ArticlePDF AvailableLiterature Review

Effects of Exercise-Based Interventions on Functional Movement Capability in Untrained Populations: A Systematic Review and Meta-Analysis

Authors:
Article

Effects of Exercise-Based Interventions on Functional Movement Capability in Untrained Populations: A Systematic Review and Meta-Analysis

Abstract

The purpose of this meta-analysis was to determine the effects of exercise-based interventions on functional movement capability in untrained populations and provide a reference for future intervention studies in this field. PubMed, Embase, Scopus, Web of Science, EBSCOhost, Cochrane Library, WanFang, and CNKI databases were systematically searched from inception until February 2022, for randomized or non-randomized controlled trials, addressing the effect of physical activity on functional movement capability in untrained populations. Two researchers independently conducted study selection, data extraction, and quality evaluation. Meta-analysis was performed using RveMan 5.3 and Stata 16.0 software. Twenty studies with 1596 participants were included in the meta-analysis. The results of meta-analysis demonstrated that exercise-based interventions were associated with improved asymmetry functional patterns (RR = 0.40; 95% CI [0.31, 0.50]; p < 0.00001), FMS composite score (MD = 3.01; 95% CI [2.44, 3.58]; p < 0.00001), deep squat (MD = 0.57; 95% CI [0.37, 0.77]; p < 0.00001), hurdle step (MD = 0.56; 95% CI [0.38, 0.75]; p < 0.00001), in-line lunge (MD = 0.54; 95% CI [0.43, 0.66]; p < 0.00001), shoulder mobility (MD = 0.37; 95% CI [0.15, 0.60]; p = 0.001), active straight leg raise (MD = 0.42; 95% CI [0.24, 0.60]; p < 0.00001), trunk stability push up (MD = 0.40; 95% CI [0.16, 0.63]; p = 0.001), and rotary stability (MD = 0.45; 95% CI [0.24, 0.67]; p < 0.0001). Exercise-based interventions were effective in improving functional movement capability in untrained populations. However, there is a need for high-quality, sufficiently powered RCTs to provide a more definitive conclusion.
Citation: Huang, J.; Zhong, M.;
Wang, J. Effects of Exercise-Based
Interventions on Functional
Movement Capability in Untrained
Populations: A Systematic Review
and Meta-Analysis. Int. J. Environ.
Res. Public Health 2022,19, 9353.
https://doi.org/10.3390/
ijerph19159353
Academic Editors: Valerio
Bonavolontàand Francesca Latino
Received: 6 July 2022
Accepted: 27 July 2022
Published: 30 July 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Environmental Research
and Public Health
Review
Effects of Exercise-Based Interventions on Functional
Movement Capability in Untrained Populations: A Systematic
Review and Meta-Analysis
Jiafu Huang , Mengting Zhong and Jinghao Wang *
School of Physical Education & Sports Science, South China Normal University, Guangzhou 510006, China;
2019020902@m.scnu.edu.cn (J.H.); 2021021125@m.scnu.edu.cn (M.Z.)
*Correspondence: 20161044@m.scnu.edu.cn
Abstract:
The purpose of this meta-analysis was to determine the effects of exercise-based inter-
ventions on functional movement capability in untrained populations and provide a reference for
future intervention studies in this field. PubMed, Embase, Scopus, Web of Science, EBSCOhost,
Cochrane Library, WanFang, and CNKI databases were systematically searched from inception
until February 2022, for randomized or non-randomized controlled trials, addressing the effect of
physical activity on functional movement capability in untrained populations. Two researchers
independently conducted study selection, data extraction, and quality evaluation. Meta-analysis
was performed using RveMan 5.3 and Stata 16.0 software. Twenty studies with 1596 participants
were included in the meta-analysis. The results of meta-analysis demonstrated that exercise-based
interventions were associated with improved asymmetry functional patterns (RR = 0.40; 95% CI [0.31,
0.50];
p< 0.00001
), FMS composite score (MD = 3.01; 95% CI [2.44, 3.58];
p< 0.00001
), deep squat
(MD = 0.57; 95% CI [0.37, 0.77];
p< 0.00001
), hurdle step (MD = 0.56; 95% CI [0.38, 0.75];
p< 0.00001
),
in-line lunge (
MD = 0.54
; 95% CI [0.43, 0.66];
p< 0.00001
), shoulder mobility (MD = 0.37; 95% CI [0.15,
0.60];
p= 0.001
), active straight leg raise (MD = 0.42; 95% CI [0.24, 0.60]; p< 0.00001), trunk stability
push up (MD = 0.40; 95% CI [0.16, 0.63]; p= 0.001), and rotary stability (MD = 0.45; 95% CI [0.24, 0.67];
p< 0.0001). Exercise-based interventions were effective in improving functional movement capability
in untrained populations. However, there is a need for high-quality, sufficiently powered RCTs to
provide a more definitive conclusion.
Keywords:
exercise-based interventions; functional movement capability; untrained populations;
functional movement screen
1. Introduction
Functional movement capability is the ability to move effectively and competently
in various fundamental movement patterns and motor skills, which is specifically charac-
terized by the mobility, stability, coordination, and symmetry of fundamental movements
in the human body [
1
,
2
]. Functional movement capability, as an important indicator to
reflect the physical function of humans, represents an important building block for life-
long engagement and potentially injury-free engagement in sport activity [
3
]. Functional
movement capacity is closely related to sports injuries. Studies have shown that nearly
80% of sports injuries are closely associated with the musculoskeletal system [
4
] and more
than 70% of musculoskeletal injuries are caused by intrinsic risk factors [
5
]. Researchers
believe that the main internal factor of musculoskeletal injuries is the functional movement
dysfunction in the body, which is a neuromuscular symptom caused by dynamic postural
instability [
6
,
7
]. The musculoskeletal screening test can identify and diagnose these dys-
functions of functional movement capacity, so that appropriate intervention programs can
be developed to improve functional movement capability and prevent sports injury [8].
Int. J. Environ. Res. Public Health 2022,19, 9353. https://doi.org/10.3390/ijerph19159353 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 9353 2 of 16
The functional movement screen (FMS) is used to evaluate fundamental movement
patterns to identify potential risk factors, such as dysfunction, asymmetry, and pain, which
is the most commonly used assessment tool for functional movement capability [
9
,
10
]. It
comprises seven individual tests: deep squat, hurdle step, in-line lunge, shoulder mobility,
active straight-leg raise, trunk stability push up, and rotary stability. Each test is scored
on a scale of 0–3 to produce a total score out of 21. When the subjects obtain lower scores
in FMS testing, they indicate less than optimal functional movement capability and the
risk of injury during sports will increase [
9
,
10
]. A meta-analysis by Bonazza et al. reported
that scoring
14 was associated with a small threefold increase in all-cause injury odds in
an athlete, firefighting, and military population [
11
]. Another study indicated that higher
FMS composite scores were associated with better dynamic balance in an active young
male and female population, and participants who score > 14 on the FMS exhibited better
dynamic balance than those with scores
14 [
12
]. Meanwhile, several studies found that
FMS performance is significantly correlated with important health markers in the elderly,
as well as gait stability and motor performance in young adults [1315].
A lack of physical activity and exercise is one of the reasons for dysfunctional move-
ment patterns. The stability and movement control components contained in exercise
have a positive effect on functional movement capability [
8
]. Researchers have carried
out a large number of related studies on the impact of exercise-based or physical activity
interventions on functional movement capability recently. These studies mainly focused on
athletes, firefighters, soldiers, and other professionally trained special occupational popula-
tions, and have reported relatively consistent results regarding exercise interventions, in
that they can improve their functional movement capability and reduce the risk of sports
injuries. For example, several studies have shown that exercise intervention programs
can improve the FMS composite scores of athletes, firefighters, and military personnel,
reduce asymmetry functional patterns, and reduce their risk of sports injuries [
16
20
]. A
recent meta-analysis also indicated that functional correction training can improve athletes’
FMS composite scores and functional movement patterns and reduce their risk of sports
injuries [
21
]. However, although researchers have also conducted some studies on the effect
of exercise-based interventions on the functional movement capability of the untrained
populations, there is still no consistent conclusion due to the influence of sample size, study
design, and intervention program. For example, Shim et al. found that aerobic exercise can
improve functional movements and FMS scores in elderly women, but the sample sizes in
the experimental group (n = 9) and the control group (n = 10) in this study were smaller [
22
].
Yeon et al. concluded that Pilates can improve college students’ FMS scores and improve
their functional movements, but the study adopted pre- and post-test design, lacking a
control group [
23
]. In contrast, Wright et al. found that 4 weeks of fundamental movement
training could not improve FMS performance in children, which may be due to short-term
interventions [
24
]. Accordingly, it is urgent for researchers to seek an appropriate method
to solve the current conflicting results.
Although there are differences in physical conditions and sports environment between
untrained populations and professional groups, such as athletes, sports injuries are not
just features of athletes. Identifying weaknesses in an untrained population and then
trying to improve them could play an important role in lifelong physical activity and
injury prevention. Systematic review is the highest level of evidence-based evidence by
systematically collecting and screening relevant studies and strictly evaluating the quality
of the included studies [
25
]. At present, there are no published meta-analyses or systematic
reviews on the effect of exercise-based interventions on functional movement capability
in untrained populations. Therefore, the purpose of the present meta-analysis was to
investigate the effects of exercise-based interventions on functional movement capability
in untrained populations and provide a reference for practical applications and clinical
studies in this field in the future. We hypothesized that exercise-based interventions would
improve the functional movement capability of untrained populations.
Int. J. Environ. Res. Public Health 2022,19, 9353 3 of 16
2. Materials and Methods
2.1. Design
This systematic review and meta-analysis were performed according to the Preferred
Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA) [
26
] and
Cochrane Collaboration Handbook [
27
]. This review was prospectively registered with
PROSPERO (CRD42022330725).
2.2. Search Strategy
An extensive literature searchwas conducted using eight electronic databases: PubMed,
Embase, Scopus, Web of Science, EBSCOhost (including SportDiscus and Academic Search
Premiere), Cochrane Library, WanFang, and China National Knowledge Infrastructure
(CNKI) from inception to February 2022. The following combinations of terms were adapted
for each database: (functional movement screen OR FMS OR functional movement screen*)
AND (functional movement patterns OR movement quality OR injury risk OR injury pre-
diction OR injury prevention OR injur*) AND (exercise OR physical activity OR functional
training OR functional strength training OR movement training). The Chinese version of
keywords “functional movement screen, movement quality, functional movement patterns
and functional training” were also used. Any disagreements in the search process were
resolved by discussion between two researchers (J.H. and M.Z.) and consulting the third
researcher (J.W.). Additionally, reference lists of all included studies and any previous sys-
tematic reviews were also screened to identify additional eligible studies. The specific search
syntax, such as PubMed, Embase, and Scopus, is available in the Supplementary Materials.
2.3. Selection Criteria
2.3.1. Inclusion Criteria
The inclusion criteria were as follows: (1) English and Chinese language studies;
(2) randomized controlled trials (RCTs) or non-randomized controlled trials (non-RCTs);
(3) participants were untrained populations who did not engage in any systematic training
(except for special occupation groups such as professional athletes, firefighters, military
personnel, and police), without restrictions on gender, age and region; (4) intervention fo-
cusing on a preventive training program or sport, including a set of exercise-based/physical
activity interventions aimed at improving stability, mobility, coordination or symmetry;
(5) outcome measures included FMS composite score, FMS individual score and/or FMS
asymmetry after intervention in the experimental group and control group.
2.3.2. Exclusion Criteria
The studies were excluded if: (1) they were meeting abstracts, case reports, conference
proceedings, or reviews; (2) duplicated studies; (3) the topic irrelevant to this review;
(4) insufficient data or lack of outcome indicators; (5) participants were athletes; and
(6) they were cross-sectional or retrospective studies.
2.4. Study Selection and Data Extraction
The two researchers (J.H. and M.Z.) independently screened the title, abstract, and
full texts according to the predetermined criteria. Meanwhile, the following data were
independently extracted by two researchers (J.H. and M.Z.): publication details (first
author and publication date), participant characteristics (mean age/age range, and sample
size); exercise interventions (type, period, frequency and time); outcomes (FMS composite
score, FMS individual score and/or the incidence of FMS asymmetry) and study design.
Discrepancies were resolved by discussion or the third researcher (J.W.) was consulted.
2.5. Risk of Bias
The quality of the included studies was assessed by two independent researchers (J.H.
and M.Z.) and disagreements were resolved by consensus. The quality of RCT studies
was evaluated by PEDro scale, an internationally recognized and widely used evaluation
Int. J. Environ. Res. Public Health 2022,19, 9353 4 of 16
tool, which added two indicators on the basis of Delphi scale [
28
]. This scale includes
11 items as follows: eligibility criteria, randomized allocation, concealed allocation, similar
baseline, blinding of participants, blinding of therapists, blinding of assessors, less than
15% dropouts, intention-to-treat analysis, between-group comparison, and point measure
and measures of variability. The first item is not included to calculate the total PEDro score,
so the maximum score was 10 points. Each item was only scored as ‘yes’ or ‘no’. RCT
studies were classified as having excellent (9–10), good (6–8), fair (4–5), or poor (<4) quality,
respectively. The MINORS scale was used to evaluate the quality of non-RCT studies [
29
].
The MINORS scale contains 12 items, the first 8 being specifically for non-comparative
studies. Each item is scored on a scale of 0–2 for a total score of 24 points, 0 indicating
that it is not reported, 1 indicating that it is reported but insufficient, and 2 indicating that
it is reported sufficient. Non-RCT studies were classified as low quality (0–8), medium
quality (9–16), or high quality (17–24), respectively. Studies were excluded if they scored
less than 12.
2.6. Data Analysis and Synthesis
The RevMan 5.3 software (Cochrane Collaboration, Oxford, UK) was used to perform
meta-analysis: effect size combination, heterogeneity test, sensitivity analysis, and subgroup
analysis. The Stata 16.0 software (StataCorp, College Station, TX, USA) was used to carry
out funnel plot and Egger’s test to detect potential publication bias [
30
]. For dichotomous
variables, the risk ratio (RR) was used to combine the asymmetry functional patterns. For
continuous variables, the weighted mean difference (WMD) was used to combine FMS
composite score and FMS individual score. The heterogeneity of the results across studies
was evaluated using the I
2
statistical. When I
2
< 50%, the fixed effect model was adopted to
perform meta-analysis; otherwise, the random effect model was used. The heterogeneity I
2
statistic was divided into three grades: small (25%), moderate (50%), and high (75%). If the
heterogeneity was too large or the effect sizes could not be combined, which is not suitable
for meta-analysis, qualitative synthesis analysis was performed. Finally, the precision of
the effect sizes was described using 95% confidence intervals (CIs) and the significant
difference was p< 0.05.
3. Results
3.1. Study Selection
In total, 2598 studies were retrieved from eight databases and other resources. These
studies were imported into EndNote X9 (Thomson Research Soft, Stanford, CA, USA), and
duplicates (n = 864) were removed. Of the remaining 1752 studies, 1685 were eliminated
after screening the title and abstract. The remaining 67 studies were further screened by
reading full texts and 47 studies were excluded. Finally, 20 studies provided sufficient
information to be included in the meta-analysis. The detailed searching and screening
process of the study is shown in Figure 1.
3.2. Study Characteristics
In total, 20 studies were selected in this study, including 15 RCTs [
8
,
31
44
] and 5 non-
RCTs [
45
49
]. All were published between 2016 and 2022 as peer-reviewed articles or
dissertations. The study included a total of 1596 participants; 834 were included in the
experimental group and 729 in the control group. The average sample size of each study
was 80, ranging from 24 to 233. Participants involved healthy children, adolescents, and
middle-aged and elderly people, and their ages ranged from 8 to 65.42 years. The exercise-
based interventions can be divided into personalized training programs (functional training,
functional strength training, core stability training, etc.) and specific sports (Tai Chi, Yoga,
Pilates, Health Qigong, etc.). The intervention period ranged from 6 to 24 weeks, and
12 weeks was the most used. The intervention frequency ranged from 1 to 6 times per
week and 3 times per week was the most adopted. The intervention time varied from 20 to
90 min and 60 min was the most used. It is worth mentioning that the purpose of a study is
Int. J. Environ. Res. Public Health 2022,19, 9353 5 of 16
to examine the acute effects of interventions, so the intervention period and frequency are
not provided [
36
]. The outcome measures included the incidence of asymmetry functional
patterns, FMS composite scores, and FMS individual scores. In addition, two studies used
a three-arm experiment design and one study consisted of two experiments, so two sets of
data for these studies were extracted for meta-analysis [
41
43
]. The basic characteristics of
the included studies are shown in Table 1.
Int. J. Environ. Res. Public Health 2022, 19, x 5 of 18
Figure 1. Flow diagram of the study selection.
3.2. Study Characteristics
In total, 20 studies were selected in this study, including 15 RCTs [8,3144] and 5 non-
RCTs [45–49]. All were published between 2016 and 2022 as peer-reviewed articles or dis-
sertations. The study included a total of 1596 participants; 834 were included in the exper-
imental group and 729 in the control group. The average sample size of each study was
80, ranging from 24 to 233. Participants involved healthy children, adolescents, and mid-
dle-aged and elderly people, and their ages ranged from 8 to 65.42 years. The exercise-
based interventions can be divided into personalized training programs (functional train-
ing, functional strength training, core stability training, etc.) and specific sports (Tai Chi,
Yoga, Pilates, Health Qigong, etc.). The intervention period ranged from 6 to 24 weeks,
and 12 weeks was the most used. The intervention frequency ranged from 1 to 6 times per
week and 3 times per week was the most adopted. The intervention time varied from 20
to 90 min and 60 min was the most used. It is worth mentioning that the purpose of a
study is to examine the acute effects of interventions, so the intervention period and fre-
quency are not provided [36]. The outcome measures included the incidence of asym-
metry functional patterns, FMS composite scores, and FMS individual scores. In addition,
two studies used a three-arm experiment design and one study consisted of two experi-
ments, so two sets of data for these studies were extracted for meta-analysis [41–43]. The
basic characteristics of the included studies are shown in Table 1.
Figure 1. Flow diagram of the study selection.
Int. J. Environ. Res. Public Health 2022,19, 9353 6 of 16
Table 1. Characteristics of included studies (n = 20).
Authors, Year Study Design Participants (n) Age (Years) (±SD) Experimental Group Control Group Duration/Frequency
/Period Outcomes (Measures)
Buxton et al. (2020) [39] RCT College students (42)
(EG: 21; CG: 21) EG (19.38 ±1.36);
CG (20.14 ±2.63) Quadrupedal movement
training Waiting list 60 min, 2 times per week, 8 weeks FMS composite score
Guler et al. (2021) [38] RCT Middle-aged adults (46)
(EG: 26; CG: 20) EG (51.55 ±3.73);
CG (52.85 ±4.01) Functional strength
training Traditional strength
training 60 min, 3 times per week, 8 weeks FMS composite score
Han (2017) [49] Non-RCT College students (31)
(EG: 13; CG: 18) NR Yi Jinjing Routine exercise 90 min, 3 times per week, 12 weeks Asymmetry functional
patterns
Kang (2020) [31] RCT Children (40)
(EG: 20; CG: 20) EG (9.45 ±1.36);
CG (9.50 ±1.15) Functional training Routine exercise 90 min, once a week, 14 weeks FMS composite score;
FMS individual score
Li et al. (2019) [45] Non-RCT Male college students (48)
(EG: 24; CG: 24) 18.88 ±0.68 Simplified 24-form Tai Chi Waiting list 20 min, 2 times per week, 8 weeks FMS composite score;
Asymmetry functional
patterns
Liao et al. (2019) [35] RCT Girls (144)
(EG: 72; CG: 72) 12.47 ±0.57 Functional strength
training Traditional strength
training 45 min, 3 times per week, 12 weeks
FMS composite score;
FMS individual score;
Asymmetry functional
patterns
Liao (2020) [40] RCT Office sedentary
people (38)
(EG: 19; CG: 19)
EG (28.15 ±1.9);
CG (27.10 ±2.1) Elastic band resistance
training Waiting list 50–60 min, 3 times per week, 12 weeks FMS composite score;
FMS individual score
Liao et al. (2021) [43] RCT Adolescents (266)
(EG1: 72; CG1: 72; EG2: 61;
CG2: 61) 13–16 Functional strength
training Physical education 45 min, 3 times per week, 12 weeks FMS composite score;
Asymmetry functional
patterns
Liao et al. (2022) [44] RCT Adolescents (266)
(EG: 133; CG: 133) EG (14.37 ±0.55);
CG (14.03 ±0.59) Functional strength
training Physical education 45 min, 3 times per week, 12 weeks FMS composite score;
FMS individual score
Lim et al. (2019) [41] RCT Adults (90)
(EG1: 30; EG2: 30; CG: 30) 30–40 EG1:Pilates
EG2:Yoga Waiting list 60 min, 3 times per week, 8 weeks FMS composite score
Liu (2020) [33] RCT Elderly adults (24)
(EG: 12; CG: 12) EG (65.25 ±3.93);
CG (65.42 ±3.94) Wu Qinxi Waiting list 60 min, 6 times per week, 12 weeks FMS composite score;
FMS individual score
Mahdieh et al. (2020) [8] RCT Female students (34)
(EG: 19; CG: 15) EG (18.8 ±0.68);
CG (18.9 ±0.91) Dynamic neuromuscular
stabilization training Routine exercise 50 min, 3 times per week, 6 weeks FMS composite score
Sawczy et al. (2020) [37] RCT College students (33)
(EG: 16; CG: 17) 21.6 ±1.3 Functional strength
training Routine exercise 60 min, 4 times per week (1–6 wk)/2 times
per week (7–12 wk), 12 weeks FMS composite score
Scepanovic et al.(2020) [48] Non-RCT Male college students (138)
(EG: 73; CG: 65) EG (20 ±0.5);
CG (20 ±0.7) Core stabilization training Routine exercise 30 min, 3 times per week, 6 weeks FMS composite score;
FMS individual score
Strauss et al. (2020) [36] RCT Active young population
(24)
(EG: 12; CG: 12)
EG (25.7 ±4.70);
CG (27.4 ±5.50) Total Motion Release Waiting list 2 sets of 15 repetitions FMS composite score
Wang et al. (2016) [42] RCT Older adults (90)
(EG1: 30; EG2: 30; CG: 30) EG1 (65.2 ±5.0); EG2
(65.3 ±4.3); CG (65.3 ±4.4) EG1:Traditional Tai Chi
EG2:Simplified Tai Chi Routine activity 60 min, 4 times per week, 12 weeks FMS composite score
Wang (2019) [32] RCT Female college
students (82)
(EG: 41; CG: 41) NR Modified yoga Regular yoga 90 min, once a week, 12 weeks FMS composite score;
FMS individual score
Xiong (2018) [34] RCT Middle-aged women (60)
(EG: 30; CG: 30) 50 ±3.21 Yoga Waiting list 60 min, 3 times per week, 12 weeks FMS composite score;
FMS individual score
Yang (2019) [47] Non-RCT Primary school
students (60)
(EG: 30; CG: 30) 8–10 Functional training Waiting list 45 min, 2 times per week, 12 weeks FMS composite score;
FMS individual score
Zhang (2020) [46] Non-RCT College students (40)
(EG: 20; CG: 20) NR Dao Yin Routine exercise 90 min, 5 times per week, 24 weeks FMS composite score;
FMS individual score
Note: RCT = randomized controlled trial; non-RCT: non-randomized controlled trial; EG = experimental group; CG = control group; NR = not reported.
Int. J. Environ. Res. Public Health 2022,19, 9353 7 of 16
3.3. Risk-of-Bias Assessment
In this review, the PEDro scale and MINORS scale were used to evaluate the quality of
15 RCT studies and 5 non-RCT studies, respectively. The score of 15 RCTs was between
6 and 8 points, with an average score of 6.33 points, indicating the quality of RCTs was
good. Two studies were randomly assigned with concealed allocation [
36
,
44
]; three studies
were blinded, one of which was a double-blind trial [
32
], and two were a single-blind
trial [
36
,
42
]. The scores of the five non-RCT studies were between 15 and 18 points, with an
average score of 16 points, indicating that the non-RCTs were medium quality. One of the
studies was high quality [
48
] and the others were medium quality [
46
49
]. None of the five
non-RCTs reported blinding, follow-up time, and calculation of sample size, and only one
study reported the loss to follow-up rate [48] (Tables 2and 3).
Table 2. Quality assessment of the included RCT studies with PEDro criteria (n = 15).
Authors, Year Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 11 Total
Buxton et al. (2020) [39] Y Y N Y N N N Y Y Y Y 6/10
Guler et al. (2021) [38] Y Y N Y N N N Y Y Y Y 6/10
Kang (2020) [31] Y Y N Y N N N Y Y Y Y 6/10
Liao et al. (2019) [35] Y Y N Y N N N Y Y Y Y 6/10
Liao (2020) [40] Y Y N Y N N N Y Y Y Y 6/10
Liao et al. (2021) [43] Y Y N Y N N N Y Y Y Y 6/10
Liao et al. (2022) [44] Y Y Y Y N N N Y Y Y Y 7/10
Lim et al. (2019) [41] Y Y N Y N N N Y Y Y Y 6/10
Liu (2020) [33] Y Y N Y N N N Y Y Y Y 6/10
Mahdieh et al. (2020) [8] Y Y N Y N N N Y Y Y Y 5/10
Sawczy et al. (2020) [37] Y Y N Y N N N Y Y Y Y 6/10
Strauss et al. (2020) [36] Y Y Y Y N Y N Y Y Y Y 8/10
Wang et al. (2016) [42] Y Y N Y N N Y Y Y Y Y 7/10
Wang (2019) [32] N Y N Y Y Y N Y Y Y Y 8/10
Xiong (2018) [34] Y Y N Y N N N Y Y Y Y 6/10
Note: N = does not meet the criteria; Y = meet the criteria; Item 1 = Eligibility criteria; Item 2 = Random allocation;
Item 3 = Concealed allocation; Item 4 = Similar at baseline; Item 5 = Subjects blinded; Item 6 = Therapists blinded;
Item 7 = Assessors blinded; Item 8 = <15% dropouts; Item 9 = Intention-to-treat analysis; Item 10 = Between-group
comparisons; Item 11 = Point measures and variability data.
Table 3. Quality assessment of the included non-RCT studies with MINORS (n = 5).
Authors, Year Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 11 Item 12 Total
Han (2017) [49] 2 1 2 2 0 0 0 0 2 2 2 2 15/24
Li et al. (2019) [45] 2 2 2 2 0 0 0 0 2 2 2 2 16/24
Scepanovic et al.
(2020) [48]2 2 2 2 0 0 2 0 2 2 2 2 18/24
Yang (2019) [47] 2 2 2 2 0 0 0 0 2 2 2 2 16/24
Zhang (2020) [46] 1 2 2 2 0 0 0 0 2 2 2 2 15/24
Note: 0 (not reported); 1 (reported but inadequate); 2 (reported and adequate). Item 1 = A clearly stated aim; Item
2 = Inclusion of consecutive patients; Item 3 = Prospective collection of data; Item 4 = Endpoints appropriate to
the aim of the study; Item 5 = Unbiased assessment of the study endpoint; Item 6 = Follow-up period appropriate
to the aim of the study; Item 7 = Loss to follow-up less than 5%; Item 8 = Prospective calculation of the study size;
Item 9 = An adequate control group; Item 10 = Contemporary groups; Item 11 = Baseline equivalence of groups;
Item 12 = Adequate statistical analyses.
3.4. Meta-Analysis
3.4.1. Asymmetry Functional Patterns
In total, 20 studies were included in this study, of which 4 studies (n = 485) provided
sufficient data for meta-analysis of the incidence of asymmetry functional patterns of
exercise-based interventions in untrained populations. A heterogeneity test showed no
significant heterogeneity in the included studies (I
2
= 0%; p> 0.1), so the fixed-effect model
was adopted to combine the effect sizes. The result of meta-analysis showed that there
was a significant difference between the experimental group and control group (RR = 0.40;
95% CI [0.31, 0.50]; Z = 7.73; p< 0.00001), suggesting that exercise-based interventions
can significantly reduce the incidence of asymmetry functional patterns in untrained
populations (Figure 2).
Int. J. Environ. Res. Public Health 2022,19, 9353 8 of 16
Int. J. Environ. Res. Public Health 2022, 19, x 8 of 18
3.3. Risk-of-Bias Assessment
In this review, the PEDro scale and MINORS scale were used to evaluate the quality
of 15 RCT studies and 5 non-RCT studies, respectively. The score of 15 RCTs was between
6 and 8 points, with an average score of 6.33 points, indicating the quality of RCTs was
good. Two studies were randomly assigned with concealed allocation [36,44]; three stud-
ies were blinded, one of which was a double-blind trial [32], and two were a single-blind
trial [36,42]. The scores of the five non-RCT studies were between 15 and 18 points, with
an average score of 16 points, indicating that the non-RCTs were medium quality. One of
the studies was high quality [48] and the others were medium quality [46–49]. None of
the five non-RCTs reported blinding, follow-up time, and calculation of sample size, and
only one study reported the loss to follow-up rate [48] (Tables 2 and 3).
3.4. Meta-Analysis
3.4.1. Asymmetry Functional Patterns
In total, 20 studies were included in this study, of which 4 studies (n = 485) provided
sufficient data for meta-analysis of the incidence of asymmetry functional patterns of ex-
ercise-based interventions in untrained populations. A heterogeneity test showed no sig-
nificant heterogeneity in the included studies (I
2
= 0%; p > 0.1), so the fixed-effect model
was adopted to combine the effect sizes. The result of meta-analysis showed that there
was a significant difference between the experimental group and control group (RR = 0.40;
95% CI [0.31, 0.50]; Z = 7.73; p < 0.00001), suggesting that exercise-based interventions can
significantly reduce the incidence of asymmetry functional patterns in untrained popula-
tions (Figure 2).
Figure 2. Forest plot of the untrained populations’ asymmetry functional patterns [35,43–45,49].
Figure 2. Forest plot of the untrained populations’ asymmetry functional patterns [35,4345,49].
3.4.2. FMS Composite Scores
Nineteen studies (n = 1505) compared the effect of FMS composite scores between the
exercise group and the control group among the 20 included studies. The heterogeneity test
showed high heterogeneity in the 19 studies (I
2
= 94%; p< 0.00001), so the random-effect
model was used to integrate the effect sizes. The result of the meta-analysis indicated a
significant improvement in the exercise group compared with the control group (MD = 3.01;
95% CI [2.44, 3.58]; Z = 10.32; p< 0.00001), suggesting that exercise-based interventions can
improve the FMS composite scores of untrained populations (Figure 3).
Int. J. Environ. Res. Public Health 2022, 19, x 10 of 18
3.4.2. FMS Composite Scores
Nineteen studies (n = 1505) compared the effect of FMS composite scores between the
exercise group and the control group among the 20 included studies. The heterogeneity
test showed high heterogeneity in the 19 studies (I
2
= 94%; p < 0.00001), so the random-
effect model was used to integrate the effect sizes. The result of the meta-analysis indi-
cated a significant improvement in the exercise group compared with the control group
(MD = 3.01; 95% CI [2.44, 3.58]; Z = 10.32; p < 0.00001), suggesting that exercise-based in-
terventions can improve the FMS composite scores of untrained populations (Figure 3).
Figure 3. Forest plot of the untrained populations’ FMS composite scores [8,31–48].
3.4.3. FMS Individual Scores
Of the 20 included studies, 10 studies (n = 888) provided adequate information of
seven individual FMS scores after exercise-based interventions in untrained populations,
including deep squat, hurdle step, in-line lunge, shoulder mobility, active straight-leg
raise, trunk stability push up, and rotary stability. We adopted meta-analytic methods to
individually synthesize the study findings of each outcome. As there was high heteroge-
neity (I
2
> 50%), the random-effect model was used to combine the effect sizes. The overall
results showed significant benefit in favor of exercise-based interventions on improving
deep squat (MD = 0.57; 95% CI [0.37, 0.77]; Z = 5.50; p < 0.00001), hurdle step (MD = 0.56;
95% CI [0.38, 0.75]; Z = 5.90; p < 0.00001), in-line lunge (MD = 0.54; 95% CI [0.43, 0.66]; Z =
9.21; p < 0.00001), shoulder mobility (MD = 0.37; 95% CI [0.15, 0.60]; Z = 3.23; p = 0.001),
active straight-leg raise (MD = 0.42; 95% CI [0.24, 0.60]; Z = 4.61; p < 0.00001), trunk stability
push up (MD = 0.40; 95% CI [0.16, 0.63]; Z = 3.29; p = 0.001), and rotary stability (MD = 0.45;
95% CI [0.24, 0.67]; Z = 4.14; p < 0.0001) (Figure 4).
Figure 3. Forest plot of the untrained populations’ FMS composite scores [8,3148].
3.4.3. FMS Individual Scores
Of the 20 included studies, 10 studies (n = 888) provided adequate information of
seven individual FMS scores after exercise-based interventions in untrained populations,
including deep squat, hurdle step, in-line lunge, shoulder mobility, active straight-leg raise,
Int. J. Environ. Res. Public Health 2022,19, 9353 9 of 16
trunk stability push up, and rotary stability. We adopted meta-analytic methods to indi-
vidually synthesize the study findings of each outcome. As there was high heterogeneity
(I
2
> 50%), the random-effect model was used to combine the effect sizes. The overall
results showed significant benefit in favor of exercise-based interventions on improving
deep squat (MD = 0.57; 95% CI [0.37, 0.77]; Z = 5.50; p< 0.00001), hurdle step (MD = 0.56;
95% CI [0.38, 0.75]; Z = 5.90; p< 0.00001), in-line lunge (MD = 0.54; 95% CI [0.43, 0.66];
Z = 9.21
;
p< 0.00001
), shoulder mobility (MD = 0.37; 95% CI [0.15, 0.60]; Z = 3.23; p= 0.001),
active straight-leg raise (MD = 0.42; 95% CI [0.24, 0.60]; Z = 4.61; p< 0.00001), trunk stability
push up (MD = 0.40; 95% CI [0.16, 0.63]; Z = 3.29; p= 0.001), and rotary stability (MD = 0.45;
95% CI [0.24, 0.67]; Z = 4.14; p< 0.0001) (Figure 4).
3.5. Subgroup Analysis
In order to explore the source factors of heterogeneity, this review conducted a sub-
group analysis of FMS composite scores. The effect of exercise-based interventions on
the FMS composite scores of the untrained populations may be affected by different ages,
intervention types, intervention time, intervention frequency, and intervention period.
Therefore, we conducted a subgroup analysis of FMS composite score based on the above
factors. They were divided into different subgroups as follows: (1) age: under 18 years old,
18–30 years old, and above 50 years old; (2) intervention: specific exercises and functional
training programs; (3) time: Under 60 min, 60 min, and more than 60 min; (4) frequency:
under three times a week, three times a week, and more than three times a week; and
(5) period: 6 weeks, 8 weeks, and 12 weeks. Studies with unclear age and intervention
characteristics were excluded [
36
,
46
]. The results of subgroup analysis showed that the
heterogeneity of age, intervention type, intervention time, intervention frequency, and
intervention period decreased, which indicates that these factors may be the source of
heterogeneity in FMS composite scores, and intervention time is the most likely source
of heterogeneity. Additionally, the result of subgroup analysis also showed that 6-week
exercise-based interventions could not improve FMS composite scores in untrained popula-
tions (p> 0.05) (Table 4).
Table 4. Subgroup analysis of the untrained populations’ FMS composite scores.
Group Subgroup N MD 95% CI pI2
Age (year) Under 18 6 4.20 3.27, 5.12 <0.00001 90%
18–30 9 2.99 1.97, 4.01 <0.00001 92%
More than 50 5 1.95 1.28, 2.62 <0.00001 82%
Intervention Specific sports 9 2.42 1.80, 3.04 <0.00001 87%
Functional training program 13 3.38 2.47, 4.30 <0.00001 95%
Time (min)
Under 60 min 8 3.64 2.45, 4.83 <0.00001 95%
60 min 9 2.17 1.67, 2.67 <0.00001 79%
More than 60 min 3 4.25 0.71, 7.79 0.02 98%
Frequency (time/week) Under 3 times/week 5 3.65 1.74, 5.56 0.0002 96%
3 times/week 12 3.13 2.39, 3.87 <0.00001 94%
More than 3 times/week 4 2.15 0.94, 3.35 0.0005 85%
Period (week)
6 weeks 2 3.09 1.92, 8.11 0.23 98%
8 weeks 5 2.71 1.45, 3.96 <0.0001 86%
12 weeks 12 2.80 2.24, 3.35 <0.00001 92%
Int. J. Environ. Res. Public Health 2022,19, 9353 10 of 16
Int. J. Environ. Res. Public Health 2022, 19, x 11 of 18
Figure 4. Forest plot of the untrained populations’ FMS individual scores [31–35,40,44,46–48].
Figure 4. Forest plot of the untrained populations’ FMS individual scores [3135,40,44,4648].
Int. J. Environ. Res. Public Health 2022,19, 9353 11 of 16
3.6. Sensitivity Analysis
Sensitivity analysis is a method to test the stability of the obtained results by assuming
conditions. In this review, we conducted a sensitivity analysis of the meta-analysis results
for the FMS composite scores and FMS individual scores with high levels of heterogeneity
and combined the effect sizes of the remaining studies by eliminating individual studies
one by one. In this review, the combined effect size of exercise-based interventions on FMS
composite scores in untrained populations was MD = 3.01; 95% CI [2.44, 3.58];
p< 0.00001
;
I
2
= 94%. When the studies were eliminated one by one, the effect size was MD = 2.81–
3.13; I
2
= 92%–94%; p< 0.00001. The effect sizes of exercise-based interventions of FMS
individual scores in untrained populations were as follows: (1) The effect size of deep squat
was MD = 0.57; 95% CI [0.37, 0.77]; p< 0.00001; I
2
= 92%. When the studies were eliminated
one by one, the effect size was MD = 0.50–0.64; I
2
= 86%–93%; p< 0.0001. (2) The combined
effect size of hurdle step was MD = 0.56; 95% CI [0.38, 0.75]; p< 0.00001; I
2
= 86%. When
the studies were eliminated one by one, the effect size was MD = 0.49–0.61; I
2
= 79%–87%;
p< 0.00001
. (3) The combined effect size of in-line lunge was MD = 0.54; 95% CI [0.43, 0.66];
p< 0.00001; I
2
= 69%. When one study was removed [
48
], the effect size was MD = 0.60;
95% CI [0.54, 0.66]; p< 0.00001; I2= 10%. (4) the combined effect size of shoulder mobility
was MD = 0.37; 95% CI [0.15, 0.60]; p= 0.001; I
2
= 93%. When the studies were removed one
by one, the effect size was MD = 0.28–0.43; I
2
= 84%–94%; p< 0.05. (5) The combined effect
size of active straight-leg raise was MD = 0.42; 95% CI [0.24, 0.60]; p< 0.00001;
I2= 90%
.
When two studies were eliminated [
35
,
48
], the effect size was MD = 0.44; 95% CI [0.38, 0.50];
p< 0.00001; I
2
= 8%. (6) The effect size of trunk stability push-up was MD = 0.40; 95% CI
[0.16, 0.63]; p= 0.001; I
2
= 95%. When studies were eliminated one by one, the effect size of
MD = 0.31–0.44; I
2
= 91%–95%; p< 0.05. (7) The combined effect size of rotatory stability
was MD = 0.45; 95% CI [0.24, 0.67]; p< 0.0001; I
2
= 95%. When studies were eliminated one
by one, the effect size of MD = 0.37–0.51; I2= 92%–95%; p< 0.05.
3.7. Publication Bias
The funnel plot analyses is performed to examine potential publication bias if the
meta-analysis included more than 10 studies [
50
]. The meta-analysis of FMS composite
scores showed no significant publication bias, as evidenced by visual inspection of the
funnel plot and Egger’s regression test (p= 0.30 > 0.05) (Figure 5).
Int. J. Environ. Res. Public Health 2022, 19, x 13 of 18
MD = 0.60; 95% CI [0.54, 0.66]; p < 0.00001; I2 = 10%. (4) the combined effect size of shoulder
mobility was MD = 0.37; 95% CI [0.15, 0.60]; p = 0.001; I2 = 93%. When the studies were
removed one by one, the effect size was MD = 0.28–0.43; I2 = 84%–94%; p < 0.05. (5) The
combined effect size of active straight-leg raise was MD = 0.42; 95% CI [0.24, 0.60]; p <
0.00001; I2 = 90%. When two studies were eliminated [35,48], the effect size was MD = 0.44;
95% CI [0.38, 0.50]; p < 0.00001; I2 = 8%. (6) The effect size of trunk stability push-up was
MD = 0.40; 95% CI [0.16, 0.63]; p = 0.001; I2 = 95%. When studies were eliminated one by
one, the effect size of MD = 0.31–0.44; I2 = 91%–95%; p < 0.05. (7) The combined effect size
of rotatory stability was MD = 0.45; 95% CI [0.24, 0.67]; p < 0.0001; I2 = 95%. When studies
were eliminated one by one, the effect size of MD = 0.37–0.51; I2 = 92%–95%; p < 0.05.
3.7. Publication Bias
The funnel plot analyses is performed to examine potential publication bias if the
meta-analysis included more than 10 studies [50]. The meta-analysis of FMS composite
scores showed no significant publication bias, as evidenced by visual inspection of the
funnel plot and Egger’s regression test (p = 0.30 > 0.05) (Figure 5).
Figure 5. Funnel plot of publication bias for FMS composite scores.
4. Discussion
This present systematic review and meta-analysis aimed to examine the effects of
exercise-based interventions on functional movement capability in untrained popula-
tions, in order to provide reference for intervention research in this field. In total, 20 stud-
ies with 1596 participants (i.e., children, adolescents, middle-aged, and elderly) were in-
cluded in this meta-analysis. The types of exercise intervention included functional train-
ing programs and specific sports (Tai Chi, Yoga, Health Qigong, etc.). Despite the different
exercise-based interventions and participant characteristics, the findings of this review
indicated that exercise-based interventions have a positive effect on functional movement
capability in untrained populations.
4.1. Effect of Exercise-Based Interventions on Functional Movement Capability in Untrained
Populations
The results of the meta-analysis showed that exercise-based or physical activity in-
terventions can effectively improve the functional movement capability in untrained pop-
ulations, which is manifested in improvements in the untrained population’s FMS com-
posite scores and FMS individual scores and a reduction in the incidence of asymmetry
movement patterns. A recent meta-analysis of exercise interventions on athletes’ func-
tional movement capability also found that functional correction training can improve
Figure 5. Funnel plot of publication bias for FMS composite scores.
4. Discussion
This present systematic review and meta-analysis aimed to examine the effects of
exercise-based interventions on functional movement capability in untrained populations,
in order to provide reference for intervention research in this field. In total, 20 studies with
Int. J. Environ. Res. Public Health 2022,19, 9353 12 of 16
1596 participants (i.e., children, adolescents, middle-aged, and elderly) were included in
this meta-analysis. The types of exercise intervention included functional training programs
and specific sports (Tai Chi, Yoga, Health Qigong, etc.). Despite the different exercise-based
interventions and participant characteristics, the findings of this review indicated that
exercise-based interventions have a positive effect on functional movement capability in
untrained populations.
4.1. Effect of Exercise-Based Interventions on Functional Movement Capability in
Untrained Populations
The results of the meta-analysis showed that exercise-based or physical activity inter-
ventions can effectively improve the functional movement capability in untrained popula-
tions, which is manifested in improvements in the untrained population’s FMS composite
scores and FMS individual scores and a reduction in the incidence of asymmetry movement
patterns. A recent meta-analysis of exercise interventions on athletes’ functional movement
capability also found that functional correction training can improve FMS composite scores
and asymmetry movement patterns and reduce the risk of sports injuries [
21
]. There are
five symmetrical movements in FMS that need to be tested on both sides of the body. The
asymmetry functional patterns refer to at least one FMS test difference between the left
and right sides of the body during FMS testing, and the scores obtained are inconsistent.
This meta-analysis showed that exercise-based interventions can reduce the incidence of
asymmetry functional patterns among untrained populations, which is consistent with the
results of previous studies. Two studies found that Tai Chi and Yi Jinjing can effectively
improve asymmetry functional patterns in college students [
45
,
49
]. Liao et al. also reported
that functional strength training significantly improved the asymmetry functional patterns
of 12–13-year-old girls [
35
]. The effect of exercise-based interventions on asymmetry func-
tional patterns may be related to the characteristics of exercise. For example, Tai Chi, Yi
Jinjing, Baduanjin, and other sports belong to bilateral sports [
51
]. The movement charac-
teristics and arrangement form of these sports can reflect symmetry, and long-term exercise
is conducive to the coordinated development of the practitioners’ bilateral functions.
The results of this meta-analysis demonstrated that exercise-based interventions also
significantly improved the FMS composite scores of untrained populations. FMS compos-
ite scores is an important indicator of individual functional movement capability, with
higher scores indicating better movement capability [
52
]. Laurent et al. also confirmed
the conclusion that exercise-based interventions can improve FMS composite scores in an
RCT study on the effect of a suspension-trainer-based movement program on children’s
functional movements [
53
]. In addition, in the research exploring the relationship between
exercise-based interventions and FMS composite scores, researchers found that an individ-
ual’s physical activity level is positively correlated with its FMS composite scores, which
also confirmed the conclusion that exercise-based or physical activity interventions had
significant effects on functional movement capability in this study [5457].
For FMS individual scores, the results of this meta-analysis were consistent with
previous studies [
35
,
40
], showing that exercise-based interventions significantly improved
the FMS individual scores in untrained populations. Early research reported that 12 weeks
of elastic band resistance training can improve the individual FMS scores of sedentary
office workers [
40
]. Liao et al. found that functional strength training has a similar effect in
improving FMS individual scores and movement quality in untrained healthy girls, aged
12–13 years [
35
]. Furthermore, some studies believe that more attention should be paid
to the score of each task instead of the FMS composite scores when interpreting the FMS
scores [
58
]. Several studies have also shown that individual FMS scores can better reflect
individual performance and predict the risk of injury than FMS total scores [
59
61
]. At
present, however, many studies mainly focus on the FMS composite score, and individual
FMS score is easy for researchers to ignore. Therefore, more attention should be paid to the
role of FMS individual scores in future studies on the effect of exercise interventions on
functional movement capability.
Int. J. Environ. Res. Public Health 2022,19, 9353 13 of 16
4.2. Subgroup Analysis of FMS Composite Scores
Subgroup analysis showed that exercise interventions have a positive effect on the total
FMS scores in untrained populations at different ages, but the effect was more significant
for the middle-aged and elderly population over 50 years old, which may be affected
by age factors. Studies found that FMS score is correlated with the age of middle-aged
and elderly people, and FMS score decreases with age [
56
,
62
]. Therefore, compared with
children and adolescents, the FMS scores of middle-aged and elderly people improved
more significantly after exercise-based interventions under the same conditions. Subgroup
analysis of exercise interventions showed that both specific sports, such as Tai Chi, Yoga,
and functional training programs, could improve the FMS composite scores in untrained
populations. Different from rugby, volleyball, fighting, and other competitive sports that
over-emphasize the practice of sport-specific skills and ignore the development of whole-
body functional movements, it is easy to cause poor functional movement capability in
athletes and increase the risk of sports injuries. Functional training programs and specific
sports include various movements, such as step, squat, and lunge, mainly focusing on
the practice of movement forms, which are more conducive to the overall development
of individual functional movement capability. This study is unable to draw a conclusion
about which type of exercise-based or physical activity intervention is more effective, which
is a topic worthy of attention in future studies. However, according to the characteristics of
exercise intervention, we can provide some suggestions for people to choose exercise-based
or physical activity intervention. For example, compared with other types of exercise
intervention, mind–body exercises (i.e., Tai Chi, Yoga, Health Qigong, and Pilates) are low
impact, moderate intensity, and emphasize trinity of mind, body, and breathing, which
is more suitable for middle-aged and elderly people to practice [
63
65
]. The functional
training program is mainly composed of different functional movements or instrument
movements, and its exercise intensity and difficulty are relatively high, which may be
more in line with the needs of young people [
66
]. For the period, frequency, and time of
exercise-based interventions, subgroup analysis showed that exercise-based interventions
occurring more than three times per week and 60 min per session for 12 weeks had a more
significant improvement effect on the FMS composite score in untrained populations. This
is not only basically consistent with the exercise prescription guidelines recommended by
the American College of Sports Medicine for healthy people [
67
], but also confirms the
conclusion reported in previous studies that 4-week short-term exercise-based interventions
cannot improve FMS performance [24].
4.3. Sensitivity Analysis
In the present review, when the literature was removed one by one, the results of sensi-
tivity analysis for FMS composite scores, deep squat, rotary stability, hurdle step, shoulder
mobility, and trunk stability push-up showed that there was still high heterogeneity, and
the results of effect size remained significant. This suggests that exercise interventions
can improve the FMS composite scores in untrained populations, as well as the scores of
deep squat, rotary stability, hurdle step, shoulder mobility, and trunk stability push-up.
The sensitivity analysis results of in-line lunge and active straight-leg raise indicated that
the heterogeneity is significantly reduced when the literature was eliminated one by one,
but the effect size did not change significantly and there is no significant impact on the
results. In summary, this indicates that the combined effect size results of the meta-analysis
outcomes are relatively robust and reliable.
4.4. Strengths and Limitations
To the authors’ knowledge, this is the first review to investigate the effect of exercise-
based interventions on functional movement capability in untrained populations. The
quality of the literature included in this study is high, there is no publication bias among
the studies, and the sensitivity analysis results are relatively robust and reliable. However,
this review also has the following limitations: First, only English and Chinese literature
Int. J. Environ. Res. Public Health 2022,19, 9353 14 of 16
was included in this review, which may cause language bias. Second, due to the limitation
of literature quantity, both RCT and non-RCT studies were included in this review, and
only two studies displayed concealed allocation plus three studies that reported blinding.
Third, the meta-analysis results of FMS composite scores have high levels of heterogeneity.
Although subgroup analysis was conducted and the possible source factors of heterogeneity
were explored, heterogeneity still could not be eliminated, which may also have a certain
impact on the results.
5. Conclusions
This meta-analysis demonstrated that exercise-based interventions have a positive
effect on functional movement capacity in untrained populations. However, due to the lack
of adequate high-quality RCTs, the findings of this review should be interpreted carefully.
Therefore, more high-quality RCTs of exercise interventions on the functional movement
capacity in untrained populations should be conducted in future research, and the impact of
different interventions on the functional movement capacity of the untrained populations at
different ages should be considered, so as to provide more substantial evidence for clinical
research and practical applications in this field.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/10
.3390/ijerph19159353/s1, Table S1: Search history.
Author Contributions:
Conceptualization, J.H., M.Z., and J.W.; methodology, J.H., M.Z. and J.W.;
data curation, J.H. and M.Z.; software, J.H.; writing—the original draft, J.H.; writing—reviewing
and editing, J.H., M.Z., and J.W; funding acquisition, J.W. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the National Social Science Foundation of China, grant
number 18BTY013.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data included in this study are available and can be accessed by
contacting the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Cook, G.; Burton, L.; Hoogenboom, B.J.; Voight, M. Functional movement screening: The use of fundamental movements as an
assessment of function—Part 1. Int. J. Sports Phys. Ther. 2014,9, 396–409. [PubMed]
2.
Cook, G.; Burton, L.; Hoogenboom, B.J.; Voight, M. Functional movement screening: The use of fundamental movements as an
assessment of function—Part 2. Int. J. Sports Phys. Ther. 2014,9, 549–563.
3.
O’Brien, W.; Khodaverdi, Z.; Bolger, L.; Tarantino, G.; Philpott, C.; Neville, R.D. The Assessment of Functional Movement in
Children and Adolescents: A Systematic Review and Meta-Analysis. Sports Med. 2022,52, 37–53. [CrossRef]
4. Patel, D.R.; Nelson, T.L. Sports injuries in adolescents. Med. Clin. N. Am. 2000,84, 983–1007. [CrossRef]
5.
Boden, B.P.; Dean, G.S.; Feagin, J.J.; Garrett, W.J. Mechanisms of anterior cruciate ligament injury. Orthopedics
2000
,23, 573–578.
[CrossRef]
6.
Cook, G.; Burton, L.; Kiesel, K.; Bryant, M.; Torine, J. Movement: Functional Movement Systems: Screening, Assessment, and Corrective
Strategies; On Target Publications: Santa Cruz, CA, USA, 2010; Volume 24, pp. 123–127.
7.
Sahrmann, S. Diagnosis and Treatment of Movement Impairment Syndromes; Elsevier Health Sciences: Alpharetta, CA, USA, 2002;
pp. 107–112.
8.
Mahdieh, L.; Zolaktaf, V.; Karimi, M.T. Effects of dynamic neuromuscular stabilization (DNS) training on functional movements.
Hum. Mov. Sci. 2020,70, 102568. [CrossRef]
9.
Cook, G.; Burton, L.; Hoogenboom, B. Pre-participation screening: The use of fundamental movements as an assessment of
function—Part 1. N. Am. J. Sports Phys. Ther. 2006,1, 62–72.
10.
Cook, G.; Burton, L.; Hoogenboom, B. Pre-participation screening: The use of fundamental movements as an assessment of
function—Part 2. N. Am. J. Sports Phys. Ther. 2006,1, 132–139. [PubMed]
11.
Bonazza, N.A.; Smuin, D.; Onks, C.A.; Silvis, M.L.; Dhawan, A. Reliability, Validity, and Injury Predictive Value of the Functional
Movement Screen: A Systematic Review and Meta-analysis. Am. J. Sports Med. 2017,45, 725–732. [CrossRef]
Int. J. Environ. Res. Public Health 2022,19, 9353 15 of 16
12.
Scudamore, E.M.; Stevens, S.L.; Fuller, D.K.; Coons, J.M.; Morgan, D.W. Use of Functional Movement Screen Scores to Predict
Dynamic Balance in Physically Active Men and Women. J. Strength Cond. Res. 2019,33, 1848–1854. [CrossRef] [PubMed]
13.
Farrell, S.W.; Pavlovic, A.; Barlow, C.E.; Leonard, D.; DeFina, J.R.; Willis, B.L.; DeFina, L.F.; Haskell, W.L. Functional Movement
Screening Performance and Association With Key Health Markers in Older Adults. J. Strength Cond. Res.
2021
,35, 3021–3027.
[CrossRef] [PubMed]
14.
Silva, B.; Rodrigues, L.P.; Clemente, F.M.; Cancela, J.M.; Bezerra, P. Association between motor competence and Functional
Movement Screen scores. PeerJ 2019,7, e7270. [CrossRef] [PubMed]
15.
Lee, M.; Youm, C.; Noh, B.; Park, H. Low composite functional movement screen score associated with decline of gait stability in
young adults. PeerJ 2021,9, e11356. [CrossRef] [PubMed]
16.
Bodden, J.G.; Needham, R.A.; Chockalingam, N. The Effect of an Intervention Program on Functional Movement Screen Test
Scores in Mixed Martial Arts Athletes. J. Strength Cond. Res. 2015,29, 219–225. [CrossRef]
17.
Dinc, E.; Kilinc, B.E.; Bulat, M.; Erten, Y.T.; Bayraktar, B. Effects of special exercise programs on functional movement screen
scores and injury prevention in preprofessional young football players. J. Exerc. Rehabil.
2017
,13, 535–540. [CrossRef] [PubMed]
18.
Kiesel, K.; Plisky, P.; Butler, R. Functional movement test scores improve following a standardized off-season intervention
program in professional football players. Scand. J. Med. Sci. Sports 2011,21, 287–292. [CrossRef] [PubMed]
19.
Basar, M.J.; Stanek, J.M.; Dodd, D.D.; Begalle, R.L. The Influence of Corrective Exercises on Functional Movement Screen and
Physical Fitness Performance in Army ROTC Cadets. J. Sport Rehabil. 2019,28, 360–367. [CrossRef]
20.
Jafari, M.; Zolaktaf, V.; Ghasemi, G. Functional Movement Screen Composite Scores in Firefighters: Effects of Corrective Exercise
Training. J. Sport Rehabil. 2020,29, 102–106. [CrossRef]
21.
Chen, J.; Zhang, C.; Chen, S.; Zhao, Y. Effects of functional correction training on injury risk of athletes: A systematic review and
meta-analysis. PeerJ 2021,9, e11089. [CrossRef]
22.
Shim, Y.; Choi, H.; Shin, W. Aerobic training with rhythmic functional movement: Influence on cardiopulmonary function,
functional movement and Quality of life in the elderly women. J. Hum. Sport Exerc. 2019,14, 748–756. [CrossRef]
23.
Roh, S.Y. A functional movement screening of college students performing Pilates exercise. J. Cosmet. Med.
2019
,3, 33–37.
[CrossRef]
24.
Wright, M.D.; Portas, M.D.; Evans, V.J.; Weston, M. The Effectiveness of 4 Weeks of Fundamental Movement Training on Functional
Movement Screen and Physiological Performance in Physically Active Children. J. Strength Cond. Res.
2015
,29, 254–261. [CrossRef]
[PubMed]
25.
Impellizzeri, F.M.; Bizzini, M. Systematic review and meta-analysis: A primer. Int. J. Sports Phys. Ther.
2012
,7, 493–503. [PubMed]
26.
Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;
Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ
2021
,372, n71.
[CrossRef]
27.
Higgins, J.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.; Welch, V. Cochrane Handbook for Systematic Reviews of
Interventions Version 6.2 (Updated February 2021). Available online: www.training.cochrane.org/handbook (accessed on 8 April 2022).
28.
Verhagen, A.P.; de Vet, H.C.; de Bie, R.A.; Kessels, A.G.; Boers, M.; Bouter, L.M.; Knipschild, P.G. The Delphi list: A criteria list
for quality assessment of randomized clinical trials for conducting systematic reviews developed by Delphi consensus. J. Clin.
Epidemiol. 1998,51, 1235–1241. [CrossRef]
29.
Slim, K.; Nini, E.; Forestier, D.; Kwiatkowski, F.; Panis, Y.; Chipponi, J. Methodological index for non-randomized studies (minors):
Development and validation of a new instrument. ANZ J. Surg. 2003,73, 712–716. [CrossRef]
30.
Sterne, J.A.C.; Egger, M.; Smith, G.D. Systematic reviews in health care: Investigating and dealing with publication and other
biases in meta-analysis. BMJ 2001,323, 101–105. [CrossRef]
31.
Kang, Z.C. An empirical Study on the Influence of Functional Training on Physical Fitness of Children Aged 8–12 Years; Northwest
Normal University: Lanzhou, China, 2020.
32.
Wang, P. Experimental Study on the Correction of College Students’ bad Action Patterns by Fitness Yoga; Henan University: Zhengzhou,
China, 2019.
33.
Liu, J.W. Experimental Study on the Influence of Wu Qinxi on FMS Results of the Elderly; Hebei Normal University: Shijiazhuang,
China, 2020.
34.
Xiong, H. The Effect of Yoga Practice on the Basic Athletic Ability of Middle-Aged Women; Hunan Normal University: Changsha, China, 2018.
35.
Liao, T.; Li, L.; Wang, Y.T. Effects of Functional Strength Training Program on Movement Quality and Fitness Performance Among
Girls Aged 12–13 Years. J. Strength Cond. Res. 2019,33, 1534–1541. [CrossRef]
36.
Strauss, A.T.; Parr, A.J.; Desmond, D.J.; Vargas, A.T.; Baker, R.T. The Effect of Total Motion Release on Functional Movement
Screen Composite Scores: A Randomized Controlled Trial. J. Sport Rehabil. 2020,29, 1106–1114. [CrossRef] [PubMed]
37.
Sawczyn, M. Effects of a periodized functional strength training program (FST) on Functional Movement Screen (FMS) in physical
education students. Phys. Educ. Stud. 2020,24, 162–167. [CrossRef]
38.
Guler, O.; Tuncel, O.; Bianco, A. Effects of Functional Strength Training on Functional Movement and Balance in Middle-Aged
Adults. Sustainability 2021,13, 1074. [CrossRef]
39.
Buxton, J.D.; Prins, P.J.; Miller, M.G.; Moreno, A.; Welton, G.L.; Atwell, A.D.; Talampas, T.R.; Elsey, G.E. The Effects of a Novel
Quadrupedal Movement Training Program on Functional Movement, Range of Motion, Muscular Strength, and Endurance.
J. Strength Cond Res. 2020.preprint. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2022,19, 9353 16 of 16
40.
Liao, S.J. Research on the impact of elastic band resistance training on functional exercise ability of office sedentary people. J. Jilin
Sport Univ. 2020,36, 43–48.
41.
Lim, E.J.; Park, J.E. The effects of Pilates and yoga participant’s on engagement in functional movement and individual health
level. J. Exerc. Rehabil. 2019,15, 553–559. [CrossRef]
42.
Wang, H.; Wei, A.; Lu, Y.; Yu, B.; Chen, W.; Lu, Y.; Liu, Y.; Yu, D.; Zou, L. Simplified Tai Chi Program Training versus Traditional
Tai Chi on the Functional Movement Screening in Older Adults. Evid.-Based Complement. Altern.
2016
,2016, 5867810. [CrossRef]
43.
Liao, T.; Yang, Q.W.; Cheng, X. Effects of a Progressive Functional Strength Training Program on Functional Movement Quality in
Middle and High School Students. J. Wuhan Inst. of Phys. Educ. 2021,55, 85–92.
44.
Liao, T.; Duhig, S.J.; Du, G.; Luo, B.; Wang, Y.T. The Effect of a Functional Strength Training Intervention on Movement Quality
and Physical Fitness in Adolescents. Percept. Mot. Skills 2022,129, 176–194. [CrossRef]
45. Li, Y.H.; He, Y.; Li, Y.K. Effect of 24 style Taijiquan typical movements on FMS test. J. Beijing Sport Univ. 2019,42, 81–87.
46.
Zhang, Z.X.; Yu, Q.C.; Sheng, Z.J.; Cai, J. Intervention research of traditional Chinese medicine guiding exercise prescription on
the functional movement capability of sedentary college students. J. Changchun Norm. Univ. 2020,39, 89–91.
47.
Yang, X.L. Research on the Application of Functional Movement Training in Level 2 Student Physique Development—Take Sanyuanli
Primary School in Guangzhou as an Example; Guangzhou Sport University: Guangzhou, China, 2019.
48.
Š´cepanovi´c, T.; Proti´c-Gava, B.; Sporiš, G.; Rupˇci´c, T.; Miljkovi´c, Z.; Liapikos, K.; Maˇcak, D.; Madi´c, D.M.; Trajkovi´c, N. Short-Term
Core Strengthening Program Improves Functional Movement Score in Untrained College Students. Int. J. Environ. Res. Public
Health 2020,17, 8669. [CrossRef]
49.
Han, L. Research of Health Qigong Yi Jinjing to Improve College Students’ Physical Movement Function; Beijing Sport University: Beijing,
China, 2017.
50. Ge, L.; Zheng, Q.; Liao, Y.; Tan, J.; Xie, Q.; Rask, M. Effects of traditional Chinese exercises on the rehabilitation of limb function
among stroke patients: A systematic review and meta-analysis. Complement. Ther. Clin. 2017,29, 35–47. [CrossRef] [PubMed]
51.
Zou, L.; Yeung, A.; Zeng, N.; Wang, C.; Sun, L.; Thomas, G.; Wang, H. Effects of Mind-Body Exercises for Mood and Functional
Capabilities in Patients with Stroke: An Analytical Review of Randomized Controlled Trials. Int. J. Environ. Res. Public Health
2018,15, 721. [CrossRef]
52.
Moore, E.; Chalmers, S.; Milanese, S.; Fuller, J.T. Factors Influencing the Relationship Between the Functional Movement Screen
and Injury Risk in Sporting Populations: A Systematic Review and Meta-analysis. Sports Med. 2019,49, 1449–1463. [CrossRef]
53.
Laurent, C.W.S.; Masteller, B.; Sirard, J. Effect of a Suspension-Trainer-Based Movement Program on Measures of Fitness and
Functional Movement in Children: A Pilot Study. Pediatr. Exerc. Sci. 2018,30, 364–375. [CrossRef]
54.
Duncan, M.J.; Stanley, M. Functional movement is negatively associated with weight status and positively associated with
physical activity in british primary school children. J. Obes. 2012,2012, 697563. [CrossRef]
55.
Mitchell, U.H.; Johnson, A.W.; Vehrs, P.R.; Feland, J.B.; Hilton, S.C. Performance on the Functional Movement Screen in older
active adults. J. Sport Health Sci. 2016,5, 119–125. [CrossRef] [PubMed]
56.
Perry, F.T.; Koehle, M.S. Normative data for the functional movement screen in middle-aged adults. J. Strength Cond. Res.
2013
,27,
458–462. [CrossRef]
57.
Karuc, J.; Mišigoj-Durakovi´c, M.; Markovi´c, G.; Hadži´c, V.; Duncan, M.J.; Podnar, H.; Sori´c, M. Movement quality in adolescence
depends on the level and type of physical activity. Phys. Ther. Sport 2020,46, 194–203. [CrossRef]
58.
Li, Y.; Wang, X.; Chen, X.; Dai, B. Exploratory factor analysis of the functional movement screen in elite athletes. J. Sports Sci.
2015
,
33, 1166–1172. [CrossRef]
59.
Silva, B.; Clemente, F.; Camões, M.; Bezerra, P. Functional Movement Screen Scores and Physical Performance among Youth Elite
Soccer Players. Sports 2017,5, 16. [CrossRef]
60.
Silva, B.; Clemente, F.M.; Martins, F.M. Associations between functional movement screen scores and performance variables in
surf athletes. J. Sports Med. Phys. Fit. 2018,58, 583–590. [CrossRef]
61.
Armstrong, R.; Greig, M. Injury identification: The efficacy of the functional movement screen in female and male rugby union
players. Int. J. Sports Phys. Ther. 2018,13, 605–617. [CrossRef]
62.
Mitchell, U.H.; Johnson, A.W.; Adamson, B. Relationship between functional movement screen scores, core strength, posture, and
body mass index in school children in Moldova. J. Strength Cond. Res. 2015,29, 1172–1179. [CrossRef]
63.
Wang, H.; Yu, B.; Chen, W.; Lu, Y.; Yu, D. Simplified Tai Chi Resistance Training versus Traditional Tai Chi in Slowing Bone Loss
in Postmenopausal Women. Evid.-Based Complement. Altern. 2015,2015, 379451. [CrossRef]
64.
Zhang, Y.P.; Hu, R.X.; Han, M.; Lai, B.Y.; Liang, S.B.; Chen, B.J.; Robinson, N.; Chen, K.; Liu, J.P. Evidence Base of Clinical Studies
on Qi Gong: A Bibliometric Analysis. Complement. Ther. Med. 2020,50, 102392. [CrossRef]
65.
Zhang, Y.; Li, C.; Zou, L.; Liu, X.; Song, W. The Effects of Mind-Body Exercise on Cognitive Performance in Elderly: A Systematic
Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2018,15, 2791. [CrossRef] [PubMed]
66. Boyle, M. New Functional Training for Sports, 2nd ed.; Human Kinetics: Champaign, IL, USA, 2016; pp. 30–34.
67.
Haskell, W.L.; Lee, I.M.; Pate, R.R.; Powell, K.E.; Blair, S.N.; Franklin, B.A.; Macera, C.A.; Heath, G.W.; Thompson, P.D.; Bauman,
A. Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and
the American Heart Association. Circulation 2007,116, 1081–1093. [CrossRef]
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Background The Functional Movement Screen™ (FMS™) is an assessment of human movement that may signal potential deficits that could predispose an otherwise healthy person to injury risk. FMS™ scores are well reported in both athletic and adult samples. However, to date, there has been no comprehensive systematic review and meta-analysis of FMS™ data among school-aged children and adolescents. Objective We aimed to systematically review and analyse functional movement proficiency of children and adolescents, specifically when assessed using the FMS™, and to establish initial normative values for the FMS™ in this population group and to further estimate differences in functional movement proficiency between the sexes, by school level (i.e., between primary and secondary school-level children and adolescents), and based on differences in child and adolescent body mass index (BMI). Methods In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, prospective studies were identified from searches across eight databases (MEDLINE, SPORTDiscus, CINAHL, Web of Science, EMBASE, ERIC, PsychINFO and PubMed), without any date restrictions, up to December 2020. The primary meta-analysis estimated the overall FMS™ score for school-aged children and adolescents across published studies. An additional three subgroup meta-analyses estimated comparisons for FMS™ data with school level, sex, and BMI across published studies. FMS™ data were meta-analysed using a number of different meta packages (Schwarzer et al. in Meta-Analysis with R, 1st ed, Springer International Publishing, Berlin, 2015), available in R Studio. Results A total of 19 articles were included in the systematic review. Meta-analysis revealed a weighted FMS™ mean score of 14.06, with a standardised Tau value of 0.56, signalling a moderate-to-large degree of variability in FMS™ means between studies. The difference in FMS™ means between samples of males (weighted FMS™ mean 13.91) and females (weighted FMS™ mean 14.56) was compatible with a possible small effect size (standardised mean difference − 0.27). The variability in FMS™ means between studies was approximately five times greater in samples of secondary school children (factor difference in Tau values 5.16). The final meta-regression identified a negative association between BMI and FMS™ scores ( r = − 0.42), which signalled a moderate-to-large difference in FMS™ scores between healthy weight and overweight children/adolescents. Conclusion This systematic review and meta-analysis represents a novel and important synthesis of published FMS™ data from groups of children and adolescents. The study signals possible sex- and age-related differences in FMS™ scores, as well as a clear negative relationship between BMI and functional movement proficiency. More longitudinal research is needed to better understand the developmental trajectory and the effects of maturation milestones on FMS™ proficiency. Additional research is also needed to identify the types of interventions that could improve functional movement proficiency among ‘at risk’ groups, who are susceptible to functional movement deficiency, and whether changes in body composition mediate the relationship between these interventions and the improvement of FMS™ scores.
Article
Full-text available
Background The functional movement screen (FMS) TM is a screening tool used to evaluate fundamental motor function. A score of 14 for the composite total FMS score (TFMS) is generally used as the cut-off point (≤14/21). In addition, gait analysis is used to evaluate fundamental motor function in humans. Thus, evaluating the fundamental motor function using the FMS TM test and gait analysis at various speeds can provide further understanding of any decline in gait stability. In this study, we aimed to investigate the association between gait ability and fundamental movement patterns in young adults according to the cut-off point. Methods A total of 439 participants (male: 203, female: 236) successfully completed the FMS test and a 1 min treadmill test; the participants were classified into two groups: low TFMS (≤14) and high TFMS (>14). Results The low TFMS group exhibited slower and shortened walking patterns and worsen gait variability than the high TFMS group. The coefficient of variance (CV) for the double support phase at a faster speed (male) and the stride length at a slower speed (female) were classifiers between the two groups. In addition, the low TFMS group demonstrated insufficient gait adaptation at the preferred and faster speeds based on the CV of the double support phase and gait asymmetry. Lower TFMS is associated with a decline in gait ability. Therefore, participants with a lower TFMS and poor gait ability may require intervention programs to prevent risk of future injury and to enhance motor function.
Article
Full-text available
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
Article
Full-text available
Background We explored functional correction training using the Functional Movement Screen (FMS™) tool. We also analyzed the effects of training on the injuries of athletes in a systematic review and meta-analysis of non-randomized clinical trials. Methodology We collected twenty-four articles from PubMed, CENTRAL, Scopus, ProQuest, Web of Science, EBSCOhost, SPORTDiscus, Embase, WanFang, and CNKI that were published between January 1997 to September 2020. Articles were selected based on the following inclusion criteria: randomized and non-randomized controlled trials, studies with functional correction training screened by FMS™ as the independent variable, and studies with injury risk to the athlete as the dependent variable. Data conditions included the sample size, mean, standard deviation, total FMS™ scores, number of injuries, and asymmetry movement patterns after interventions in the experimental and control groups. Exclusion criteria included: conference abstracts, cross-sectional studies, articles with retrospective study design. Results Twelve non-randomized trials were included in the meta-analysis. The injury risk ratio of athletes after functional correction training was 0.39 RR (95 CI [1.50–1.93]; Z = 15.53; P < 0.0001; I ² = 2.6%), indicating an improvement of athletes functional patterns. Conclusion Grade B evidence indicates that functional correction training based on FMS™ may improve the functional patterns of athletes and Grade D evidence indicates that it may reduce the risk of sports injury. However, the true effect is likely to be different from the estimate of the effect. Therefore, further studies are needed to explore the influence of functional correction training on the injury risks of athletes. Protocol registration: CRD42019145287.
Article
Full-text available
Functional movement deficiencies cause falls and injuries in adults. Functional strength training (FST) is emerging as a new training method for athletes, middle-aged and older adults, to improve functional movement: The present study was conducted in order to investigate the effects of FST on balance and functional movement in healthy and independent middle-aged adults. The sample for this study consisted of 46 physically active individuals (24 female and 22 male). A total of 46 subjects were divided based on randomly into the functional strength training (FST) group (n = 26) aged: 51.55 ± 3.73 years; height: 168.69 ± 8.8 cm; body mass: 75.88 ± 12.18; and traditional strength training (TST) group (n = 20) age: 52.85 ± 4.01; height: 166.9 ± 9.98; body mass: 76.15 ± 10.45. Each group performed 24 sessions of a training protocol three-time a week. The functional movement was assessed using the functional movement screen (FMS) protocol. Balance performance was determined by using the balance error scoring system (BESS). Bodyweight and body fat ratio were measured using bioelectric impedance. There was a significant statistical difference between FMS total scores after an eight-week FST in the FST group. After the intervention, the functional strength group tended to have significantly better balance control than the traditional strength group (p = 0.01). Statistically, significant differences were observed between pre-test and post-test in the intervention group on BMI, body fat, and body mass (p = 0.01). There were not found significant differences in balance control and FMS score in TST group. As a result of this study, FST positively affected the FMS total score and balance performance in middle-aged adults. Early detections of the deficiencies in functional movement and balance in the middle ages may reduce the risk of insufficiency and fall in adults through targeted functional strength training intervention.
Article
Full-text available
Functional movement is an important part of developing athletes' but also untrained individuals' performance. Its monitoring also proved useful in identifying functional limitations and asymmetries, and also in determining the intervention effects. The quasi-experimental pre-test post-test study investigated the effects of core stability training program on the Functional Movement Screen (FMS) score in untrained students after six weeks. The intervention (INT) and control (CG) groups included 73 and 65 male students, respectively. Functional movement patterns were evaluated using the FMS including seven components scores representing seven basic functional patterns. Both groups significantly improved almost all FMS components scores, but the INT increased the mean performance of the hurdle step (partial ŋ 2 × 100 = 4%, p = 0.02), in-line lunge (partial ŋ 2 × 100 = 3%, p = 0.05), rotatory stability (partial ŋ 2 × 100 = 4%, p = 0.02) and total FMS (partial ŋ 2 × 100 = 3%, p = 0.04) significantly more than the CG. This justifies that core strengthening can improve FMS in untrained individuals even with the short duration programs.
Article
Full-text available
Buxton, JD, Prins, PJ, Miller, MG, Moreno, A, Welton, GL, Atwell, AD, Talampas, TR, and Elsey, GE. The effects of a novel quadrupedal movement training program on functional movement, range of motion, muscular strength, and endurance. J Strength Cond Res XX(X): 000-000, 2020-Quadrupedal movement training (QMT) is a form of bodyweight training incorporating animal poses, transitions, and crawling patterns to reportedly improve fitness. This type of training may improve multiple facets of fitness, unfortunately, little evidence exists to support commercial claims and guide practitioners in the best use of QMT. Therefore, the purpose of this study was to assess the impact of a commercially available QMT program on functional movement, dynamic balance, range of motion, and upper body strength and endurance. Forty-two active college-age (19.76 ± 2.10 years) subjects (males = 19, females = 23) were randomly assigned to a QMT (n = 21) or control (CON) (n = 21) group for 8 weeks. Quadrupedal movement training consisted of 60-minute classes performed 2×·wk in addition to regular physical activity. Active range of motion, Functional Movement Screen (FMS), Y-Balance Test (YBT), handgrip strength, and push-up endurance were assessed before and after the intervention. The QMT group showed significantly greater improvements than the CON group in FMS composite score (1.62 ± 1.53 vs. 0.33 ± 1.15, p = 0.004) and FMS advanced movements (0.81 ± 0.87 vs. 0.01 ± 0.71, p = 0.002) and fundamental stability (0.57 ± 0.75 vs. 0.05 ± 0.50, p = 0.011), along with hip flexion, hip lateral rotation, and shoulder extension (p < 0.05). No significant differences between groups were observed for dynamic balance or upper body strength and endurance. Our results indicate that QMT can improve FMS scores and various active joint ranges of motion. Quadrupedal movement training is a viable alternative form of training to improve whole-body stabilization and flexibility.
Article
This study compared a 12 week Functional Strength Training (FST) program on functional movement and physical performance to typical physical education (PE) classes for middle school (MS) and high school (HS) students. We randomly assigned 266 participants ( M age = 14.35, SD = 0.57 years; M height = 164.82, SD = 6.13 cm; M mass = 55.09, SD = 12.19 kg; M BMI = 20.11, SD = 3.54 kg/m ² ) into an FST or control group. The FST group trained in flexibility and stability, functional movement patterns, and health-related functional strength. The control group continued regular physical education (PE) classes. Each group trained three-times/week in 45 minute sessions for 12 weeks. Outcome measures included the Functional Movement Screen protocol and seven physical performance tests, assessed every four weeks over a 12 week period. We employed a mixed model ANOVA with Bonferroni post-hoc tests to examine differences between and within groups. Compared to the control group, the FST group significantly ( p < 0.01) improved Functional Movement Screen total scores (25.7%), curl-ups (70.4%), pull-ups (281.6%), and flexibility (83.6%). We suggest including the FST program in the MS and HS PE curriculum.
Article
Objectives This study examined the relationship between functional movement and physical activity (PA) levels in adolescents. Design Cross-sectional study. Setting This research is a part of the CRO-PALS longitudinal study conducted in a random sample of adolescents in Zagreb at the Faculty of Kinesiology, University of Zagreb, Croatia. Participants Seven hundred and twenty-five adolescents aged between 16 and 17 years were included. Main outcome measure Total Functional Movement Screen score (total FMS score). Results After adjusting for age, body fat and SES, both VPA and MVPA showed minor but significant effects on total FMS score among girls (β = 0.011, p = 0.001, β = 0.005, p = 0.006, respectively), but not in boys (β = 0.004, p = 0.158; β = 0.000, p = 0.780). Regarding PA type, volleyball and dance improved total FMS score (β = 1.003, p = 0.071; β = 0.972, p = 0.043, respectively), while football was associated with lower FMS score (β = −0.569, p = 0.118). Conclusion Results suggest that the PA level is positively associated with the functional movement in adolescent girls, but not in boys, where the type of PA moderates these associations. Therefore, functional movement patterns incorporated into physical education curriculum could be beneficial to the musculoskeletal health of the children.
Article
Background and Study Aim: To examine the effects of periodized functional strength training (FST) on FMS scores of sport university students with higher risk of injury. Material and Methods: Thirty three participants (age 21.6±1.3 years, height 177.8±6.9 m, mass 80.4±7.7 kg) with FMS total score ≤ 14 were selected from eighty two volunteered students of University of Physical Education and Sport in Gdańsk and randomly assigned to experimental group (n=16) and control group (n=17). The FMS test was conducted one week before and one week after the 12 week training intervention. The experimental group participated in FST program through 12 weeks. The control group did not engaged in any additional physical activity than planned in their course of study. The collected data were analysed using Statistica 13.3 pl (StatSoft Inc). Wilcoxon signed rank test was used to establish the statistical significance of the difference between FMS total scores within each group and Mann Whitney U test between groups before and after the 12 week training intervention. Results: 45 % of volunteers in the first FMS testing showed total scores ≤14. The experimental group that participated in FST program changed significantly FMS total scores after 12 weeks (p<0.05). There were also significant differences in FMS total score between groups after the experiment (p<0.05). Conclusions: There is a need for injury prevention programs for students of University of Physical Education and Sport in Gdańsk. It is clear from this study that FST is effective in improving FMS total score in students with cut off score ≤14.