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Biomechanical parameters for gait analysis: a systematic review of healthy human gait

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Abstract Background: Modern gait analysis offers a broad variety of biomechanical parameters through which to quantify gait. However, no consensus has yet been established with regards to which biomechanical parameters are most relevant to evaluate during gait analysis in the healthy population. Purpose: The purpose of the current systematic review was to determine the most relevant biomechanical parameters for gait analysis in the healthy adult population. Methods: PubMed, EMBASE and Web of Science databases were searched. Two independent reviewers participated in the article selection and attributed a Level of Evidence score to each article to account for quality based on participant selection, intervention and analysis. A score combining both frequency and number of articles was calculated. Correlations were carried out between the Level of Evidence score, Journal Impact Factor and the frequency of biomechanical parameters. Results: Spatio-temporal parameters were found to be the most often measured biomechanical parameters and reported by the greatest number of articles; walking velocity, cadence and step/stride length appearing to be the most relevant biomechanical parameters for gait analysis in the healthy adult population. No correlation was found between Level of Evidence score and Journal Impact Factor, nor between the frequency of parameters and Level of Evidence score. Conclusion: This systematic review provides recommendations for variables to assess in future gait evaluations in healthy adults. Keywords: Gait, biomechanics, gait analysis, healthy, adult
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Review Open Access
Biomechanical parameters for gait analysis: a systematic
review of healthy human gait
Mary Roberts1*, David Mongeon1 and Francois Prince2
1Department of Kinesiology, University of Montreal, Montreal, Quebec, Canada.
2Faculty of Medicine, Department of Surgery, University of Montreal, Montreal, Quebec, Canada.
*Correspondence: mary.roberts@umontreal.ca
Abstract
Background: Modern gait analysis offers a broad variety of biomechanical parameters through which
to quantify gait. However, no consensus has yet been established with regards to which biomechanical
parameters are most relevant to evaluate during gait analysis in the healthy population.
Purpose: The purpose of the current systematic review was to determine the most relevant biomechanical
parameters for gait analysis in the healthy adult population.
Methods: PubMed, EMBASE and Web of Science databases were searched. Two independent reviewers
participated in the article selection and attributed a Level of Evidence score to each article to account for
quality based on participant selection, intervention and analysis. A score combining both frequency and
number of articles was calculated. Correlations were carried out between the Level of Evidence score,
Journal Impact Factor and the frequency of biomechanical parameters.
Results: Spatio-temporal parameters were found to be the most often measured biomechanical parameters
and reported by the greatest number of articles; walking velocity, cadence and step/stride length appearing
to be the most relevant biomechanical parameters for gait analysis in the healthy adult population.
No correlation was found between Level of Evidence score and Journal Impact Factor, nor between the
frequency of parameters and Level of Evidence score.
Conclusion: This systematic review provides recommendations for variables to assess in future gait
evaluations in healthy adults.
Keywords: Gait, biomechanics, gait analysis, healthy, adult
© 2017 Roberts et al; licensee Herbert Publications Ltd. is is an Open Access article distributed under the terms of Creative Commons Attribution License
(http://creativecommons.org/licenses/by/3.0). is permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Walking is the most common form of locomotion and it is part
of almost all activities of daily living [
1
,
2
]; therefore, the ability
to walk is an indicator of overall health as it dictates autonomy
[
3
]. Although walking is usually learned at a young age, the
mechanics of walking are not as simple as they may appear [1].
From the first studies of human walking elaborated through
a series of photographic images, by early Biomechanics en-
thusiasts Edweard Muybridge and Étienne-Jules Marey, gait
analysis as it is known today has evolved significantly [4]. The
walking pattern of individuals has become an area of broad
interest and the focus of much research as seen by the numer-
ous journals and articles published. The importance of gait
analysis lies in its application; through years of research and
experimentation, gait analysis has become widely used as a
means to diagnose pathology, set a prognosis and establish
and evaluate a treatment plan [
5
,
6
]. Today, a variety of differ-
ent parameters of various types exist and are readily used to
examine and explain human gait [7-10].
In clinical settings, gait analysis is often carried out solely
through clinician observation [
11
]. Although clinicians have
developed good expertise through many years of practice and
training, these observations remain subjective [12]. Principal
reason for main, and perhaps sole use of clinician observation
as means of gait analysis, is ease of measurement [8,13,14].
In the research setting, numerous parameters have been
used to quantitatively describe gait. Parameters of various types
such as spatio-temporal parameters, ground reaction forces,
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joint kinematics and the energy expense are a few [1,15,16].
In accordance with evidence-based-medicine, the bio-
mechanical parameters chosen are as important as rigorous
gait analysis technique [17]. Because of the quasi-infinite
number of parameters available, it seems reasonable that
certain parameters would be best suited for gait analysis in
the healthy population.
Systematic reviews have been realized in an attempt
to organize and add understanding to the practice of gait
analysis in various populations. For example, a systematic
review carried out by Sagawa and colleagues [18], using an
original methodological approach, was able to identify the
most relevant biomechanical parameters for assessing gait in
individuals with a lower limb amputation. The results obtained
by Sagawa and colleagues [18] leads to question whether the
same biomechanical parameters are most relevant for gait
analysis in the healthy adult population.
The aim of this systematic review is to determine the most
relevant biomechanical parameters used for gait analysis in
a healthy adult population.The term relevant was defined as
those biomechanical parameters being able to identify gait
abnormalities in the healthy adult population and applicable
to the clinical and rehabilitation setting. This definition is an
adaptation of that used by Sagawa et al. [18].
Methods
Procedure for the identication of selected articles
We performed an online search in three databases: PubMed,
EMBASE and Web of Science. These three databases were select-
ed for search because of their broad inclusion of multidiscipli-
nary topics within the Biomedical and Health Sciences domain.
Each database was searched for all years included in the respec-
tive databases with the last search completed in May 2016.
The following search was inputted to all three databases:
[abstract/title] (speed OR cadence OR (stride time) OR (swing
time) OR (step time) OR (single support time) OR (double
support time) OR (foot flat time) OR (stance time ratio) OR
(swing time ratio) OR timing OR (stride length) OR (step length)
OR (step width) OR angle OR moment OR power OR (center
of mass) OR (ground reaction force) OR (ground reaction im-
pulse) OR (center of pressure) OR rotation OR symmetry OR
velocity OR (stance phase) OR (swing phase) OR (cycle time)
OR (spati* temporal) OR hip Or knee OR ankle OR foot) OR
(biomechanic*) AND ([MeSH] gait OR walking OR locomotion).
In databases where applicable, certain additional parameters
were used to narrow the search. In PubMed, filters including
human studies of adults aged 18 to 65 years old, published
in French and English and with regards to the nature of the
study (i.e., original articles, review articles, case study) were
applied. In the EMBASE and Web of Science databases, filters
were applied to include human studies, French and English
language publications and specific nature of study (i.e., original
articles, review articles, case study).
Inclusion and exclusion criteria
The inclusion and exclusion criteria were developed based
upon the purpose of the systematic review, to examine the
biomechanical parameters used to study healthy gait. Thus,
studies including participants living with pathologies, dis-
abilities, health concerns and/or neurological deficits were
excluded. To be selected, articles had to evaluate adults
aged 18 to 65 years old with no walking aids. Participants
could have been evaluated barefoot, wearing socks, wearing
shoes and/or any combination of these three situations. As
well, no studies were included if they measured the effect of
a treatment or equipment. Selected articles had to at least
evaluate participants walking at their self-selected speed on
an overground and flat surface.
Analysis of selected articles
A census of all biomechanical parameters measured was
undertaken by two evaluators by carefully reading and ana-
lyzing the chosen articles. First, all methodological aspects of
the selected articles were tabulated and briefly summarized.
Second, the biomechanical parameters measured in all articles
were tallied. For each parameter, all articles which measured
this parameter were reported and counted. Third, because of
the many various instruments, techniques, planes of measure-
ment, etc. used to quantify parameters in the studies selected,
the parameters measured were summarized under broader
parameter names (i.e. sagittal, frontal and transverse plane knee
power were combined under the broader name of knee power).
Lastly, after a summation of parameters, the number of dif-
ferent articles measuring a type of biomechanical parameter
was counted; this was also done for single parameters. Indeed,
it seems inevitable to consider not only the most frequently
measured parameters, but as well the number of different arti-
cles which measure a parameter to observe any disparities be-
tween the number of times a parameter was measured versus
the number of different articles which measured this parameter.
In an attempt to evaluate relevance of biomechanical pa-
rameters, both the frequency of measure and the number of
different articles which measure the parameter were combined
to produce a score using the summarized parameters. For the
first factor, all frequency of measurement scores were divided
by the parameter having been measured the most times (hip
power: 66 times) and multiplied by 0.5. For the second factor,
all number of articles were divided by the parameter having
been measured by the most amount of different articles and
multiplied by 0.5. Both values were then added to obtain a
score weighting both factors. It was deemed that both factors
were as important as the other, each contributing to 50% of
the score. The following is an example of the calculation for
walking velocity, which was measured 50 times by 50 articles:
Walking velocity: ((50/66)*0.5) + ((50/50)*0.5)= 0.879.
Quality of selected articles
We evaluated quality of the selected articles by attributing
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a Level of Evidence score for each selected article. Our Level
of Evidence score was a modified version of that used by
Sagawa and colleagues [
18
], since they were interested in
gait analysis in a population with a lower limb amputation
and the current systematic review addresses healthy adult
gait analysis. The 14 criteria were subdivided between three
main article elements: 1) selection of participants, 2) interven-
tion and assessment, and 3) statistical validity. The maximum
possible score was therefore 14, with each article receiving
a score of 1 (if they met the requirements) or 0 (if they did
not meet the requirements) for each criterion (score of 1 for
a non-applicable criterion). Two independent evaluators as-
sessed the score of all articles. For any disparities between
scores, both evaluators determined the best suited scoring
through discussion. If a consensus could not be reached by
the two evaluators, a third evaluator intervened in order to
break tie between both scores suggested.
Data/ Statistical analysis
A Spearman correlation was carried out in order to determine
if higher Level of Evidence articles were published in higher
Impact Factor journals. Also, a Spearman correlation was
sought between the mean Level of Evidence attributed to
all articles measuring a given parameter and its frequency of
measurement. All statistical analyses were carried out using
SPSS 22 (IBM Corp., NY). Level of significance was set at p<0.05.
Results
Selection of articles
The preliminary database search, using the previously
mentioned keyword combination, yielded 16 023 abstracts
throughout all three databases. Upon reading the titles and
applying the inclusion and exclusion criteria, 1388 articles
were retained for further selection. After reading the ab-
stract, 515 articles remained. Finally, after a careful reading,
65 articles fulfilled the inclusion and exclusion criteria and
were selected for further analysis (Figure 1). Table 1 outlines
the main methodological aspects of these selected articles.
Participant characteristics
The main participant characteristics of the 65 selected articles
are outlined in Table 1.
Article data quality
The Level of Evidence score attributed to each article was in
agreement between reviewers. The mean Level of Evidence
for all articles was 11.8±1.8, with scores ranging from 6 to 14.
The Level of Evidence scores attributed to the 65 articles are
outlined in Table 1.
Parameters for gait analysis
Table 2 indicates that parameters of various types were
measured and counted in the selected articles. Parameters
from power, work and/or torque were recorded 269 times,
Figure 1. Article selection owchart.
Flowchart as per PRISMA guidelines [19] summarizing the
procedure for the selection of articles aer the interrogation
of three databases. All articles were retained or dismissed
for analysis by the application of the inclusion and exclusion
criteria (see methods). First, the articles were retained or
dismissed on the basis of the article titles. A second step
consisted of the reading of the article abstracts. Finally, all
retained articles were read and a nal selection was made.
spatio-temporal parameters were recorded (256 times), joint
angles (177), moments (115) and force (115). A total of 1097
parameters were counted in 65 articles.
All measured biomechanical parameters in the selected
articles are outlined in
Table 2
. The parameter most often
measured and/or calculated was the walking velocity (50
times) followed by cadence (30 times), stride length (23 times)
and step length (21 times).
Parameter summation
As stated, a summation of parameters was carried out (results
outlined in Table 3) and the results show that the hip power is
the most often measured biomechanical parameter (66 times)
followed by the knee power (61 times), walking velocity (50
times) and the ankle angle (47 times).
Also outlined in Table 3 is the number of different articles
measuring summarized single parameters. Spatio-temporal
parameters were measured in 59 of the 65 articles, angles by
29 different articles and forces in 16 articles. When considering
summarized single parameters, walking velocity was meas-
ured in 50 different articles and stride length and cadence
were measured in 36 and 35 different articles, respectively.
The calculation to account for both frequency of measure-
ment and number of articles was carried out with the highest
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Reference Number & Sex of
participants
Age of participants Main Objectives Level of Evidence
[20]30 (15 F: 15 M) 20-29 Basic gait data on groups of healthy young adult Kuwaitis of
both genders was collected to determine if they duplicated the
data published in the Swedish study.
10
[21]19 (19 M) 25.3 (4.1) To determine, over two consecutive strides, if the right and
le lower limbs developed similar power patterns and if their
associated mechanical energies were equal or not in all 3 planes
of motion.
14
[22]17 (8M: 9F) 27.5(5.3) To compare overground and treadmill ambulation for possible
dierences in gait temporal variables and leg joint kinematics.
12
[23]20 (8 M: 12F) 37-62 To evaluate the time-varying behavior, the test-retest reliability
and the concurrent validity of lateral trunk lean and toe-out
angles during prolonged walking in healthy adults.
12
[24]11 (11 M) 28.3 (12.4) An examination of the angular momenta of healthy adult males
walking at three speeds; 0.7, 1.0, and 1.3 times their self-selected
comfortable walking speed.
11
[25]10 (3F: 7M) 27.5; 20-33 To conrm the hypothesis that stride duration variability
exhibits long-range autocorrelations among young healthy
subjects walking on level ground, by using an integrated
approach that combines distinct methods in order to increase
the level of condence. Also, to determine whether the treadmill
disrupts long-range autocorrelations present in stride duration
variability and to determine if the outcomes obtained from the
treadmill were reproducible across two dierent testing days.
11
[26]6 (4M: 2F) 25-45 To improve the understanding of how the central nervous
system (CNS) chooses gait parameters for the modulation of
velocity by proposing a method for characterizing gait strategies
from step frequency and step length analysis.
10
[27]30 (15F: 15M) 23.6 (2.7) To dene the walking speed and gender eects on the center of
pressure (COP) pathway.
11
[28]98 (51 M: 47 F) 23.5(2.7); 22.9(4.9) e research hypothesis was that healthy adults would walk
dierently according to their gender when walking barefoot at
their comfortable speed.
14
[29]14 (8M: 6F) 22.5(3); 23.8(4.1) To determine if there are changes in temporal gait parameters
with a focus on the pelvis when comparing overground and
treadmill ambulation, and to assess the eect of sex.
11
[30]30 (6 groups of 5)
(15 M: 15F)
20-30;
31-45;
46-60
To investigate the eects of age, gender and walking speed on
dierent gait performance measures including joint motion,
ground reaction forces (GRF), electromyography (EMG), heart
rate (HR), and perceived exertion during walking at dierent
percentage of preferred walking speed (PPWS).
12
[31]8 (6M: 2F) 22-30 To examine trunk, neck and head movements to determine a
mechanism for upper body stabilization during walking.
12
[32]10 (5M: 5F) 27.10 (3.25) To demonstrate that the processes responsible for maintaining
local dynamic stability of walking act across multiple consecu-
tive strides of gait.
11
[33]14 (4M: 10F) 30-55 To analyze foot and ankle kinematics from gait recordings of
healthy subjects walking at comfortable and slower speeds.
11
[34]10 (7M: 3F) 23 (2) To analyze the 3D angle between the joint moment and the joint
angular velocity vectors at the ankle, knee and hip during the
gait cycle and to investigate if these joints are predominantly
driven or stabilized.
11
[35]46 (32M: 14F) -- Velocity, stride length and stride frequency were treated as in-
dependent variables in relation to each other in a graphic form
to see how they interact in gait. To achieve this, a Velocity Field
Diagram (VFD) was described.
6
[36]9 (9M) 28.5 (5) To characterize the basic features of the moment-angle curves in
normal walking at dierent velocities.
12
Table 1. Methodological aspects of selected articles.
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[37]39 (21M: 18F) 27(4.2);
22.9 (4.1)
To characterize and compare the dynamic joint stiness (DJS) of
the ankle in the sagittal plane during natural cadence walking in
both genders.
12
[38]10 (10M) 23.3 (2.4) To investigate the variability and symmetry of ground reaction
force (GRF) measurements during walking, using time and
frequency domain analysis.
13
[39]16 (16M) 22.8 (1.6) To demonstrate that data from a video-based system could
be used to estimate the net eect of the external forces during
gait, to determine the contribution of the trunk and upper and
lower limbs using their accelerated body masses, and to test the
hypothesis that the thigh mainly assumed lower limb propulsion
during able-bodied locomotion.
14
[40]20 (20M) 23.8 (2.2.) To investigate the changes in horizontal velocity which are
known to inuence many biomechanical characteristics of
human locomotion, with respect to the interlimb symmetry of
walking in a normal population.
14
[41]14 (8M: 6F) 19-56 1) To determine whether asymmetries exist between limbs of
healthy individuals during gait and 2) to examine the rela-
tionship between lower extremity lateral dominance and any
observed dierences.
12
[42]10 (10M) 18-29 1) To determine whether long-range correlations in gait extend
over very long-time scales; 2) to dene the conditions under
which such correlations may exist; and 3) to evaluate potential
mechanisms underlying this fractal property of gait.
11
[43]11(11F) 27.4(4.0); 22-30 To investigate the inuence of walking speed on the amount and
structure of the stride-to-stride uctuations of the gait cycle.
9
[44]13 (7M: 6F) 23.3(3.0) To examine how gait speed inuences healthy individual’s lower
trunk motion during overground walking and to assess if Prin-
cipal Component Analysis (PCA) can be used to gain further
insight into postural responses that occur at dierent walking
speeds.
13
[45]10 23 (4) To investigate the relationship between oscillatory dynamics of
the head and trunk in each plane of motion during walking.
13
[46]68 (32M: 36F) 34 (11) To examine the changes, if any that occur in peak lower
extremity net joint moments while walking in industry recom-
mended athletic footwear.
12
[47]110 (57F: 53M) 29.1(8.9);
28.3(5.04)
To determine if knee joint torques, which are likely relevant to
the development and, possibly, progression of knee osteoar-
thritis, are equivalent between genders during natural, barefoot
walking.
14
[48]30 (17M: 13F) 24.6 (4.0) To evaluate the eect of pelvic rotation, originally described as
the rst determinant of gait, on reducing the vertical displace-
ment of the center of mass (COM) during comfortable speed
walking.
13
[49]20 (10M: 10F) 27-56 To determine three-dimensional foot and ankle kinematics,
using a three-segment foot model and to determine ground
reaction forces, temporal force factors and time-related factors
in normal subjects.
12
[50]25 (25M) 26.2 (5.2) To test if the lower limb joint and thoraco-lumbar moments are
similar in subjects who maintain an average natural forward or
backward trunk inclination during gait and verify if the lower
limbs are equally aected.
12
[51]16 (8M: 8F) 18-28 To study the familiarization time required for reliable sagittal-
plane knee kinematics and temporal-distance gait measure-
ments to be obtained from treadmill walking and whether knee
kinematics and temporal-distance gait measurements obtained
from familiarized treadmill walking can be generalized to over-
ground walking.
14
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[52]20 (5M: 15F) 18-30 To investigate the short-term relationships between footstep
variables during steady state, straight-line, over-ground walking
in healthy adults and to explore the extent to which the perfor-
mance of a step or stride is dependent on the performance of an
earlier step or stride in a sequence.
12
[53]10 (7M: 3F) 26.9 (5.7) To examine the eect of walking speed on center of mass
(COM) displacement in the medial-lateral (ML) and vertical
directions.
13
[54]10 (6M: 4F) 23 (5) 1) Quantifying gait pseudo-periodicity using information
concerning a single stride; 2) investigating the eects of walking
pathway length on gait periodicity; 3) investigating separately
the periodicity of the upper and lower body part movements; 4)
verifying the validity of foot-oor contact events as markers of
the gait cycle period.
12
[55]8 (4M: 4F) 24-38 To determine if walking at the predicted frequency produced
greater shock attenuation through the body when compared
with other frequencies at the same walking speed and to assess
the role played by the individual segments in attenuating shock
under dierent frequency-stride length combinations at a con-
stant speed.
12
[56]26 (13M: 13F) 18-35 (1) To compare the kinematics of treadmill gait to overground
gait obtained in laboratory, comparing the present ndings to
those previously reported and (2) to quantify any kinetic dif-
ferences between overground and treadmill gait, including, for
the rst time an analysis of the joint moments and powers of
treadmill gait.
7
[57]48 (10M: 38F) 23-62 To simultaneously statistically test whether the three factors
gender, age and walking speed signicantly aect kinematic gait
data in a reference population.
14
[58]22 (9M: 13F) 35-55 To determine if the variability in the characteristics of the
net external hip adduction moment can be explained by the
strength of the hip abductor musculature, subject anthropomet-
rics, gait velocity and the corresponding characteristics of the
gluteus medius electromyogram captured during gait in healthy
individuals.
12
[59]32 (20M: 12 F) 24.9 (2):
24.1 (1.6)
Gait analysis was conducted on Korean subjects in their 20s and
these gait characteristics were compared to those reported in
previously published studies conducted in Western countries.
13
[60]20 (20M) 25.3 (4.1) To test the hypothesis that limb propulsion is mainly associated
with the interaction of a number of muscle power bursts devel-
oped throughout the stance phase and that the control actions
are mainly achieved by the contralateral limb through dierent
power-burst interactions.
12
[61]19 (19M) 26.2 (3.2) To test the hypothesis that the trailing limb contributes mainly
to forward progression, whereas the trailing limb provides con-
trol and propels the lower limb to a lesser extent.
14
[62]20 (20 M) 25.3 (4.1) (a) To identify the main functions of the ankle and hip muscle
moments and their contribution to support and propulsion
tasks, and (b) to illustrate the interaction between ankle and hip
moment activities.
14
[63]19 (19 M) 25.3 (4.1) To demonstrate that the ankle frontal muscle power absorption
and generation at push-o are related to the foot’s initial posi-
tion at heel-strike with respect to the body center of mass.
13
[64]20 (10M: 10F) 24 (3) To compare bilateral ground reaction force impulses to evaluate
functional asymmetry as an explanation for gait asymmetries.
13
[65]25 (8M: 17 F) 19-32 To report the reproducibility of the invariant walk ratio in re-
peated trials involving young healthy adults walking at a variety
of speeds.
12
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[66]22 (10M: 12F) 25.9 (4.1):
20.6 (1.4)
To examine whether there is an optimal walking speed with
minimum intrasubject variability in step length and step width
during free walk and whether there is an optimal step rate with
minimum step length variability during walking with imposed
step rates.
12
[67]28 (14M: 14F) 20-34 To test the applicability a control scheme to the unconstrained
portion of the gait cycle- the swing phase.
11
[68]40 (20M: 20F) 24.1 (3.1):
22.5 (3.2)
To determine the kinematic variability of the lower extreity
joints using methods from the mathematical chaos theory in
a normal walking environment in conjunction with a large
population of healthy young adults and to test the hypothesis
that variability characteristics are dierent between joints and
to further investigate dierences between male and female and
right and le subgroups.
13
[69]10 (5M: 5F) 19-34 1) To introduce the knee moment arm length as a measure to
evaluate knee pre- and postoperatively; (2) to determine the
variability in trials done minutes apart and trials done days
apart; (3) to present some normative data for healthy subjects
for use as reference values in assessment of patients with knee
deformities; and (4) to determine the variability in the hip, knee
and ankle moments in the frontal and sagittal planes, in trials
done minutes apart and days apart.
11
[70]16
(slow:3M: 5F)
(fast: 3M: 5F)
Slow: 20.74
Fast: 19.75
To determine the familiarization period required to obtain con-
sistent measurements of the angular movements of the lumbar
spine and pelvis during treadmill walking.
13
[71]27
(slow: 7M: 6F)
(fast: 5M: 9F)
Slow: 23.5 (5.1)
Fast: 20.6 (2.8)
To study the eect of walking at a self-selected and at a slower
speed on the angular movements of the pelvis and lumbar spine
and how interpretation of speed eects on lumbar spine move-
ments was inuenced by frame of reference, either relative to the
pelvis or relative to a global reference frame.
8
[72]14 (7M: 7F) 46 (13.3) To employ an analytical model to estimate the eects of walking
cadence and laterality on the positive and negative mechani-
cal work performed by the hip, knee and ankle muscles in the
sagittal plane.
12
[73]8 (3M: 5F) 23-34 To measure the mechanical energy changes of the center of
gravity (CG) of the body in forward, lateral and vertical direc-
tion during normal level walking at intermediate and low
speeds.
11
[74]18 (9M: 9F) 35.9 (10) To test the 2D PL (power law) compliance of motion of the
center of mass (CM) within the step, as a premise to further 3D
modeling, so far applied to upper limb motion.
11
[75]62 (21M: 41 F) 41.4 (11.0) To investigate if the detailed pressure data of the footprints of
normal gait add essential information to the spatio-temporal
variables of gait.
6
[76]19 (19M) 25.3 (4.1) To determine if more than one gait pattern exists in able-bodied
young men, by analyzing the dissimilarities in the three-dimen-
sional (3-D) muscle powers developed at the joints of the right
lower limb.
14
[77]9 (9M) 28.7 (4.4) To determine the dierences between angular oscillation curves
of the lumbar spine and pelvis during walkway and treadmill
ambulation.
14
[78]15 (4M: 11F) 25.5 (4.5) To determine if limb dominance aects the vertical ground reac-
tion force and center of pressure (COP) during able-bodied gait.
9
[79]10 (5M: 5F) 24.3 (4.0) Sole-oor reaction forces were measured from ve anatomically
discrete points in the human sole during locomotion on the
treadmill and on the laboratory oor.
14
[80]24 (11M: 13 F) 27 (7) To compare vertical ground reaction forces walking overground
with vertical foot-belt forces for treadmill gait.
10
[81]20 (9M: 11F) 24 (4) To investigate the contribution of passive mechanisms to lower
extremity joint kinetics in normal walking at slow, comfortable
and fast speeds.
12
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[82]12 28.5 (3.3) To investigate whether multiple short bouts of gait can be used
for the valid and reliable assessment of variability and local
dynamic stability, and how many bouts are required for their
reliable estimation.
11
[83]21 (10M: 11F) 26.9 (4.5) To assess the validity of the anatomical landmark data derived
from the Kinect’s skeleton tracking algorithm for examining
the spatiotemporal characteristics of gait in young, healthy
individuals.
11
[84]10 (10M) 28.8 (8.3) To demonstrate how vector eld statistics can be used to more
objectively analyse CoP trajectories.
10
Methodological aspects of all selected articles. e above chart depicts the reference, number of participants, sex of participants (M for
male: F for female), the participant age (mean, standard deviation in parentheses and range separated by a hyphen), as well as the main
objectives of the study and the Level of Evidence score attributed to each article (on a possible 14 total points). All available information
concerning participant characteristics was provided. If the articles reported participant age and sex characteristics per group (i.e. fast and
slow walking group), the information is provided as such.
POWER, WORK & TORQUE (269)
Parameter Total Articles Parameter To t a l Articles
Sagittal hip power peak 1 5[60,61,72,76,81]Sagittal hip power peak 2 2[60,61]
Sagittal hip power peak 2 5[60,61,72,76,81]Sagittal hip power peak 3 2[60,61]
Sagittal hip power peak 3 5[60,61,72,76,81]Frontal hip power peak 1 2[60,61]
Sagittal knee power peak 1 5[60,61,72,76,81]Frontal hip power peak 2 2[60,61]
Sagittal knee power peak 2 5[60,61,72,76,81]Frontal hip power peak 4 2[60,61]
Sagittal knee power peak 3 5[60,61,72,76,81]Transverse hip energy peak 1 2[60,61]
Sagittal ankle power peak 1 5[60,61,72,76,81]Transverse hip energy peak 2 2[60,61]
Sagittal ankle power peak 2 5[60,61,72,76,81]Transverse hip energy peak 3 2[60,61]
Frontal ankle power peak 2 4[60,61, 63,76,]Sagittal knee energy peak 1 2[60,61]
Frontal hip power peak 1 3[60,61,76]Sagittal knee energy peak 2 2[60,61]
Frontal hip power peak 2 3[60,61,76]Sagittal knee energy peak 3 2[60,61]
Frontal hip power peak 3 3[60,61,76]Frontal knee energy peak 1 2[60,61]
Transverse hip power peak 1 3[60,61,76]Frontal knee energy peak 2 2[60,61]
Transverse hip power peak 2 3[[60,61,76]Transverse knee energy peak 1 2[60,61]
Transverse hip power peak 3 3[60,61,76]Transverse knee energy peak 2 2[60,61]
Sagittal knee power peak 4 3[72,76,81]Transverse knee energy peak 3 2[60,61]
Frontal knee power peak 1 3[60,61,76]Sagittal ankle energy peak 1 2[60,61]
Frontal knee power peak 2 3[60,61,76]Sagittal ankle energy peak 2 2[60,61]
Transverse knee power peak
1
3[60,61,76]Frontal ankle energy peak 1 2[60,61]
Transverse knee power peak
2
3[60,61,76]Frontal ankle energy peak 2 2[60,61]
Transverse knee power peak
3
3[60,61,76]Sagittal plane knee power 2[21,81]
Frontal ankle power peak 1 3[60,61,76]Sagittal plane hip power 2[21,81]
Frontal hip power peak 3 3[60,61,63]Sagittal plane ankle power 2[21,81]
Frontal hip power peak 4 2[60,61]Frontal plane ankle power 2[21,63]
Sagittal hip power peak 1 2[60,61]
SPATIO-TEMPORAL PARAMETERS (256)
Parameter Total Articles Parameter To t a l Articles
Walking velocity 50 [20,21,27-30,32,33,40,42-
46,48,50-53,55-61,60-
67,61,76,81, 71-
77,79,80,81,83,84]
Stride width 4[28,33,53,59]
Table 2. Measured biomechanical parameters.
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Cadence 30 [20,21,22,37,24,26-30,49-56,59,
60,61,72,75,76,79,63,65,66,
67,80]
Swing time 4[22,24,59,75]
Stride length 23 [22,21,35,36,24,28,32,33,43,50,
51,53,56,59,60,61,72,75,76,63,6
7,80,83]
% Stance time 3[21,28,38]
Step length 21 [20,21,26,33,41,43,44,48,52,53,5
4,59,60,61,73,75,76,65,66,67,83]
Step time 4[20,41,75,83]
Stance time 12 [38,24,29,39,40,41,49,59,60,61
75,79]
Time of heelstrike 3[25,31,39]
Gait cycle (%) 10 [34,28,29,44,46,47,50,54,57,59]Time of toe-o 3[31,33,39]
Stride time 12 [25,22,35,29,32,33,44,51,54,56,
80,83]
Breaking phase 2[60,61]
Double support time 7[24,31,33,52,60,61,75]Stride interval 2[42,43]
% Stance phase 6[33,49,50,63,75,76]Stride frequency 2[32,35]
Gait cycle time 5[41,49,75,67,77]Terminal double support time 2[60,61]
% Double support 4[21,28,56,75]Step width 2[54,66]
ANGLES (177)
Parameter Total Articles Parameter To t a l Articles
Maximum ankle sagittal
plane dorsiexion
4[30,41,49,56]Maximum hip sagittal plane exion 2[30,56]
Maximum ankle sagittal
plane plantarexion
4[30,41,49,56]Maximum hip sagittal plane exten-
sion
2[30,56]
Hip sagittal angle 3[22,81,67]Maximum knee sagittal plane exten-
sion
2[41,56]
Knee sagittal angle 3[22,81,67]Frontal plane ankle angular velocity 2[21,63]
Ankle sagittal angle 3[22,81,67]Upward maximum pelvic obliquity
angle
2[29,56]
Maximum knee sagittal
plane exion
3[30,41,56]Downward minimum pelvic obliq-
uity angle
2[29,56]
Foot progression angle 2[57,58]Max knee extension angle 2[22,32]
Sagittal plane ankle angle
position
2[36,49]Pelvic rotation angle 2[48,57]
Sagittal plane hip angle
position
2[36,28]
MOMENTS (115)
Parameter Total Articles Parameter To t a l Articles
Sagittal plane hip moment 6[21,36,46,50,81,60]Peak knee exion moment 2[46,56]
Sagittal plane ankle moment 6[21,36,46,50,81,60]Peak knee varus moment 1 2[46,56]
Sagittal plane knee moment 5[21,36,46,50,81]Peak knee external rotation
moment
2[46,56]
Peak hip extension moment 4[46,50,56,60]Peak knee internal rotation moment 2[46,56]
Peak hip exion moment 4[46,50,56,60]Peak ankle eversion moment 2[46,56]
Peak ankle dorsiexion mo-
ment
3[46,56,60]Peak ankle external rotation
moment
2[46,56]
Peak ankle plantarexion
moment
3[50,56,60]Peak ankle internal rotation
moment
2[46,56]
Frontal plane ankle moment 3[21,46,63]Transverse plane knee moment 2[21,46]
Peak knee extension moment
1
2[50,56]Frontal plane knee moment 2[21,46]
Peak hip adduction mo-
ment 1
2[46,56]Transverse plane hip moment 2[21,46]
Peak hip external rotation
moment
2[46,56]Frontal plane hip moment 2[21,46]
Peak hip internal rotation
moment
2[46,56]Transverse plane ankle moment 2[21,46]
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FORCES (115)
Parameter Total Articles Parameter To t a l Articles
Fz1 8[38,30,40,43,46,49,78,80]V1 (vertical maximum force) 3[39,56,59]
Fz3 8[38,30,40,43,46,49,78,80]S1 (sagittal maximum force) 3[39,56,59]
Fz2 7[38,30,40,43,49,78,80]T1 (maximum transverse force) 3[39,56,59]
Fy1 4[38,40,46,49]Fx3 2[46,49]
Fy2 4[38,40,46,49]Time to Fx1 2[38,49]
Fx1 3[38,46,49]Time to Fx2 2[38,49]
Fx2 3[38,46,49]V3 (vertical maximum force) 2[39,59]
Time to Fz1 4[38,40,49,80]S2 (sagittal minimum force) 2[39,56]
Time to Fz2 4[38,40,49,80]S3 (sagittal maximum force) 2[39,59]
Time to Fz3 4[38,40,49,80]T2 (minimum transverse force) 2[39,56]
Time to Fy1 3[38,40,49]T4 (maximum transverse force) 2[39,59]
Time to Fy2 3[38,40,49]Ground reaction forces AP 2[64,74]
ACCELLERATION (52)
Parameter Total Articles Parameter To t a l Articles
VT head acceleration 2[45,55]AP head acceleration 2[45,55]
SYMMETRY (25)
Parameter Total Articles Parameter To t a l Articles
Symmetry of Fz1 2[38,78]Symmetry of Fz3 2[38,78]
Symmetry of Fz2 2[38,78]AP COP displacement 2[78]
CENTER OF MASS (22)
Parameter Total Articles Parameter To t a l Articles
COM displacement (vertical) 3[48,53,74]COM velocity (VT) 2[53,74]
COM displacement (M/L) 2[53,74]COM velocity (M/L) 2[53,74]
COM displacement (A/P) 2[63,74]
LOCAL DYNAMIC STABILITY (19)
VARIABILITY (18)
COP (11)
OTHER (36)
Biomechanical parameters measured in included studies. is chart tabulates each biomechanical parameter as it was measured in the
designated study. e reference measuring each given parameter is given, as well as the total for single parameters. e following parameters
are grouped according to their type and a total of number of parameters measured per type is given in parentheses. Only parameters
measured more than once are shown here.
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POWER, WORK & TORQUE (269) 13
Parameter Total Number of articles Parameter Tot a l Number of articles
Hip power 66 9 Limb energy 6 1
Knee power 61 9 Limb work 6 1
Ankle power 36 9 Foot momentum 6 1
Arm momentum 12 1 Shank momentum 6 1
Energy 8 1 igh momentum 6 1
Hip work 6 1 Knee torque 5 2
Knee work 6 1 Head & neck mo-
mentum
3 1
Ankle work 6 1 Torso momentum 3 1
Hip energy 6 1 Total body momen-
tum
3 1
Knee energy 6 1 Hip torque 1 1
Ankle power 6 1 Ankle torque 1 1
SPATIO-TEMPORAL PARAMETERS (256) 59
Parameter Total Number of articles Parameter Tot a l Number of articles
Walking velocity 50 50 Double support 13 11
Stride length 43 36 Step width 6 6
Gait cycle 37 23 Swing time 6 5
Cadence 37 35 Single support 2 2
Stance time 22 19 OTHER 14 4
Stride time 16 16
ANGLES (177) 29
Parameter Total Number of articles Parameter Tot a l Number of articles
Ankle angle 47 17 Lumbar angle 7 1
Pelvis angle 37 9 Spine angle 6 1
Hip angle 30 13 Neck angle 2 1
Knee angle 29 14 Head angle 2 1
Trunk angle 8 4 Sacrum angle 1 1
orax angle 7 1
MOMENTS (115) 13
Parameter Total Number of articles Parameter Tot a l Number of articles
L5 moment 5 1 Ankle moment 35 12
Hip moment 37 11 Other 8 2
Knee moment 30 9
FORCES (115) 16
Parameter Total Number of articles Parameter Tot a l Number of articles
Vertical ground reaction force 43 13 Lower limb ground
reaction forces
3 1
Anterior-posterior ground reaction
forces
27 9 Upper limb ground
reaction forces
3 1
Medial-lateral ground reaction forces 22 6 Other 8 3
Head and trunk ground reaction forces 3 1
ACCELLERATION (52) 2
Parameter Total Number of articles Parameter Tot a l Number of articles
Head velocity 21 2 Ankle velocity 6 1
Trunk velocity 13 1 Knee velocity 4 1
Shoulder velocity 6 1 Ankle velocity 2 1
SYMMETRY (25) 4
Table 3. Summation of parameters.
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Parameter Total Number of articles Parameter Tot a l Number of articles
Ground reaction symmetry 12 3 COP symmetry 7 2
Spatio-temporal symmetry 8 1
CENTER OF MASS & GRAVITY (22) 4
Parameter Total Number of articles Parameter Tot a l Number of articles
Center of mass 19 4 Center of gravity 3 1
LOCAL DYNAMIC STABILITY (19) 3
Parameter Total Number of articles Parameter Tot a l Number of articles
Local Dynamic Stability 18 3
VARIABILITY (18) 3
Parameter Total Number of articles Parameter Tot a l Number of articles
Coecient of variation 8 1 Standard deviation 7 1
COP (11) 3
Parameter Total Number of articles Parameter Tot a l Number of articles
COP velocity 5 2 COP position 2 1
COP displacement 3 1
OTHER (36) 7
Summation of all biomechanical parameters measured in included studies. is chart tabulates each parameter under a broader
theme of parameters as well as the number of dierent articles which measure this summarized parameter. e total number of
parameters measured per type is shown in parentheses beside the parameter type; the total number of dierent articles measuring
a type of parameter is given beside these parentheses. e breakdown of the summation is not shown here. Only parameters
measured more than once are shown.
frequency of measurement being the hip power (66 times)
and the greatest number of articles being walking velocity (50
articles). Walking velocity obtained the highest score (0.879),
followed by stride length (0.686), cadence (0.630), hip power
(0.590) and knee power (0.552). The results of this score are
presented in Table 4.
Level of evidence score and Journal Impact Factor
It was sought whether a correlation existed between the
article Journal Impact Factor (not shown) and the Level
of Evidence score attributed to each article by means of a
Spearman correlation. The result of this correlation is a very
weak, negative and non-significant correlation (rs=-0.133,
p=0.105). The Impact Factor scores of 4 articles [21,54,59,67]
were unavailable and were therefore excluded.
Frequency of parameters and Level of Evidence score
When the frequency of the most often reported parameters
was correlated with the mean Level of Evidence score of arti-
cles (not shown), via a Spearman correlation, a weak, negative
and non-significant correlation was found (r
s
=-0.224, p=0.06).
Discussion
Number of articles
The current review was based on 65 articles. This number
may appear small knowing that the review of Sagawa and
colleagues [18] included 89 articles of a clinical population.
The present study reflects the restrictiveness of our inclusion
and exclusion criteria.
Type, single and summation of biomechanical parameters
Types of biomechanical parameters
Considering types of parameters, it was found that power,
work and energy parameters were measured most often (269
times): spatio-temporal parameters followed closely being
measured 256 times. Joint angle parameters were measured
177 times, joint moment parameters were measured 115 times
and forces were also measured 115 times. In comparison, the
systematic review of Sagawa and colleagues [18], revealed
that parameters of spatio-temporal type were measured 153
times, joint angles 78 times, platform parameters (i.e. ground
reaction forces and center of pressure) 72 times, powers 64
times and joint moments 58 times. Thus, in general, the num-
ber of times a type of parameter was measured was less in
the review of Sagawa and colleagues [18] than in the present
review despite the fact that fewer articles were included for
analysis in the current review.
These larger numbers are explained by the fact that both
studies did not group parameters in the same manner;
therefore, the number of parameters in relation to the total
number of articles included in each study is different. Also,
Sagawa and colleagues [18] carried out a summation of pa-
rameters in which both time sub-parameters and amplitude
sub-parameters were grouped separately. For the purpose
of our systematic review, it was thought more appropriate
to group parameters accordingly, since all are yielded from
one measure.
Omitting these disparities, it is possible to note that spatio-
temporal parameters are of high relevance in both systematic
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reviews. As well, all most frequent types of parameters are the
same, although they differ in number and order of relevance.
Single parameters
When looking at single parameters, the walking velocity (50
times), cadence (30 times), stride length (23 times) and step
length (21 times) were those parameters most frequently
measured. These results are in agreement with Sagawa and
colleagues [
18
] who conclude the same parameters were most
often measured: walking velocity, cadence, stride and step
length. It is interesting to note that for these two different
populations the same parameters would appear to be most
relevant. This may be because parameters of spatio-temporal
type have a certain ease of measurement in comparison to
other parameters.
Despite the fact that in the current systematic review,
power, work and energy parameters were the most frequently
reported measures as a type of parameter, when consider-
ing single parameters, the most frequently measured were
spatio-temporal. Interestingly, for power, work and energy
type of parameters, no single parameter was reported more
than 10 times and most parameters were measured only once.
In fact, for these types of parameters, a given parameter can
be measured at different instances of the gait cycle, in three
different planes and for minima and maxima values, making
the number of parameters somewhat inflated.
As well, more minima and maxima power values exist at the
hip joint when compared to the ankle joint, for example. This
may also explain some disparity in the frequency of measure-
ment of some parameters, especially kinematic parameters
of the lower limb joints.
Summation of parameters
After a summation of parameters, we observe that those pa-
rameters most frequently measured are hip power (66 times),
knee power (61 times), walking velocity (50 times) and ankle
angle (47 times). Following parameter summation, Sagawa
and colleagues [
18
] concluded walking velocity (43 times),
knee angle (31), knee moment (27 times) and hip power (26
times) were most often measured. These differences might
reflect that the results are somewhat inflated and the angle,
moment and power parameters need to be interpreted cau-
tiously. Indeed, Sagawa and colleagues [
18
] did not group
parameters in the same way as was done in the present review
and the frequency obtained for angle, moment and power
parameters are smaller. As well, for certain types of parameters
(i.e. power, work and energy), the number of total parameters
measured (i.e. 269 times) may also be inflated. Again, this may
explain some disparity between the number of parameters
measured with regards to the total number of articles.
Another explanation for these differences is the type of
population studied. Indeed, their choice of clinical popula-
tion implied the absence of the ankle joint which can explain
the lack of ankle joint measures in their population with a
transtibial amputation. In the healthy adult population, ankle
joint measures were in the top four most relevant parameters
after parameter summation.
Also interesting is that articles which measure hip mo-
Parameter Frequency Number of articles Relevance score
POWER, WORK & TORQUE
Hip power 66 9 0.590
Knee power 61 9 0.552
Ankle power 36 9 0.363
SPATIO-TEMPORAL PARAMETERS
Walking velocity 50 50 0.879
Stride length 43 36 0.686
Cadence 37 35 0.510
Gait cycle 37 23 0.630
Stance time 22 19 0.357
ANGLES
Ankle angle 47 17 0.526
Pelvis angle 37 9 0.370
Knee angle 29 14 0.360
Hip angle 30 13 0.357
FORCES
Vertical ground reaction force 43 13 0.456
Table 4. Relevance score.
is relevance score is calculated based on the frequency of measurement and the number of
dierent articles measuring the given parameter, as described in the methods section. Only
parameters which scored more than 0.300 are shown here.
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ments, also tend to measure joint moments at the knee and
ankle, as they are necessary in inverse dynamic calculations.
As well, it is interesting to note that forces are needed in the
calculation of moments and angular kinematics are needed
for power calculations. Therefore, articles measuring powers,
would also measure kinematics, forces and moments and
this plays an important role when looking at frequency and
relevance of parameters.
In addition to the frequency of measurement, it is also
important to consider the number of different articles meas-
uring a given parameter. Out of the total 65 articles included
in our systematic review, spatio-temporal parameters were
reported by 59 different articles, joint angles were reported
by 29 articles, followed by forces (16 articles), joint moments
(13 articles) and power, work and energy (13 articles). So for
power, work and energy parameters (measured 269 times),
the type of parameters which appear to have been measured
most often, only 13 out of 65 articles measured these types
of parameters. In comparison, spatio-temporal parameters
(measured 256 times), were evaluated in 59 of the 65 articles.
As for the type of parameters discussed above, using the
summarized parameters, the walking velocity remained the
most often measured (50 articles out of 65 total articles) fol-
lowed by cadence (35 articles), stride length (36 articles), gait
cycle parameters (23 articles) and stance time (19 articles).
However, when comparing these results to those of Sagawa
and colleagues [
18
], we observe that a higher number of
articles reported the most common parameters in our pre-
sent study: walking velocity was measured only 43 times in
89 articles, cadence 19 times and step and stride length 19
times and 15 times, respectively. An important note must be
made here that parameters were summarized differently by
both reviews. The differences in the number of articles can,
in large part, be explained by the choice of inclusion and
exclusion criteria.
As shown by our results, both frequency of measurement
and the number of different articles measuring a parameter
are of importance when investigating the most relevant
biomechanical parameters for gait analysis. The results of
the score combining both these factors show that walking
velocity, stride length and cadence appear to be most relevant.
Level of Evidence score
The mean Level of Evidence score for all articles was 11.8±1.8
out of 14 points. This mean score is high; one can argue that
it almost reaches a ceiling effect. It is perhaps because the
Level of Evidence was not discriminatory enough in the limits
for scoring. This can also be due to high quality and soundly
based studies. It is perhaps simpler to carry out quality ex-
perimentation in a healthy population since there may be
less physical restrictions and/or needs as compared to other
clinical populations. This may also be due to the inclusion/
exclusion criteria weeding out the lower quality articles. A
Level of Evidence score with a wider array of possible scores
would be needed.
Relation between the Level of Evidence score and Journal
Impact Factor
The Level of Evidence score of articles were correlatedwith the
Journal Impact Factor. The weak, negative and non-significant
Spearman correlation found is in agreement with that of Sa-
gawa and colleagues [18] who carried out this same analysis
but with the Journal Impact Factors of the year of publica-
tion of their systematic review. It is possible to conclude that
both Level of Evidence score and the Journal Impact Factor
are not related.
Relation between the frequency of parameters and their
mean Level of Evidence score
The mean Level of Evidence of the articles was correlated
with the frequency of the parameter measured. As stated
in the results section, a weak, negative and non-significant
Spearman correlation was found. It is therefore possible to
conclude that the frequency of measurement of a parameter
is not related to the mean Level of Evidence of the articles
which measure this parameter.
Most relevant biomechanical parameters
Spatio-temporal parameters, namely walking velocity, cadence
and step and stride length, appear to be the most relevant
biomechanical parameters in both individuals with a transtibial
amputation and healthy adults. In addition, walking velocity
is of even greater relevance since it also measures, and has a
direct effect on such parameters as cadence and stride length.
Additionally, these spatio-temporal parameters have a cer-
tain ease of measurement: measuring simple spatio-temporal
parameters such as walking velocity would appear to be an
effective and simple manner to add objectivity to clinical
gait analysis which is primarily aimed at ease of measure-
ment [8,13,14].
Future studies should aim to identify if the most relevant
biomechanical parameters for gait analysis found in healthy
adults are also relevant to other clinical populations. Individu
-
als with a transtibial amputation and healthy adults yielded
the same most relevant parameters, but perhaps the results
obtained in other populations would be different, such as
in populations with a neurological disorder (i.e.: Parkinson’s,
Stroke or Cerebral Palsy) or with a more severe mechanical
impairment (i.e.: bilateral trans-femoral amputation).
Conclusion
A systematic review of the literature pertaining to healthy
adult gait was performed and the most relevant biomechani-
cal parameters were identified. Spatio-temporal parameters
were those parameters most often measured and by the most
amount of articles. Additionally, many specific spatio-temporal
parameters were those most often measured (walking veloc-
ity, cadence and step/stride length), walking velocity being
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measured most often, and by the greatest number of articles.
Walking velocity, and other spatio-temporal parameters would
therefore appear to be the most relevant biomechanical
parameters to healthy adult gait analysis.
To our knowledge, this is a first systematic review of its
kind in a healthy adult population and the implications of
these findings are important for choosing the most relevant
biomechanical parameters for gait analysis.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Acknowledgement
We would like to thank the Programme de collaboration
Université-Collège du MERST for their financial support.
Publication history
Editor: Mohammad H. Hadadzadeh, Wheeling Jesuit University, USA.
Received: 23-Feb-2017 Final Revised: 26-Apr-2017
Accepted: 24-Jul-2017 Published: 17-Aug-2017
Authors’ contributions MR DM FP
Research concept and design ✓ ✓
Collection and/or assembly of data ✓ --
Data analysis and interpretation ✓ ✓
Writing the article ✓ --
Critical revision of the article ✓ ✓
Final approval of article -- ✓
Statistical analysis -- --
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Citation:
Roberts M, Mongeon D and Prince F. Biomechanical
parameters for gait analysis: a systematic review of
healthy human gait. Phys er Rehabil. 2017; 4:6.
http://dx.doi.org/10.7243/2055-2386-4-6
... Because many diseases lead to abnormal gait, gait is usually used to assess the health of the human body and even can diagnose some motor neuron diseases in the medical and ergonomic fields [1]. According to the review from Roberts et al., most gait studies chose stride/step length and speed as representative gait parameters [2]. Meanwhile, foot clearance (i.e., the highest height in a gait cycle) is an important indicator in evaluating the ability of obstacle avoidance [3] and the trip risk of older adults [2,4,5]. ...
... According to the review from Roberts et al., most gait studies chose stride/step length and speed as representative gait parameters [2]. Meanwhile, foot clearance (i.e., the highest height in a gait cycle) is an important indicator in evaluating the ability of obstacle avoidance [3] and the trip risk of older adults [2,4,5]. Thus, this paper focuses on three gait parameters: stride length, speed, and foot clearance. ...
Article
Gait can reflect locomotion and physical condition and thus is used to assess people's health. Traditional high-precision gait analysis devices are expensive and are limited to laboratory conditions, so the trend is to use wearable devices to analyze gait. The existing wearable inertial-based gait-analysis methods have stride-length and foot-clearance estimation accuracy of 2.0 cm to 5.0 cm (in root mean square) and have limitations. They have not considered the regularity of the movement between the toe and the heel, the varying dual-feet distance due to foot dynamics, and the diversity of sensors used. Involving these factors, this paper achieves an improved gait-analysis system that provides stride-length and foot-clearance accuracy of 1.5 cm and 1.0 cm, respectively. Such accuracy is state-of-the-art for low-cost inertial systems and is even competitive with those from visual-sensor-based gait-analysis systems. A key to the proposed method is a new multi-level information fusion architecture and the extraction of human-walking constraints. The information-fusion architecture involves data fusion from the sensor, single-foot, and dual-foot levels. Two gait-characteristic-based motion constraints are presented to achieve such fusion, including the toe-heel constant distance constraint and the dual-foot flexible distance constraint. To implement these constraints, a constrained Kalman filter is constructed. The corresponding hardware system has been designed using multiple dollar-level inertial measurement units.
... The strong clinical characteristics of the VFD have enabled diverse clinical studies in developed and developing countries involving plantar lipoatrophy [27], knee osteoarthritis [17], hemiparesis [28], high-heeled walking [16], and diabetes [18,29,30] among others [18,29,30]. A systematic review provided evidence that the VFD has basic scientific applications relevant to clinical studies [31], thus justifying its potential as a fall screening tool [11]. ...
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Full-text available
Background Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults. Method This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD’s intersection point of the stride frequency, and velocity regression lines (E1) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E1 ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD’s and TUG’s sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05. Results The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC’s AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively). Conclusion The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls.
... GRF were recorded in mediolateral (GRF ML ), posteroanterior (GRF PA ) and vertical (GRF V ) directions by a fully instrumented dual-belt treadmill with separate embedded force plates (Bertec, Columbus, USA; TM-09). Selected peak GRF ML , GRF PA and GRF V were based on curves previously described among healthy adults [43] (Fig. 2). The GRF ML curve for normal walking contains one lateral and two medial peaks: the lateral thrust force (LTF) at heel contact, followed by the medial impact force (MIF) during weight acceptance, and lastly the medial propulsive force (MPF) during terminal stance. ...
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Background: Achilles tendinopathy (AT) may affect ground reaction force (GRF) and muscle synergy (MS) during walking due to pain, biological integrity changes in the tendon and neuroplastic adaptations. The objective of this study was to compare GRF asymmetries and MS attributes between symptomatic and asymptomatic lower limbs (LL) during walking at natural and fast speeds in adults with unilateral AT. Methods: A convenience sample consisting of twenty-eight participants walked on an instrumented treadmill at natural (1.3 m/s) and fast (1.6 m/s) speeds. Peak GRF were measured in mediolateral, anteroposterior and vertical directions. Individualized electromyography (EMG) activation profiles were time- and amplitude-normalized for three consecutive gait cycles and MS were extracted using non-negative matrix factorization algorithms. MS were characterized by the number, composition (i.e., weighting of each muscle) and temporal profiles (i.e., duration and amplitude) of the MS extracted during walking. Paired Student's t-tests assessed peak GRF and MS muscle weighting differences between sides whereas Pearson correlation coefficients characterized the similarities of the individualized EMG and MS activation temporal profiles within sides. Results: AT had limited effects on peak GRF asymmetries and the number, composition and temporal profiles of MS between symptomatic and asymptomatic LL while walking on a level treadmill at natural and fast speeds. In most participants, four MS with a specific set of predominantly activated muscles were extracted across natural (71 and 61%) and fast (54 and 50%) walking speeds for the symptomatic and asymptomatic side respectively. Individualized EMG activation profiles were relatively similar between sides (r = 0.970 to 0.999). As for MS attributes, relatively similar temporal activation profiles (r = 0.988 to 0.998) and muscle weightings (p < 0.05) were found between sides for all four MS and the most solicited muscles. Although the faster walking speed increased the number of merged MS for both sides, it did not significantly alter MS symmetry. Conclusion: Faster walking speed increased peak GRF values but had limited effects on GRF symmetries and MS attribute differences between the LL. Corticospinal neuroplastic adaptations associated with chronic unilateral AT may explain the preserved quasi-symmetric LL motor control strategy observed during natural and fast walking among adults with chronic unilateral AT.
... Gait speed can be controlled either by separately adjusting cadence or step (stride) length, or by a combination of both. Thus, it is evident that gait speed has significant impact on the gait pattern and therefore influences the majority of other gait parameters [15,24,47,53,150,244,280]. Together with cadence and step or stride length, gait speed appears to be amongst the most relevant parameters of human gait [215]. It is reported that the main strategy to increase gait speed, at least from slow walking to the preferred walking speed, is to increase the cadence [256]. ...
Thesis
Full text available at: https://resolver.obvsg.at/urn:nbn:at:at-ubl:1-46055 ------------------------------------------------------------------------------------------------------------------------------------------------------------------- Over the last decades a continuous trend of the worldwide population growing older could be observed. Increasing life expectancy and falling birth rates are identified as the main factors driving this demographic change. The demand for motion assistance and rehabilitation of age-related movement disorders is growing accordingly, which also facilitates interest in robotic solutions. Two of such devices will be addressed in this thesis, a commercial robotic hippotherapy system and an exoskeleton for the human lower body, developed at the JKU Institute of Robotics. In connection with these robotic systems, several methods and approaches in the context of rehabilitation and motion assistance are introduced. A major topic thereby is the generation of artificial motion patterns based on motion capture data. Hippotherapy refers to horse riding in the context of rehabilitation. It is a medical treatment that has been successfully employed in various fields, e.g. for improving locomotion performance of patients with movement disorders. Robotic systems enable the application of hippotherapy in clinical environments. Additional benefits, amongst others, are the continuously adjustable speed and high repeatability. Fundamental for a therapy outcome equivalent to classical hippotherapy is that the horse motion reproduced by the robotic system is as realistic as possible. Based on an analytical and time-continuous motion description a method to reproduce the horseback movement during typical horse gaits is presented in this thesis. This method allows for motion synthesis with any desired time span and time resolution, generating realistic trajectories applicable to robotic systems for riding simulation in general and robotic hippotherapy in particular. An adjustable time scaling parameter enables the adaptation of the generated motion according to the physical abilities of the patient or the capabilities of the robotic system. Serving as testing platform, where novel control strategies and motion assistance approaches can be implemented in a low-level manner, an exoskeleton prototype was developed in the course of this thesis. In addition to the design and construction of the setup and the pre-requisite implementations facilitating communication and operability, robust recognition of the user's motion intention and accurate timing of the provided assistance are focused on. Two approaches addressing these issues, namely gait assistance by learning periodic motions, as well as classification of gait phases based on measured muscle activities, are introduced. Physiologically consistent motion patterns of healthy human gait are intended to serve as a sound scientific basis for the developed methods and simulations. To this end, a holistic approach to simultaneously identify the geometric parameters of a kinematic human lower limb model and the parameters defining a cyclic gait trajectory, based on three-dimensional motion capture marker positions, is introduced. The presented methodology is then utilized to analyze several datasets of measured gait trials. The influence of person-specific factors, like age or physique, onto the motion pattern is analyzed by applying the presented biomechanical model to gait data of different male and female participants walking at various speeds. Modeling the dependency of gait motions on the considered variables enables the generation of artificial joint angle trajectories according to a simulated person with defined body parameters walking at a prescribed speed. . The presented methods show promising experimental results, which are deemed valuable for future research projects focusing on technologies for rehabilitation or movement assistance.
... The inclusion criteria of the healthy elderly were self-reported healthy with normal daily activity ability and no walking difficulty. The exclusion criteria included a history of neurological, cardiac, pulmonary or musculoskeletal diseases and memory or cognitive problems with mobility impairment [22], as well as joint pain, low back or leg pain within the past 30 days. A total of 12 healthy young adults under 55 years old were recruited after the test of elderly group. ...
Article
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Background The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects. Methods Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results. Results A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal. Conclusions The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.
... Quantitative gait analysis, including spatiotemporal, kinematic, and kinetic parameter assessment, has been proven useful in evaluating gait function, disease progression, and/or a given intervention's effect on patients' mobility [12,13]. Gait spatiotemporal parameters (STPs), in particular, are key metrics in gait function evaluation [14] and are indicators of gait adaptations in people with inflammatory arthritis [15]. Instrumented gait analysis studies in people with PsA demonstrated impairments in gait STP, including reduced cadence, gait speed, stride length, and increased double support time, indicating impaired gait function and ultimately implying difficulties in walking activities [16][17][18]. ...
Article
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Introduction Foot involvement is a significant concern in psoriatic arthritis (PsA) as it can lead to severe levels of foot pain and disability and reduced mobility and quality of life. Previous studies have shown moderate efficacy for custom-made foot orthoses (CFO) in reducing foot pain and disability in people with rheumatoid arthritis. However, evidence on the efficacy of CFO in people with PsA is lacking. Objectives To explore the effects of CFO on foot function, foot and lower limb pain, gait function, and free-living walking activities (FWA) in people with PsA. Methods A pre-experimental study including twenty participants with PsA (mean age: 54.10 ± 9.06 years and disease duration: 11.53 ± 10.22 years) was carried out. All the participants received and wore CFO for 7 weeks. Foot and lower limb pain and foot function were measured before and after the intervention using the numerical rating scale (NRS) and the foot function index (FFI). Gait function was assessed by recording spatiotemporal parameters (STPs) during a 10-m walk test using an instrumented gait analysis system (Mobility Lab). Free-living walking activities (step count, free-living cadence, time spent in different ambulatory physical activities (APA)) were recorded over 7 days using an accelerometer-instrumented sock. Results The FFI reported scores demonstrated severe baseline levels of foot pain (54.46 ± 14.58 %) and disability (46.65 ± 16.14%). Statistically and clinically significant improvements in foot pain and foot function and large effect sizes (Cohen’s effect size > 1, p < 0.005) were observed after the intervention period. A strong correlation ( r = −0.64, p < 0.01) between the CFO wearing time and foot function was demonstrated. However, no significant changes were found for gait STP or free-living walking activities after 7 weeks of CFO use. Conclusion Results support the clinical and biomechanical plausibility of using CFO in people with PsA to reduce pain and improve foot function. Large-scale and controlled studies are needed to confirm these findings. Moreover, a multidisciplinary approach including the prescription of exercise therapy and physiotherapy combined with CFO could be required to improve STP and promote APA in people with PsA. Trial registration ClinicalTrials.gov , NCT05075343 . Retrospectively registered on September 29, 2021
... maximum sagittal knee angle during stance phase) ( Fig. 1.3). While sagittal plane kinematics are most frequently investigated [13], also frontal and transverse (horizontal) plane angles can be determined. A frequently investigated variable in running biomechanics is the foot strike pattern [14,15]. ...
Chapter
The purpose of this chapter is to describe the interdisciplinary field of biomechanics and its importance in the evaluation of running. We will shortly describe the basic principles and origins of this scientific field and go into further details of new technical innovations. Then, a comprehensive overview of laboratory and field-based biomechanical analyses will be discussed in the light of implication for injury aetiology and prevention. Finally, we will end with a discussion on new research areas and implications for future research.
Article
Purpose Human movement is considered one of the important factors for maintaining an independent life. Individuals in different age groups have different characteristics of locomotion patterns and some health conditions can affect or be affected by mobility changes. Few studies clarify or present data about the influence of different ages and biopsychosocial factors on accelerometry features. The aim of this study was to identify characteristics and variables in the frequency signals for different age groups and their relationship with associated health conditions in raw accelerometry data obtained from the use of a triaxial accelerometer during 7 days of activities of daily living. Method A cross-sectional study was conducted based on the database of the first evaluations of the Epidemiological Study of Movement (EPIMOV) cohort. Frequency, signal amplitude, and entropy accelerometry features of EPIMOV participants who used a triaxial accelerometer for 7 days were extracted. Sociodemographic, clinical, anthropometric and physical activity assessments were also performed. Two-way ANOVA was performed to compare accelerometry features within different age groups. A series of stepwise multiple regressions were performed on accelerometry variables to analyze their relationships with demographic, anthropometric and cardiovascular risk variables. Results The sample consisted mostly of female, white, and high school graduates. The most prevalent cardiovascular risk factors were sedentary behavior and obesity. When analyzing the accelerometry variables, it was possible to observe that the entropy feature, and the counts, decrease in the group of older adults, while the feature of harmonic components of gait (frequency × amplitude) increases in the group of older adults. Regarding the amplitude feature, there were no significant differences between the groups. Through stepwise multiple linear regression, it was possible to observe that demographic, anthropometric and cardiovascular risk factors are associated with most accelerometry variables. Conclusion The results confirm that human movement can be influenced by different ages, sex, demographic, anthropometric and cardiovascular risk factors. Future studies and clinical analyzes can use the methods proposed in this research to adjust movement patterns for sex and different age groups, thus obtaining new interpretations about human movement.
Article
To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.
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Background: Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults. Method: This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD's intersection point of the stride frequency, and velocity regression lines (E1) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E1 ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD's and TUG's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05. Results: The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC's AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively). Conclusion: The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls.
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Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.