ORIGINAL RESEARCH
1 SAJSM VOL. 34 NO.1 2022
Creative Commons Attribution 4.0 (CC BY 4.0) International License
Changes in training activity post COVID-19 infection in recreational
runners and cyclists
A Emeran,¹,²,³ BSc (Hons); EV Lambert,¹,² PhD;
T Paruk,¹,² MSc; A Bosch,¹,² PhD
¹ UCT Research Centre for Health through Physical Activity Lifestyle and
Sport (HPALS), Department of Human Biology, Faculty of Health Sciences,
University of Cape Town, Cape Town, South Africa
2 International Federation of Sports Medicine (FIMS) Collaborative Centre
of Sports Medicine, University of Cape Town, Cape Town, South Africa
3 National Research Foundation (NRF), Cape Town, South Africa
Corresponding author: A Bosch (andrew.bosch@uct.ac.za)
The COVID-19 pandemic has had a major
impact on morbidity and mortality globally.[1]
Fortunately, most COVID-19 cases have been
classified as mild to moderate.[2] However,
even with mild illness severity, individuals may still require a
prolonged time to full recovery, particularly when returning
to exercise. Anecdotal evidence shows that many individuals,
including athletes, have difficulty returning to their normal
level of exercise post infection.[3] Symptoms challenging
athletes’ return to exercise include coughing, tachycardia, and
fatigue.[4] There is limited research on the effect of COVID-19
on the exercise activity of athletes post infection, with several
studies reporting minimal impairment of exercise capacity on
cardiopulmonary exercise testing (CPET) in athletes post
COVID-19.[5,6] To our knowledge, no research has been
undertaken on objectively measured training data obtained
from runners and cyclists pre- and post COVID-19 infection.
The current study aims to address the gap in knowledge for
the above by describing the perceptions of recreational runners
and cyclists recovering from COVID-19 on their training
activity and general well-being. In a sub-sample, we will
compare objectively measured training data in runners and
cyclists pre- and post COVID-19 infection with non-infected
controls who experienced a training interruption. The study
differs from existing studies, as instead of using exercise
testing, exercise data from participants’ Global Positioning
System (GPS) wearable devices were used to determine
whether a change in exercise activity occurred post infection.
To our knowledge, this is the first study to measure exercise
activity of athletes pre- and post infection using GPS wearable
data. This study will potentially drive interest in conducting
further studies on different populations and in various sporting
fields using similar methods to better understand the potential
impact of the SARS-CoV-2 virus on the exercise activity of
athletes.
Methods
Study design and participants
The study is an observational study on recreational runners and
cyclists predominantly from South Africa (two international
participants were included). Participants were recruited using
convenient, non-randomised sampling, via emailing running
and cycling clubs in South Africa, and advertising the study on
social media and at the Sports Science Institute of South Africa.
The advertisement included a basic description of the study
and a link to the study’s website. The website page contained
links to the study’s participation information sheet containing
instructions on how to download their GPS data, the informed
consent form, and a study questionnaire via Google forms.
Inclusion criteria were runners and cyclists over the age of 18
that reportedly tested positive for COVID-19, had attempted to
return to exercise post infection, and used a GPS wearable
device that measures heart rate. A control group that had not
been infected with COVID-19 but had an interruption in
training of two weeks or more was also included in the study.
Training interruption was defined as a complete cessation of
running or cycling training, not associated with COVID-19
infection.
All participants provided informed consent via the study’s
online Google forms questionnaire. The study received ethics
approval from the Faculty of Health Sciences Human Research
Background: Anecdotal evidence suggests that athletes
struggle to return to exercise post COVID-19 infection.
However, studies evaluating the effect of COVID-19 on
athletes’ exercise activity are limited.
Objectives: The objectives of this study were: (i) to describe
the perceptions of recreational runners and cyclists recovering
from COVID-19 on their training activity and general well-
being, (ii) to compare device-measured training data in
runners and cyclists pre- and post COVID-19, with non-
infected controls that had a training interruption.
Methods: Participants who were recruited via social media
completed an online questionnaire (n=61), including
demographic, health and COVID-19 descriptive data. In a
sub-sample, device-measured training data (heart rate, time,
distance and speed, n=27) were obtained from GPS devices for
four weeks before infection and on resumption of training.
Similar data were collected for the control group (n=9) whose
training had been interrupted but by factors excluding
COVID-19.
Results: Most participants experienced a mild to moderate
illness (91%) that was associated with a training interruption
time of two-four weeks. Decreases in heart rate, relative
exercise intensity, speed, time and distance were observed
during the first week of returning to training for both groups,
followed by an increase from Week two onwards.
Discussion: Results failed to support a ‘COVID-19 effect’ on
exercise activity as reductions in training variables occurred
in both the COVID-19 and control groups. A possible
explanation for the reductions observed is a deliberate
gradual return to training by athletes post-COVID-19.
Conclusion: More research is needed using device-measured
training data prior to and post COVID-19 infection to better
understand the impact of the SARS-CoV-2 virus on the
exercise activity of athletes.
Keywords: SARS-CoV-2, coronavirus, physical activity,
training activity, relative exercise intensity
S Afr J Sports Med 2022;34:1-7. DOI: 10.17159/2078-516X/2022/v34i1a13758
mailto:andrew.bosch@uct.ac.za
http://dx.doi.org/10.17159/2078-516X/2022/v34i1a13758
https://orcid.org/0000-0003-2217-6608
https://orcid.org/0000-0003-4315-9153
https://orcid.org/0000-0002-9543-9408
https://orcid.org/0000-0001-9073-702X
ORIGINAL RESEARCH
SAJSM VOL. 34 NO.1 2022 2
Ethics Committee at the University of Cape Town (Ref. No.
409/2021). This study complies with the latest version of the
Declaration of Helsinki (2013), as well as the Department of
Health: Ethics in Health Research: Principles Structures and
Processes (2004).
This study was a “proof-of-concept study”. Thus, a sample
size calculation was not performed.
Questionnaire
A questionnaire on Google forms was provided for the
participants to complete. Demographic, health, and training
self-reported data were obtained. For the 42 persons who
reported having tested positive for COVID-19, additional data
were captured, including the duration of their COVID-19,
COVID-19 symptoms, and COVID-19 severity. For the control
group, the reason for their training interruption and training
interruption duration were also obtained.
GPS data
Participants were required to download their GPS wearable
device data by following the instructions provided in the
participation information sheet. Thereafter, participants sent
their training data to the researcher to be analysed. Participants
were required to send GPS data from four weeks pre-training
interruption to four weeks post return to training.
Raw data were sorted and filtered on Microsoft Excel (version
2110) to include the following variables: average and peak heart
rate during training per week (bpm), average and maximum
speed per week (min/km), total time training per week (min),
total distance per week (km), with variables being averaged for
each week. Change in training activity was measured by
comparing the above training variables pre and post-training
interruption. Relative exercise intensity was determined by
calculating age-corrected maximum heart rate using the HUNT
equation (211-0.64*age)[7] and dividing the average heart rate
for each week by the maximum heart rate.
Statistical analysis
The program IBM SPSS Statistics 27 and GraphPad Prism 9
were used to conduct statistical analyses. Data were expressed
as number and percentage for categorical variables and mean
and standard deviation for continuous variables. Pearson Chi-
squared tests and independent t-tests were used to determine
any differences in categorical and continuous variables
between the two groups. Repeated measures analysis of
variance (ANOVA) was run to determine within-group and
between-group differences in the GPS data of participants.
Correlations were determined using Spearman’s rank
correlation tests. Statistical significance was set at a P-value of
<0.05.
Results
A total of 61 participants provided consent to participate in the
study and completed the questionnaires, with 42 individuals
being in the COVID-19 group, and 19 in the control group. Of
those that completed the questionnaire, 38 participants
provided their GPS data (COVID-19: N=27; Controls: N= 11).
The data of two control participants were removed prior to
analysis due to training data not meeting inclusion criteria,
reducing the GPS control sample to nine.
Questionnaire
COVID-19 and control group characteristics
No statistically significant differences were found in baseline
characteristics of participants (p>0.05) (Table 1). Most
participants in both groups were above the age of 40 (~70%),
were male (~74%), had a normal BMI (~67%), and were runners
(~48%). Most participants used Garmin wearable devices
(without a chest strap to measure heart rate, ~59%). The most
frequent training interruption length was between 2-4 weeks
for the COVID-19 group (45%), and 1-3 months for the control
group (32%) (p=0.054). The most common cause of training
interruption for the control group was COVID-19 lockdown
restrictions.
Table 1. Demographic and training characteristics of the COVID-19
and control group
Characteristics
COVID-19
n=42
Control
n=19
P
value
Age (years)
19-29
30-40
40-50
50-60
>60
3 (7)
11 (26)
17 (41)
10 (24)
1 (2)
1 (5)
4 (21)
6 (32)
7 (37)
1 (5)
0.806
Sex
Male
Female
27 (64)
15 (36)
16 (84)
3 (16)
0.114
BMI (kg/m²)
Underweight
Normal weight
Overweight
Obese
1 (2)
28 (67)
10 (24)
3 (7)
0 (0)
13 (68)
3 (16)
3 (16)
0.605
Sport
Runner
Cyclist
Runner and Cyclist
20 (48)
5 (12)
17 (41)
9 (47)
2 (11)
8 (42)
0.985
Chest strap
Yes
No
19 (45)
23 (55)
7 (20)
12 (63)
0.539
Training interruption time
(weeks)
0-2 weeks
2-4 weeks
1-3 months
>3months
10 (24)
19 (45)
13 (31)
0 (0)
1 (5)
5 (26)
8 (32)
5 (26)
0.054
Reasons for training
interruption of control group
Lockdown restrictions
Illness (excluding COVID-19)
Injury
Other
14 (74)
1 (5)
1 (5)
3 (16)
Data are shown as the number of participants and column percentage (%). P
values from Pearson Chi-squared test. Underweight, <18.5kg/m2; normal
weight, 18.5-29.9kg/m2; Overweight, 25-29.9kg/m2; Obese, >30kg/m2.
ORIGINAL RESEARCH
3 SAJSM VOL. 34 NO.1 2022
COVID-19 group characteristics
Table 2 shows health and COVID-19
related characteristics of the COVID-19
group. Most participants had no
comorbidities (69%) and were
unvaccinated before and after being
infected with COVID-19 (57%). The most
frequent acute COVID-19 symptoms
reported were headaches (79%), body
aches (74%), and fatigue (74%). Symptom
duration was most frequently between 0-
2 weeks (60%). COVID-19 severity was
mild to moderate for most participants
(91%) with only one participant being
hospitalised. The type of treatment
reported varied, with most participants
using over-the-counter medication such
as paracetamol (56%). About 58% of
participants said that they followed
guidelines for return to training from
their doctors. However, only 16% of
participants reported being medically
screened before returning to exercise. The
most frequent symptoms reported when
returning to training were increased
heart rate (58%), and fatigue (53%).
Positive correlations were found
between COVID-19 severity and number
of symptoms (r=0.50 [95% CI= 0.21-0.70];
p=0.001), symptom duration (r=0.55 [95%
CI=0.27-0.74]; p<0.001), and training
interruption time (r=0.52 [95% CI= 0.24-
0.72]; p<0.001). There were also
correlations between number of
symptoms and symptom duration (r=0.42
[95% CI=0.12-0.65]; p=0.006), and
interruption time (r=0.49 [95% CI=0.20-
0.70]; p=0.001).
GPS data
Training interruption time
There was a statistically significant
difference in the training interruption
time between the COVID-19 group and
the control group with GPS data. The
control group had a longer training
interruption time than the COVID-19
participants (33 ± 11 days vs 20 ± 13 days,
Table 2. Comorbidities, COVID-19 presentation, and treatment in the COVID-19 group (N=42)
N (%)
Comorbidities*
Diabetes 1 (2)
High Blood pressure 3 (7)
Cholesterol 4 (10)
Obesity 1 (2)
Asthma 8 (19)
Autoimmune disease 0 (0)
None 29 (69)
COVID-19
presentation
COVID-19 Symptoms*
Cough 25 (58)
Fever 23 (54)
Sore throat 21 (49)
Fatigue 32 (74)
Body aches 31 (74)
Loss of taste or smell 21 (49)
Headache 34 (79)
Diarrhoea 8 (19)
Difficulty breathing 12 (28)
Chest pain 12 (28)
Other 4 (9)
None 1 (2)
Number of symptoms 5 ± 2
Symptom duration
0-2 weeks 25 (60)
2-4 weeks 12 (29)
1-3 months 2 (5)
>3 months 3 (7)
COVID-19 Severity
Asymptomatic 2 (5)
Mild 20 (48)
Moderate 18 (43)
Severe 2 (5)
Critical 0 (0)
Symptoms when returning to training*
Cough 3 (7)
Fatigue 23 (54)
Shortness of Breath 12 (28)
Increased Heart Rate 25 (58)
Other 3 (7)
None 6 (14)
Treatment and
protective
measures
Hospitalisation
Yes 1 (2)
No 41 (98)
Treatment*
Corticosteroids 8 (18)
Antibiotics 7 (16)
Oxygen 2 (5)
Over the counter medication 24 (56)
Ivermectin 4 (10)
Vitamins/Supplements 15 (35)
None 6 (14)
Fully vaccinated
Pre COVID-19 3 (7)
Post COVID-19 15 (36)
Not vaccinated 24 (57)
Guidelines followed before returning to training
Yes, guidelines from medical practitioner 23 (58)
Yes, guidelines from scientific journal articles 3 (7)
Yes, guidelines from internet 3 (7)
No 13 (31)
Medical screening before return to training
Yes 7 (17)
No 35 (83)
Data are shown as the number of participants and
percentage (%) or as mean ± SD. * participants can
fall into multiple categories. COVID-19 severity:
Asymptomatic, no symptoms but tested positive for
COVID-19; Mild, had flu-like symptoms excluding
shortness of breath at rest or during exertion;
Moderate, flu-like symptoms and/or shortness of
breath, may or may not be hospitalized; Severe,
requires hospitalization and oxygen administration;
Critical, requires hospitalization and ventilation,
multi-organ involvement may be present.[8,9]
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p=0.014) As mentioned previously, the most common reason
for a training interruption for the control group was lockdown
restrictions, with other reasons for training interruptions
including injury and illnesses other than COVID-19. There
were no other differences in baseline characteristics such as
age, sex, and BMI.
Peak heart rate, average heart rate, and relative exercise
intensity
Decreases in peak and average heart rate, and relative exercise
intensity were observed in both groups, one-week post return
to training, compared to one-week pre-training interruption.
(Mean peak heart rate: COVID-19:171 beats per minute (bpm)
to 158 bpm; Control:178 bpm to 161 bpm. Mean average heart
rate: COVID-19:147 bpm to 140 bpm; Control:146 bpm to 138
bpm. Mean relative exercise intensity: COVID-19:80% to 76%;
Control: 82%-77%). These decreases were statistically
significant in both groups for peak heart rate (p=0.017), but not
statistically significant for average heart rate and relative
exercise intensity (p=0.095; p=0.091).
Following the above decreases at one week post return to
training, increases in peak and average heart rate, and relative
exercise intensity (Figure 1) were observed
in both groups from the second to the
fourth week post return to training (Mean
peak heart rate: COVID-19: 158 bpm to 172
bpm; Control: 160 bpm to 177 bpm. Mean
average heart rate: COVID-19: 140 bpm to
149 bpm; Control: 138 bpm to 150 bpm.
Relative exercise intensity: COVID-19: 76%
to 80%; Control: 77% to 84%) These
increases were statistically significant in
both groups for all three variables (p<0.04),
and variables increased close to their
original values pre-training interruption.
No between-group differences were found
for peak heart rate, average heart rate and
relative exercise intensity (p=0.182;
p=0.360; p=0.338).
Maximum and average speed
Maximum and average speed were
analysed separately for runners and
cyclists due to the difference in the nature
of training modality (cyclists’ speed is
naturally faster than runners’ speed).
Cyclists’ maximum and average speeds
were omitted from analysis due to a very
small sample size as a result of missing
data values (n=6).
A non-statistically significant decrease in
maximum and average speed was
observed (increase in min/km) for runners
in both groups, at one week post return to
training, compared to one week pre-
training interruption (Mean maximum
speed: COVID-19: 4.36 min.km-1 to 5.99
min.km-1; Control: 5.41 min.km-1 to 5.98
min.km-1; p=0.07). Mean average
speed:COVID-19: 6.45 min.km-1 to 9.26
min.km-1; Control: 7.40 min.km-1 to 8.98
min.km-1; p=0.066). This was followed by a
non-statistically significant increase in
maximum and average speeds at Weeks
two to four post return to training. The
COVID-19 group had a faster average
speed than the control group pre-training
interruption (Figure 2.). However, no
statistically significant group effect was
found at any point in training (p=0.132).
Fig. 1. Change in relative exercise intensity of COVID-19 and control groups four weeks pre
interruption to four weeks after return to training post interruption; *statistical significant
differences in relative exercise intensity between one week post return to training, and two,
three and four weeks post return to training (p=0.03); TI: training interruption period; error
bars 95% confidence interval.
Fig. 2. Change in average speed of COVID-19 and control groups four weeks pre-
interruption to four weeks after return to training post interruption. TI: Training
interruption period; error bars 95% confidence interval.
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Training time and distance
A statistically significant decrease in training time (p<0.001)
and distance (p=0.002) was observed in the control group, one
week post return to training compared to one week pre-
training interruption (Time trained: 291 min to 59 min;
Distance: 77 km to 23 km). In contrast, a non-statistically
significant decrease in time and distance was observed in the
COVID-19 group, at one week post return to training compared
to one week pre-training interruption (Time: 153 min to 109
min; Distance: 33 km to 14 km). Additionally, the distance and
time trained at one week pre-training
interruption were statistically significantly
different between the two groups (p=0.002;
p=0.003) (Figures 3 and 4).
The decreases in both groups were
followed by increases in time and distance
trained from Week two to four post return
to training. However, these increases were
not statistically significant for either group.
For the COVID-19 group, the participants
appeared to recover their pre-interruption
training time and distance levels.
However, the control group’s training time
and distance remained lower than their
pre-training interruption values (Figures 3
and 4).
Discussion
Questionnaire
COVID-19 clinical presentations
The findings of this study show that the
COVID-19 clinical presentation in
recreational runners and cyclists was mild
to moderate (90%) with fatigue, body ache
and headache being the most common
symptoms, and 0-2 weeks being the most
frequent symptom duration. This clinical
presentation correlates with findings in
previous studies on athletes post COVID-
19 infection. Studies by Schwellnus et al.[10]
and Hull et al.[11] both observed headache
and fatigue as one of the most prevalent
COVID-19 symptoms in athletes, with
athletes having predominantly mild illness
severity.[10,11] A possible reason for the
milder illness severity found in athletes is
that regular exercise of moderate intensity
can potentially improve one’s immune
response to infection, decrease
inflammation, and reduce the risk of
metabolic conditions, such as diabetes and
obesity, which are considered risk factors
for severe COVID-19.[12] The correlations
with COVID-19 severity found in this
study also suggest that the more severe the
illness, the longer the training interruption
time, the greater the number of symptoms,
and the longer the symptom duration.
GPS data
In the first week of the return to training,
decreases in peak and average heart rate,
relative exercise intensity, maximum and
Fig. 3. Change in time trained of COVID-19 and control groups four weeks pre-interruption
to four weeks after return to training post interruption; *statistical significant difference in
time trained for the control group, between one week pre-training interruption and one week
post return to training (p<0.001);**statistical significant difference in time trained between
the two groups at one week pre-training interruption (p=0.002) TI: Training interruption
period; error bars 95% confidence interval.
Fig. 4. Change in distance trained of COVID-19 and control groups four weeks pre-
interruption to four weeks after return to training post interruption; *statistical significant
difference in distance trained for the control group, between one week pre-training
interruption and one week post return to training (p=0.002);**statistical significant
difference in distance trained between the two groups at one week pre-training interruption
(p=0.003) TI: Training interruption period; error bars 95% confidence interval.
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average speed, time, and distance were observed for both the
COVID-19 and control groups, compared to one week pre-
training interruption. Because these decreases were observed
in both the COVID-19 and control groups, this suggests that
there was no specific ‘COVID-19’ effect on training activity
post infection in this group of athletes. The above decreases
were then followed by increases in all training variables at
Weeks two to four post return to training.
A possible reason for the decreases in the measured training
variables at Week one post return to training, could be a
conscious choice of the athletes to start training at a lower
volume and intensity compared to before the COVID-19
infection and training interruption. This hypothesis is further
strengthened by the ability of athletes to increase their
training values back to pre-training interruption values,
suggesting a maintained exercise ability. Furthermore, 58% of
participants in the COVID-19 group said that they followed
the guidelines for return to training from their doctors, which
could also be a reason for their gradual return to training.
Based on the results, it could be suggested that following a
gradual return to training post COVID-19 infection is a safe
and beneficial way to approach returning to training, as
participants were able to return to their original levels of
training by four weeks post training interruption.
In contrast, the control group did not return to their original
values for time and distance trained, with values being lower
than pre-training interruption values. This could possibly be
due to the control group having a longer time off than the
COVID-19 group (33 ± 11 days vs 20 ± 13 days, p=0.014), thus
making it more difficult to return to original values pre-
training interruption. This could potentially be due to a
detraining effect. Detraining is the partial or complete reversal
of physiological adaptations induced from training, due to a
reduction or cessation of training stimuli.[13] However, the
peak and average heart rates and relative exercise intensity
after the training interruption remained similar to the pre-
training interruption values. One would expect an increase in
heart rate if detraining occurred. Therefore, it is more likely
that the control group was less motivated to return to their
original levels of exercise due to the prolonged time off
training, as opposed to a detraining effect.
In conclusion, the data above suggest that COVID-19
infection is associated with an interruption in training in
recreational runners and cyclists. However, COVID-19 did
not have a more serious effect on return to training compared
to other forms of training interruption.
Study limitations
This study has several limitations that may influence the
validity of the results. Many of the changes in training
variables over time were not statistically significant, with the
confidence intervals of training values being wide. This is
most likely due to the small sample sizes. Furthermore, the
study did not include any objectively physiologically
measured data, such as data from cardiopulmonary exercise
testing. Researchers were also limited by the data provided by
the participants, which contained a limited number of training
variables. Having variables such as resting heart rate and time
trial data would have been beneficial. Additionally, the
training load pre-training interruption and the training
interruption time of the COVID-19 and control groups were not
matched, with the control group having a larger time and
distance trained pre-training interruption and a longer training
interruption time. Additionally, GPS wearable heart rate
measurements are known to often be inaccurate when the
optical wrist-based pickup is used rather than a chest strap.[14]
Furthermore, equations used to calculate age-corrected
maximum heart rate could result in overestimations or
underestimations of readings.[15]
Other limitations include the small sample size of
participants, which further decreased during the analysis due
to missing data values. In addition, the control group’s sample
size is much smaller than the COVID-19 group, thus decreasing
the comparator group effect. Despite its limitations, this study
shows the value of using GPS wearable training data to
determine the effect of COVID-19 on the training activity of
athletes post infection. Future studies could improve on the
current study by recruiting a larger number of participants,
including resting heart rate and time trial data and matching
the training loads and interruption times of cases and controls.
Conclusion
To our knowledge, this is the first study investigating the effect
of COVID-19 on the training activity of recreational athletes
using GPS data. Most participants had mild to moderate
COVID-19, with associations found between COVID-19
severity and number of symptoms, symptom duration, and
training interruption time. COVID-19 was also associated with
a self-reported training interruption time of two-four weeks. At
one week post training interruption decreases in peak and
average heart rate, relative exercise intensity, maximum and
average speed, time and distance trained were observed for
both the COVID-19 and control groups. This was followed by
an increase in these variables between two to four weeks’ post
return to training in both groups. The decreases in training
variables were observed for both groups, thus eliminating the
possibility of a specific ‘COVID-19 effect’ on training activity
post infection. A possible reason for the pattern of changes
observed in training variables post COVID-19 could be
participants deliberately returning to exercise at lower volumes
and intensities in order to return to training safely. The study
demonstrates the value of using GPS wearable device data to
evaluate athletes’ training activity post training interruptions.
However, due to the many limitations of the study, the results
should be taken with caution, with more research being
required to further expand on the study's results.
Conflict of interest and source of funding: The authors declare
no conflict of interest. This work is based on the research
supported wholly by the National Research Foundation of
South Africa (NRF) (MND200804549962).
Acknowledgements: The authors would like to extend their
gratitude to the University of Cape Town and the NRF for the
enablement of this study. Additionally, we would like to thank
the participants for completing the study questionnaire and
sharing their GPS data for analysis.
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7 SAJSM VOL. 34 NO.1 2022
Author contributions:
All authors contributed to the design of this research and the
writing of the article ((i) conception, design, analysis, and
interpretation of data; (ii) drafting or critical revision for
important intellectual content; and (iii) approval of the
version to be published.
References
1. World Health Organisation. World Health Organisation
Coronavirus (COVID-19). Geneva: World Health Organisation,
2021. https://www.afro.who.int/health-topics/coronavirus-
covid-19 (accessed 7 April 2021)
2. Metzl JD, McElheny K, Robinson JN, et al. Considerations for
return to exercise following mild-to-moderate COVID-19 in the
recreational athlete. HSS J 2020;16(Suppl 1):102–107. [doi:
10.1007/s11420-020-09777-1] [PMID: 32837412]
3. Salman D, Vishnubala D, Le Feuvre P, et al. Returning to
physical activity after covid-19. BMJ 2021;372 m4721.
[http://dx.doi.org/10.1136/bmj.m4721] [PMID: 33419740]
4. Wilson MG, Hull JH, Rogers J, et al. Cardiorespiratory
considerations for return-to-play in elite athletes after COVID-
19 infection: a practical guide for sport and exercise medicine
physicians. Br J Sports Med 2020;54:1157–1161. [doi: 10. 1136/
bjsports- 2020- 102710] [PMID: 32878870]
5. Milovancev A, Avakumovic J, Lakicevic N, et al.
Cardiorespiratory fitness in volleyball athletes following a
COVID-19 infection: a cross-sectional study. Int J Environ Res
Public Health 2021;18(8):4059. [doi: 10.3390/ijerph18084059]
[PMID: 33921458]
6. Komici K, Bianco A, Perrotta F, et al. Clinical characteristics,
exercise capacity and pulmonary function in post-COVID-19
competitive athletes. J Clin Med 2021;10(14):3053. [doi:
10.3390/jcm10143053] [PMID: 34300219]
7. Nes BM, Janszky I, Wisløff U, et al. Age-predicted maximal
heart rate in healthy subjects: the HUNT fitness study. Scand J
Med Sci Sports 2013;23(6):697-704. [doi: 10.1111/j.1600-
0838.2012.01445.x] [PMID: 22376273]
8. Löllgen H, Bachl N, Papadopoulou T, et al. Recommendations for
return to sport during the SARS-CoV-2 pandemic. BMJ Open
Sport Exer Med 2020;6(1):e000858 [doi:10.1136/bmjsem-2020-
000858] [PMID: 34192007]
9. National Institutes of Health. Clinical Spectrum of SARS-CoV-2
Infection. National Institute of Health 2021;
https://www.covid19treatmentguidelines.nih.gov/overview/clini
cal-spectrum/(accessed August 2021)
10. Schwellnus M, Sewry N, Snyders C, et al. Symptom cluster is
associated with prolonged return-to-play in symptomatic
athletes with acute respiratory illness (including COVID-19): a
cross-sectional study-AWARE study I. Br J Sports Med 2021;
55(20): 1144-1152. [doi: 10.1136/bjsports-2020-103782] [PMID:
33753345]
11. Hull JH, Wootten M, Moghal M, et al. Clinical patterns, recovery
time and prolonged impact of COVID-19 illness in international
athletes : the UK experience. Br J Sports Med 2022; 56(1): 4-11.
[doi:10.1136/ bjsports-2021-104392] [PMID: 34340972]
12. Brandenburg JP, Lesser IA, Thomson CJ, et al. Does higher self-
reported cardiorespiratory ftness reduce the odds of
hospitalization from COVID-19? J Phys Act Health 2021;18
(7):782–788. [doi: 10.1123/jpah.2020-0817] [PMID: 33984837]
13. Chen YT, Hsieh Y-Y, Ho J-Y, et al. Two weeks of detraining
reduces cardiopulmonary function and muscular fitness in
endurance athletes. Eur J Sport Sci 2022; 22(3): 399-406. [doi:
10.1080/17461391.2021.1880647][ PMID: 33517866]
14. Wang R, Blackburn G, Desai M, et al. Accuracy of wrist-worn
heart rate monitors. JAMA Cardiol 2017; 2(1):104–106. [DOI:
10.1001/jamacardio.2016.3340] [PMID: 27732703]
15. Nikolaidis PT, Rosemann T, Knechtle B. Age-predicted maximal
heart rate in recreational marathon runners: A cross-sectional
study on Fox’s and Tanaka’s equations. Front Physiol 2018; 9: 226.
[doi: 10.3389/fphys.2018.00226] [PMID: 29599724]