RESEARCH ARTICLE
Factors associated with high-level endurance
performance: An expert consensus derived
via the Delphi technique
Magdalena J. KonopkaID1,2☯*, Maurice P. Zeegers1,2,3☯, Paul A. Solberg4☯,
Louis Delhaije2☯, Romain Meeusen5,6☯, Geert Ruigrok2☯, Gerard Rietjens5☯,
Billy Sperlich7☯
1 Care and Public Health Research Institute, Maastricht University, Maastricht, Limburg, Netherlands,
2 Department of Epidemiology, Maastricht University Medical Centre, Maastricht, Limburg, Netherlands,
3 School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Limburg,
Netherlands, 4 Norwegian Olympic and Paralympic Committee and Confederation of Sports, Oslo, Norway,
5 Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels,
Brussels-Capital Region, Belgium, 6 Brussels Human Robotics Research Center (BruBotics), Vrije
Universiteit Brussel, Brussels, Brussels-Capital Region, Belgium, 7 Integrative & Experimental Exercise
Science & Training, Institute of Sport Science, University of Wu¨rzburg, Bavaria, Germany
☯ These authors contributed equally to this work.
* magdalena.konopka@maastrichtuniversity.nl
Abstract
There is little agreement on the factors influencing endurance performance. Endurance perfor-
mance often is described by surrogate variables such as maximum oxygen consumption, lac-
tate threshold, and running economy. However, other factors also determine success and
progression of high-level endurance athletes. Therefore, the aim was to identify the relevant
factors for endurance performance assessed by international experts by adhering to a struc-
tured communication method (i.e., Delphi technique). Three anonymous evaluation rounds
were conducted initiated by a list of candidate factors (n = 120) serving as baseline input vari-
ables. The items that achieved 70% of agreement in round 1 were re-evaluated in a second
round. Items with a level of agreement of 70% in round 2 reached consensus and items with
a level of agreement of 40–69% in round 2 were re-rated in a third round followed by a consen-
sus meeting. Round 1 comprised of 27 panellists (n = 24 male) and in round 2 and 3 18 (n = 15
male) of the 27 panellists remained. Thus, the final endurance expert panel comprised of 18
international experts (n = 15 male) with 20 years of experience on average. The consensus
report identified the following 26 factors: endurance capacity, running economy, maximal oxy-
gen consumption, recovery speed, carbohydrate metabolism, glycolysis capacity, lactate
threshold, fat metabolism, number of erythrocytes, iron deficiency, muscle fibre type, mitochon-
drial biogenesis, hydrogen ion buffering, testosterone, erythropoietin, cortisol, hydration status,
vitamin D deficiency, risk of non-functional overreaching and stress fracture, healing function of
skeletal tissue, motivation, stress resistance, confidence, sleep quality, and fatigue. This study
provides an expert-derived summary including 26 key factors for endurance performance, the
“FENDLE” factors (FENDLE = Factors for ENDurance Level). This consensus report may
assist to optimize sophisticated diagnostics, personalized training strategies and technology.
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OPEN ACCESS
Citation: Konopka MJ, Zeegers MP, Solberg PA,
Delhaije L, Meeusen R, Ruigrok G, et al. (2022)
Factors associated with high-level endurance
performance: An expert consensus derived via the
Delphi technique. PLoS ONE 17(12): e0279492.
https://doi.org/10.1371/journal.pone.0279492
Editor: Daniel Boullosa, Universidade Federal de
Mato Grosso do Sul, BRAZIL
Received: July 8, 2022
Accepted: December 8, 2022
Published: December 27, 2022
Copyright: © 2022 Konopka et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Introduction
High performance in endurance sports is the result of the interplay of various factors including
optimal training [1], recovery [2, 3], nutritional strategies [4–6], the use and handling of envi-
ronmental [7] and psycho-social factors [8, 9] as well as high-tech equipment [10–12]. In addi-
tion, optimal biological factors are crucial to achieve world class competition level. For
instance, recent scientific evidence suggests that a complex network of genetic and other bio-
logical mechanisms (e.g., transcriptomics, epigenomics, proteomics, metabolomics) contribute
to the performance level of an athlete [13]. By integrating such novel research disciplines
(omics) and advanced technology (e.g., machine learning) exercise science is shifting towards
“precision exercise” offering training and recovery strategies tailored to the individual needs of
an athlete [14–16]. The main idea of precision exercise is to base training and recovery deci-
sions on individual characteristics including various biomarkers, ultimately attempting to per-
sonalize training programs, minimize injury risks, optimize performance outcomes, and to
identify talents in near future [16]. These attempts, e.g., in the field of precision medicine, are
based on prediction models and the predictive ability of these models tend to increase with the
amount and quality of data input [17]. Thus, from a “precision endurance exercise” perspec-
tive, the more factors associated with high-level endurance performance are integrated, the
better the prediction of the model should become as evidenced in medical research [18].
However, from a practical and a scientific point of view little agreement exists on the most
important factors that influence high-level endurance performance. A first step towards devel-
oping precision exercise for endurance athletes would be to identify the relevant key factors
for further modelling. In addition, endurance athletes, their coaches, and researchers could be
informed and guided how to set priorities regarding training strategies and future research.
For example, the identified key factors could be assessed with innovative wearable technology.
Therefore, the aim of this study was to reach consensus about the key factors that are consid-
ered important for high-level endurance performance among international experts. The Del-
phi technique allows a comprehensive and structured group communication process aiming
to achieve convergence of expert opinions by employing iterative data collection [19–24].
Materials and methods
Study design and consensus threshold
The study protocol is available at the open science framework (doi:10.17605/OSF.IO/YH5V4).
We conducted a consensus study by employing the Delphi technique according to the descrip-
tion by Hsu et al., [19] the checklist by Sinha et al., [25] and Hasson et al., [26]. We also fol-
lowed the recommendations for Conducting and REporting DElphi Studies (CREDES) [27].
The 12-item CREDES checklist is enclosed in S1 Table. The study was implemented in three
phases: 1) preparation, 2) conduction, and 3) analysis [28]. The Delphi process (i.e., conduc-
tion phase) consisted of three iterative rounds of web-based questionnaires, participant
response, and controlled feedback.
We employed a dichotomous scale (relevant vs. not relevant) to determine the relevance of
the items. A cut-off value of 70% level of agreement was a priori chosen as the consensus
threshold [27]. Level of agreement was categorized into low (0–39%), moderate (40–69%), and
high (70–100%). Items with a low or moderate level of agreement were considered not relevant
whereas items with a high level of agreement (70%) represented relevant factors and/or con-
sensus. The steering committee advised on study as well as survey design, methodology, and
content, but did not interact with or act on behalf of the panellists. Ethical approval for con-
ducting the study was obtained from the Ethical Review Committee Health, Medicine, and
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Life Science of Maastricht University (FHML-REC/2019/021) and conducted in accordance
with the Declaration of Helsinki for human research.
Study flow
An initial list of candidate factors (n = 120) established by the steering committee served as
input for the first round. Before distribution, the survey was pilot-tested and subsequently sent
to the panellists of round 1. The items receiving a high level of agreement in round 1 were re-
evaluated in a second round. All high level of agreement items in round 2 were included in the
‘consensus report’ presenting the level of agreement for each item. Moderate level of agree-
ment items after round 2 were resubmitted to round 3 and in case the 70% threshold was
attained, these factors were also added to the consensus report. Finally, a ‘consensus decision’
with the steering committee took place in which the remaining items of round 3 were consid-
ered for additional inclusion into the consensus report. The consensus report thus contains
the factors that achieved a high level of agreement (i.e., the “FENDLE” factors). The study flow
is illustrated in Fig 1.
Panellists
In accordance with the CREDES recommendations [27] we involved experts with diverse
backgrounds (e.g., high-level coaches, exercise scientists, physicians, etc.) and geographical
locations. Inclusion criteria to participate in this study were: 1) age of 18 years or older; 2)
good command of English language; 3) competence in or experience with (elite) endurance
training. The panellists were recruited through non-probability purposive sampling to ensure
that the invited experts represent sufficient experience, knowledge, and interest in the topic.
Eligible panellists were professional athletes or coaches, exercise-scientists, physiotherapists,
exercise-psychologists, or medical physicians. The panellists had to possess extensive experi-
ence and knowledge of elite endurance performance. The qualifications and responsibilities of
the expert panel are displayed in S2 Table. Invitations to take part in this study were sent
through email by the steering committee members using purposive sampling. The invitation
letter included the participant information leaflet containing information regarding the aim of
the study, the Delphi process, what was expected from the panellists, the projected timeline,
and the amount of time, effort, and commitment required to finish the study. The panellists
remained anonymous except for the main investigator (MK) for communication purposes.
Each survey took approximately 15 minutes to complete.
Data collection
The web-based surveys were conducted using Qualtrics (Qualtrics, Provo, UT). The panellists
provided written informed consent for the study at the start of the first survey. In all three
rounds of questionnaires, the panellists were asked to rate the proposed items as either ‘rele-
vant’ or ‘not relevant’: “Which of the following factors, if any, are relevant for endurance per-
formance?” Following each round the responses were analysed and anonymously reported
back to the experts (e.g., number of panellists, level of agreement for each item) allowing them
to reconsider pervious decisions.
Preparation (PHASE I)
The purpose of the preparation phase was to develop a comprehensive list of candidate factors
that are possibly associated with high-level endurance performance and could potentially be
incorporated into the consensus report. First, a literature search was performed in the Medline
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Fig 1. Study flow.
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database to identify candidate factors (last search date: 05. November 2019). All retrieved arti-
cles were read by the main investigator (MK). The potentially relevant factors were extracted
using Excel and compiled into a list. Risk of bias of individual studies was not assessed. The list
was subsequently revised (e.g., wording of factors, adding/ removing factors) based on recom-
mendations from the steering committee. Finally, the list comprised of 120 factors (S3 Table)
and served as input for the first round of the Delphi process [19]. Before distribution, the sur-
vey was pilot tested by five experts with varying backgrounds (exercise scientist, athletes,
coaches) and 14 years of experience on average, different from those recruited for the main
study, to verify the clarity of questions. The testers were instructed not to add or remove fac-
tors from the list. Suggestions from the testers were used to revise the survey.
Conduction (PHASE II)
Round 1. For reason of efficiency and proposed by the pilot testers, the panellists were
asked to ‘uncheck’ the non-relevant factors from the list in the first round. We also invited the
experts to add missing factors and to provide free text comments. Finally, we requested rele-
vant demographic data, such as name, age-group, email address, profession, place of employ-
ment, and quantifiable level of expertise.
Round 2. Based on the results from round 1, the steering committee agreed to eliminate
the candidate factors that did not reach the threshold of 70%. Thus, items that did not reach
the 70% threshold in round 1 were discarded from the survey and not resubmitted in round 2.
In round 2, the items that attained a level of agreement of 70% in round 1 were distributed
to the panellists who completed round 1 together with the average level of agreement, the
newly proposed items, and the anonymized free text comments from round 1. This time, the
panellists were asked to ‘check’ the relevant factors. After round 2, the items that achieved a
level of agreement of 70% reached consensus and were incorporated in the consensus report.
Round 3 and consensus decision of the steering committee
In round 3, we provided the anonymized results of the second survey to the experts who com-
pleted both rounds (i.e., the “FENDLE PANEL”) including their own rating and group sum-
mary statistics. In this round, the experts had the option to re-rate the items that reached a
moderate level of agreement (40–69%) in round 2. In this ‘consensus decision’ of the FENDLE
PANEL, the items that achieved a level of agreement of 70% after round 3 were also added to
the consensus report. The remaining factors from round 3 were set aside for the ‘consensus
decision’ amongst the steering committee members. Each member of the steering committee
could propose items which were consequently discussed by the group. In case each member
agreed (level of agreement = 100%), the proposed factor was added to the consensus report.
Lastly, in round 2 and 3, there was no option for free text responses. S4 Table contains a link to
the web-based questionnaire of round 1 and 2 as well as an illustration of round 3.
Analysis (PHASE III)
After each round the raw data was downloaded from Qualtrics. We checked for incomplete
submissions and duplicates. In case duplicate panellists were identified, the first survey submit-
ted was analysed. Incomplete submissions were excluded. In case a valid email address was
provided, the research team invited the participant to finish incomplete surveys. For the data
analysis, we randomly assigned identification numbers to the experts so that they remained
pseudonymous. Descriptive (frequency, percentage) and inferential statistics (mean, median,
interquartile range) were used to describe the demographics and to analyse the extent of con-
sensus [26]. The main outcome variable was binary. Secondary outcome variables were both
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categorical (gender, age-group, country, and occupation) and continuous (years of experi-
ence). All analysis were performed with R (version 4.0.3).
Results
Survey distribution and demographics of panellists.
Twenty-seven panellists completed
the first round of questionnaire (July–December 2020) and were consequently invited to par-
ticipate in round 2. Eighteen panellists (66.7%) from nine countries finished round 2 (Janu-
ary–February 2021) and 3 (March–April 2021) and thus completed all three rounds of
questionnaires. Of the 18 panellists, 15 (83.3%) were male and 72.2% were aged between 31
and 60 years. The median time in years (interquartile range) of practical experience with
endurance athletes was 20.0 (10.0–33.5). Table 1 demonstrates the demographics of the panel-
lists. Finally, the consensus decision of the steering committee took place in May 2021.
Survey results
Fig 1 illustrates the flow of the factors through the Delphi process. In the first round, 99 of the
120 candidate items achieved the 70% threshold and consequently were resubmitted in round
2. In addition, one item (sedentary lifestyle) was proposed as new factor and therefore added
to round 2. In the second round, 24 of 100 items achieved consensus 70% and thus were
included in the consensus report. Further, 22 items were rated with a moderate level of agree-
ment and therefore resubmitted in round 3. During the ‘consensus decision’ of the FENDLE
PANEL in round 3, one item “endurance capacity” was rated as relevant (level of agreement
72%) and hence integrated into the consensus report. S5–S7 Tables displays the results of
round 1–3, respectively. After round 3, the ‘consensus decisions’ of the steering committee
took place, in which 13 of the 21 remaining items from round 3 were subject for discussion.
The steering committee agreed (100%) on one item, “recovery speed”, to be additionally
included in the consensus report. S8 Table presents the results of the consensus decision of the
steering committee. The final consensus report contained 26 factors (Table 2) considered rele-
vant for high-level endurance training and/or performance. Lastly, 20 factors were rated with a
moderate (S9 Table) and 54 with a low level of agreement, representing the factors rated as not
relevant (S10 Table).
Discussion
Based on an international expert consensus process (i.e., Delphi technique) the aim of this
study was to identify key factors that are considered important for high-level endurance per-
formance. In total, 26 factors achieved consensus. The 26 FENDLE factors comprised of five
different clusters: i) physiology ii) nutrition, iii) injuries, iv) psychological traits and v) fatigue.
Physiology
Traditionally, three physiological factors have been identified explaining endurance perfor-
mance: i) ‘maximum oxygen consumption’ (i.e., the maximal capacity to take up, transport,
and utilize oxygen), ii) the ability to maintain high velocity without accumulating blood lactate
(‘lactate threshold’), and iii) ‘running economy’ often expressed as the oxygen utilized while
running at a given constant speed [29]. Since these three physiological factors have been exten-
sively investigated and linked to elite endurance performance, it seems reasonable that the
expert panel identified these factors as relevant for high-level of endurance performance. Inter-
estingly, this consensus report also identified the ability to quickly recover during and after
endurance events (‘recovery speed’) as important factor for high-level endurance performance.
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Replenishing energy storage efficiently and quickly reduces time between training and compe-
tition and simultaneously can prevent injuries when training again with full energy storages
[30]. The fuelling of the different energy pathways is a key factor for many endurance sports
[4–6] and since most endurance disciplines involve either long-duration tasks (e.g., marathon
running) and/or high-intensity exercise (e.g., 800m running) it seems plausible that the experts
reached consensus about ‘glycolytic capacity’, ‘hydrogen iron buffering’, as well as ‘fat and car-
bohydrate metabolism’ as key factors influencing endurance performance. Furthermore, the
Table 1. Demographics of the panellists.
ROUND 1
ROUND 2 & 3
i.e., FENDLE PANEL
Total (n, %)
27 (100)
18 (100)
Gender (n, %)
Males
24 (88.9)
15 (83.3)
Females
2 (7.4)
2 (11.1)
Missing
1 (3.7)
1 (5.6)
Age group in years (n, %)
18–30
5 (18.5)
4 (22.3)
31–60
20 (74.1)
13 (72.1)
60+
1 (3.7)
1 (5.6)
Missing
1 (3.7)
0
Country (n, %)
Canada
1 (3.7)
1 (5.6)
USA
1 (3.7)
1 (5.6)
United Kingdom
2 (7.4)
1 (5.6)
Belgium
4 (14.8)
3 (16.7)
Germany
3 (11.1)
2 (11.1)
Italy
3 (11.1)
2 (11.1)
Netherlands
8 (29.7)
5 (27.6)
Norway
1 (3.7)
1 (5.6)
Sweden
2 (7.4)
2 (11.1)
Finland
1 (3.7)
0
Spain
1 (3.7)
0
Occupation (n, %)
Chief Executive Officer (triathlon)
1 (3.7)
1 (5.6)
PhD student (sport science)
5 (18.5)
4 (22.2)
Physician (sports and rehabilitation)
2 (7.4)
1 (5.6)
Physiotherapist
1 (3.7)
1 (5.6)
Professor (sport science)
6 (22.3)
6 (33.1)
Director (triathlon)
1 (3.7)
1 (5.6)
Head Coach
Road cycling
1 (3.7)
1 (5.6)
Soccer
1 (3.7)
1 (5.6)
Triathlon
2 (7.4)
2 (11.1)
Unknown
4 (14.8)
0
Missing
3 (11.1)
0
Practical experience in years (median, IQRa)
20.0 (9.0–33.0)
20.0 (10.0–33.5)
FENDLE: acronym for Factors for ENDurance Level.
aIQR = interquartile range.
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muscle fibre spectrum [31, 32] including the density and efficiency of mitochondria [33] have
substantial impact on endurance performance. It is well known that ‘fibre type distribution’
and ‘mitochondrial biogenesis’ are important for increasing oxygen utilization for energy pro-
duction stimulated by various training methods [34, 35]. Oxygen delivery and utilization are
critical components for the energy turnover in the muscle cells [36]. Substantial evidence indi-
cates that improving oxygen transportation by the augmentation of haemoglobin levels (e.g.,
through high-altitude training, medication, or blood transfusion) significantly improves
endurance performance [37]. Thus, it seems reasonable that the experts rated the ‘number of
red blood cells’ and ‘iron deficiency’ as key factors for high-level endurance performance.
Finally, three hormones reached consensus as key physiological factors for endurance perfor-
mance: ‘testosterone’, ‘erythropoietin’, and ‘cortisol’. Testosterone is known to stimulate mus-
cle mass and to reduce body fat [38]. Erythropoietin induces erythropoiesis, the maturation
and proliferation of oxygen-delivering red blood cells [39]. Finally, in the skeletal muscle the
level of cortisol plays a fundamental role in regulating energy homeostasis [40]. During exer-
cise, the high level of cortisol increases the availability of metabolic substrates, protects from
immune cell activity, and maintains vascular integrity [41].
Table 2. Consensus report describing the FENDLE factors, the 26 factors considered the most important to influence endurance training and/or performance.
Cluster
Factor
Level of agreement (%)
Physiology
General
Endurance capacitya
72.2
Economy of movement
88.9
Maximal oxygen consumption
94.4
Recovery speedb
66.7
Metabolism
Carbohydrate metabolism
100.0
Glycolysis capacity
100.0
Lactate threshold
88.9
Fat metabolism
88.9
Blood
Number of red blood cells
100.0
Iron deficiency
94.4
Muscle
Muscle fibres—type 1 vs. type 2a/x
94.4
Mitochondrial biogenesis
88.9
Hydrogen ion buffering
88.9
Hormones
Testosterone
94.4
Erythropoietin
83.3
Cortisol
77.8
Nutrition
Electrolyte balance/ hydration status
77.8
Vitamin D deficiency
72.2
Injuries
Risk of non-functional overreaching
88.9
Risk of stress fracture
77.8
Healing function of skeletal muscle tissue
88.8
Psychology
Motivation capacity
94.4
Stress resistance
88.9
Self-confidence
72.2
Fatigue
Sleep quality
94.4
Level of fatigue
77.8
FENDLE: acronym for Factors for ENDurance Level.
aAdded after the consensus decision of the FENDLE PANEL (round 3).
bAdded after the consensus decision of the steering committee.
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Injury and nutrition
Good health is essential for achieving high training volumes necessary for maximal endurance
performances. Since endurance athletes engage in numerous low- to high intensity trainings
and thus are exposed to constant muscle and tissue damage, it seems reasonable that the
experts identified the ‘healing function of skeletal muscle tissue’, ‘risk of stress fracture’, and
‘risk of non-functional overreaching’ as key factors for high-level endurance performance.
Moreover, tissue repair is supported by various nutritional factors. For example, a low level of
vitamin D has been associated with maladaptation of skeletal muscle and bone tissue [42].
Proper muscle renewal in response to exercise is required for optimal hypertrophic effects
[43]. Therefore, it is plausible that ‘vitamin D deficiency’ has reached consensus in the present
analysis. Further, ‘hydration status’ and ‘electrolyte balance’ were rated as key endurance fac-
tors by the panellists. The loss of body fluids during exercise is mostly due to sweating and the
replacement of sodium loss is widely recommended [44]. The hydration of athletes tends to
vary according to individual factors (e.g., thirst response, acclimation, gut training) and envi-
ronmental factors (e.g., ambient temperature, provision of drinks at aid stations) as well as the
type and intensity of exercise, making individualized fluid replacement strategies necessary for
high-level endurance performance [5, 45].
Psychology and fatigue
This report contains three psychological key factors relevant for high-level endurance perfor-
mance: ‘motivation’, ‘stress resistance’, and ‘self-confidence’. Motivation is a key factor when
describing (long-term) success in endurance athletes. Intrinsic motivation determines the high
engagement in disciplined training and therefore is crucial for any athlete [46]. Professional
athletes must be able to deal with stress during training and competitions [47]. In literature,
self-confidence is one of the most cited factors thought to affect athletic performance [48, 49].
Finally, it is well known that engaging in extensive endurance training induce central and
peripheral fatigue [50]. Therefore, the ‘sleep quality’ and ‘level of fatigue’ are not surprising to
be identified as key factors influencing high-level endurance performance.
Areas of disagreement
The number of relevant endurance factors declined from 99 factors in the first round to 24 fac-
tors in the second round. For instance, the volume of heart (85%) and lungs (78%) were rated
as relevant in the first round but have been discarded after the second round (level of agree-
ment round 2: 33% and 17%, respectively). In addition, although myoglobin plays an impor-
tant role in the oxidative capacity of endurance trained runners [51], ‘myoglobin storage
capacity’ was rated by 89% of the experts as relevant in round 1, but only by 33% in round 2.
Furthermore, zinc (level of agreement: 85% round 1, 28% round 2), magnesium (level of agree-
ment: 85% round 1; 39% round 2), vitamin C (level of agreement: 78% round 1; 22% round 2),
and Vitamin E (level of agreement: 74% round 1; 11% round 2) deficiency were eliminated
after round 2. Zinc and magnesium are essential trace elements and normal level of zinc are
needed for proper immune system function [52]. However, there is no evidence of reduced
performance in zinc deficient endurance athletes [53]. Magnesium supplementation, in turn,
may enhance athletic performance in deficient athletes [54], although is not beneficial when
magnesium status is normal [55]. Based on current scientific knowledge, antioxidants (level of
agreement: 82% round 1; 22% round 2) including Vitamin C and E supplements, may not pro-
vide additional benefits for athletes. Finally, the ‘risk of upper respiratory tract infections’
achieved a level of agreement of 67% after round 3 and thus has not been included in the con-
sensus report (threshold 70%). Except for injuries, upper respiratory symptoms are the most
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common medical presentation in endurance athletes [56]. All in all, the factors that did not
make it on the consensus report remain subject for discussion and should not be overlooked
by athletes and coaches.
Research implications
This consensus report provides athletes, coaches, and exercise scientists a holistic overview of
the key factors contributing to high-level endurance performance. Consequently, these factors
need to be recognized and prioritized in the future. The 26 factors included in the consensus
report demonstrate where to focus on in the future. For instance, the current results can be
integrated into novel research disciplines such as -omics approaches. The aim of such
approaches is the implementation of precision medicine in sports [14], which attempt to per-
sonalize performance by employing prediction models [16]. The predictive ability of such
models tends to increase with the amount and quality of data input [17]. Hence, the identifica-
tion of key factors and integration into future models the better the predictions will become
[18]. Moreover, the current findings can be integrated into new technology such as wearables
for data-informed decision-making enabling endurance athletes and coaches to monitor the
athletes’ health, risk of injury, and performance in real time [57]. In near future, through rapid
advances in technology, more opportunities will arise to integrate the FENDLE factors. Finally,
replication studies are warranted to validate the current findings and update the consensus
report.
Strengths and limitations
We would like to highlight several strengths. i) First, we strictly followed the study protocol
and reported the methodological considerations undertaken transparently. ii) In the prepara-
tion phase, a literature review was performed. The identified studies served as theoretical
framework for establishing a comprehensive list. This list may not have captured all endurance
factors, although only one new item was proposed from the expert panel. iii) Before distribu-
tion, the survey was pilot tested to assure comprehension of the survey [58]. iv) The steering
committee consisted of international experts from the field of sport science who advised on
study and survey design, methodology, and content. v) The authors assured geographic disper-
sion and anonymity of the panellists. Furthermore, the expert panel did not interact directly
with each other so that social pressure was avoided [28]. The panellists had also the option to
provide free text comments in round 1 and could reconsider initial ratings. vi) Finally, the
response rates in round 2 (66%) and 3 (100%) were high [59]. Engaging 15–20 panellists is suf-
ficient as long as the background of panellists is homogenous, and three rounds is appropriate
for reaching consensus within a Delphi process [19].
We would like to acknowledge some limitations: i) Purposive sampling might have intro-
duced selection bias [60] and personal perceptions of the experts could have influenced the
results. ii) We are aware that the final expert selection is a limitation of the current study. We
advise to recruit a higher number of experts from more diverse backgrounds/ disciplines (e.g.,
accredited registered sports dietitians) representing in-depth knowledge in each area. iii) We
used different approaches to answer the questions in round 1 and 2. Hence we recommend a
follow up study employing coherent methods throughout the Delphi process. iv) Some factors
may be classified as “higher-level” (e.g., maximal oxygen consumption) or as “lower-level” fac-
tors (e.g., ‘metabolic’, ‘blood’ related factors) with the lower-level factors influencing endur-
ance performance indirectly by affecting the higher-level factors. Hence, some potentially
meaningful (and likely lower-level) factors may have been omitted (e.g., ‘strength/ power’;
level of agreement 33.3%). v) Although experts from various disciplines were recruited, most
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factors identified pertain to running. Factors related to sport-specific skills, such as ‘technique
in swimming’ or ‘power in cycling’ are underrepresented. vi) Factors such as ‘exogenous car-
bohydrate/ caffeine/ post exercise protein ingestion’, ‘neuromuscular control’, ‘strategy’ or
‘decision making’ were not examined in the current study, although these factors have exten-
sively been reported and linked to endurance performance or recovery [5, 6, 61–64]. vii)
Expert opinion remains among the lowest levels of empirical evidence [65] and the findings
are only as valid as the opinions of the experts constituting the panel. Nonetheless, the experts
of our study had on average 20 years of practical experience, reflecting a trained panel with
good knowledge of the topic. viii) Finally, Delphi studies are considered evidence-based
approaches with emphasis on value of expert judgement, which is not accessible through clini-
cal trials [27]. This technique has the potential to arrive at valid and credible results—if per-
formed properly. We therefore strictly followed the quality criteria for conducting and
reporting Delphi studies to increase the validity of the results.
Conclusions
This study provides an expert-derived consensus report identifying the important factors for
high-level endurance performance, the FENDLE factors. We offer professional coaches, ath-
letes, and scientist insights into 26 key endurance factors and strongly recommend considering
these factors when optimizing personalized training strategies and technology in the future.
Supporting information
S1 Table. CREDES checklist.
(PDF)
S2 Table. Qualifications and responsibilities of the expert panel.
(PDF)
S3 Table. List of candidate factors (n = 120).
(PDF)
S4 Table. Survey outline.
(PDF)
S5 Table. Results of round 1.
(PDF)
S6 Table. Results of round 2.
(PDF)
S7 Table. Results of round 3.
(PDF)
S8 Table. Consensus decision.
(PDF)
S9 Table. Moderate level of agreement factors.
(PDF)
S10 Table. Low level of agreement factors.
(PDF)
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Acknowledgments
The research team thanks the 27 panellists. We specially thank Mr. Amatori, Mr. Gonzalez,
Mr. Leary, and Mr. Tonessen for participation. We also would like to thank the Steering Com-
mittee: Paul A. Solberg, Louis Delhaije, Romain Meeusen, Geert Ruigrok, Gerard Rietjens, and
Billy Sperlich.
Protocol register name: Open science framework.
Registration doi: 10.17605/OSF.IO/PGNWT.
Author Contributions
Conceptualization: Magdalena J. Konopka, Maurice P. Zeegers, Paul A. Solberg, Louis Del-
haije, Romain Meeusen, Geert Ruigrok, Gerard Rietjens, Billy Sperlich.
Data curation: Magdalena J. Konopka.
Formal analysis: Magdalena J. Konopka.
Investigation: Magdalena J. Konopka.
Methodology: Magdalena J. Konopka, Maurice P. Zeegers, Paul A. Solberg, Louis Delhaije,
Romain Meeusen, Geert Ruigrok, Gerard Rietjens, Billy Sperlich.
Project administration: Magdalena J. Konopka.
Software: Magdalena J. Konopka.
Supervision: Maurice P. Zeegers, Billy Sperlich.
Visualization: Magdalena J. Konopka.
Writing – original draft: Magdalena J. Konopka, Billy Sperlich.
Writing – review & editing: Magdalena J. Konopka, Maurice P. Zeegers, Paul A. Solberg,
Louis Delhaije, Romain Meeusen, Geert Ruigrok, Gerard Rietjens, Billy Sperlich.
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Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique.
Konopka, Magdalena J,Zeegers, Maurice P,Solberg, Paul A,Delhaije, Louis,Meeusen, Romain,Ruigrok, Geert,Rietjens, Gerard,Sperlich, Billy