common use of the Stryd and Runscribe wearable sensors, there are other options
for running power estimation commercially available. Cerezuela-Espejo and colleagues [15] also
analysed Garmin Running Power (v1.6, Olathe, KS, USA) and Polar Vantage V (ﬁrmware 3.1.7, Polar,
OY, Kempele, Finland). The Garmin device estimates PW data derived from the combination of a
Garmin sport watch and one of the sensors recommended by the manufacturer (i.e., HRM-Run or
HRM-Tri heart rate monitor and Running Dynamics Pod on the waist belt). Polar Vantage V estimates
power production with no need of an extra sensor (e.g., foot pods). This multisport watch is capable of
calculate indirectly several metrics such as average power, maximum power and laps power using
the built-in barometer and GPS sensors. Although a positive relation with VO2 was found for both
devices (r ≤ 0.841), they exhibited limited test-retest reliability, particularly Garmin Running Power
in laboratory settings and Polar Vantage V outdoors. Myotest device, usually ﬁxed onto a belt and
fastened and placed level with the navel’s runner (according to manufacturer’s guidelines), provides,
amongst others (i.e., cadence, runner’s centre of mass vertical movement, contact time, ﬂight time,
step length, stiﬀness, pace, distance), running PW. Unfortunately, the way Polar, Garmin, and Myotest
estimate PW remains unrevealed.
Every wearable sensor that provides power metrics employs some form of running power model
combined with diﬀerent assumptions. Therefore, there exist conditions in which such models do not
concur until all the diﬀerent wearable sensors standardise and implement the same model for running
PW estimation.
4.3. How Valid and Reliable is PW during Running When Measured by These Devices?
Despite the lack of a concurrent validity study where any of the commercially available power
meters are compared with the ‘Gold Standard’ to measure running power (i.e., force-plate-instrumented
treadmill or a long force platform system), the accuracy of the PW when running provided by these
wearable devices might be limited. The variety of available technologies for running gait analysis
(e.g., accelerometers, gyroscopes, force plates, pressure plates, and photoelectric cells) implies a variety
of devices should exist for analysing stride characteristics. However, some of these devices have not
been validated yet. The validity and reliability of a gait analysis system are essential to determine
whether results are due to changes in gait pattern or are simply systematic measurement errors.
As already mentioned, white (non-peer-reviewed) papers provided by manufacturers to promote
the likely potential of their devices, attribute the diﬀerent values of running power obtained by the
diﬀerent devices to diﬀerences in estimating power. Indeed, Myotest attempted to demonstrate
validity and repeatability of Myotest App on an Apple watch for PW analysis in comparison
with Garmin-Garmin Pod, Polar Vantage V, Stryd (White paper provided by the manufacturer,
https://www.myotest.com/technology). A sample of 7 runners executed a 2000 m run protocol with an
elevation gain of 22.8 m where 500 m were run on ﬂat ground, 500 m uphill at a constant slope, 500 m
of constant-slope downhill, and 500 m on ﬂat ground at a self-selected speed over the entire protocol.
It was reported that given the outputs shape and the existence of similar peaks, a correlation between
the analysed systems is seemingly demonstrated considering that the diﬀerent systems are sensitive
to elevation changes (i.e., lower power at uphill/downhill shift and higher power at uphill running).
Sensors 2020, 20, 6482
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Mean-normalised power signal was used to remove the constant shift in the signals, and it was shown
that PW measured with Myotest is closer to power measured with Garmin and Stryd. These ﬁndings
must be taken cautiously as it is well-known that white papers lack the peer-review process.
Concerning the reliability of such wearables, a recent study analysed the repeatability diﬀerent
devices (Stryd, Runscribe, Garmin Running Power, and Polar Vantage V) show when measuring power
when running as well as their concurrent validity against VO2 [15]. For such a purpose, 12 highly-trained
endurance runners executed a submaximal incremental running speed test and a submaximal
incremental body weight test in two diﬀerent settings (i.e., outdoor and indoor). An additional increasing
slope gradient at submaximal speed test was executed only indoor. After completion, the authors
found Stryd to be the most repeatable device for power estimation. Additionally, Stryd concurrent
validity assessment for power estimation was found to show the closest relationship with the VO2max
measured directly by metabolic cart [15]. Of note, the authors of this study distinguish between Stryd
App and Stryd Watch. Although the Stryd sensor is found to be the same using both app and watch,
the variations reported by the authors between these systems is not justiﬁed. It might be arguable
that the normalisation applied by each system (i.e., Stryd app and watch) diﬀers from one other,
but this is not mentioned by the authors. Nevertheless, the ﬁndings reported by Cerezuela-Espejo and
colleagues [15] constitute a huge contribution providing clinicians, coaches, and practitioners a reliable
wearable sensor to quantify running power in training, retraining, and competition.
Some of these devices have been used previously for measuring running kinetics
(i.e., PW amongst others) and kinematics parameters (i.e., running spatiotemporal gait characteristics).
The aforementioned GOVSS model [52] and Jenny’s model [41] for estimating mechanical power
rely mainly on runners anthropometry, environmental factors (i.e., air density and wind speed) and
running spatiotemporal parameters (i.e., speed, step rate and ground contact time). With this in
mind the measurement of spatiotemporal parameters is essential for an accurate power estimation.
Regarding this, some studies have shown good reliability of wearable sensors when measuring such
parameters [11–15]. García-Pinillos and colleagues [11], over a speed incremental running protocol on
a treadmill, tested the reliability of Stryd for running spatiotemporal parameters (i.e., contact time,
ﬂight time, step length, and step frequency) against a proved reliable photoelectric cell system for
such purpose (i.e., Optogait system) [56]. The authors found that Stryd measures accurately step
length and step frequency but underrates slightly contact time overrates ﬂight time in comparison
with such system. Likewise, the intra-Stryd reliability has also been analysed [12] over two diﬀerent
5-min tasks (i.e., two self-paced walks along a trail a and two trail runs separated by a 5-min rest
period) with 20 healthy individuals (it was not mentioned whether the participants had any running
experience). The authors assessed all the data provided by the Stryd power meter. Regarding trail
running, all variables were found to have relative test-retest reliability, meeting the set the intraclass
correlation coeﬃcient (ICC) threshold. When considering an interval of conﬁdence equals to 95%,
pace, average elapsed power, average elapsed form power, average elapsed leg spring, and vertical
oscillation were deemed to have good to excellent reliability; maximal power, average elapsed ground
time, and distance were reported to exhibit moderate to excellent reliability [12].
The intra-validity analysis of the Runscribe sensor has also been examined [13,14]. This sensor
was used to measure spatiotemporal (i.e., contact time, step length, and cycle time), kinematic
(i.e., foot pronation excursion and pronation velocity), and kinetic parameters (i.e., impact ground
force and braking ground force) on two diﬀerent surfaces (i.e., track and grass) at two diﬀerent
running speeds (comfortable self-selected speed and an increased speed) [13]. Over two 1600-m
runs, ﬁrst at a slow pace and then fast on two randomised-ordered surfaces (i.e., track and grass),
Runscribe foot pod sensors were found to be valid to determine variations in the aforementioned
spatiotemporal, kinetic, and kinematic parameters in diﬀerent conditions (i.e., diﬀerent surfaces) [13].
Furthermore, validity measurements regarding the Runscribe placement on the running shoes have
also been examined [14]. In this study, the location of the Runscribe on the running shoes (i.e., heel or
shoelace) was assessed against a reference technology (i.e., high-speed video camera at 1000 Hz).
Sensors 2020, 20, 6482
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The authors found Runscribe to be a valid system to examine spatiotemporal variables in treadmill
running. Additionally, the location of the Runscribe needs to be considered as it was found to be
sensitive to metrics accuracy. When analysing contact time, ﬂight time, and step length, the shoelace
placement is recommended as smaller errors were found when comparing to the Runscribe attached to
the heel. In contrast, the heel showed higher accuracy when analysing step frequency [14]. In a recent
study [15] where test-retest reliability of several wearable sensors was tested, Runscribe was found to
be the second most repeatable sensor for speed, slope gradient, and body weight (standard error of
measurement ≥ 30.1 W, coeﬃcient variation [CV] ≥ 7.4%, ICC ≤ 0.709), only after the Stryd power
meter, for indoor settings. However, when employed in outdoor, Runscribe exhibits both the highest
errors and poorest repeatability (SEM ≥ 59.3 W, CV ≥ 14.8%, ICC ≤ 0.563) [15]. When its concurrent
validity between PW estimation and VO2 consumption examined over an increasing speed test by a
metabolic cart, Runscribe exhibited values of r ≥ 0.582 and standard error of estimate (SEE) ≤ 13.7%
for indoor and outdoor settings. Moreover, the power estimation and VO2 agreement was reduced
over both conditions (body weight, SEE = 10.3%; slope, SEE = 18.5%). Regarding data collection, it is
worth highlighting that the authors did not specify the placement of the Runscribe wearable sensors
aﬀecting, as previously discussed, the possible interpretation of the measured outcomes.
5. Conclusions
The previous works on running PW and the theoretical approaches provided for its estimation are,
from a practical standpoint, hard to include in the everyday routine of an athlete. This study provides a
critical evaluation of available scientiﬁc information regarding PW quantiﬁcation in endurance running
as well as the diﬀerent accessible devices for its estimation. The inexistence of studies attempting to
evaluate concurrent validity of PW estimation measured by wearable sensors when running (apart from
non-peer-reviewed manufacturer’s white papers), the limited available information about the dynamic
of PW during running and its short-term response to acute inﬂuencing factors (e.g., velocity, slope,
fatigue) and long-term training adaptations (i.e., PW as a tool for monitoring training adaptations) made
the analysis reported here especially diﬃcult. However, it is arguable that the outcomes stated here are
tremendously useful as PW stabilises earlier than other variables commonly used (i.e., heart rate or
VO2). Furthermore, running power increases alongside velocity, resembling their linear relationship at
diﬀerent submaximal speeds. Additionally, the reliability of commercially available wearables has
been assessed, ﬁnding Stryd to be the most reliable and accurate wearable device for running PW
estimation. Ultimately, given their novelty and potential application, the analysis of PW while running
and its estimation by wearable devices needs more attention from a research perspective in order to
provide practitioners a reliable, valid, and friendly tool to improve both training and performance
quality in running.
Author Contributions: Conceptualization, D.J.-C., A.C.-L. and F.G.-P.; Methodology, D.J.-C., L.E.R.-S. and F.G.-P.;
Software, A.C.-L. and L.E.R.-S.; Validation, R.R.-C., A.C.-L; Formal Analysis, D.J.-C., L.ER.-S. and F.G.-P;
Investigation, A.C.-L., R.R.-C.; Resources, D.J.-C., L.E.R.-S and A.C.-L.; Data Curation, F.G.-P., D.J.-C., L.E.R.-S.
and R.R.-C.; Writing-Original Draft Preparation, D.J.-C., A.C.-L. and F.G.-P.; Writing-Review & Editing, D.J.-C.
and F.G.-P.; Visualization, A.C.-L.; Supervision, L.E.R.-S., F.G.-P. and R.R.-C.; Project Administration, L.E.R.-S.
and A.C.-L.; Funding Acquisition, D.J.-C. and A.C.-L. All authors have read and agreed to the published version
of the manuscript.
Funding: The authors declare no funding has been received for this research.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
References
1.
Passﬁeld, L.; Hopker, J.G.; Jobson, S.; Friel, D.; Zabala, M. Knowledge is power: Issues of measuring training
and performance in cycling. J. Sports Sci. 2017, 35, 1426–1434.
2.
Halliday, D.R.; Resnick, R. Fundamentals of Physics; Wiley: New York, NY, USA, 2007.
3.
Vance, J. Run with Power: The Complete Guide to Power Meters for Running; VeloPress: Boulder, CO, USA, 2016.
Sensors 2020, 20, 6482
18 of 20
4.
Paquette, M.R.; Napier, C.; Willy, R.W.; Stellingwerﬀ, T. Moving Beyond Weekly ‘Distance’: Optimizing
Quantiﬁcation of Training Load in Runners. J. Orthop. Sports Phys. Ther. 2020, 1–20. [CrossRef]
5.
Kram, R.; Griﬃn, T.M.; Donelan, J.M.; Chang, Y.H. Force treadmill for measuring vertical and horizontal
ground reaction forces. J. Appl. Physiol. 1998, 85, 764–769.
6.
Cavanagh, P.R.; Kram, R.J.M.S.S.E. Stride length in distance running: Velocity, body dimensions, and added
mass eﬀects. Med. Sci. Sports Exerc. 1989, 21, 467–479.
7.
Cavagna, G.; Mantovani, M.; Willems, P.; Musch, G.J.P.A. The resonant step frequency in human running.
Pﬂügers Arch. 1997, 434, 678–684.
8.
Clark, K.P.; Ryan, L.J.; Weyand, P.G.J.J. A general relationship links gait mechanics and running ground
reaction forces. J. Exp. Biol. 2017, 220, 247–258.
9.
Samozino, P.; Rabita, G.; Dorel, S.; Slawinski, J.; Peyrot, N.; Saez de Villarreal, E.; Morin, J.B. A simple method
for measuring power, force, velocity properties, and mechanical eﬀectiveness in sprint running. Scand. J.
Med. Sci. Sports 2016, 26, 648–658.
10.
Van Dijk, H.; Van Megen, R. The Secret of Running: Maximum Performance Gains through Eﬀective Power Metering
and Training Analysis; Meyer & Meyer Sport: Aachen, Germany, 2017.
11.
Garcia-Pinillos, F.; Roche-Seruendo, L.E.; Marcen-Cinca, N.; Marco-Contreras, L.A.; Latorre-Roman, P.A.
Absolute Reliability and Concurrent Validity of the Stryd System for the Assessment of Running Stride
Kinematics at Diﬀerent Velocities. J. Strength Cond. Res. 2018. [CrossRef]
12.
Navalta, J.W.; Montes, J.; Bodell, N.G.; Aguilar, C.D.; Radzak, K.; Manning, J.W.; DeBeliso, M. Reliability of
Trail Walking and Running Tasks Using the Stryd Power Meter. Int. J. Sports Med. 2019, 40, 498–502.
13.
Hollis, C.R.; Koldenhoven, R.M.; Resch, J.E.; Hertel, J. Running biomechanics as measured by wearable
sensors: Eﬀects of speed and surface. Sports Biomech. 2019, 1–11. [CrossRef]
14.
García-Pinillos, F.; Chicano-Gutiérrez, J.M.; Ruiz-Malagón, E.J.; Roche-Seruendo, L.E. Technology. Inﬂuence
of RunScribe™ placement on the accuracy of spatiotemporal gait characteristics during running. J. Sports
Eng. Technol. 2019, 17543371–19876513. [CrossRef]
15.
Cerezuela-Espejo, V.; Hernández-Belmonte, A.; Courel-Ibáñez, J.; Conesa-Ros, E.; Mora-Rodríguez, R.;
Pallarés, J.G. Are we ready to measure running power? Repeatability and concurrent validity of ﬁve
commercial technologies. Eur. J. Sport Sci. 2020, 1–22. [CrossRef]
16.
Jones, A.M.; Vanhatalo, A. The ‘critical power’concept: Applications to sports performance with a focus on
intermittent high-intensity exercise. Sports Med. 2017, 47, 65–78.
17.
Burnley, M.; Jones, A.M. Power–duration relationship: Physiology, fatigue, and the limits of human
performance. Eur. J. Sport Sci. 2018, 18, 1–12.
18.
Peters, M.D.J.; Godfrey, C.M.; Khalil, H.; McInerney, P.; Parker, D.; Soares, C.B. Guidance for conducting
systematic scoping reviews. Int. J. Evid.-Based Healthc. 2015, 13, 141–146. [CrossRef]
19.
Munn, Z.; Peters, M.D.J.; Stern, C.; Tufanaru, C.; McArthur, A.; Aromataris, E. Systematic review or scoping
review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med.
Res. Methodol. 2018, 18, 143. [CrossRef]
20.
Liberati, A.; Altman, D.G.; Tetzlaﬀ, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.; Clarke, M.; Devereaux, P.J.;
Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of
studies that evaluate health care interventions: Explanation and elaboration. Ann. Intern. Med. 2009, 151,
W-65–W-94.
21.
Aromataris, E.; Riitano, D. Constructing a search strategy and searching for evidence. A guide to the
literature search for a systematic review. Am. J. Nurs. 2014, 114, 49–56. [CrossRef]
22.
Lundh, A.; Gøtzsche, P.C. Recommendations by Cochrane Review Groups for assessment of the risk of bias
in studies. Bmc Med Res. Methodol. 2008, 8, 22.
23.
Downs, S.H.; Black, N. The feasibility of creating a checklist for the assessment of the methodological quality
both of randomised and non-randomised studies of health care interventions. J. Epidemiol. Community Health
1998, 52, 377–384.
24.
Landis, J.R.; Koch, G.G. The measurement of observer agreement for categorical data. Biometrics 1977, 33,
159–174.
25.
Snyder, K.L.; Mohrman, W.P.; Williamson, J.A.; Li, K. Methodological Flaws in Aubry, RL, Power, GA, and
Burr, JF. An Assessment of Running Power as a Training Metric for Elite and Recreational Runners. J. Strength
Cond. Res. 2018, 32, e61.
Sensors 2020, 20, 6482
19 of 20
26.
Aubry, R.L.; Power, G.A.; Burr, J.F. An assessment of running power as a training metric for elite and
recreational runners. J. Strength Cond. Res. 2018, 32, 2258–2264.
27.
Dobrijevic, S.; Ilic, V.; Djuric, S.; Jaric, S. Force-velocity relationship of leg muscles assessed with motorized
treadmill tests: Two-velocity method. Gait Posture 2017, 56, 60–64.
28.
García-Pinillos, F.; Soto-Hermoso, V.M.; Latorre-Román, P.Á.; Párraga-Montilla, J.A.; Roche-Seruendo, L.E.
How Does Power During Running Change when Measured at Diﬀerent Time Intervals? Int. J. Sports Med.
2019, 40, 609–613.
29.
Austin, C.L.; Hokanson, J.F.; McGinnis, P.M.; Patrick, S. The relationship between running power and
running economy in well-trained distance runners. Sports 2018, 6, 142.
30.
Mulligan, M.; Adam, G.; Emig, T. A minimal power model for human running performance. PLoS ONE
2018, 13, e0206645.
31.
Vandewalle, H. Modelling of Running Performances: Comparisons of Power-Law, Hyperbolic, Logarithmic,
and Exponential Models in Elite Endurance Runners. Biomed Res. Int. 2018, 2018, 1–23. [CrossRef]
32.
Cerezuela-Espejo, V.; Hernández-Belmonte, A.; Courel-Ibáñez, J.; Conesa-Ros, E.; Martínez-Cava, A.;
Pallarés, J.G. Running power meters and theoretical models based on laws of physics: Eﬀects of environments
and running conditions. Physiol. Behav. 2020, 112972. [CrossRef]
33.
Leuchanka, A.; Switaj, Z.; Clark, T. Exploring kinematic asymmetry by means of wearable sensors during
marathon race. Footwear Sci. 2019, 11, S193–S194.
34.
Leuchanka, A.; Switaj, Z.; Clark, T. Use of wearable sensors for measurement of spatiotemporal variables
during marathon race. Footwear Sci. 2019, 11, S191–S192.
35.
Gregory, C.; Koldenhoven, R.M.; Higgins, M.; Hertel, J. External ankle supports alter running biomechanics:
A ﬁeld-based study using wearable sensors. Physiol. Meas. 2019, 40, 044003.
36.
García-Pinillos, F.; Latorre-Roman, P.A.; Roche-Seruendo, L.E.; García-Ramos, A. Prediction of power output
at diﬀerent running velocities through the two-point method with the Stryd™ power meter. Gait Posture
2019, 68, 238–243.
37.
Brayne, L.; Barnes, A.; Heller, B.; Wheat, J. Using a wireless consumer accelerometer to measure tibial
acceleration during running: Agreement with a skin-mounted sensor. Sports Eng. 2018, 21, 487–491.
38.
Koldenhoven, R.M.; Hertel, J. Validation of a wearable sensor for measuring running biomechanics.
Digit. Biomark. 2018, 2, 74–78.
39.
Cavagna, G.A.; Saibene, F.P.; Margaria, R. Mechanical work in running. J. Appl. Physiol. 1964, 19, 249–256.
40.
Driss, T.; Vandewalle, H. The measurement of maximal (anaerobic) power output on a cycle ergometer:
A critical review. Biomed. Res. Int. 2013, 2013, 589361. [CrossRef]
41.
Jenny, D.F.; Jenny, P. On the mechanical power output required for human running—Insight from an
analytical model. J. Biomech. 2020, 110, 109948. [CrossRef]
42.
Vandewalle, H.; Vautier, J.F.; Kachouri, M.; Lechevalier, J.M.; Monod, H. Work-exhaustion time relationships
and the critical power concept. A critical review. J. Sports Med Phys. Fit. 1997, 37, 89–102.
43.
Scherrer, J.; Samson, M.; Paleologue, A. Etude du travail musculaire et de la fatigue. 1. Donnes ergometriques
obtenues chez lhomme. J. De Physiol. 1954, 46, 887–916.
44.
Kennelly, A.E. An approximate law of fatigue in the speeds of racing animals. JSTOR 1906, 42, 275–331.
45.
García-Manso, J.; Martín-González, J.; Vaamonde, D.; Da Silva-Grigoletto, M. The limitations of scaling laws
in the prediction of performance in endurance events. J. Theor. Biol. 2012, 300, 324–329.
46.
Monod, H.; Scherrer, J. The work capacity of a synergic muscular group. Ergonomics 1965, 8, 329–338.
47.
Moritani, T.; Nagata, A.; Devries, H.A.; Muro, M. Critical power as a measure of physical work capacity and
anaerobic threshold. Ergonomics 1981, 24, 339–350.
48.
Morton, R.H. The critical power and related whole-body bioenergetic models. Eur. J. Appl. Physiol. 2006, 96,
339–354.
49.
Weyand, P.G.; Lin, J.E.; Bundle, M.W. Sprint performance-duration relationships are set by the fractional
duration of external force application. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2006, 290, R758–R765.
50.
Wilkie, D. Man as a source of mechanical power. J. Ergon. 1960, 3, 1–8.
51.
Willy, R.W. Innovations and pitfalls in the use of wearable devices in the prevention and rehabilitation of
running related injuries. J. Phys. Ther. Sport 2018, 29, 26–33.
52.
Skiba, P.F. calculation of power output and quantiﬁcation of training stress in distance runners:
The development of the gOVSS algorithm. J Revis. Sep 2006, 16, 9.
Sensors 2020, 20, 6482
20 of 20
53.
Batliner, M.E.; Kipp, S.; Grabowski, A.M.; Kram, R.; Byrnes, W.C.J.S.m.i.o. Does metabolic rate increase
linearly with running speed in all distance runners? Sports Med. Int. Open 2018, 2, E1–E8.
54.
Bramble, D.M.; Lieberman, D.E.J.N. Endurance running and the evolution of Homo. Nature 2004, 432,
345–352.
55.
Zuccarelli, L.; Porcelli, S.; Rasica, L.; Marzorati, M.; Grassi, B. Comparison between slow components of HR
and V· O2 kinetics: Functional signiﬁcance. Med. Sci. Sports Exerc. 2018, 50, 1649–1657.
56.
Jaén-Carrillo, D.; García-Pinillos, F.; Cartón-Llorente, A.; Almenar-Arasanz, A.J.; Bustillo-Pelayo, J.A.;
Roche-Seruendo, L.E. Test–retest reliability of the OptoGait system for the analysis of spatiotemporal running
gait parameters and lower body stiﬀness in healthy adults. Proc. Inst. Mech. Eng. Part P: J. Sports Eng. Technol.
2020, 234, 17543371–19898353.
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Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running.
Jaén-Carrillo, Diego,Roche-Seruendo, Luis E,Cartón-Llorente, Antonio,Ramírez-Campillo, Rodrigo,García-Pinillos, Felipe