“Question Your Categories”: the Misunderstood Complexity of Middle-Distance Running Profiles With Implications for Research Methods and Application

Abstract

Middle-distance running provides unique complexity where very different physiological and structural/mechanical profiles may achieve similar elite performances. Training and improving the key determinants of performance and applying interventions to athletes within the middle-distance event group are probably much more divergent than many practitioners and researchers appreciate. The addition of maximal sprint speed and other anaerobic and biomechanical based parameters, alongside more commonly captured aerobic characteristics, shows promise to enhance our understanding and analysis within the complexities of middle-distance sport science. For coaches, athlete diversity presents daily training programming challenges in order to best individualize a given stimulus according to the athletes profile and avoid “non-responder” outcomes. It is from this decision making part of the coaching process, that we target this mini-review. First we ask researchers to “question their categories” concerning middle-distance event groupings. Historically broad classifications have been used [from 800 m (~1.5 min) all the way to 5,000 m (~13–15 min)]. Here within we show compelling rationale from physiological and event demand perspectives for narrowing middle-distance to 800 and 1,500 m alone (1.5–5 min duration), considering the diversity of bioenergetics and mechanical constraints within these events. Additionally, we provide elite athlete data showing the large diversity of 800 and 1,500 m athlete profiles, a critical element that is often overlooked in middle-distance research design. Finally, we offer practical recommendations on how researchers, practitioners, and coaches can advance training study designs, scientific interventions, and analysis on middle-distance athletes/participants to provide information for individualized decision making trackside and more favorable and informative study outcomes.

Full text

REVIEW
published: 26 September 2019
doi: 10.3389/fspor.2019.00028
Frontiers in Sports and Active Living | www.frontiersin.org 1September 2019 | Volume 1 | Article 28
Edited by:
Olivier Girard,
Murdoch University, Australia
Reviewed by:
Jean Slawinski,
Institut National du Sport, de
l’Expertise et de la
Performance, France
Marco Cardinale,
Aspire Academy for Sports
Excellence, Qatar
*Correspondence:
Gareth N. Sandford
gsandford@csipacific.ca
Specialty section:
This article was submitted to
Elite Sports and Performance
Enhancement,
a section of the journal
Frontiers in Sports and Active Living
Received: 13 May 2019
Accepted: 02 September 2019
Published: 26 September 2019
Citation:
Sandford GN and Stellingwerff T
(2019) “Question Your Categories”:
the Misunderstood Complexity of
Middle-Distance Running Profiles With
Implications for Research Methods
and Application.
Front. Sports Act. Living 1:28.
doi: 10.3389/fspor.2019.00028
“Question Your Categories”: the
Misunderstood Complexity of
Middle-Distance Running Profiles
With Implications for Research
Methods and Application
Gareth N. Sandford 1,2,3
*and Trent Stellingwerff 1,2, 3
1School of Kinesiology, University of British Columbia, Vancouver, BC, Canada, 2Physiology, Canadian Sport Institute-Pacific,
Victoria, BC, Canada, 3Athletics Canada, Ottawa, ON, Canada
Middle-distance running provides unique complexity where very different physiological
and structural/mechanical profiles may achieve similar elite performances. Training and
improving the key determinants of performance and applying interventions to athletes
within the middle-distance event group are probably much more divergent than many
practitioners and researchers appreciate. The addition of maximal sprint speed and other
anaerobic and biomechanical based parameters, alongside more commonly captured
aerobic characteristics, shows promise to enhance our understanding and analysis within
the complexities of middle-distance sport science. For coaches, athlete diversity presents
daily training programming challenges in order to best individualize a given stimulus
according to the athletes profile and avoid “non-responder” outcomes. It is from this
decision making part of the coaching process, that we target this mini-review. First
we ask researchers to “question their categories” concerning middle-distance event
groupings. Historically broad classifications have been used [from 800 m (∼1.5 min)
all the way to 5,000 m (∼13–15 min)]. Here within we show compelling rationale from
physiological and event demand perspectives for narrowing middle-distance to 800
and 1,500 m alone (1.5–5 min duration), considering the diversity of bioenergetics
and mechanical constraints within these events. Additionally, we provide elite athlete
data showing the large diversity of 800 and 1,500 m athlete profiles, a critical
element that is often overlooked in middle-distance research design. Finally, we
offer practical recommendations on how researchers, practitioners, and coaches can
advance training study designs, scientific interventions, and analysis on middle-distance
athletes/participants to provide information for individualized decision making trackside
and more favorable and informative study outcomes.
Keywords: anaerobic speed reserve, training science, fiber type, individualized training, coaching, bioenergetics

Sandford and Stellingwerff Middle-Distance Running Profile Complexity
INTRODUCTION
In the book Factfulness the late Professor Hans Rosling addresses
“Ten reasons why we’re wrong about the world” (Rosling et al.,
2018). Specifically, Rosling explains how we subconsciously
employ bias in our decision making and interpretation of the
world based on self-narrative, which can drive false positive
and negative understanding of reality. Accordingly, we will
apply one of Professor Rosling’s ten-principles: “Question your
categories” to an approach generally employed by many middle-
distance researchers; in that, generally, many take a singular
approach to the treatment and analysis of middle-distance
athletes and/or study participants. Biological first principles
consistently demonstrate a huge variability in adaptation to a
given exercise stimulus and is prevalent across multiple sports
(Gaskill et al., 1999; Vollaard et al., 2009; Timmons et al.,
2010; Sylta et al., 2016). Therefore, a “one-size-fits-all” approach
needs re-consideration based on substantial individual responses
to a given stimulus, that is especially unique to the middle-
distance event group resulting in very different physiological
and mechanical profiles achieving similar elite performances
(Schumacher and Mueller, 2002; Sandford et al., 2019a). Many
in the middle-distance coaching community already generally
implement individualized training (Horwill, 1980; Daniels,
2005), but highlight the need for deeper information surrounding
how to best address the complexity of middle-distance athletes. It
is from this coaching perspective that we target this mini-review,
providing recommendations on how researchers/practitioners
can advance training study designs, scientific interventions, and
analysis in middle-distance athlete profiles research to provide
more beneficial information for individualized decision making
and/or more favorable and informative study outcomes.
WHAT CONSTITUTES MIDDLE-DISTANCE?
Consistency of both sport science terminology (Chamari and
Padulo, 2015; Winter et al., 2016) and grouping of middle-
distance events and athletes within the literature is lacking.
Therefore, initially, we put forward a framework for defining
middle-distance running events as solely the 800 and 1,500 m
events (∼1.5 to 5 min duration; Table 1); primarily due to the
demarcation of average 800 and 1,500 m race pace intensity in
relation to a given physiological threshold (beyond VO2max;
Table 1).
The distinction of middle-distance as solely 800 and 1,500 m
is critical for advancing current understanding. First, between
0 and 5 min, performance decrements of all-out efforts are
exponential as a function of time (Bundle and Weyand, 2012).
Therefore, within this time frame, a varying blend, but still
large contributions, of (1) aerobic, (2) anaerobic, and (3)
neuromuscular/mechanical characteristics are implemented to
achieve optimal performance (Schumacher and Mueller, 2002;
Sandford et al., 2019a). An appreciation of the differences in these
three distinct performance determinants between, and within,
middle-distance events has received limited consideration within
the middle-distance literature, largely perhaps due to our
limitations in accurately and reliably quantifying anaerobic
energetics (Haugen et al., 2018). If one extends the middle-
distance category beyond ∼5 min (for example to 7,8, or
15 min) a much smaller decline in performance is seen
(between e.g., 5 and 9 min than between 1 and 5), due to the
similar nature of aerobic contribution support the extended
duration (e.g., 5–9 min) of exercise (Bundle and Weyand,
2012;Table 1). Second, the average race pace of 800–1,500 m
as a % VO2max, are beyond VO2max, providing distinctly
different metabolic consequences to those events that are
below VO2max, both of which are distinctly different to those
events that reside, on average, below critical velocity (defined
as the last wholly oxidative physiological intensity; Table 1).
Therefore, it is important that in establishing middle-distance
event specific performance determinants, and/or appropriate
performance enhancing interventions, that the bioenergetics and
neuromuscular/mechanical requirements represent the actual
demands from ∼1.5 to 5 min of duration, which are much
different if one includes middle to long and long distance athletes
(Table 1).
Consequently, training and applying interventions to athletes
within this middle-distance event group are much more
divergent than many practitioners and researchers appreciate.
Indeed, recent work in elite 800 m runners has shown
huge diversity of profiles presenting along the continuum of
middle-distance running (Sandford et al., 2019a), which we
will discuss below. Accordingly, we suggest that given the
unique bioenergetics (Table 1) and neuromuscular/mechanical
constraints, that middle-distance are exclusively defined as the
800 and 1,500 m athletics events, or ∼1.5 to 5 min of duration.
MIDDLE-DISTANCE RUNNING—THE
EVENT GROUP WITH LARGEST DIVERSITY
OF ATHLETE PROFILE?
The middle-distance events are described as the “middle-ground”
of aerobic and anaerobic energetics (Billat, 2001;Table 1), where,
accordingly, athletes may approach the same performance time
from distinctly different perspectives, as shown by diversity of
aerobic energetics within the 800 m (Table 1). Indeed, most
coaches appreciate the large variability of aerobic energetics
across the 800 m event when programming training (Gamboa
et al., 1996), which actually aligns well with the published
diversity of energetic contributions (Table 1). As an example,
published case studies on world-class 1,500 m runners Henrik
Ingebrigtsen (Tjelta, 2013) and Peter Snell (Carter et al.,
1967) show substantial diversity in physiology (VO2max) and
performance profile at 800 and 3,000 m, despite similar 1,500 m
race performances (3:35.43 and 3:37.60, respectively, at time of
publication). For example Ingebrigtsen presents with a VO2max
of 84.4 vs. Snell’s value of 72.2 ml/kg/min. Ingebrigtsen’s personal
best at 800 and 3,000 m are 1:48.60 and 7:58.15, respectively.
By comparison, over the 800 m Snell recorded 1:44.30 world-
record and had no recorded 3,000 m race performances (but
did record 9:16 on grass and 9:12.5 on cinder tracks over 2
miles (Steve Willis personal communication), which converts to
8:36.10 and 8:32.90 3,000 m (IAAF scoring tables). Therefore,
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Sandford and Stellingwerff Middle-Distance Running Profile Complexity
TABLE 1 | Proposed framework for standardizing researcher and practitioner categories of events 800 m—marathon considering both average race velocity and
subsequent physiological consequences of a given race demand.
Parameter Middle-distance Middle-long distance Long distance
Events 800 m
(min:ss:ms)
1,500 m
(min:ss:ms)
3,000 m
(min:ss:ms)
5,000 m
(min:ss:ms)
10, 000 m
(min:ss:ms)
60 min record
(∼l/2 marathon)
(min:ss)
Marathon
(hr:min:ss)
Male world record event duration (hr:min:ss:ms) 1:40.91 3:26.00 7:20.67 12:37.35 26:17.53 58.18 2:01:39
Average race pace intensity (% VO2max; Billat, 2001) 115–130 105–115 ∼100 95–100 90–95 85–90 75–80
Physiological threshold Above VO2max ≤VO2max, ≥Critical velocity <Critical velocity
% Aerobic energy contribution (Billat, 2001) 65–75 80–85 85–90 90–95 97 98 99.9
% Aerobic energy contribution (Spencer and Gastin, 2001) 66 ±4 84 ±3 n/a n/a n/a n/a n/a
% Aerobic energy contribution (Duffield et al., 2005a,b) 60.3 ±9 77 ±7 86 ±7 n/a n/a n/a n/a
Coach interpretation of % aerobic energy contribution
(Gamboa et al., 1996)
35–65 n/a n/a n/a n/a n/a n/a
% difference in aerobic contribution to 800 m – 5–20 10–25 20–30 22–32 23–33 24.9–34.9
Adapted from Gamboa et al. (1996),Billat (2001),Spencer and Gastin (2001),Duffield and Dawson (2003),Duffield et al. (2005a,b).
despite these two athletes having similar 1,500 m race times, it
is obvious that Snell’s 1,500 m performance comes much more
from a speed/anaerobic physiological profile compared to the
more aerobic profile from Ingebrigtsen. In further support of
divergent middle-distance athlete profiles, a recent global sample
by Sandford et al. (2019a) revealed substantial diversity of athlete
profile within elite male 800 m athletes, that can be categorized
by three distinct sub-groups (400–800 m speed types, 800 m
specialists and 800–1,500 m endurance types) across a continuum
(Figure 1A). The same within 800 m subgroups are also found
in elite females (Figure 1B; where velocity at 4 mmol (v4mmol)
has also been added). Providing just three measures of an athletes
profile, such as v4mmol (aerobic indicator), velocity at VO2max
(vVO2max; aerobic power indicator) and maximal sprinting
speed (MSS; biomechanical/structural indicator, measured over
50 m from standing start, Sandford et al., 2019a) provides a great
“first layer insight” for any researcher, practitioner, and coach.
From this, one can more easily identify: (a) the physiological
and biomechanical strengths and limitations of an individual;
and (b) which “sub-group” the athlete/participant is currently
in. This, in turn, potentially enables more targeted interventions
by sub-group to improve depth of understanding on stimulus-
response of interventions across the middle-distance continuum
which ultimately aid the ability to inform individualized
training prescription.
It is important to note, that without the characterization
of MSS in these middle-distance athletes, something which
is rarely reported in middle-distance studies, this continuum
characterization is not possible (Figures 1A,B). Interestingly,
these identified middle-distance athlete sub-groups (Sandford
et al., 2019a) supports longstanding coaching observations
of middle-distance athlete variability that requires careful
individual considerations (Horwill, 1980).
We suggest that appreciating the continuum of middle-
distance diversity is currently poorly implemented in many
research study designs, and poorly appreciated amongst
middle-distance researchers and practitioners. Accordingly,
as outlined below and in Table 2, considerations of this
diversity should occur with: (l) section Study Athlete/Participant
Characterization and Description (II) section Selection of
Appropriate Intervention - Are All Stimulus Created Equal?; and
(III) section Analysis of Effects per Sub-group.
Study Athlete/Participant Characterization
and Description
All studies are required to profile and characterize their
participants (Begg et al., 1996). Typically, many middle-distance
based studies tend to limit this reporting to primarily aerobic
based physiological parameters, such as VO2max (or vVO2max),
lactate threshold, and performance times and participant age
and anthropometrics. Sometimes, depending on the scope of
the study, some anaerobic metrics are provided, appreciating
sport science currently has limited validity in accurately and
sensitively measuring the anaerobic domain (Haugen et al.,
2018). Furthermore, middle-distance coaching education is
predominated by aerobic based energy system teaching (Berg,
2003; Thompson, 2016; Sandford, 2018), which may skew the
over-emphasis on these performance elements.
In Rosling’s words we should look to “get-a tool box not
a hammer.” Accordingly, we suggest that neuromuscular and
mechanical qualities, such as MSS and the anaerobic speed
reserve (ASR, defined as the speed range from Velocity at
VO2max to MSS, Blondel et al., 2001; Buchheit and Laursen,
2013), offer potential to deepen our understanding of athlete
profile diversity. At the very least, the addition of MSS allows
for enhanced potential analysis (see Figures 1A,B), and is a
technically and methodologically easy addition to most study
designs. However, very rarely, are any maximal speed/power
and/or biomechanics based metrics reported or considered
(despite considerable evidence showing them to be important
performance determinants of MSS Weyand et al., 2000, 2010;
Morin et al., 2011; Rabita et al., 2015; Nagahara et al., 2019) as well
as determinants of middle-distance race performance (Nummela
et al., 1996; Bachero-Mena et al., 2017; Sandford et al., 2019a).
Furthermore, many papers only report single event
performance, which does not inform the reader on where
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Sandford and Stellingwerff Middle-Distance Running Profile Complexity
FIGURE 1 | (A) Anaerobic speed reserve profiles of 19 elite male 800 and 1,500 m athletes across 800 m sub-group continuum as described in Sandford et al.
(2019a). All participants seasons best (SB) 800 m ≤1:47.50 and 1,500 m SB ≤3:40.00. vVO2max estimated from 1,500 m race time as per methods of Bellenger et al.
(2015) and validated in elite male runners in Sandford et al. (2019b).(B) Anaerobic speed reserve and velocity at 4 mmol/l lactate (v@4 mmol/l) across three elite
middle-distance female profiles from each of the 800 m sub-groups tested in 2017. Note the between individual diversity across v@4 mmol/l, vVO2max and Maximal
sprint speed—despite all having a season’s best over 800 m within 1.3s of each other. Rankings in brackets from 2017 season. vVO2max generated using methods
developed by Bellenger et al. (2015) and utilized in Sandford et al. (2019a) (A). Informed consent was obtained through Auckland University of Technology ethics
committee as part of Sandford et al. (2019a).
the strengths and weaknesses of a given athletes/participants lie
(e.g., Figures 1A,B). Athlete/participant profiles may be further
characterized by concepts such as ASR and the speed reserve
ratio (SRR; MSS/vVO2max) allowing authors to describe the
distribution of their athlete/participants sub-group(s). As a
minimum authors should show a spread of performance times,
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Sandford and Stellingwerff Middle-Distance Running Profile Complexity
TABLE 2 | Study design principles for middle-distance running populations.
Example Research Question: “The effect of high intensity training interventions at or beyond VO2max on middle-distance race performance”
Traditional approach Issues with approach Emerging approaches Rationale
I. Participant
description
“18 middle-distance
runners,” height, weight,
age, international ranking
level, VO2max,
middle-distance
performance times
Does not provide enough information to
distinguish what type of middle-distance
athlete the participants since MSS is not
assessed
MSS measured over 50 m used to
complement the aerobic
characterization of vVO2max. Elite
Athletes presented across the
middle distance continuum,
n=400–800, n=800, and n=
800–1,500 and categorized using
the SRR (MSS/vVO2max) (Sandford
et al., 2019a) Performance times
provided across multiple distances
(400, 800, 1,500) Training volume,
time in zones quantified to
understand training history
Allows for sub-group characterization even
in underpowered studies that can have
closer application and relevance for
coaches and support staff frontline or for
future study hypothesis generation
II. Exercise
prescription
%vVO2max (>VO2max)
%HRmax (>VO2max) Event
personal best running
speeds (e.g., intervals at
800 or 1,500 m race pace)
Human locomotor performance (i.e., time
to exhaustion) at intensities beyond
vVO2max can be surprisingly “predicted”
using only 2 locomotor entities: vVO2max
and MSS (Bundle et al., 2003; Alexander,
2006; Bundle and Weyand, 2012)
Therefore, without acknowledgment of the
MSS differences, the intervention will have
athletes working at different relative
intensities of a given workload. For
example, the lower the use of the ASR, the
greater the exercise tolerance (Blondel
et al., 2001; Buchheit et al., 2012;
Buchheit and Laursen, 2013) and thus a
big confounder often overlooked in these
types of studies
% ASR (or exercise prescription
decisions set relative to both
%vVO2max and %MSS)
Accounts for mechanical differences
between athletes and allows the same
relative physiological stimulus to be
applied (Buchheit and Laursen, 2013)
Ill. Analysis (1) All runners grouped
together for analysis
(despite some studies
having 800 m (1.5–2 min) to
marathon (130–160 min)
specialists.
(2) Periodically a sub-group
responders vs.
non responders
Misrepresentation of athletes ability to
“respond.” Should we expect all athletes
to respond equally to the same stimulus
despite having very different event
specialty or diverse profile to approach the
same event?
Analyze data as a single group,
BUT also display individual and
sub-group response and
differences between subgroups
Further understanding the appropriateness
of a stimulus for a given sub-group profile
for example 400, 800, and 1,500 m personal bests. Expanding
upon the athlete/participant profile in a study design allows for
significant improvement in analysis as well as for applied sport
practitioners and coaches to determine the relevance of study
findings to the athletes they coach.
Selection of Appropriate Intervention—Are
All Stimulus Created Equal?
Many papers report responder or-non-responder outcomes
following a blanket intervention without inspection of
participant profile diversity (see: Gaskill et al., 1999; Vollaard
et al., 2009; Timmons et al., 2010; Sylta et al., 2016). This
may result in assuming a “non-response.” Conversely, perhaps
an inappropriate stimulus was implemented for their unique
sub-group profile that has created the “non-responder” outcome,
rather than the athlete’s inability to adapt. Equally, the same
stimulus may have favored other uniquely identified subgroups
in the sample resulting in responders. Such scenarios are daily
challenges in coaching and an area where furthering our scientific
approach could add great resolution to inform frontline decision
making. Interestingly, a recent paper highlighting the value of an
individualized training intervention, albeit in a team sport group
by Jiménez-Reyes et al. (2017) demonstrate that individualized
programming based on a subjects baseline force-velocity profile
led to greater improvements in jump performance, with less
variability, compared to a generic non-individualized strength
training programme.
One major mechanism (but not exclusive from other neural
and morphological components) underpinning the diversity of
middle-distance athletes and unique adaptive profiles might be
muscle fiber typing. Slow twitch muscle are characterized by
myosin heavy chains (MHC) I and fast twitch by MHC II
[sum total of MHC lla (fast oxidative) and IIx (fast glycolytic)],
and shall be discussed using these isoforms herein. Historical
understanding of fiber typing at the extremes of speed and
endurance have been well-understood since the 1970s (Costill
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Sandford and Stellingwerff Middle-Distance Running Profile Complexity
et al., 1976), but the blend of these qualities in the middle-
distance are less clear (van der Zwaard et al., 2017). MHC
IIa and IIx fiber composition is a common characteristic
underpinning elite speed and power performance. For instance,
a former world champion sprint hurdler demonstrated an
impressive 71% MHC II (24%llx) (Trappe et al., 2015). MHC
II also has a superior ability to hypertrophy (Billeter et al.,
2003). Further characteristics of MHC II muscle include larger
baseline muscle carnosine content (Parkhouse et al., 1985;
Baguet et al., 2011) that have been related to frequency of
movement (i.e., more MHC II, higher frequency of movement)
(Bex et al., 2017); and enhanced muscle buffering. In addition,
greater creatine content is also found at rest in MHC
II muscle, which supports more anaerobic based exercise
(Tesch et al., 1989). All of these facets have implications for
muscle buffering capacity, sensitivity to supplementation and
intervention designs with ergogenic aids (Stellingwerff et al.,
2019).
By contrast distance runners (5 km–to marathon) have shown
a MHC I fiber range of 63.4 to 73.8%, with 1972 Olympic
Marathon gold-medalist Frank Shorter having 80% MHC I
(Costill et al., 1976). Taken together, differences in fiber typing
and specific hypertrophy, highlights the complexity of any one
type of stimulus to a phenotypic adaptive outcomes that requires
careful future sub-group investigation.
In the middle of this MHC I-to-MHC II continuum
lie the middle-distance athletes (Costill et al., 1976; Baguet
et al., 2011). The concurrent event demand for middle-
distance athletes of speed and endurance is at conflict with
the inverse relationship between oxidative capacity and muscle
cross sectional area (CSA—where MHC II are larger), alongside
the strong relationship between MHC I and oxidative enzyme
activity (Zierath and Hawley, 2004; van der Zwaard et al., 2016).
Therefore, a given middle-distance athlete may present from
varying points along this fiber-type continuum. For example,
Costill et al. (1976) revealed a large MHC I range of 44.0–73.3%
and 40.5–69.4% in female and male middle-distance runners,
respectively. Interestingly, these fiber type ranges overlaps
with the aerobic contribution to the 800 and 1,500 m events
(Table 1).
Without separating the presenting diversity into sub-groups in
our study designs, we are potentially blurring the individuality of
responses that may be present, and thus, perhaps losing effects
that work for some sub-groups and not others. An alternative
may be to consider what interventions may be appropriate for a
given sub-group within a study population, rather than applying
a generic intervention to all participants; much like a coach does
daily in prescribing training for their athletes.
Analysis of Effects per Sub-group
The consequence of employing blanket interventions to one
group is the “signal” of the effect may be lost in the diversity
of the athlete sample, which presents as non-significant “noise.”
Therefore, approaches such as ASR, alongside measures of
critical velocity/v4mmol/l, can allow for significantly enhanced
data analysis. In the end, it is best to not choose one
model, but a broad perspective (multidisciplinary approach) to
fully develop the athlete profile and subsequent analysis. In
addition consideration of mechanical differences such as aerial
or terrestrial profiles (Lussiana and Gindre, 2016) or baseline
muscle carnosine (Baguet et al., 2011), representative of fiber-
typing could add huge value in characterizing and determining
effective interventions for the different sub-groups. Given some
research interventions will have more relevant categories than
others (e.g., aerial vs. terrestrial biomechanics vs. ASR sub-group
using SRR vs. baseline muscle carnosine/creatine), consider
presenting results using multiple layered sub-groups (e.g., 400–
800 m athlete aerial profile vs. 400–800 m terrestrial profile),
to potentially provide a more complete understanding of the
complex characteristics between and within middle-distance
athletes. Finally, the smallest worthwhile change to competitive
performance in elite—middle-distance running (defined as
<3 km) is 0.5% (Hopkins, 2005). Bringing to question whether
our investigations and groupings of 800–5,000 m as “middle-
distance,” with up to 30% difference in aerobic energetic demands
(Table 1) are too broad to determine an effect that matters to
performance within the sub-group complexity.
CONCLUSION AND RECOMMENDATIONS
In the present mini-review we, first, provide a call to action
for authors to “Question your categories” with regards to broad
unidimensional classification of the middle-distance running
events. Second, we outline multiple areas at an athlete/participant
level where research design and consideration for sub-group
outcomes at multiple steps (section Study Athlete/Participant
Characterization and Description, Selection of Appropriate
Intervention—Are All Stimulus Created Equal?, Analysis of
Effects Per Sub-group; Table 2) can enhance the application
of research to the coach and practitioner frontline. Until the
inherent diversity of athlete profiles are appreciated by the
middle-distance research and practitioner community, many
current generic middle-distance sport science recommendations
and associated research methods will continue to provide a
misleading narrative and understanding of effective middle-
distance interventions. It is for sport scientists at the frontline
to connect the sub-group understanding and characterization
from the lab to the track, enabling our coaches to make the most
informed recommendations about individualizing interventions
based on the athlete presenting in front of them.
To conclude, in the words of Professor Hans Rosling “It
will be helpful to you if you always assume your categories are
misleading. Here are five powerful ways to keep questioning your
favorite categories: look for differences within and similarities
across groups; beware of the majority; beware of exceptional
examples; assume you are not normal; and beware of generalizing
from one group to another.”
It is from this paradigm that we believe more progress
will be made in understanding the complexities, and training
stimulus approaches in applied sport science application to
middle-distance running.
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Sandford and Stellingwerff Middle-Distance Running Profile Complexity
AUTHOR CONTRIBUTIONS
GS and TS were involved in the conceptual ideas, writing a first
draft of the paper, selection and production of figures and tables,
and revising the manuscript.
ACKNOWLEDGMENTS
The authors would like to thank Steve Willis for
his personal communications regarding Peter Snell’s
historical performances.
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Conflict of Interest: The authors declare that the research was conducted in the
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potential conflict of interest.
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