Citation: Guerra, M.; Garcia, D.;
Kazemitabar, M.; Lindskär, E.; Schütz,
E.; Berglind, D. Effects of a 10-Week
Physical Activity Intervention on
Asylum Seekers’ Physiological
Health. Brain Sci. 2022, 12, 822.
https://doi.org/10.3390/
brainsci12070822
Academic Editor: Fiorenzo
Moscatelli
Received: 17 May 2022
Accepted: 20 June 2022
Published: 24 June 2022
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brain
sciences
Article
Effects of a 10-Week Physical Activity Intervention on Asylum
Seekers’ Physiological Health
Matheus Guerra 1,2,*, Danilo Garcia 2,3,4,5,6,*
, Maryam Kazemitabar 2,7, Erik Lindskär 2, Erica Schütz 2,8
and Daniel Berglind 1,9
1
Department of Global Health, Karolinska Institute, 171 77 Stockholm, Sweden; daniel.berglind@ki.se
2
Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being,
New Haven, CT 06510, USA; maryam.kazemitabar@yale.edu (M.K.); erik.lindskar@regionblekinge.se (E.L.);
erica.schutz@lnu.se (E.S.)
3
Department of Behavioral Sciences and Learning, Linköping University, 581 83 Linköping, Sweden
4
Centre for Ethics, Law and Mental Health (CELAM), University of Gothenburg, 405 30 Gothenburg, Sweden
5
Department of Psychology, Lund University, 221 00 Lund, Sweden
6
Department of Psychology, University of Gothenburg, 405 30 Gothenburg, Sweden
7
School of Public Health, Yale University, New Haven, CT 06510, USA
8
Department of Psychology, Linnaeus University, 450 85 Kalmar, Sweden
9
Center for Epidemiology and Community Medicine (CES), Region Stockholm, 104 31 Stockholm, Sweden
*
Correspondence: matheus.guerra@ki.se (M.G.); danilo.garcia@icloud.com (D.G.)
Abstract: Introduction: The rise in armed conﬂicts has contributed to an increase in the number of
asylum seekers. Prolonged asylum processes may negatively affect asylum seekers’ health and lead
to inactivity. Studies show that physical activity interventions are associated with improvements
in health outcomes. However, there are a limited number of studies investigating the associations
of physical activity on asylum seekers’ health. Methods: Participants (263 males and 204 females),
mostly from Syria, were assessed before and after a 10-week intervention for VO2 max, body mass
index (BMI), skeletal muscle mass (SMM), body fat, and visceral fat. Linear mixed models were
used to test differences within groups, and a linear regression model analysis was performed to
test whether physiological variables predicted adherence. Results: Participants’ VO2 max increased:
males by 2.96 mL/min/kg and females 2.57 mL/min/kg. Increased SMM percentages were seen
in both genders: females by 0.38% and males 0.23%. Visceral fat area decreased: males by 0.73 cm2
and females 5.44 cm2. Conclusions: Participants showed signiﬁcant increases in VO2 max and SMM
and decreased visceral fat. This study provides an insight into asylum seekers’ health and serves
as a starting point to new interventions in which physical activity is used as a tool to promote and
improve vulnerable populations’ health.
Keywords: physical activity; intervention; asylum seekers; physiological health; VO2 max
1. Introduction
One of the biggest challenges in the 21st century is the increasing number of armed
conﬂicts in the world [1], which has contributed to an increased number of displaced
populations and asylum seekers. In 2015, about 163,000 individuals sought asylum in
Sweden, compared to an average of 28,575 per year during 2000–2010 [2]. In Blekinge,
Sweden, the number of asylum seekers registered in the county’s ﬁve municipalities in
2015 amounted to a total of 4069—which represents a signiﬁcant increase when compared
to the previous years (2014: 2126; 2013: 1280; and 2012: 1032) [2]. Lengthy application
processes, which in some cases can take several years to complete, may adversely affect
the physical and mental health of asylum seekers [3]. Additionally, other difﬁcult and
unusual situations, combined with limited language skills, may prevent asylum seekers
from receiving the information required to navigate their newly adopted society [4].
Brain Sci. 2022, 12, 822. https://doi.org/10.3390/brainsci12070822
https://www.mdpi.com/journal/brainsci
Brain Sci. 2022, 12, 822
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Even though asylum seekers are a very diverse group regarding health status, research
shows that they generally have more health problems, such as lower physical and mental
health, than native populations [5–7]. For example, a previous study with an Iraq-born
population in Sweden found a higher level of physical inactivity and a higher risk of
the development of non-communicable diseases such as diabetes type 2 when compared
with native Swedes [8]. Other studies have also found that Middle Eastern immigrants in
Sweden have a four-fold higher risk of developing diabetes when compared with native
Swedes [9], which can be partly attributed to a high prevalence of obesity in non-European
immigrants [10]. Consequentially, there is a need to increase and promote physical activity
among foreign-born populations in Sweden.
It is a well-known fact that low cardiorespiratory ﬁtness is one of the leading risk
factors causing non-communicable diseases and overall mortality, while physical activity
contributes to physical and mental well-being and increases the possibilities for creating
social networks as well as being part of society [11]. For example, higher cardiorespiratory
ﬁtness is prospectively associated with lower all-cause mortality, in which a 1.0 mL/min/kg
higher maximal oxygen consumption (VO2 max) is associated with a 9% relative risk
reduction in all-cause mortality [12].
Moreover, the loss of skeletal muscle mass, together with the excessive gain of fat mass,
is associated with several metabolic disorders such as metabolic syndrome, diabetes, and
cardiovascular diseases [13]. However, skeletal muscle loss is, to a large extent, reversible
through the adoption of resistance training and diet [14]. According to research conducted
by Roth et al. [15], physical activity and resistance training are effective for the prevention
of the loss of skeletal muscle mass which, in turn, improves quality of life.
Last but not the least, excess visceral fat is closely associated with the development
of many non-communicable diseases such as hypertension, type 2 diabetes, and hyper-
lipidemia and constitutes an independent risk factor for developing heart disease [16].
Diet and regular exercise have been shown to be effective in reducing visceral fat, and the
association between physical exercise and the reduction in visceral fat volume has been
well established [17,18].
Despite the vast amount of evidence linking physical activity to improved quality
of life and a reduced outcome of non-communicable diseases [19–21], there are few stud-
ies exploring the associations of physical activity interventions with asylums seekers’
health [22,23]. One such study [24] investigated the impact of an eight-week training pro-
gram in 45 young males (mean age = 25.6, SD = 7.1) living in a refugee camp in Greece. The
participants were invited to engage in physical activities three to ﬁve times per week for
approximately one hour, focusing on a combination of weight and endurance training. The
study found that higher participation rates were associated with fewer anxiety symptoms,
higher health-related quality of life, higher self-perceived ﬁtness, greater handgrip strength,
and improved cardiovascular ﬁtness.
The project “Health for Everyone-Sport, Culture and Integration” was an initiative
created by the Ronneby municipality in Blekinge, Sweden, in partnership with the Blekinge
Sports Association. Within the project, asylum seekers were invited to engage in physical
activity once a week during a 10-week period in groups of 20 to 30 individuals. In addition
to this, participants were invited to a once-a-week class on health promotion in their native
language and a visit to the Blekinge Museum in order to introduce them to Scandinavian
history and Blekinge’s cultural heritage.
The aim of this study was to evaluate whether there were any signiﬁcant changes in
physiological health among asylum seekers who participated in the “Health for Everyone”
project and to investigate whether physiological health measurements at baseline predicted
adherence to the intervention. We expected to see improvements in most of the physiolog-
ical health measures and hypothesized that individuals who were more physically ﬁt at
baseline would have higher rates of attendance.
Brain Sci. 2022, 12, 822
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2. Materials and Methods
2.1. Participants and Procedure
The project “Health for Everyone-Sport, Culture and Integration” was created by the
Ronneby municipality and carried out in partnership with the Blekinge Sports Association.
It started in the fall of 2016 and lasted for two years, with a total of 18 months of active
operation. The recruitment was carried by Blekinge’s municipalities, being scheduled
within the framework of the social orientation for asylum seekers from countries currently
experiencing armed conﬂicts. In total, 467 individuals (263 males and 204 females) with a
mean age of 35.9 years (SD = 11.9) were enrolled in the project.
The participants engaged in a combination of resistance and aerobic training designed
in a circuit format alternating different exercise stations. The participants were provided
with transportation from their settlements in each of the municipalities to the training
facility in Karlskrona (Blekinge Health Arena) for the once-a-week training session.
At the beginning of the intervention, the participants received information (verbally
and written) about the activities in Arabic, Somali, and Persian languages, with the assis-
tance of community-appointed translators, and they were subsequently asked to participate
in the evaluation carried out by the research group. The participants were informed that
their data were conﬁdential, and that the data would be used for scientiﬁc analysis and
publication. All the participants in this study gave their consent to participate in writing.
The participants underwent physiological tests consisting of a bioelectrical impedance
measurement using an InBody 720 body composition analyzer (Biospace Co., Ltd., Seoul,
Korea) to measure body weight, body mass index (BMI), skeletal muscle mass (SMM), body
fat, visceral fat, and cardiorespiratory ﬁtness (VO2 max) through a beep test both at the
beginning of the study and at the end of the intervention. The participants were also asked
to answer questions regarding their background (demographical data, age, and gender, etc.)
and other self-reports of validated psychological measures. In total, the data collection took
approximately 1.5 h and was performed at baseline (week 0) and at endpoint (week 10),
leading to a total of 8 training sessions within the intervention.
2.2. Measures
2.2.1. Attendance
Attendance was logged by the Blekinge Health Arena instructors on each scheduled
once-a-week training day. A total attendance of eight times was the maximum attendance
rate. Therefore, we divided the number of recorded attendances for each participant by
eight, which gave us the attendance percentage for each participant in the project.
2.2.2. Cardiorespiratory Fitness
The multi-stage ﬁtness test, also known as the beep test, was used to estimate the
participants’ cardiorespiratory ﬁtness (i.e., VO2 max). The test has been widely used due
to its simplicity in providing an accurate approximation of an individuals’ VO2 max [25].
The test requires participants to run 20 m back and forth across a marked track, keeping
time with beeps. Every minute, the next level starts; the time between beeps gets shorter,
which requires participants to run faster to keep up with the next level. If the participant
fails to reach the relevant marker in time, a ﬁrst warning will be given, with a second
warning meaning the end of the test. The number of shuttles successfully completed
is therefore registered, and the ﬁnal score is given according to which level and the to-
tal number of shuttles the participant was able to complete. The following formula is
used to transform the beep test results to VO2 max: VO2 max = 3.46 × (Level + No. of
Shuttles/(Level × 0.4325 + 7.0048)) + 12.2.
2.2.3. Body Weight and Body Mass Index (BMI)
BMI is a statistical index calculated by a person’s weight divided by height in square
meters or BMI = weight (kg)/height2 (m) [26]. The number obtained by the equation is the
individual’s BMI, and it is used to deﬁne an individual as underweight, normal weight,
Brain Sci. 2022, 12, 822
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overweight, or obese. A higher BMI indicates a higher likelihood of obesity. A commonly
used reference range for normal weight is between 18.5 and 24.9 kg/m2 [26].
The BMI calculation was performed using measures obtained from a direct segmental
multi-frequency bioelectrical impedance analysis (DSM-BIA) with an InBody 720 body com-
position analyzer. The DSM-BIA technique provides an accurate assessment of segmental
and body composition [27,28].
2.2.4. Skeletal Muscle Mass (SMM)
In humans, skeletal muscle is a type of striated muscle tissue which is under voluntary
control of the somatic nervous system. It constitutes approximately 40% of the total body
mass [29] and can be inﬂuenced by a person’s nutritional status, hormonal balance, physical
activity levels, or disease. The SMM calculation was conducted using measures obtained
from a DSM-BIA with an InBody 720 body composition analyzer.
2.2.5. Body Fat Mass
Body fat mass refers to the amount of adipose tissue that constitutes the human
body. The excessive accumulation of fat represents obesity, typically classiﬁed through
the BMI with the underlying assumption that a higher BMI indicates increased body fat.
However, BMI does not measure body fat mass directly and provides no information on
the location of fat mass in different body sites. Hence, as a complement, body fat mass
calculation was conducted using measures obtained from a DSM-BIA with an InBody
720 body composition analyzer.
2.2.6. Visceral Fat
Visceral or abdominal fat refers to adipose tissue accumulated in the abdominal
cavity between internal organs such as the liver, stomach, and intestines. The cut-off
value of visceral fat area associated with an increased risk of obesity-related disorder,
according to the receiver operating characteristics curve, was 103.8 cm2 [30]. The visceral
fat calculation was conducted using measures obtained from a DSM-BIA with an InBody
720 body composition analyzer.
2.3. Statistical Analysis
First, we removed the outliers. Outliers are values that deviate remarkably from
other values [31], which make data distribution non-normal and create signiﬁcant changes
in parameter estimates, especially when the maximum likelihood estimation method is
used [32]. In this study, outliers were detected using boxplots and scatterplots. A total
of 93 extreme outliers in the variables of VO2 max, body weight, BMI, skeletal muscle
mass (%), body fat mass (%), and visceral fat were removed in order to acquire normal
distribution of the data. Then, the normality of the data was measured by investigating
the skewness and kurtosis of the variables. All these values were within the range of ±1,
and therefore, we considered the data distribution as normal for the dependent variables.
Additionally, an MCAR test was conducted, and the results showed that the missing data
were completely at random (Chi-Square = 51.65, df = 52, p = 0.49).
The study had an interventional and longitudinal design. In short, a set of physiologi-
cal variables was measured before and after the 10-week physical activity intervention for
each participant. We used two linear mixed models to test differences within groups with
regard to physiological health variables (i.e., cardiorespiratory ﬁtness, body weight, BMI,
SMM%, body fat mass%, and visceral fat) at the start (T1) and end (T2) of the intervention.
In Models 1 and 2, T1 and T2 measures of physiological health were entered as dependent
variables. In Model 2, gender, age, and attendance percentage were included as covariates
into the regression model. The covariates’ intercepts effects were ﬁxed, and individuals’
intercepts were set at random to test the differences within individuals with regard to the
dependent variables.
Brain Sci. 2022, 12, 822
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We used the restricted maximum likelihood estimation method, which provides
more accurate and unbiased results compared to other methods [33]. In addition, we
used intraclass correlation coefﬁcients (ICCs) as a measure of the variance explained by
individuals; that is, an estimation of the group mean reliability across T1 and T2 [34]. This
was the ratio of between-group variance to total variance. It was calculated using the
following formula for each linear regression model:
ICC =
σ2
0
σ2
0 + σ2e
in which σ2
0 is the variance of random intercept and σ2
0 + σ2
e is the total variance (i.e.,
random intercept variance and residual variance). The result is usually between 0 and 1;
higher values suggest greater between-group variability.
As the last analysis, we performed a linear regression model analysis to test whether
variables in physiological health at baseline (i.e., T1) could predict adherence to the physical
intervention (i.e., attendance percentage). All the statistical analyses were conducted using
IBM SPSS Statistics v.26 software, and statistical signiﬁcance for all analyses was set at
p < 0.05. The statistical power (1 − β) for the total sample at α = 0.05 was equal to 0.99.
3. Results
3.1. Sample Characteristics
Table 1 indicates the descriptive characteristics of the sample used in this study. The
comparison of the mean differences in attendance percentage among females and males
indicated that both genders participated in the physical activity sessions to roughly the
same extent. Table 2 indicates the physiological health variables related to before (T1) and
after (T2) the intervention.
Table 1. Descriptive characteristics of the study sample.
Variables
Gender
Mean/SD
Age
Female
40.29 ± 9.34
Male
39.28 ± 10.16
Total
39.70 ± 9.81
Attendance Percentage
Female
70% ± 0.33
Male
76% ± 0.26
Total
73% ± 0.29
Differences between females and males in attendance percentage
t-value
p-value
1.99
0.05
Note: min: minimum, max: maximum, SD: standard deviation.
Table 2. Physiological health measures at week 0 (T1) and week 10 (T2).
Physiological Health
Gender
T1 Mean (SD)
Total Mean T1
(SD)
T2 Mean (SD)
Total Mean T2
(SD)
Mean Change T1
to T2
Cardiorespiratory Fitness
(VO2 max; mL/min/kg)
Female
28.58
(SD = 4.91)
31.46
(SD = 5.97)
31.22
(SD = 4.87)
34.35
(SD = 6.42)
2.64
Male
33.54
(SD = 5.81)
36.44
(SD = 6.49)
2.90
Body Weight (kg)
Female
69.41
(SD = 11.93)
75.81
(SD = 14.64)
68.35
(SD = 10.29)
75.95
(SD = 13.98)
−1.06
Male
80.37
(SD = 14.71)
80.83
(SD = 13.88)
0.46
Brain Sci. 2022, 12, 822
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Table 2. Cont.
Physiological Health
Gender
T1 Mean (SD)
Total Mean T1
(SD)
T2 Mean (SD)
Total Mean T2
(SD)
Mean Change T1
to T2
BMI (kg/m2)
Female
26.57
(SD = 4.23)
26.56
(SD = 4.34)
26.34
(SD = 3.72)
26.63
(SD = 4.09)
−0.23
Male
26.55
(SD = 4.43)
26.81
(SD = 4.31)
0.26
Skeletal Muscle Mass (%)
Female
34.60
(SD = 3.50)
39.04
(SD = 5.51)
34.98
(SD = 3.46)
39.54
(SD = 5.52)
0.38
Male
42.21
(SD = 4.39)
42.45
(SD = 4.53)
0.24
Body Fat Mass (%)
Female
36.69
(SD = 6.38)
30.01
(SD = 9.07)
35.96
(SD = 6.15)
29.21
(SD = 8.90)
−0.73
Male
25.26
(SD = 7.58)
24.91
(SD = 7.63)
−0.35
Visceral Fat (cm2)
Female
126.60 (SD =
44.10)
107.91
(SD = 45.57)
114.96
(SD = 39.42)
104.53
(SD = 43.50)
−11.64
Male
94.55 (SD = 41.83)
97.27
(SD = 44.84)
2.72
Note: SD: standard deviation.
3.2. Linear Mixed Model Analysis: Effect of the Intervention
Table 3 shows the results of Models 1 (null model), 2 (random effects model), and
3 (ﬁxed effects model) regarding predictors of physiological health. For Model 1, the
results indicated that there were differences in physiological health measures between
T1 and T2 within individuals (p < 0.00) f or all the various physiological measures (i.e.,
cardiorespiratory ﬁtness, body weight, BMI, SMM%, body fat mass%, and visceral fat). In
Model 2 (random effects model), we estimated the differences within individuals regarding
physiological health measures in T1 and T2 by controlling for gender, age, and attendance
percentage as predictors in the equation and putting individuals as random effects. The
results showed that the predictor variables in Model 2 (i.e., gender, age, and attendance
percentage) signiﬁcantly affected the intercepts of the dependent variables in Model 1 (i.e.,
the physiological health measures). The ICCs for all the equations showed that the added
predictors in Model 2 changed the independent intercepts in Model 1.
To be more precise, the results of the linear mixed model for Model 2 showed that the
changes from T1 to T2 regarding cardiorespiratory ﬁtness, BMI, body weight, and SMM
percentage were signiﬁcant (p ≤ 0.01). Gender predicted changes in body fat percentage
and SMM percentage (p < 0.05) from T1 to T2. Thus, this suggests that females had a greater
relative increase in SMM percentage and a greater relative decrease in body fat percentage
compared to males.
All the intercepts for visceral fat and the predictors were non-signiﬁcant (p > 0.05).
Hence, there were no differences within individuals concerning visceral fat, and gender,
age, and attendance percentage did not predict changes in visceral fat values. Impor-
tantly, attendance percentage did not have any association with changes in physiological
health variables.
For a comparison of the ﬁxed versus random effects models, the ﬁxed effects model
was also measured. The results of the linear mixed model for Model 3 (ﬁxed effects model)
yielded similar outputs as the random effects model with several differences. In the ﬁxed
effects models, age predicted cardiorespiratory ﬁtness, body fat percentage, and SMM
percentage. In addition, gender predicted cardiorespiratory ﬁtness in addition to those
signiﬁcant relationships in the random effects model. The comparison of the Akaike
Information Criteria (AIC) showed that the random effects model better ﬁt to the data. In
this study, the outputs of the random effects model were considered for further analysis
with regard to the model’s advantages over the ﬁxed effects model. The random effects
model is capable of estimating shrunken residuals [35] and provides estimates that overall
are closer to the true value in any particular sample [36].
Brain Sci. 2022, 12, 822
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Table 3. Linear mixed model analysis of the predictors of physiological health.
Variables
Model 1
Model 2 (Random Effects)
Model 3 (Fixed Effects)
Est.
SE
p-Value
ICC
AIC
Est.
SE
p-Value
AIC
Est.
SE
p-Value
Cardiorespiratory Fitness
(VO2 max)
32.38
0.33
0.00
0.80
3115.81
50.93
8.91
0.00
3222.57
54.13
7.97
0.00
Gender
−9.01
5.52
0.10
−10.70
4.98
0.03
Age
−0.43
0.24
0.07
−0.50
.22
0.02
Attendance Percentage
5.39
11.24
0.63
3.45
9.85
0.727
BMI
26.61
0.22
0.00
0.98
2664.25
20.58
6.94
0.00
3455.94
22.96
5.75
0.00
Gender
−0.15
4.34
0.97
−1.84
3.70
0.62
Age
0.22
0.19
0.24
0.20
0.16
0.20
Attendance Percentage
2.20
8.81
0.80
−1.08
7.15
0.88
Body Fat Percentage
29.90
0.47
0.00
0.97
3492.50
−4.59
12.14
0.71
4057.93
−5.21
9.36
0.58
Gender
17.57
7.55
0.02
17.77
6.01
0.00
Age
0.56
0.30
0.06
0.65
0.26
0.01
Attendance Percentage
−2.45
15.39
0.87
2.62
11.65
0.82
Body Weight (kg)
75.97
0.75
0.00
0.99
4020.39
79.26
24.14
0.00
4847.40
88.60
11.12
0.00
Gender
−14.48
15.01
0.34
−20.20
11.65
0.08
Age
0.56
0.60
0.35
0.51
0.50
0.31
Attendance Percentage
0.66
30.63
0.98
−9.61
22.52
0.67
SMM Percentage
39.12
0.29
0.00
0.98
2830.52
61.28
6.90
0.00
3380.54
62.45
5.35
0.00
Gender
−12.08
4.29
0.01
−12.64
3.43
0.00
Age
−0.34
0.17
0.05
−0.41
0.15
0.00
Attendance Percentage
1.92
8.75
0.83
−1.99
6.66
0.77
Visceral Fat
107.66
2.34
0.00
0.98
5576.84
3.62
73.55
0.96
6331.85
67.66
55.91
0.23
Gender
30.16
45.76
0.51
10.62
35.45
0.77
Age
2.17
1.83
0.24
0.27
1.52
0.86
Attendance Percentage
−40.44
93.34
0.67
−58.05
70.71
0.41
Note: Est.: regression coefﬁcient, SE: standard error, SMM: skeletal muscle mass. ML: maximum likelihood. ICC:
intraclass correlation coefﬁcients. AIC: Akaike Information Criteria.
3.3. Effect Size and Minimum Detectable Change Calculation for Each Physiological Measure
Table 4 indicates the effect size Cohen’s f2 for each measure across females and males,
which were calculated using GPower v3.1. The level of effect sizes was small for VO2 max,
skeletal muscle mass, body fat mass for females, and visceral fat. The effect sizes for body
weight, BMI for both females and males, and body fat mass for males were not signiﬁcant.
Cohen [37] suggested cut-off points of f 2 ≥ 0.02, f2 ≥ 0.15, and f 2 ≥ 0.35 representing
small, medium, and large effect sizes, respectively. Moreover, small differences in effect
sizes between females and males with respect to skeletal muscle mass and body fat mass
were observed. Changes in physiological health measures were also estimated through
minimum detectable change (MDC)—a statistical estimate of the smallest amount of change
that can be detected by a measure that corresponds to a noticeable change in the variable
under study over time which is not related to measurement error. MDC is calculated using
the following formula:
MDC = SEM × 1.96 × square root of 2
SEM = SD ×
2q
(1 − r)
where 1.96 is a z-score which represents the conﬁdence interval from a normal distribution,
SD is the standard deviation at baseline, r is the test–retest reliability coefﬁcient, and SEM
is the standard error of measurement.
Brain Sci. 2022, 12, 822
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Table 4. Effect size, SEM, and MDC for all physiological health measures.
Physiological Health
Gender
Cohen’s f2
SEM
MDC95
Cardiorespiratory Fitness
(VO2 max; mL/min/kg)
Female
0.04
1.38
1.96
Male
0.04
Body Weight (kg)
Female
0.01
1.46
1.69
Male
0.01
BMI (kg/m2)
Female
0.01
0.43
0.92
Male
0.01
Skeletal Muscle Mass (%)
Female
0.03
0.55
1.04
Male
0.02
Body Fat Mass (%)
Female
0.02
0.91
1.34
Male
0.01
Visceral Fat (cm2)
Female
0.02
4.56
2.99
Male
0.02
Note: SEM: standard error of measurement, MDC95: minimum detectable change at 95% conﬁdence interval.
The MDC value was assumed to be the minimum amount of change that needs to
be observed so that it could be considered a real change [38] or a change to which the
amount of change in performance was likely to be greater than the amount of random
measurement error. Since the MDC95 values were greater than the SEM values, the changes
in physiological health measures were not related to measurement error and therefore could
be considered as real changes. Moreover, the visceral fat’s SEM was greater than MDC95,
thus being consistent with the results from the linear mixed method in which the changes
in visceral fat were not signiﬁcant. Finally, although the linear mixed analysis showed that
changes in body fat percentage were not signiﬁcant, the MDC95 was greater than the SEM
values for this variable. Perhaps this reﬂects the signiﬁcant body fat percentage reduction
in females shown using the linear mixed model method.
3.4. Regression Analysis: Baseline Physiological Health as Predictor of Attendance
We conducted a linear regression analysis to investigate whether physiological health
variables at baseline (T1) as well as age and gender predicted the attendance percent-
age. As expected, cardiorespiratory ﬁtness at baseline was signiﬁcantly and positively
(F(1, 307) = 9.25, p < 0.01, adj. R2 = 0.03) related to asylum seekers’ attendance to the physical
intervention sessions (p < 0.01). Nevertheless, this correlation was relatively low (r = 0.17,
p < 0.01). See Table 5 for details.
Table 5. Linear regression analysis of the predictors of attendance percentage.
Parameter
B
Std. β Estimate
Std. Error
p-Value
Cardiorespiratory ﬁtness (VO2 max)
0.008
0.171
0.003
0.003
BMI
0.000
0.004
0.004
0.947
Body Weight
5.639
0.003
0.001
0.957
SMM Percentage
0.004
0.082
0.003
0.123
Body Fat Percentage
−0.003
−0.079
0.002
0.140
Visceral Fat
0.000
−0.067
0.000
0.214
Age
0.002
0.071
0.002
0.171
Gender
−0.060
−0.102
0.030
0.048
4. Discussion
The study’s aim was to evaluate the signiﬁcance of the 10-week physical activity inter-
vention, “Health for Everyone”, on asylum seekers’ physiological health. The results of the
linear mixed model conﬁrmed that the “Health for Everyone” intervention was associated
with a beneﬁcial impact on the asylum seekers’ physiological variables in both males and
females, with improvements in their body composition and cardiorespiratory ﬁtness.
In general, the participants showed a signiﬁcant increase in cardiorespiratory ﬁtness,
with females showing a decrease in their total body weight, while males showed a slight
increase. Both genders showed an increase in skeletal muscle mass and a decrease in body
Brain Sci. 2022, 12, 822
9 of 15
fat mass percentages and in visceral fat area. For all the participants, the total attendance
percentage for the duration of the program was 73%. Using minimum detectable changes
(MDC) and effect sizes, we found signiﬁcant differences in the physiological health variables
over time. This justiﬁes the changes we obtained from the linear mixed model analysis.
Similar results have been presented in a systematic review and meta-analysis of
65 physical activity interventions [39], showing signiﬁcant improvements in the car-
diometabolic health of the participants. From baseline to post-intervention, short-term
interventions of <12 weeks signiﬁcantly improved cardiorespiratory ﬁtness in populations
with overweight/obesity, along with a decrease in cardiometabolic risk factors.
Moreover, in a culturally adapted lifestyle intervention, including seven sessions
addressing healthy diet and physical activity [40] in a sample of 96 Iraq-born immigrants
residing in Malmö, Sweden, beneﬁcial effects on insulin levels, body weight and LDL
cholesterol were found.
4.1. Cardiorespiratory Fitness
The participants showed signiﬁcant increases in cardiorespiratory ﬁtness from T1
(week 0) to T2 (week 10). At the end of the intervention, males showed an increase in VO2
max of 2.90 mL/min/kg (M = 36.44, SD = 6.49) and females an increase of 2.64 mL/min/kg
(M = 31.22, SD = 4.87). For comparison, a population-based study of 579 men aged 42 to
60 [12] found that a 1.0 mL/min/kg increase in VO2 max was prospectively associated
with a 9% risk reduction in all-cause mortality, emphasizing the importance of increasing
and maintaining cardiorespiratory ﬁtness levels to promote long-term health.
Albeit an increase in cardiorespiratory ﬁtness was seen, and considering the partici-
pants’ mean age of 35.9 (SD = 11.9), the results show that males in our study population
had lower cardiorespiratory ﬁtness levels when compared to females. Using the cardiores-
piratory ﬁtness classiﬁcation scale for the age group of 30–39 years old [41], males were
classiﬁed as having poor cardiorespiratory levels at T1 and T2, with females being classiﬁed
as having fair cardiorespiratory ﬁtness level at both points in time. It is worth noting that
although both groups remained in the same cardiorespiratory ﬁtness classiﬁcation category,
males and females increased their VO2 max during the 10-week intervention period.
Comparing our sample’s results with the Swedish population, a study [42] of relative
VO2 max trends in the working force from 1995 to 2017 (N = 354 277 participants; 44%
women, 56% men, aged 18–74 years) showed that within the age group of 35–49 years,
females had a mean relative VO2 max of 35.7 mL/kg/min (SD = 1.23) and males a mean of
34.9 mL/kg/min (SD = 1.38). Hence, at least for the males in our asylum seeker sample,
their VO2 max improved and reached similar levels to native Swedes.
4.2. Skeletal Muscle Mass
In our study, both genders increased their skeletal muscle mass percentages from
baseline to endpoint. While female participants at endpoint showed an increase of 0.38%,
male participants showed an increase in skeletal muscle mass percentage of 0.24% from T1
to T2. Contrasting with the European native population, a study [43] including a total of
1664 Hungarian adults (1198 females and 466 males) found that the mean skeletal muscle
mass for the age group of 20–40 years was 46.51% and 39.60% for males and females,
respectively. Another investigation with a sample of 1 924 Serbian women with a mean age
of 35.5 years [44] registered the average skeletal muscle mass for the total sample as 39.3%.
Bearing in mind that our study’s population consisted of foreign-born asylum seekers,
and although skeletal muscle mass percentages increased for both genders, our results
showed considerably lower skeletal muscle mass percentages when compared to the native
European population described above.
4.3. Body Fat
Despite the relatively limited number of training sessions offered to the participants,
both genders showed a reduction in total body fat percentages, with males registering a
Brain Sci. 2022, 12, 822
10 of 15
decrease of 0.35% at T2 and females a decrease of 0.73%. At endpoint, females reduced
more in body fat percentages than their male counterparts. Although both genders showed
a reduction in percentual points, males and females still showed comparably higher per-
centages at T2 when compared to European populations. Ihász et al. [43] showed that the
mean body fat percentages in the age group of 20–40 years were 18.27% for males and
27.75% for females. In a study [45] consisting of 433 healthy Caucasians (253 men and 180
women) aged 18–94 years, in the age group of 35–59 years, the fat mass percentages values
in males were determined to be 21.2% and 29.0% for females. Moreover, the American
College of Sports Medicine [41], in their classiﬁcation scale for body composition, states for
the age group of 30–39 years that good body fat percentages are 15.9–18.4% for men and
17.5–21.0% for women.
In this context, the males in our study presented poor body fat percentages followed
by very poor percentages for females at T1 and T2, while the above-mentioned studies
sampling European populations presented fair/good percentages for males and poor/fair
percentages for females, according to their respective age groups.
4.4. Visceral Fat
The current literature maintains that healthy levels for visceral fat area should be
sustained at <100 cm2, with values of ≥100 cm2 associated with an increased risk of obesity-
related disorders such as hypertension, hyperglycemia, and dyslipidemia [46]. In a study
of 413 subjects (174 men and 239 women) to determine cut-off values for visceral fat area
associated with an increase in the risk of obesity disorders and metabolic syndrome [30],
the value of visceral fat area associated with an increased risk of obesity-related disorders
was 103.8 cm2.
A previous investigation with a sample of 233 middle-aged and older women (45
to 73 years) showed that a visceral fat area of ≥106 cm2 is associated with elevated risks
for having low HDL cholesterol concentrations, hypertriglyceridemia, a high LDL/HDL
cholesterol ratio, impaired glucose tolerance, and hyperinsulinemia [47], with a visceral
fat area of ≥163 cm2 being predictive of even greater risks for metabolic risk factors for
coronary heart disease when compared to lower visceral fat levels.
In relation to the values presented above, in our study, males had healthy visceral
fat levels at both measure points (94.55–97.27), while females, despite having a signif-
icant reduction in their mean visceral fat area, continued to be at an elevated risk for
cardiometabolic disorders (126.60–114.91).
4.5. Physiological Health Variables as Predictors of Attendance
Before discussing which physiological health variables predicted attendance, we
would like to point out that in our study, females registered a larger increase in skeletal
muscle mass and a larger decrease in body fat when compared to males. While age pre-
dicted a decrease in skeletal muscle mass in older individuals, a reduction in skeletal muscle
mass was expected for older individuals since the association of aging and progressive
muscle loss are well stablished [48], with skeletal muscle mass decreasing at a rate of
approximately three to eight percent per decade after 30 years, and an even higher rate
of decline after the age of 60 [49]. These results, albeit outside our aim, are important to
understand further analyses.
Although the results showed differences in physiological health from T1 to T2 within
individuals for the variables analyzed, the attendance percentages did not have any as-
sociations with changes in physiological variables. However, cardiorespiratory ﬁtness
had a signiﬁcant relationship with an individual’s attendance, i.e., individuals with bet-
ter cardiorespiratory ﬁtness at baseline had higher attendance rates. Other studies have
shown similar results, as seen in a previous review performed to determine exercise ad-
herence rates and their predictors in 21 randomized control trials [50]. As per our results,
individuals with better cardiorespiratory ﬁtness at baseline had the best adherence to
physical training.
Brain Sci. 2022, 12, 822
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Nevertheless, one of the challenges is to increase adherence to physical activity inter-
ventions. Some studies indicate that, among different populations, the more physically
ﬁt a person is at the start of the program, the higher attendance rate they have [50,51].
Since asylum seekers in general present lower ﬁtness levels when compared to native
populations, it could be speculated that the group may not have a high adherence rate to
physical activity interventions.
4.6. Strengths and Limitations
The study’s strength was ﬁrst and foremost its longitudinal study design with a
predeﬁned set of speciﬁc variables and objective measures. It allowed for an insight into
physiological health developments over a 10-week period in a rarely studied population.
The primary methodological limitation of the study refers to the lack of randomization
and a control group providing a standard for comparison when measuring the physiological
outcomes. This limitation did not allow us to draw deﬁnitive conclusions on the effects
of physical activity in the group studied; thereby, the results can only be interpreted
as associations.
Moreover, the mixed models we applied w particularly useful in longitudinal studies
and are often preferred to other approaches because they can be used with missing values.
Nevertheless, even if in our models the covariates’ intercepts effects were ﬁxed and individ-
uals’ intercepts were set at random in order to test the differences within individuals with
regard to the dependent variables, the lack of a control group still presents a limitation.
Upon ﬁrst glance, our results might suggest that a short physical activity intervention
combining resistance and aerobic training yields small but positive results in physiological
variables for this speciﬁc population. For instance, a short intervention (8 to 10 sessions in
a 10-week period, for example) would allow a much larger number of people to take part
of the training program and still have positive impacts on their health. Indeed, the amount
of training in “Health for Everyone” was dictated by ﬁnancial and logistical constraints.
However, the amount of physical activity that is recommended for adults is signiﬁcantly
higher. In order to improve and sustain cardiorespiratory ﬁtness and reduce the risk of
non-communicable diseases, the WHO [52] recommends at least 150 min of moderate-
intensity physical activity per week or at least 75 min of vigorous-intensity physical activity
per week. Therefore, the volume of exercise recommended is considerably higher than
the volume that was provided by the intervention. Hence, even if a short intervention
would be economically and logistically more feasible and still give positive effects, it would
probably not have any long-term physiological effects.
In addition, socioeconomic variables, which could potentially have affected the par-
ticipants’ physiological outcomes, were not taken into consideration in the present study.
The participants’ educational levels were not analyzed, and as reported in previous stud-
ies [53,54], lower educational levels are linked to higher risks of physical inactivity and a
higher incidence of non-communicable diseases [55].
Income levels and participation in the workforce were also not examined in this
study. Higher physical activity levels are noted among those with higher income and
a steady source of revenue. Meanwhile, less disposable income and unemployment are
strongly related to low physical activity levels [56]. Even though information on income
levels was not collected, it could be assumed that the vast majority of the participants
were still navigating the Swedish immigration system and relied heavily on subsidies
and ﬁnancial support from the Swedish state, since the recruitment was carried out by
Blekinge’s municipalities within the framework of social orientation for asylum seekers
from countries currently experiencing armed conﬂicts.
Although this study provides a valuable insight into asylum seekers’ physiological
health, due to the relatively small number of participants in the project, we do not aim to
use the cardiorespiratory ﬁtness values found in this investigation to set reference values
for VO2 max for asylum seekers in Sweden. Based on its limitations and study design,
caution should be applied when arriving to conclusions based on the study ﬁndings.
Brain Sci. 2022, 12, 822
12 of 15
5. Conclusions
The results from our study are in accordance with previous research on the associ-
ations of physical activity interventions in the physical health of adult populations. The
participants showed signiﬁcant improvements in physiological health variables of physical
ﬁtness and body composition, most noticeably in a signiﬁcant increase in cardiorespiratory
ﬁtness. Moreover, individuals with higher initial cardiorespiratory ﬁtness levels were more
likely to adhere to the intervention, which leads to the assumption that participants who
were already physically active were more inclined to maintain a physically active lifestyle.
Given the current format and the limitations of the project “Health for Everyone-
Sport, Culture and Integration”, it would be of interest to investigate whether an extended
version could lead to long-term improvements in the participants’ physiological health and
whether it could beneﬁcially impact their exercise attitudes and their psychological and
social health.
With the complexity of asylum seekers’ physical and mental health needs, barriers
and facilitators could also be identiﬁed in order to increase participation in a physical
activity intervention and achieve more signiﬁcant and lasting results. For example, Haith-
Cooper et al. [57] found that, among asylum seekers, stress, poverty, and temporary living
conditions acted as barriers for participating in physical activity.
A point to be explored in future studies is the potential effects physical activity
interventions may have on immigrant populations in the context of language acquisition.
Evidence shows that physical activity interventions in young asylum seekers have a positive
impact on their second language learning outcomes [58]. Since higher physical ﬁtness is
associated with improved cognition and literacy [59], it may therefore facilitate integration
into a new society.
Overall, the results from this study provide an insight into asylum seekers’ health
status and could serve as a base for implementing an intervention scale-up, where culturally
sensitive approaches to physical activity are used to improve vulnerable populations’
physical and psychological health, and act as a guide for future policies towards health
equality and inclusion in society.
Author Contributions: M.G. analyzed and interpreted the data results and prepared the manuscript
draft for submission. D.G. gave input on the initial versions of the manuscript and guided the
interpretation of the results. M.K. is responsible for the ﬁnal data analysis. E.L. is responsible for
the preliminary data analysis. E.S. provided manuscript inputs and insights into data interpretation.
D.B. provided manuscript inputs and guided the data analysis and interpretation of the results. All
authors have read and agreed to the published version of the manuscript.
Funding: The project “Health for Everyone-Sport, Culture and Integration” was ﬁnanced by Region
Blekinge and the County Administrative Board in Blekinge. The project was managed by the Ronneby
municipality with the collaboration of the Blekinge Sports Association and Blekinge Museum. The
ﬁnancers had no role in the study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Institutional Review Board Statement: The evaluation of the project “Health for Everyone-Sport,
Culture and Integration” was approved by the Swedish Ethical Review Authority (Dnr. 2017/604).
The study was conducted in accordance with the ethical standards of the 1964 Helsinki declaration
and further amendments. Hence, all the participants were provided with the necessary information to
obtain verbal and written informed consent (e.g., aims of the study, that participation was voluntary,
conﬁdential, etc.).
Informed Consent Statement: Informed consent was obtained from all the participants.
Data Availability Statement: The data supporting the ﬁndings of this study are available from
the research group, but restrictions apply to the availability of these data, and the data are not
publicly available.
Acknowledgments: We would like to express our gratitude to Olof Ljungberg, Integration Coordi-
nator at the Blekinge Sports Association, Henrik Lövgren, Head of Social Communication at the
Brain Sci. 2022, 12, 822
13 of 15
Blekinge Integration and Education Center, and Region Blekinge for allowing us to investigate the
“Health for Everyone-Sport, Culture and Integration” intervention. Last but not the least, we would
like to thank the participants and the staff at Blekinge Health Arena for their assistance during the
data collection stage.
Conﬂicts of Interest: The authors declare that they have no conﬂict of interests.
Abbreviations
BMI
Body mass index
DSM-BIA
Direct segmental multi-frequency bioelectrical impedance analysis
ICC
Intraclass correlation coefﬁcients
SMM
Skeletal muscle mass
VO2
max: Maximal oxygen consumption
WHO
World Health Organization
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Effects of a 10-Week Physical Activity Intervention on Asylum Seekers' Physiological Health.
Guerra, Matheus,Garcia, Danilo,Kazemitabar, Maryam,Lindskär, Erik,Schütz, Erica,Berglind, Daniel