Keywords
functional movement screen - resistance training - breast cancer
Introduction
As exercise, activity levels, muscle mass, and decreased fat mass are associated with
improved survival in women undergoing treatment for breast cancer, efforts are
underway to include resistance training as a routine part of breast cancer (BC)
treatment [1]. However, efforts to maximize
changes in body composition while optimizing safety are important in this patient
population. There is currently no reliable and quantifiable test that measures
mobility and can be utilized for research and to quantify mobility changes during
progression of exercise prescriptions. The functional movement screen (FMS) is an
exercise pre-participation screen that can help guide the exercise prescription to
maximize the benefits of exercise, such as muscle hypertrophy, fat loss, and
improved strength, while minimizing potential injury risk [2]
[3]
[4]. It is comprised of seven
movements, with each movement scored from 0 to 3; a score of 3 indicates perfect
movement without compensation, while 0 represents pain during the movement. Thus,
the total score can range from 0 to 21 [5]
[6]. The FMS can uncover
compensatory movement during basic movement patterns. Compensatory movement patterns
may predispose individuals to injuries. As such, the FMS has been shown to predict
which individuals are at a heightened risk of injury during certain movement
patterns [6].
Previous literature suggests that, in middle and older aged healthy adults, the FMS
score correlates negatively with body mass index (BMI), percent body fat, and age,
and positively correlates with activity levels [7]
[8]. Individuals with BC are
anthropometrically similar to individuals without BC, except for potential mobility
issues from surgical treatment and radiation therapy [1]. Thus, it may be reasonable to assume
that the relationship of the FMS to BMI, percent body fat, age, and activity levels
would be similar in individuals with BC compared to the general population. However,
the relationship of FMS score to BMI, percent body fat, age, and activity levels has
not been tested in individuals with BC. Furthermore, it remains unknown whether
individuals undergoing an exercise regimen with higher FMS scores lose more weight
than those individuals with lower FMS scores. This represents a critical gap in our
understanding of implementing a proper exercise regimen to maximize the efficacy and
minimize risk of exercise programs, particularly as we dose-escalate exercise
programs in individuals treated for BC [9].
Therefore, the aims of this study in women with BC were to 1) assess the relationship
of FMS to BMI, percent body fat, age, and activity levels; and 2) assess if
individuals with higher FMS scores lost more weight and/or percent body fat, than
individuals with lower FMS scores from pre- to post-intervention.
We hypothesized that 1) the FMS score would have a strong relationship with BMI,
percent body fat, age, and activity levels; and 2) individuals with higher FMS score
would lose more weight and a greater percent of body fat compared to individuals
with lower FMS scores.
Methods
Participants
We reviewed all participants of EXERT-BC, EXERT-BCN, and EXERT-C, which were
three institutional review board-approved prospective exercise studies in women
aged 20–89 with biopsy-proven ductal carcinoma in situ (DCIS) or BC. All
participants signed consent forms prior to any testing. Participants in each
study were required to be able to get up and down from the ground, squat their
body weight, and participate in a group exercise regimen. Individuals with
severe arthritic, joint, cardiovascular, and/or musculoskeletal condition deemed
unsafe to engage in resistance training were excluded. Participants actively
receiving systemic cytotoxic chemotherapy were excluded from the studies, while
participants treated with radiation, anti-estrogen and targeted systemic
therapies were allowed. Participants were screened by study personnel at the
time of oncologic consultation or a follow-up visit.
Recruitment occurred between September 15, 2022, and October 17, 2023, at the
Allegheny Health Network (AHN) departments of surgical, medical, and radiation
oncology, along with the AHN Cancer Institute Exercise Oncology and Resiliency
Center (EOC). Consent was obtained for each participant prior to enrollment in
the study. The studies are registered at ClinicalTrials.gov (NCT05747209,
NCT05978960, and NCT06083324).
Functional Movement Screen
Prior to initiation of the exercise regimen and at completion, each participant
underwent an FMS assessment performed by expert personnel. The physician who
screens all patients is level I and II FMS certified and has performed the test
in over 1 000 individuals. The other exercise physiologist who performs the test
was trained by him and showed proficiency in the FMS under close observation
before being able to implement it. Both practitioners observe each other weekly
during the test to enhance accuracy and proficiency. Each participant performed
7 movements (deep squat, hurdle step, in-line lunge, shoulder mobility, active
straight-leg raise, trunk stability push-up, and rotary stability), which were
scored from 0 to 3. A score of 0 was given if there was any pain associated with
the movement by the participant. A score of 1 was given if the individual could
not perform the movement. A score of 2 was given if the movement was performed
with compensatory movements, and a score of 3 was given if the movement was
completed without compensatory movements [10]. The composite score of all seven movements was summed, with the
absolute lowest score of 0 and highest of 21. For the unilateral movements
(hurdle step, inline lunge, shoulder mobility, active straight-leg raise, and
rotary stability), each movement was scored independently on the left and right
sides of the body. If a movement was scored differently from one side to the
other, the lower of the two scores was used in the composite score [5]
[11].
Exercise intervention
The exercise regimen included a combination of compound movements focusing on
closed kinetic chain movements (CKC), utilizing linear progression and following
guidelines from the National Strength and Conditioning Association (NSCA).
Activation and reset exercises focused on mobility, muscle activation, and range
of motion were performed prior to each workout to reduce the risk of injury.
Each workout provided full body resistance training focusing on the basic
movement patterns of push, pull, hip hinge, squat, and core activation. To
maximize safety, each individual exercise workout progressed from high
intensity, CKC, compound, athletic movements, such as squats and deadlifts, to
low intensity, more isolated focused exercises throughout the workout [12]. The entire program lasted 12 weeks,
with each exercise session ranging from 45–60 minutes. Lastly, there was a
two-week ramp-up period at the start of the program, and weights lifted utilized
a combination of repetition speed, number “left in the tank,” and rating of
perceived exertion.
The study took place at the Allegheny Health Network Cancer Institute’s Exercise
Oncology and Resiliency Center. The center is a state-of-the-art, 3
000-square-foot exercise and research facility where the exercise regimens are
created and monitored by Certified Strength and Conditioning Specialists (CSCS)
and a medical doctor, utilizing exercise principles to increase strength,
conditioning, performance, and overall health.
Exercise class attendance was recorded and planned missed days were able to be
performed remotely if the individual had access to similar workout equipment.
This was allowed only after the first month of the regimen. Exercises were
progressed or regressed around specific core movement patterns (push, pull, hip
hinge, squat, and core) based on participant ability. For example, if an
individual was unable to perform a bodyweight split squat, they would be
assisted in the movement until they could progress to the weighted lift. Weight
lifted, repetitions, sets, and notes were recorded.
Experimental design
All participants were enrolled in a three-month, thrice weekly dose-escalated
exercise regimen utilizing multi-joint compound movements and linear progression
balanced with resistance training volume to elicit hypertrophy, as previously
described [1]
[13]. The following study includes data
from three different cohorts: EXERT-BC (n=40), EXERT-C (n=29), and EXERT-BCN
(n=43). All groups underwent the same intervention, except for individuals in
the EXERT-BCN cohort, who underwent an additional nutrition intervention
requiring nutrient dense food sources high in vitamins, minerals, and nutrients.
They were encouraged to limit processed foods, sugar, bread, pasta, and other
simple carbohydrates, and to eat plenty of colorful and non-starchy vegetables.
They were also advised to avoid snacking between meals, cook most/all meals, eat
with family and friends, avoid eating food in the car or on the run, and focus
on whole foods that require preparation. The goal of protein consumption was at
least 1.3–1.8 g/kg per day.
Body composition and resting metabolic rate
Prior to initiation and at completion of the exercise regimen, each participant
underwent body composition analysis via an InBody 970 bioimpedance analysis
(BIA) machine (InBody Co., Seoul, South Korea). Body composition analysis
included total body fat (lbs.) and total muscle mass (lbs.). Additionally, an
ultrasound (US) was performed to measure percent body fat, fat-free mass (FFM),
and resting metabolic rate (RMR) [14],
which was calculated utilizing Body Metrix software (BodyMetrix, Brentwood, CA,
USA).
Strength and load lifted
Prior to initiation and at completion of the exercise regimen, each participant
underwent bilateral grip strength assessments with each arm at the neutral
position, utilizing a Jamar Hand Dynamometer grip strength measurement device.
Load was calculated continuously throughout the regimen by multiplying weight
lifted (lbs.) by repetitions and sets. These calculations were done at the
fourth, eighth, and the final week of the exercise regimen to ensure dose
escalation. Split squat, trap bar deadlift, incline dumbbell bench press, and
birddog row were compared as these encompass squat, hip hinge, push, and pull
movement patterns.
Activity levels
Preceding the initiation of the exercise regimen, each participant completed a
Godin Leisure-Time Exercise Questionnaire [15]. The questionnaires were completed at the end of their exercise
regimen as well.
Statistical analysis
Sample size was determined a priori for each cohort using G*Power. Using pilot
data from our lab, we observed a moderate effect size (Cohen’s d=0.57) for the
change in percent body from pre-to post-intervention. Using this effect size, an
alpha set at 0.05, and power set at 0.95, the power analysis determined a total
of 37 participants would be needed. Therefore, we aimed to recruit approximately
37 participants for each cohort (EXERT-BC, EXERT-C, and EXERT-BCN). Given our
previous pilot data, we did not anticipate any drops, and thus we did not
consider dropouts in the power analysis.
Spearman’s correlation was used to examine bivariate relationships between FMS
scores and BMI, percentage body fat, age, and activity level at baseline.
Dependent t-tests were used to assess the change from pre- to post-intervention
for all continuous variables. Linear regression was performed to assess whether
baseline FMS score was associated with weight loss during the three-month
intervention. Data that did not meet the assumptions for normality were
log10-transformed; untransformed data are presented for ease of
interpretation. For variables that were negative (i. e., weight loss), a
constant was added to make the variable a positive integer, thus the variable
could then be log10-transformed. The regression models were run with
the cohorts individually, and subsequently with all the cohorts combined. Data
were analyzed using SPSS 28.0 (IBM Corp., Armonk, NY, USA) for the analysis of
descriptive statistics, comparison of means, correlations, and regressions, with
significance set at p≤0.05. Data are mean±SEM.
Results
Patient characteristics
Baseline participant characteristics are shown in [Table 1]. At baseline, 112 women with
BC were assessed and completed one of three protocols. Eighteen women were
undergoing systemic targeted treatment, 29 were receiving radiation therapy, and
91 were on anti-estrogen therapy. No difference was found between the three
cohorts for the percentage of individuals undergoing chemotherapy, radiation, or
anti-estrogen therapy (Supplemental Table 1). Of the 112 participants,
only one injury was noted, a knee injury that resulted in missing a planned
workout. An average of 3.3 missed workouts per participant were recorded, with
26 individuals not missing a single workout during the duration of the 3-month
trial (data not shown).
Table 1 Descriptive characteristic from pre- to
post-intervention in all individuals who completed the trial.
Descriptive (n)
|
Pre-intervention
|
Post-intervention
|
Cohen’s d
|
P-value
|
BMI (112)
|
29.3±0.6
|
28.8±0.6
|
0.49
|
<0.001
|
Body fat (%) (112)
|
35.0±0.6
|
32.1±0.6
|
0.43
|
<0.001
|
Muscle mass (lbs.) (112)
|
56.4±0.8
|
57.4±0.8
|
0.11
|
<0.001
|
FFM (%) (112)
|
29.5±0.4
|
30.5±0.4
|
0.21
|
<0.001
|
RMR (111)
|
1,441.4±15.2
|
1,474.1±15.9
|
0.20
|
<0.001
|
FMS (110)
|
10.2±0.3
|
12.5±0.3
|
0.75
|
<0.001
|
BMI, body mass index; CI, confidence interval; FFM, fat free mass; RMR,
resting metabolic rate; FMS, functional movement screen. Mean difference
is difference from post- to pre-intervention; P-value is pre- vs.
post-intervention; data is mean±SEM.
Baseline assessment
At baseline, no difference was noted in age, BMI, percent body fat, FFM, RMR and
FMS score between all three different cohorts (Supplemental Table 2). The
FMS score negatively correlated with BMI (r=–0.466 [–0.603, –0.301],
p<0.001), percent body fat (r=–0.512 [–0.641, –0.356], p<0.001), and age
(r=–0.334 [–0.494, –0.153], p<0.001) ([Fig. 1]). Furthermore, baseline FMS correlated positively with
activity level(r , 0.395 [0.221, 0.545], p<0.0001), right hand grip strength
(r=0.428 [0.258, 0.573], p<0.001), and left hand grip strength (r=0.493
[0.333, 0.626], p<0.001) ([Fig.
2]).
Fig. 1 Correlation of FMS to BMI (a), body fat %
(b), and age (c).
Fig. 2 Correlation of FMS to right hand grip strength (a),
left hand grip strength (b), and activity level (c).
Moreover, activity level also negatively correlated with BMI and percent body fat
(r=–0.207 and –0.203, respectively; both p≤0.01). Therefore, we decided to
assess the relationship of FMS to BMI and percent body fat while controlling for
activity level. Both the relationship of the FMS to BMI and percent body fat
remained significantly negatively correlated (r=–0.440 and –0.481, respectively;
both p<0.001).
Longitudinal assessment
From pre- to post-assessment, significant decreases in BMI and percent body fat
were observed. Furthermore, significant increases in FMS score, FFM, and RMR
were noted pre- to post-assessment (all p<0.05) ([Table 1]).
Linear regression revealed that baseline FMS score was not significantly
associated with changes in BMI ([Table
2]) or percent body fat ([Table
3]). When BMI and body fat were calculated as percentages of
respective baseline values, the baseline FMS score was still not significantly
associated with changes in BMI ([Tables
2] and [3]). Similarly,
changes across pre- and post-intervention FMS scores were not associated with
changes in BMI or percent body fat ([Tables
2] and [3]).
Table 2 Regression models for change in BMI.
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
R Square Change
|
t
|
Sig.
|
|
|
β
|
Std. Error
|
Beta
|
|
|
|
1
|
Constant
|
0.504
|
0.103
|
|
|
5.341
|
<0.001
|
|
FMS score
|
0.094
|
0.103
|
0.087
|
0.008
|
0.916
|
0.362
|
2
|
Constant
|
1.138
|
0.186
|
|
|
6.129
|
<0.001
|
|
FMS score
|
–0.313
|
0.186
|
–0.159
|
0.025
|
–1.689
|
0.094
|
3
|
Constant
|
0.646
|
0.039
|
|
|
16.369
|
<0.001
|
|
Change in FMS score
|
–0.007
|
0.056
|
–0.013
|
0.0001
|
–0.130
|
0.897
|
4
|
Constant
|
0.517
|
0.109
|
|
|
4.724
|
<0.001
|
|
FMS score
|
0.004
|
0.003
|
0.108
|
0.039
|
1.106
|
0.082
|
Model 1 is the association of baseline FMS score to change in BMI. Model
2 is the association of baseline FMS score to change in the percent BMI,
relative to baseline. Model 3 is the association of the change in FMS
score, relative to baseline, to change in BMI. Model 4 is the
association of baseline FMS score to change in BMI while adjusting for
missed workouts.
Table 3 Regression models for change in BF%.
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
R Square Change
|
t
|
Sig.
|
|
|
β
|
Std. Error
|
Beta
|
|
|
|
1
|
Constant
|
1.154
|
0.203
|
|
|
5.698
|
<0.001
|
|
FMS score
|
–0.146
|
0.202
|
–0.069
|
0.005
|
–0.724
|
0.471
|
2
|
Constant
|
1.521
|
0.151
|
|
|
10.093
|
<0.001
|
|
FMS score
|
0.035
|
0.151
|
0.022
|
0.001
|
0.234
|
0.815
|
3
|
Constant
|
1.019
|
0.077
|
|
|
13.325
|
<0.001
|
|
Change in FMS score
|
–0.009
|
0.108
|
0.008
|
0.0001
|
0.088
|
0.930
|
4
|
Constant
|
1.004
|
0.213
|
|
|
4.898
|
<0.001
|
|
FMS score
|
–0.013
|
0.007
|
0.187
|
0.032
|
1.923
|
0.064
|
Model 1 is the association of baseline FMS score to change in BF. Model 2
is the association of baseline FMS score to change in the percent BF
relative to baseline. Model 3 is the association of the change in FMS
score relative to baseline to change in BF. Model 4 is the association
of baseline FMS score to change in BF while adjusting for missed
workouts.
Furthermore, a hierarchical multiple regression revealed that total days missed
did not improve the association of baseline FMS for change in BMI ([Table 2]). However, a hierarchical
multiple regression revealed that total days missed did improve the association
of baseline FMS for change in body composition, although significance was not
fully reached ([Table 3]).
Lastly, when assessing each cohort individually, baseline FMS score was not
associated with change in BMI or percent body fat, even when expressed as a
percentage compared to baseline ([Tables
2] and [3]).
Discussion
Our current study revealed that 1) baseline FMS score negatively correlated with BMI,
percent body fat, and age; 2) the baseline FMS score positively correlated with
activity level; and 3) the baseline FMS score was not associated with changes in BMI
or body composition. These findings suggest that, while the FMS can be used to guide
exercise prescription for individuals with BC, the baseline FMS is not associated
with changes in body weight or body composition after undergoing an exercise regimen
utilizing intense resistance training.
Improved body composition is of critical significance for women with BC. Individuals
with obesity are at risk of worse outcomes after treatment for BC [16]
[17], emphasizing fat reduction to potentially improve BC outcomes.
However, during weight loss, as much as 38% of weight can be lost from muscle
tissue, jeopardizing body composition [18].
For individuals with BC, this may be particularly harmful, as low muscle mass is
associated with higher mortality rates [19]. Further highlighting the importance of muscle mass for individuals with
BC, it is estimated that cachexia accounts for up to 20% of cancer-related deaths
[20]. Resistance training is the most
potent non-pharmacological intervention to increase muscle mass [21]. Our clinically relevant findings of an
increase in muscle mass, despite a loss in body mass, should be used as a proof of
concept that an exercise regimen focused on closed chain, intense resistance
training can be implemented safely and effectively in individuals with BC, even in
those with poor initial mobility. Such results are encouraging and translatable to
the general BC population as entrance criteria for these studies were broad.
Additionally, no injuries were reported in either study, besides one episode of
transient knee pain, further supporting progression during resistance training for
these individuals.
The present study was the first of its kind to assess the FMS as a pre-participation
exercise screen for individuals with BC. Previously, we have shown that a majority
of exercise oncology studies observing women with BC use mostly open chain kinetic
exercises [22]. Safe and effective
implementation of CKC movements into an exercise intervention may have been limited
by the lack of a pre-participation movement screen for individuals with BC. Our
findings suggest that the FMS can help exercise practitioners guide these closed
chain, intense full-body exercise sessions to maximize the potential benefit and
safety. Although our decision to FMS-screen all participants was done a priori, we
used it to guide and modify each individual’s exercise regimen, if needed. This may
ultimately be responsible for the low injury rates. Moreover, the implementation of
FMS score-guided exercise modification may have led to an enjoyable and challenging
exercise experience, as before the intervention only 12 individuals had any prior
experience with resistance training, and afterwards, 54 planned on continuing
resistance training (data not shown).
Our data further support the utility of the FMS as a useful tool to guide exercise
prescription. We found that the correlation of FMS score with BMI, percent body fat,
and activity level was comparable to those reported among non-cancer individuals
[7]
[23]. Additionally, these correlations remained significant after
accounting for activity level. Similar findings and implications were found by Perry
et al. in middle-aged adults without cancer [24]. Thus, our data suggest that the FMS screen can be used for
individuals with BC. Such implementation could potentially result in an exercise
intervention that includes more CKC movements, which has the potential to result in
profound anthropometric and metabolic changes.
Another key finding from this study is that the baseline FMS score was not associated
with weight loss or reductions in percent body fat. Similarly, the change in FMS
score, from pre- to post-intervention, was also not associated with weight loss or
reductions in percent body fat. However, when running a hierarchical regression
model with accounting for workouts missed, a trend towards significance was found
for the association of baseline FMS score to change in body composition. Thus,
potentially suggesting that for individuals who want to improve body composition,
baseline movement capabilities are not as impactful as total volume of exercise. It
should be noted that, while a significant amount of weight loss did occur, this
study was not designed to elicit weight loss, and future studies should examine if
a
higher baseline FMS score is a predictor of weight loss.
The strengths of the present study include 1) a first-time assessment of the
relationship of FMS to BMI, percent body fat, age, and activity levels among BC
patients; 2) a first-time evaluation assessing the predictive value of FMS score to
changes in body weight and composition; and 3) a large group with a wide range of
ages and varying stages of breast cancer with varying treatments. While the lack of
uniformity in cancer stage and treatment may be perceived as a limitation, our
participants are more representative of the BC population, providing more
generalizable findings. One potential limitation is that these findings may not be
translatable without the guidance of CSCS and FMS trained individuals. Without
proper training, implementing the FMS to guide exercise prescription may not be
feasible for an individual who is exercising independently. While modern
bioimpedance analysis utilizing multicompartment measurements is considerably more
accurate than prior ones, it can still be affected by hydration status, providing
further limitations on the data. We also utilized BMI for some of our calculations,
which does not account for muscle mass versus fat mass.
Conclusion
In summary, our study reveals that in women with BC, the FMS 1) correlates with
anthropometric markers, grip strength and activity levels; and 2) does not predict
changes in anthropometric markers during a three-month exercise program.
Our data suggest that the FMS can be used in individuals with BC to optimize their
exercise regimen. However, baseline FMS values are not associated with changes in
pre- and post-intervention changes in body fat or BMI and should not be used to
predict changes in anthropometric markers during an exercise intervention. These
data support utilizing FMS to guide safe dose escalation of intense CKC movements
for BC patients. Future studies should assess if baseline FMS scores can predict
changes in strength-based measures, such as grip strength.
Bibliographical Record
Jared Rosenberg, Jytosna Natarajan, David J Carenter, Chris Peluso, Christie Hilton,
Colin E. Champ. Do Functional Movement Screens Predict Body Composition Changes after
Resistance Training?. Sports Med Int Open 2025; 09: a25564182.
DOI: 10.1055/a-2556-4182