Citation: Drum, S.N.; Rappelt, L.;
Held, S.; Donath, L. Effects of Trail
Running versus Road
Running—Effects on Neuromuscular
and Endurance Performance—A Two
Arm Randomized Controlled Study.
Int. J. Environ. Res. Public Health 2023,
20, 4501. https://doi.org/10.3390/
ijerph20054501
Academic Editors: Joanna Baran and
Justyna Leszczak
Received: 27 December 2022
Revised: 22 February 2023
Accepted: 28 February 2023
Published: 3 March 2023
Copyright:
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International  Journal  of
Environmental Research
and Public Health
Article
Effects of Trail Running versus Road Running—Effects on
Neuromuscular and Endurance Performance—A Two Arm
Randomized Controlled Study
Scott Nolan Drum 1,*
, Ludwig Rappelt 2, Steffen Held 2
and Lars Donath 2
1
Department of Health Sciences—Fitness Wellness, College of Health and Human Services,
Northern Arizona University, Flagstaff, AZ 86001, USA
2
Department of Intervention Research in Exercise Training, German Sport University Cologne,
50933 Cologne, Germany
*
Correspondence: scott.drum@nau.edu; Tel.: +1-970-371-2620
Abstract: Running on less predictable terrain has the potential to increase the stimulation of the
neuromuscular system and can boost aerobic performance. Hence, the purpose of this study was
to analyze the effects of trail versus road running on neuromuscular and endurance performance
parameters in running novices. Twenty sedentary participants were randomly assigned to either
a trail (TRAIL; n = 10) or road running (ROAD; n = 10) group. A supervised and progressive,
moderate intensity, and work-load-matched 8 wk endurance running program on TRAIL or ROAD
was prescribed (i.e., randomized). Static balance (BESS test), dynamic balance (Y-balance test), gait
analysis (RehaGait test, with regard to stride time single task, stride length dual task, velocity single
task), agility performance (t-test), isokinetic leg strength (BIODEX), and predicted VO2max were
assessed in pre- and post-tests. rANOVA analysis revealed no signiﬁcant time–group interactions.
Large effect sizes (Cohen’s d) for pairwise comparison were found for TRAIL in the BESS test (d = 1.2)
and predicted (pred) VO2max (d = 0.95). Moderate effects were evident for ROAD in BESS (d = 0.5),
stride time single task (d = 0.52), and VO2max predicted (d = 0.53). Possible moderate to large effect
sizes for stride length dual task (72%), velocity single task (64%), BESS test (60%), and the Y-balance
test left stance (51%) in favor of TRAIL occurred. Collectively, the results suggested slightly more
beneﬁcial tendencies in favor of TRAIL. Additional research is needed to clearly elucidate differences
between TRAIL and ROAD, not only in novices but also in experienced exercisers.
Keywords: postural balance; gait; agility; muscle strength; long distance running; endurance training;
running surface
1. Introduction
Regular physical activity, such as running, enhances cardiorespiratory and neuromuscu-
lar performance and is associated with a delay in all causes of mortality and morbidity [1–4].
Lee et al. [5] found that minimal running training volumes of 30–59 min a week, or 5–10 min a
day are associated with lower risks of all-cause and cardiovascular mortality. Despite proven
health benefits of physical exercise, the number of sedentary people worldwide is large and
steadily growing [6–8] in both sexes and with increasing age [7,9]. Physical inactivity acceler-
ates aging-induced functional decrements and compromises physical performance which can
lead to impairments in activities of daily living [3,10,11].
At approximately 30 years of age, muscle mass and muscle strength begin to decrease
gradually by 10–15% each decade [3]. Progressive skeletal muscle atrophy is accompanied
by a loss in muscle coordination and a decline in balance [11], which can already be evident
in individuals of ≥40 years of age [12]. Balance impairments and related spatiotemporal
gait deﬁcits both represent crucial risk factors for falls and fall-related injuries [13–15].
Falls and fall-related injuries as well as general health impairments not only occur in
Int. J. Environ. Res. Public Health 2023, 20, 4501. https://doi.org/10.3390/ijerph20054501
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2023, 20, 4501
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the elderly but are a frequent problem in middle-aged and young people [16,17]. Few
studies have investigated falls and the frequencies of falls in young and middle-aged
individuals [16]. In a longitudinal study by Niino et al. [17], the prevalence of falls among
middle-aged individuals (40–59 years) was 12.9%, compared to 16.5% among the elderly
group (60–79 years). Talbot et al. [16] observed a prevalence of one or more reported falls
within a two-year period in 18.5% of young adults, 21% of middle-aged adults and 35% of
older adults. In addition to the direct consequences of falls, many people develop a fear of
falling after such an event which often leads to a vicious cycle of reduced physical activity,
decreased mobility and muscle strength, and a subsequent higher risk for future falls [14,18,19].
To refute the natural decline in neuromuscular properties with aging and augment
spontaneous balance and maintenance of strength, our main study objective was to de-
termine the effectiveness of exercising on uneven surfaces (i.e., dirt trails) vs. familiar (or
predictably even road) surfaces in a younger adult population on the prior mentioned
variables (e.g., neuromuscular or gait training, balance, strength). For instance, running
has been shown to improve or amplify several task-speciﬁc, metabolic, and neuromuscular
factors [20]. However, few studies have focused on neuromuscular variables (e.g., gait
parameters via a wearable gait analysis system) resulting from endurance training on
distinctly different surfaces [20]. As a suggestion, future researchers should theoretically
look at the protective effects of frequent running on uneven surfaces related to unexpected
falls, especially in the elderly. Ultimately, the impact of trail running, which is attracting
an increasing number of recreational and competitive runners [21,22], compared to road
running, has not been extensively compared.
In the present project, we hypothesized trail running would lead to more pronounced
improvements in neuromuscular and endurance performance than road running. These
assumptions are based on the different characteristics of surface type and gradients between
the two conditions. Trail running tends to invoke higher challenges for the neuromus-
cular system, especially regarding involved muscle coordination, proprioception, and
activation [23–26] compared to road running. Furthermore, since uphill running is an
effective stimulus for improving endurance running performance [27,28] and submaximal
running economy [27,29] we expected a more pronounced performance at posttest in the
submaximal incremental treadmill test for TRAIL.
2. Materials and Methods
2.1. Participants and Experimental Setting
This pilot study adheres to CONSORT guidelines [30]. Participants were recruited via
ﬂyers, posters, word-of-mouth, and local advertisement as well as via “batch” emails among
faculty and staff at the university where the project was conducted. Inclusion criteria [31]
for participation were: (i) 18–59 years of age; (ii) currently sedentary or not exercising
more than twice a week for the last three months; (iii) free from any injury or illness and
currently no intake of any medication; (iv) and non-smoker. Importantly, according to
ACSM, sedentary, healthy (e.g., free of disease, non-smoker, uninjured) individuals will
showcase a greater physiological change from pre to post exercise intervention.
To ensure that participants met the inclusion criteria, all subjects were asked to complete
several physical activity questionnaires. The questionnaires included: (a) International Physi-
cal Activity Questionnaire—Short Form (IPAQ-SF) [32], (b) the Physical Activity Readiness
Questionnaire (PAR-Q&YOU) [33], and the (c) American College of Sports Medicine (ACSM)
Risk Stratification [31] to assess individual current health and activity levels. If a participant
reported two risk factors related to cardiovascular diseases, he/she had to consult a physician
for medical clearance to participate in moderate to vigorous exercise.
The study was conducted according to the Code of Ethics for Human Experimentation
of the World Medical Association and the Declaration of Helsinki [34]. Participants were
informed in detail about the design of the study, including the potential risks and beneﬁts
of included procedures, before providing their informed written consent to participate. The
study protocol was approved by the Institutional Review Board of the Northern Michigan
Int. J. Environ. Res. Public Health 2023, 20, 4501
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University (Trial registration number: ID Proposal Number HS16-786; Date of registration:
September, 2017).
Participants were anonymously assigned by the researcher via simple randomization
using a random number generator to either TRAIL (n = 20) or ROAD (n = 19) and entered
into an endurance exercise program. The program consisted of 8 weeks of gradually
increasing running workouts with a total amount of 29 training sessions. This randomized
controlled pilot trial compared two training groups (i.e., TRAIL vs. ROAD) in terms of
balance, gait, agility, along with strength and endurance performance measures in a pre-
and post-intervention testing format. Participants in the TRAIL group ran outdoors on
uneven and soft trails with varying gradients and under-foot terrain (e.g., rocks, roots,
more consistent undulating routes). Participants from the ROAD group ran on predictable
terrain or roads with asphalt, concrete or paved surfaces exhibiting no or infrequent
gradients. An adherence rate of a minimum of 80% (24 runs) was required for inclusion in
the ﬁnal analysis.
To conﬁrm, a total of 39 healthy adults were initially assigned, whereof 6 subjects did
not start the program; 5 participants dropped out during the intervention due to injuries;
3 participants did not meet the required 80% adherence rate and 1 participant was not
available for post testing. Additionally, 2 participants (i.e., “4” total) from each group
were excluded from analysis due to other exclusion criteria—not following the prescribed
training load and for participating in additional training during the period of the study.
Then end total of analyzed participants equaled 20.
Demographic data at baseline for all participants who received the allocated interven-
tion are depicted in Table 1.
Table 1. Demographic data at baseline.
TRAIL 1 (n = 10, 6 fem)
ROAD 1 (n = 10, 7 fem)
Total (n = 20)
Female/male (n)
6/4
7/3
13/7
Age (years)
33.2 ± 6.8
29 ± 10.5
31.3 ± 8.8
Height (cm)
171.1 ± 8.0
170.9 ± 6.6
171 ± 7.3
Weight (kg)
77.4 ± 17.6
74.5 ± 15.6
76.1 ± 16.5
BMI (kg/m2)
26.2 ± 4.1
25.4 ± 4.5
25.8 ± 4.3
Physical Activity (min/week)
1904.8 ± 957.5
2105.3 ± 1679.5
2000.5 ± 1445.6
1 Values are mean (±SD). TRAIL = trail running group. ROAD = road running group.
2.2. Experimental Design
Qualifying participants were asked to report to an Exercise Science Laboratory for
pre- and post-intervention testing. Post-testing sessions were scheduled at a similar time
of the day as pre-testing and within a week upon completion of the training program in
November and December 2017, depending on pre-testing dates. Testing order, as well as
the examiner were kept constant for each participant.
Finally, ten participants in each group were included in the statistical analysis. The
study ﬂow is depicted following the CONSORT criteria, which is easily referenced [30].
Notably, 10 participants in each group provided signiﬁcant differences (alpha error proba-
bility: 0.05) and notable study power (i.e., 1-beta error probability: 0.9) when moderate to
large effects size differences between group were presumed for balance performance as the
primary outcome.
Lastly, mandatory running meetings were held twice a week and coaching appoint-
ments were scheduled as required. Furthermore, participants were contacted by email or
phone once a week for feedback. As an additional motivation, a ﬁnal joint 5k running event
was held upon completion of the intervention.
Int. J. Environ. Res. Public Health 2023, 20, 4501
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2.3. Heart Rate and Blood Pressure
Prior to baseline testing, a blood pressure cuff (Adcuff™, Hauppauge, NY, USA) and
stethoscope (Littmann, St. Paul, MN, USA) were employed for blood pressure measures;
then, pre-exercise resting heart rate (Polar monitor and watch, Lake Success, NY, USA), as
well as body height (Seca stadiometer, Chino, CA, USA) and weight (Health O Meter scale,
Mccook, IL, USA) were measured. Maximal heart rate (HRmax) in beats per minute (bpm)
was predicted using the following formula according to Tanaka et al. [35]: 207—(age × 0.7)
for men and 206—(age × 0.88) for women. The lateral preference inventory for measurements
of footedness [36] was used to evaluate leg dominance. Limb length was measured from the
umbilicus to the medial malleolus of the right leg using a tape measure [37]. Blood pressure,
pre-exercise resting heart rate, as well as body height and weight measurements were repeated
before post-testing as well.
2.4. Warm-Up
Warm-up consisted of walking on a treadmill for 5 min at a rate of perceived exertion
(RPE) of 3 on the Borg CR-10 scale [38], followed by dynamic stretching and muscle
activation (Knee Hug to Forward Lunge–Elbow to Instep, Heel to Butt Moving Forward
with Arm Reach, Handwalk, Lateral Squat Low).
2.5. Static Balance Testing
Static balance was tested with the Balance Error Scoring System (BESS) [39], which
evaluates 3 stance variations in the following order: (1) double leg, (2) single leg, and
(3) tandem or feet in line with one another. The test takes place on 2 different surfaces,
starting on ﬁrm for all “3” conditions and ending on foam for all “3” conditions while
wearing no shoes. Each trial lasts 20 s, during which the number of deviations from the
proper testing position were counted. Deviations from the proper testing position in the
BESS test are: (a) moving hands off the hips; (b) opening the eyes; (c) step, stumble or fall;
(d) abduction or ﬂexion of the hip beyond 30◦; (e) lifting the forefoot or the heel off of the
testing surface; and (f) remaining out of proper testing position for more than 5 s. Proper
position consists of the hands on the iliac crest, eyes closed, and consistent foot position.
For the double leg stance, feet need to touch and remain ﬂat on the testing surface. For
the single leg stance, the participant stands on the non-dominant leg with the other leg
held in approximately 20◦ of hip ﬂexion, 45◦ of knee ﬂexion, and neutral position in the
frontal plane. For the tandem stance, one foot is placed in front of the other with the heel
of the anterior foot touching the toes of the posterior foot, and the non-dominant leg in
the posterior position. The maximum amount of errors for any single condition was set at
10. If multiple errors were committed simultaneously, only one was recorded. To improve
reliability, the test was repeated 3 times by the same examiner [39] and the mean score of
the three trials was calculated for ﬁnal analysis.
2.6. Dynamic Balance Testing
The Y Balance test (YBT) was performed to evaluate dynamic postural stability
and functional symmetry during single leg stance in three (anterior, posteromedial,
posterolateral) directions [40]. In a Y pattern, each posterior line was marked with
tape 135◦ from the anterior line and 90◦ apart from one another. Subjects performed
a practice trial followed by three test trials for each direction and each leg and were
instructed to reach as far as possible, thereby pushing a pen held by the examiner to
mark the reaching distance. The testing order started with standing on the left foot
and reaching in the anterior direction followed by the trials standing on the right foot
for the same direction. This procedure was repeated for all directions. Trials were
considered invalid and were repeated if the participant either made a heavy touch
or rested the reaching foot on the ground, could not return in a controlled way to
the starting position, raised or moved the stance foot, or kicked the marker with the
Int. J. Environ. Res. Public Health 2023, 20, 4501
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reaching foot to gain more distance [40]. Results were calculated as a composite score
with the help of following formula:
(((anterior length + posteromedial length + posterolateral length)/3 × leg length) × 100).
(1)
2.7. Gait Analysis
Spatiotemporal gait parameters (stride time [s], stride length [m], and stride velocity
[m/s]) were measured during 20 m (65.6 feet) of level walking at self-selected habitual
walking speed by using the portable gait analysis system RehaGait® (Hasomed GmbH,
Magdeburg, Germany). The RehaGait® system consists of two mobile sensors which are
attached to the lateral part of each shoe to measure linear acceleration, angular velocity, and
the magnetic ﬁeld of the foot at a sampling rate of 500 Hz [41]. Each participant performed
a familiarization trial followed by 2 trials with single task condition and 2 trials with
dual task condition. For dual task trials, participants were asked to perform a double-digit
subtraction task while walking. The combination of gait analysis with cognitive interference
tasks was applied to distract participants and limit the cognitive resources for gait control.
The mean score for each condition was included in further analysis. For all trials, the phases
of gait initiation and deceleration at the end of the walkway were excluded from analysis.
For both pre- and post-testing, participants were wearing their running shoes.
2.8. Agility Testing
The t-test evaluates the subjects’ agility, leg power and leg speed [42]. Four cones are
set out in a T pattern. The test starts at the ﬁrst cone with a forward sprint of 9.14 m to the
second cone, continues with shufﬂing sideways for 4.57 m to another cone on the right,
then 9.14 m to the one on the left, and again 4.57 m back to the middle, before ultimately
running backwards 9.14 m to return to the starting point. The base of the cone always has
to be touched with the hand further away from the cone when performing the test. The
fastest out of 3 trials was used for analysis.
2.9. Strength Testing
Unilateral isokinetic concentric leg strength was assessed for the dominant leg using
the BIODEX Multi-Joint System 4 Pro (Biodex Medical Systems, New York, NY, USA).
Knee extension and ﬂexion as well as ankle plantar- and dorsi-ﬂexion were tested for
peak torque (PT) and total work (TW). Subjects were seated with chair and dynamometer
position at 90◦ and the dynamometer positioned outside the testing leg. The anatomical
axis rotation (lateral femoral condyle on a sagittal plane for the knee and through the body
of talus, ﬁbular malleolus, and tibial malleolus for the ankle) was in alignment with the
dynamometer shaft for both knee and ankle attachment, ensuring that the testing pattern
was consistent with the proper biomechanics of the joint. Body parts on either side of the
tested joint were ﬁrmly secured with straps, in order to restrict motion as much as possible
to the area of interest. Range of motion was set for each subject and joint individually. After
a 12-repetition warm-up trial at 180◦/s and low effort, participants performed two sets of
5 repetitions at 60◦/s and maximal effort with a 60 s break between sets.
2.10. Aerobic Endurance Testing
Oxygen consumption was measured by indirect calorimetry on a treadmill during
the walking-based Pepper protocol [43] with the Parvo Medics TrueOne 2400 automated
gas analysis system (Sandy, UT, USA). The Pepper protocol is an incremental submaximal
test starting at an inclination of 0% and a velocity of 2.5 mi (4 km) per hour. Intensity
increases each minute by elevating either inclination or velocity. The test is ended at 85%
of predicted HRmax [35]. Gas exchange variables (VO2 and VCO2, RER), RPE on the
Borg CR-10 scale [38] and HR were monitored and averaged to 15s time-intervals. Finally,
maximal oxygen consumption (VO2max) was predicted from the highest value recorded at
HR85% using the formula VO2max pred = VO2max at HR 85%/85 × 100. Prediction was
used to minimize cardiovascular risk of pushing to maximum in this mixed age group (2).
Int. J. Environ. Res. Public Health 2023, 20, 4501
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2.11. Training Program
The training program started with 3 training sessions per week in weeks 1–3. Each
training session had a duration of 22–36 min (which was the standard range throughout
most of the 8 wk intervention) of running interspersed with 2 min walk rest periods. Novice
participants progressed to 4 running (with prescribed intermittent walking) sessions per week
in weeks 4–6 with 2-min walk breaks before gradually omitting the walk breaks in week 7 and
finishing the program at the end of week 8 with a 45 min continuous run (i.e., their 4th run of
week 8). Exercise training started for each participant after the pretest and was performed
individually on self-selected outdoor trails (i.e., TRAIL) and roads (i.e., ROAD) at a perceived
exertion of 3–4 on Borg CR-10 (although the average RPE approached “5” for both groups
upon end analysis). Each participant was provided with a running log in which they recorded
training duration, perceived exertion levels, location, and estimated percentage of each session
on TRAIL or ROAD. Actual training loads for both groups are summarized in Table 2.
Table 2. Training load of trail and road physical activity interventions. Values are mean ± SD.
Training Load
TRAIL (n = 10, 6 fem)
ROAD (n = 10, 7 fem)
Total (n = 20, 13 fem)
Weeks (n)
9.0 (0.7)
9.6 (0.8)
9.3 (0.8)
Trainings (n)
26.5 (1.7)
27.9 (2.6)
27.2 (2.3)
Sessions/week (n)
3.0 (0.3)
2.9 (0.4)
2.9 (0.3)
Time/session (min)
35.4 (1.7)
34 (1.6)
34.7 (1.8)
Intensity/session (RPE)
4.9 (1.1)
4.6 (0.8)
4.8 (1)
Total Training Time (min)
938.4 (68.3)
946.7 (82.9)
942.5 (74.8)
2.12. Statistical Analysis
Group means of all variables for all pre- and post-test data were calculated based on
individual test scores in order to compare changes between groups. All data are presented
as means with standard deviations (SD). Data analysis was computed using the statistical
software program SPSS for Windows V.14.0 (SPSS Inc., Chicago, IL, USA). After adjustment
for baseline scores (note, baseline values were added as covariate in order to adjust for
potential baseline differences), repeated-measures ANOVA procedures were conducted to
determine signiﬁcant between-group differences. Group (TRAIL and ROAD) served as the
between-subject factor, and time (pre- and post-test) as the within-subject factor. Statistical
signiﬁcance level was set at p < 0.05. Because of the small sample size, partial eta squared
(ηp2) and Cohen’s d (d < 0.2 = trivial effect; d ≥ 0.2 = small effect; d ≥ 0.5 = moderate
effect; d ≥ 0.8 = large effect), as the standardized mean difference, were calculated to
estimate effect sizes from pre- to post-testing for all ANOVAs. The probability for an effect
being practically worthwhile in favor of either TRAIL or ROAD was calculated accord-
ing to the magnitude-based inference (MBI) approach (25–75%, possibly; 75–95%, likely;
95–99.5%, very likely; >99.5%, most likely) using the Hopkins [44] spreadsheet for analysis
of controlled trials with adjustments for a predictor in Microsoft® excel.
3. Results
In review, of the 33 subjects that received the allocated intervention, 5 people (4 in
TRAIL; 1 in ROAD) ended the program prematurely due to injuries and/or pain. A total of
3 people (2 in TRAIL; 1 in ROAD) did not meet the required attendance rate and 1 person
from ROAD never reported to the post-testing. Two more subjects of each group were
excluded from further evaluation based on exclusion criteria (age, amount of previous
physical activity, adherence rate, ≤2 risk factors according to the ACSM Risk Stratiﬁcation).
A total of 10 participants from each group were included in the ﬁnal analysis. Higher
baseline test scores and differences between the two groups were seen for leg strength in
knee ﬂexion PT (19.9% higher in TRAIL) and ankle plantar ﬂexion PT (18.5% higher in
TRAIL), and for VO2max pred (24.3% higher in ROAD).
Int. J. Environ. Res. Public Health 2023, 20, 4501
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The mean overall attendance rate for the intervention was 93.8% or 27.2 ± 2.3 out of
29 total trainings; 91.4% (26.5 ± 1.7) for TRAIL and 96.2% (27.9 ± 2.6) for ROAD.
3.1. Static and Dynamic Balance
The repeated-measures ANOVA revealed no statistically signiﬁcant differences be-
tween groups for any balance measures. However, for the BESS test, a signiﬁcant time-effect
between pre-and post-testing was noted (p = 0.001, ηp2 = 0.46) and large and moderate
effect sizes according to Cohen’s d for TRAIL (d = 1.2) and ROAD (d = 0.5), respectively.
Results for static and dynamic balance testing are presented in Table 3.
Table 3. Effect on balance of an 8-week trail and road running training intervention.
TRAIL
ROAD
rANOVA
TEST
Pre-Test
Post-Test
Cohen’s D
Pre-Test
Post-Test
Cohen’s d
Time
ηp2
Group × Time
ηp2
BESS
12.4 (2.7)
9.5 (2.3)
1.2
11.6 (3.9)
10.0 (2.8)
0.5
p = 0.001
0.46
p = 0.38
0.05
YBT left
94 (8.2)
95.9 (7.7)
0.25
94.4 (7.3)
94.7 (7.2)
0.04
p = 0.19
0.10
p = 0.31
0.06
YBT right
93.8 (8.5)
96.4 (8.2)
0.31
92.4 (8.1)
93.8 (7.4)
0.18
p = 0.15
0.12
p = 0.3
0.06
Values are mean (±SD); statistical signiﬁcance level is set at p < 0.05.
3.2. Gait
The spatiotemporal gait analysis rANOVA showed no notable improvements over
time in any parameter for either TRAIL or ROAD, as displayed in Table 4. According to
Cohen’s d, a moderate effect size for stride time ST in ROAD (d = 0.52) as well as small
effects for velocity DT in TRAIL (d = 0.32), velocity ST in ROAD (d = 0.23), and for stride
time DT in both groups (d = 0.43 in TRAIL; d = 0.45 in ROAD) were calculated.
Table 4. Effect on spatio-temporal gait characteristics.
TRAIL
ROAD
rANOVA
Pre-Test
Post-Test
Cohen’s d
Pre-Test
Post-Test
Cohen’s d
Time
ηp2
Group × Time
ηp2
Stride time [s]
ST
1.1 (0.1)
1.1 (0.1)
0.01
1.1 (0.1)
1.1 (0.1)
0.52
p = 0.7
0.009
p = 0.37
0.05
DT
1.2 (0.1)
1.2 (0.1)
0.43
1.2 (0.1)
1.2 (0.1)
0.45
p = 0.89
0.001
p = 0.35
0.05
Stride length [m]
ST
1.4 (0.1)
1.4 (0.1)
−0.09
1.4 (0.1)
1.4 (0.1)
0.09
p = 0.65
0.01
p = 0.35
0.05
DT
1.3 (0.1)
1.3 (0.1)
0.19
1.3 (0.1)
1.3 (0.1)
−0.17
p = 0.84
0.002
p = 0.37
0.05
Velocity [m/s]
ST
1.3 (0.2)
1.3 (0.2)
−0.06
1.3 (0.2)
1.4 (0.2)
0.23
p = 0.8
0.006
p = 0.34
0.05
DT
1.1 (0.2)
1.2 (0.2)
0.32
1.2 (0.2)
1.2 (0.2)
0.06
p = 0.45
0.03
p = 0.3
0.06
Values are mean (±SD); ST, single task condition; DT, dual task condition; statistical significance level is set at p < 0.05.
3.3. Agility
Both groups improved their t-test performance by 4.6% (TRAIL) and 6.8% (ROAD),
respectively. Yet, no signiﬁcant change over time or between groups was observed. Effects
from the intervention on agility are shown in Table 5.
Table 5. Effect on agility.
TRAIL
ROAD
rANOVA
Agility
Pre-test
Post-test
Cohen’s d
Pre-test
Post-test
Cohen’s d
time
ηp2
group × time
ηp2
t-test [s]
15.6 (3.2)
14.9 (2.4)
0.26
15.1 (3.2)
14.1 (2.4)
0.36
p = 0
0.69
p = 0.15
0.12
Values are mean (±SD); signiﬁcance level is set at p < 0.05.
3.4. Strength
Gains in isokinetic concentric leg strength were only recorded in knee extension TW
(8.2%) and knee ﬂexion TW (11.8%) for TRAIL, and knee extension TW (1.6%) as well as
ankle dorsi ﬂexion TW (1.9%) for ROAD. Thereof, only knee ﬂexion TW in favor of TRAIL
resulted in a close to signiﬁcant between-group difference over time (p = 0.06; ηp2 = 0.19;
d = 0.25). This ﬁnding was reinforced by a 76% likely probability of a substantial worthwhile
effect according to the MBI approach. A signiﬁcant negative time-effect in ankle plantar
Int. J. Environ. Res. Public Health 2023, 20, 4501
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ﬂexion PT (p = 0.02; ηp2 = 0.29) was recorded for ROAD. All other strength measures
showed small declines between pre- and post-testing, as shown in Table 6.
Table 6. Effect on strength.
TRAIL
ROAD
rANOVA
Pre-Test
Post-Test
Cohen’s d
Pre-Test
Post-Test
Cohen’s d
Time
ηp2
Group × Time
ηp2
Knee
KE PT (Nm)
175.5 (74.6)
172.7 (68)
−0.04
163.9 (55.5)
154.3 (49.1)
−0.18
p = 0.06
0.2
p = 0.37
0.05
KF PT (Nm)
96.3 (41)
91.6 (37.6)
−0.12
80.3 (25.7)
77.0 (21.9)
−0.14
p = 0.06
0.2
p = 0.77
0.005
KE TW (J)
870.9 (419.4)
945.3 (406.3)
0.18
833.9 (283.8)
847.7 (303.6)
0.05
p = 0.56
0.02
p = 0.21
0.09
KF TW (J)
487.2 (245.4)
548.3 (244.8)
0.25
447.8 (151.3)
446 (164.5)
−0.01
p = 0.67
0.01
p = 0.06
0.19
Ankle
PF PT (Nm)
60.2 (32.3)
55.8 (26.5)
−0.15
50.8 (18.1)
45 (16.5)
−0.33
p = 0.02
0.29
p = 0.49
0.03
DF PT (Nm)
25.5 (7.4)
24.1 (7.7)
−0.18
24.2 (6)
23.7 (5.8)
−0.09
p = 0.5
0.03
p = 0.5
0.03
PF TW (J)
167 (93.1)
166.6 (86.5)
−0.004
140.6 (54.1)
134.7 (55.8)
−0.106
p = 0.14
0.12
p = 0.57
0.02
DF TW (J)
104.6 (28.3)
97.6 (31.6)
−0.24
94.8 (20.3)
96.6 (26.4)
0.07
p = 0.9
0.001
p = 0.28
0.07
Values are mean (±SD); KE, knee extension; KF, knee ﬂexion; PF, plantar ﬂexion; DF, dorsi ﬂexion; PT, peak torque;
TW, total work; i.f.o., in favor of; statistical signiﬁcance level is set at p < 0.05.
3.5. VO2max
The results of the aerobic endurance testing (VO2max pred) show the greatest proba-
bility for a substantial beneﬁcial effect between pre- and post-testing with 97% in favor of
TRAIL. These ﬁndings are supported by the calculated Cohen’s d effect sizes (d = 0.95 in
TRAIL; d = 0.53 in ROAD). Time-effect (p = 0.14) and between-group differences (p = 0.13)
did not reach statistical signiﬁcance. Results for VO2max pred are depicted in Table 7.
Table 7. Effect on VO2max of an 8-week trail and road running training intervention.
TRAIL
ROAD
rANOVA
Pre-test
Post-test
Cohen’s d
Pre-test
Post-test
Cohen’s d
time
ηp2
group × time
ηp2
pred. VO2max
28.4 (6)
35.8 (9.2)
0.95
35.3 (8.8)
40.5 (10.4)
0.53
p = 0.14
0.12
p = 0.13
0.13
Values are mean (±SD); i.f.o., in favor of; statistical signiﬁcance level is set at p < 0.05.
4. Discussion
This is the ﬁrst study that comparatively investigated the impact of trail running
versus road running on neuromuscular performance parameters in healthy adults. We
hypothesized that running on natural trails would lead to more pronounced improvements
in static and dynamic balance, gait patterns, agility, and leg strength between pre- and
post-testing compared to road running. This assumption was based on previous ﬁndings
which have shown that the navigation of the body on varying surface densities, inclines
and speeds evoked higher muscle activation and coordination as opposed to moving on
more ﬁrm and ﬂat terrain [23–26,45,46]. Greater physiological strain on softer terrain is
associated with a greater degree of energy absorption by the training surface that results
in a loss of elastic energy, followed by greater concentric work and overload stimulus in
the lower-limb muscles [26,45]. Against this background, we expected gains in concentric
quadriceps and hamstring muscle strength as well as in ankle strength and stability in
favor of TRAIL from navigating in uneven terrain. However, according to the BIODEX
isokinetic concentric leg strength testing, knee ﬂexion TW was the only parameter that
resulted in close to signiﬁcant improvements. On the other hand, for ankle dorsi ﬂexion PT,
a signiﬁcant negative time-effect was recorded. A possible explanation for this decrease
could be found in a reduction in ankle work and range of motion that has been seen when
running on uneven and unpredictable terrain in order to stabilize the joint [26]. The fact
that all other strength measures showed small declines between pre- and post-testing might
be attributed to fatigue as a result of the newly increased exercise routine. It is also possible
that the reduced strength outcomes especially for PT values are a consequence of endurance
training-speciﬁc adaptations. When interpreting leg strength results, baseline differences
Int. J. Environ. Res. Public Health 2023, 20, 4501
9 of 14
and high standard deviations in both groups should be taken into account. Especially
in TRAIL, large discrepancies in strength scores among subjects in pre- and post-testing
were observed. Another factor that added to these inconsistencies is the fact that most
participants from both groups had no experience in resistance training, much less with
the applied strength-testing device. The lack of experience might have inﬂuenced the
test performances.
We found no statistically signiﬁcant differences in the rANOVA analysis between
TRAIL and ROAD for static and dynamic balance measures. But a signiﬁcant time-effect
between pre-and posttest was calculated (p = 0.001, ηp2 = 0.46) for the BESS test. In addition,
large (d = 1.2) and moderate (d = 0.5) effect sizes for Cohen’s d for TRAIL and ROAD
respectively indicate potential balance improvements from running, especially on trails. In
a review on sports participation and balance performance, Hrysomallis et al. [47] stated
that athletes generally have a superior balance ability compared to control subjects as
a result of repetitive experience and improved motor responses to proprioceptive and
visual cues. Additionally, the same authors observed improved coordination, strength
and range of motion. However, it remains unclear whether proprioception can actually be
improved by exercise or if athletes just become more skilled at reacting to sensory cues.
In a study on functional ﬁtness gains through various types of exercise in older adults,
Takeshima et al. [48] reported improvements in dynamic balance (functional reach test)
in all intervention groups (balance, aerobic, and resistance training). They also predicted
that training on unstable surfaces not only leads to improvements in balance but also
in lower-body strength due to greater muscle activation when counteracting increased
sway following unexpected perturbations. A few other studies report improvements in
locomotion in older adults after aerobic training interventions involving walking, treadmill
walking, jogging, and step aerobics [19]. The results of the BESS test in this pilot study
support previous ﬁndings that physical exercise, speciﬁcally running, may have a positive
inﬂuence on balance. Nevertheless, beneﬁts from running for dynamic and functional
balance could not be proven with the administered tests for the lack of signiﬁcant results in
the Y-Balance test and gait analysis.
Despite the close relationship between balance and gait performance in regards to
fall- and injury-risk factors [14,19,26,47–49], the spatiotemporal gait analysis in this study
showed no notable characteristics or changes in any parameter for either TRAIL or ROAD.
rANOVA, Cohen’s d, as well as MBI calculations show inconsistent results and no conclu-
sions can be drawn about the inﬂuence of trail or road running on gait stability. Likewise,
no statistically signiﬁcant differences for time or between groups were recorded in agility
performances. Nevertheless, most participants achieved faster T-test times after the in-
tervention and demonstrated noticeably increased conﬁdence and security levels in their
sprint performances. Increased conﬁdence levels and sprint ability might result in an over-
all increased gait stability and thus reduce fall risk. When discussing the lack of evidence
for gait and agility in this study, testing devices and procedures need to be considered.
More task-speciﬁc trials might elicit more pronounced changes.
Aerobic endurance testing showed the highest probability for a substantial worthwhile
effect in favor of TRAIL (97%, very likely) together with a large Cohen’s d effect size
(d = 0.95). Relative VO2max outcomes from the gas analysis test improved by 23.1%
and 13.7% from pre- to post-testing for TRAIL and ROAD, respectively. Still, time-effect
(p = 0.14; ηp2 = 0.12) and between-group differences (p = 0.13; ηp2 = 0.13) did not reach
statistical signiﬁcance. Moreover, big baseline differences (24.3% higher in ROAD) need to
be considered when interpreting predicted maximal oxygen consumption. Lower baseline
values in TRAIL might have facilitated the larger responses to the training intervention
in that group. Even so, it is probable that trail running may elicit greater beneﬁts for
cardiovascular ﬁtness. Several studies [26,50–53] documented that running on natural
surfaces such as irregular trails required a higher energy expenditure and metabolic cost,
which translated to a higher training intensity and higher aerobic training adaptations.
However, recorded RPE from the running logs revealed no group differences (4.6 ± 1.1 for
Int. J. Environ. Res. Public Health 2023, 20, 4501
10 of 14
TRAIL; 4.9 ± 0.8 for ROAD), an interesting ﬁnding if greater energy expenditure is realized
on TRAIL versus ROAD without a concurrent rise in RPE. Therefore, TRAIL could be a
strategy or modality for advanced energy output and weight loss, leading to better motor
control at a lower perceived exertion.
To date, a lot of research regarding neuromuscular adaptations from running has
focused on different types of footwear or foot strike patterns and related kinematic,
metabolic, and biomechanical parameters of the lower limb, as well as running-related
injuries [20,24,54]. Various research groups examined the effects of training on different
outdoor terrain, mainly focused on grass or sand surface [23,45,55–57], or deﬁned trail
running as an ultra-endurance activity. In this understanding, Easthope et al. [58] analyzed
performance levels between young and older master runners in a 55-km ultra-endurance
trail run. They observed equal performances in both groups despite structural and func-
tional age-related alterations and conﬁrmed that the decline in physical performance can be
prevented with regular endurance training such as running. In a study that compared the
different effects of concrete road, synthetic track, and woodchip trail on dynamic stability
and loading in runners, Schütte et al. [22] revealed signiﬁcant performance differences from
a biomechanical perspective. Running on woodchip trail altered measures of dynamic
stability and lower-limb musculature compared to running on concrete road due to com-
pression and displacement of the woodchips under the foot causing destabilization and
directional shift with each stride. Similarly, Boey et al. [59] looked at running on concrete,
synthetic running track, grass, and woodchip trail at two different speeds and the different
vertical impacts on the lower leg. Their results showed that running on woodchip trails
and at a slower speed, reduced the injury risk at the tibia.
Running related injuries (RRI) of the lower extremities are a common negative side
effect in runners [60,61]. The prevalence is usually higher for overuse musculoskeletal
injuries than for acute injuries [21,60,62]. There is a large heterogeneity of injuries that
originates from different methods and deﬁnitions when evaluating RRI [21,60]. Among
the most commonly reported RRIs in the literature are to the Achilles tendon, plantar
fascia, calf muscle, knee, meniscus, shin, foot, ankle, hip/pelvis, lower back, hamstring,
and thigh [21,60,63,64]. Risk factors for RRI appear to be previous injuries to the same
anatomical area, high training loads and little running experience [64,65].
In the current study, 5 out of 33 people reported an injury during the 8-week interven-
tion that prevented them from completing the training program. Affected body sites and
type of injuries are all in line with the formerly reported common injury types and risk
factors. Two participants from TRAIL developed reoccurring overuse injuries (i.e., knee
and lower back) that had probably not been fully and appropriately cured. The other par-
ticipants suffered from tibial stress syndrome (1 in TRAIL) and ankle sprains (1 in TRAIL;
1 in ROAD). The recorded amount and type of injuries in this study seem to reinforce
the fact that previous injuries, little running experience, and an increase in training load
within a relatively short amount of time may be risk factors for RRI. Meanwhile, as stated
by Taunton et al. [64], previous activity, cross-training and running surface appear to be
non-signiﬁcant injury risk factors for either gender. Interestingly, 4 of the 5 injured subjects
in this study were part of the trail running group, which contrarily seems to imply a
connection between surface and injury prevalence. Trail running might be more strenuous
for physiological parameters due to its speciﬁc surface characteristics and the resulting
challenges for involved muscle groups and the metabolic system. Therefore, running
on natural and more compliant trails may be more likely to cause overuse injuries in an
untrained population. Despite the mentioned risk factors, authors agree that health beneﬁts
from running outweigh the related risks and costs of RRI [21].
Limitations to this study are the small group sizes and baseline differences between
groups in VO2max pred and certain strength parameters, as well as the fact that the running
intervention itself was not supervised and subjects performed most of the training units
individually. Consequently, even though participants were instructed to exercise at a com-
fortable, moderate to somewhat hard intensity (3–4 on the Borg CR-10 scale), it is possible
Int. J. Environ. Res. Public Health 2023, 20, 4501
11 of 14
that some trained at intensities that were too high for their level of ﬁtness. Additionally, the
program was based on running time and not distance, which may have resulted in a differ-
ent training volume dependent on different training pace among individuals. The training
log was a way of controlling for these interferences. Regarding adherence, a slightly lower
attendance rate in the trail running group was expected since trails require more effort and
planning to access and may become impassable in bad weather or darkness. As a ﬁnal
point, MBI’s should be interpreted carefully, especially implications drawn from them, and
one should be mindful of how the performed tests may have related to the intervention.
5. Conclusions
The results of this training intervention show no statistically signiﬁcant between-group
differences. This suggests that beneﬁts derived from running on uneven and soft natural
terrain as opposed to a more ﬂat and concrete road surface in respect to static and dynamic
balance, gait, agility, and lower limb strength should not be overrated. Based on current
knowledge and the outcomes of this study, no well-founded recommendations for an
integrative training approach in regard to trail running and the prevention of falls and
fall-related injuries can be given. More research is needed on the inﬂuence of running on
trails or similar natural surfaces on different neuromuscular performance parameters.
Nevertheless, the ﬁndings of this intervention indicate slightly more beneﬁcial ten-
dencies for balance and leg strength improvements when running on trails as opposed to
road; and, therefore, potential beneﬁts for the prevention of falls and fall-related injuries.
While a signiﬁcant time-effect between pre- and post-testing in static balance was recorded
for both groups (p = 0.001, ηp2 = 0.46), the trail running group also showed large effect
sizes (d = 1.2) for static balance, compared to only moderate effect sizes (d = 0.5) in the road
running group. Trail running also seems to have positive impacts on upper leg strength
performance, which is indicated by gains in knee extension (8.2%) and ﬂexion (11.8%) total
work and a close to signiﬁcant between-group difference over time (p = 0.06; ηp2 = 0.19;
d = 0.25) in knee ﬂexion TW.
For more detailed and speciﬁc results, future studies should target larger group sizes
of recreational runners within smaller age ranges and in a longitudinal approach over
a longer time period. Moreover, the scope of the intervention should be limited to one
particular neuromuscular parameter. Thereby, the combined effects for cardiovascular and
neuromuscular performance factors from running on different surfaces might be disen-
tangled more clearly. Finally, repeating this study in an older, untrained population and
tracking at-home falls throughout a pre-determined follow-up period (e.g., over 5-years)
post intervention could yield more precise commentary regarding TRAIL’s effectiveness in
or lack of promoting better neuromuscular coordination.
Author Contributions: Conceptualization, S.N.D. and L.D.; methodology, S.N.D. and L.D.; software,
L.D.; validation, L.D.; formal analysis, L.D.; investigation, S.N.D. and L.D.; resources, S.N.D., L.R.,
S.H. and L.D.; data curation, S.N.D., L.R., S.H. and L.D.; writing—original draft preparation, S.N.D.,
L.R., S.H. and L.D.; writing—review and editing, S.N.D., L.R., S.H. and L.D.; visualization, S.N.D.
and L.D.; supervision, S.N.D.; project administration, S.N.D.; funding acquisition, N/A. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The project was approved by Northern Michigan University
Institutional Review Board for human participants, Proposal Number HS16-786 (December, 2017).
Further, the study was conducted in accordance with the Declaration of Helsinki.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data for this project are maintained by the primary authors—S.N.D.
and L.D.—on personal and password protected laptops.
Int. J. Environ. Res. Public Health 2023, 20, 4501
12 of 14
Acknowledgments: We wish to acknowledge the efforts of the graduate students involved in training
the participants for their energy with participant recruitment and the implementation of the training
protocols. The authors would also like to thank the volunteer participants for their valuable time and
effort during participation.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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