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Accuracy of swimming wearable watches for estimating energy expenditure

Authors:
  • Korea institute of Sport Science
Mihyun Lee1, Hyojin Lee2, & Saejong Park3*
1Sungkyul University, Gyeonggi-do, Korea
2Yongin University, Gyeonggi-do, Korea
3Korea Institute of Sport Science, Seoul, Korea
Abstract
With the recent installation of waterproof function on wearable watches, various sports activities
including walking, running and even swimming are monitored. Commercially available swimming
wearable watches automatically identified stroke type, swim distance, stroke counts and energy
expenditure (EE). Although the accuracy of estimating EE of walking, bilking and activities of daily life
on activity monitors have been evaluated, it has not been examined for swimming. Thus, the purpose of
the study was to evaluate the accuracy of estimating EE for swimming wearable watches (Apple Watch
S2, Apple and Garmin Finex 3HR, Garmin). A total of 78 swimmers aged 20-59 years (female: 48%)
participated in the study. All the participants wore Apple and Garmin and completed a set of swimming
protocol comprising various speeds (0.4, 0.6, 0.8, 1.0, 1.2 m/s). At each swimming speed they were
asked to swim for four minutes. Lap counts, stroke counts and energy expenditure (EE) from the Apple
and Garmin were evaluated with the criterion measures. Lap counts and stroke counts were directly
counted by the research assistant. The portable respiratory gas analyzer (K4b2, Cosmed, Italy) and a
swimming snorkel (Aqua Trainer Snorkel, Cosmed, Italy) was used as the criterion measure of EE. The
mean absolute percentage error (MAPE) of lap counting and stroke counts at various swimming speed
were within 10% for Apple (lap counts: 0.5-6.1%, stroke counts: 6.2-9.3%) and about 20% for Garmin
(lap counts: 0-20.6%, stroke counts: 6.8-17.6%). However, the MAPE of EE was higher for Apple
(17.1%-151.7%) than for Garmin (17.9%-32.7%). The accuracy of estimating EE tended to improve with
increasing swimming speed for both Apple and Garmin. The EEs from Apple were outside the
equivalence zone except for at 1.2 m/s and were overestimated compared to the criteria. On the other
hand, EEs from Garmin were within the equivalence zone at all speeds except for 1.2 m/s. In
conclusion, Apple and Garmin wearable watches accurately measure lap counts and stroke counts.
However, the accuracy of estimating EE are poor at slow to medium swimming speed. Further
improvement is needed to estimate energy expenditure of swimming at various speed.
Key words: Wearable device, Swimming, Lap count, Stroke count, Energy expenditure
Most wearable watches on the market have physical
Submitted : 28 March 2018
Revised : 24 May 2018
Accepted : 11 June 2018
Correspondence : seajpark@sports.re.kr
activity monitoring functions such as step counts, heart rate
and energy expenditure (EE) etc. Wearable watches are
used as tools to maintain and/or promote health by
motivating physical activities (Chowdhury, Western,
Nightingale, Peacock, & Thompson, 2017). Wearable-based
behaviors changes may be effective to promote physical
International Journal of Applied Sports Sciences ISSN 2233-7946 (Online)
2018, Vol. 30, No. 1, 80-90. ISSN 1598-2939 (Print)
https://doi.org/10.24985/ijass.2018.30.1.80 Korea Institute of Sport Science
Accuracy of Swimming Wearable Watches
81
activity with instantaneous feedback and monitoring
exercise intensity. As the wearable devices are utilized as
intervention tools in clinical and research setting, the
accuracy of the wearables are important. Accordingly,
many studies on the accuracy of wearable devices are now
emerging. Previous studies were limited to the validity of
wearable devices that included measures of step counting,
heart rate and EE while walking and running. These
studies have reported that counting is relatively accurate
(Case et al., 2015), however, the accuracy of heart rate and
EE vary depending on the device and the intensity of
exercise (Chowdhury et al. 2017; Dooley et al. 2017;
Wallen et al., 2016; Stahl et al., 2016).
Swimming is one of the most popular cardiorespiratory
exercise involving rhythmic movements of large muscles
along with walking and running. Swimming can be
enjoyed through the lifespan regardless of age and health
status. Swimming is widely recommended not only for
healthy people but also for overweight/obese people,
patients with arthritis, and elderly persons with joint
problems for health (American College of Sports Medicine,
2013). With the recent installation of waterproof function
on wearable watches, swimming performance is easily
monitored. The commercial wearable watches include
features such as stroke counts and swim speed, and types
of stroke performed atomically. Swimming wearable
watches are primarily aimed for recreational swimmers as
opposed to elite swimmers. Thus, the accuracy of basic
features such as lap counts, stroke counts and EE are
prominent to be used as an intervention tool for
recreational swimmers to promote physical activity.
Mooney et al. (2017) investigated the accuracy of the
swimming movement recognition (stroke type, swim
distance, stroke rate, stroke length, average speed etc.) of
wearable devices. The accuracy of Garmin SwimTM and
Finis Swimsense
was tested on national swimmers, and
the results were reported to be similar. The authors,
however, proposed a study on recreational swimmers since
their participants were elite swimmers whose swimming
form is outstandingly consistent. Also one of the key
features of wearables, EEs of swimming is not evaluated
yet. Previously, the accuracy of step counts, heart rate and
EE were examined for walking, running, cycling, and
rowing (Brazeau et al., 2011; Case et al., 2015;
Chowdhury et al. 2017; Dooley et al. 2017; Erdogan et al.,
2010; Wallen et al., 2016; Stahl et al., 2016). Therefore,
the purpose of the present study was to evaluate the
accuracy of the information on lap count, stroke count, and
EE provided by wearable devices (Apple and Garmin)
during swimming.
The participants of this study were healthy adults, aged
from 20 to 59 years, free from illnesses or injuries,
unrestricted physical function and able to swim at various
speeds. Exclusion criteria were health risk factors such as
hypertension, or a dental implant because of problems
wearing a snorkel. The five participants (4 male and 1
female) were excluded due to missing data or dropping out
in the experiment. In the study, a total of 78 participants
(40 male and 38 female) were collected for statistical
analysis. The demographic characteristics are shown in
Table 1. All participants were given an overview of the
procedures and signed the Institutional Review Board
approved informed consent document.
The experiment was conducted in a 25 m indoor pool
with the water temperature was 28.8°C. Participants
completed the Physical Activity Readiness Questionnaire
and resting heart rate, blood pressure, body height, weight
and body fat percentage were measured using standard
methods.
All participants wore Apple and Garmin and completed
a set of swimming protocol (0.4, 0.6, 0.8, 1.0, 1.2 m/s)
comprising various speed. An experimental protocol was
chosen based on the intensity used in a previous study
related to swimming EE (Holmer et al., 1972; Pendergast
et al.; 1977).
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Mihyun Lee et al.
N
Metabolic equivalents
Heart rate (bpm)
Rating of perceived
exertion
Resting
78
1.03±0.30
88.61±12.44
8.26±1.82
0.4m/s
46
3.80±0.83
115.26±11.06
9.67±2.09
0.6m/s
75
5.31±1.33
123.21±12.39
10.41±2.25
0.8m/s
68
7.15±1.51
142.00±15.65
12.33±1.99
1.0m/s
53
8.89±1.73
159.35±14.87
15.85±3.20
1.2m/s
10
11.65±1.75
165.20±9.11
16.64±1.80
bpm: beats per minute
Table 2.
Objective and subjective exercise intensity by swimming speed (mean ± SD)
Characteristics
Male (n=40)
Female (n=38)
Total (n=78)
Age (year)
38.7±11.05
39.1±10.67
37.1±10.79
Height (cm)
173.7±5.99
161.0±5.72
167.5±8.66
Weight (kg)
73.9±7.20
57.8±6.20
66.0±10.49
BMI (kg/m2)
24.5±1.85
22.3±1.84
23.4±2.13
Body fat (%)
19.1±5.08
26.1±6.28
22.5±6.67
Swimming experience (year)
8.8±5.97
10.5±5.96
9.6±5.98
50m best record (s)
34.4±5.69
40.35±6.33
37.3±6.67
BMI: body mass index, weight (kg) divided by height (m)2, %BF: %body fat
Table 1.
Demographic characteristics of the participants (mean±SD)
To help the participant maintain appropriate time for
each protocol, a research assistant provided feedback the
time at every 25 m turn from the swimming pool floor.
To standardize the amount of swimming, underwater
swimming was controlled, and the side turn was the
only turn method permitted to use snorkel.
In the present study, the total lap counts and stroke
counts for four minutes of each protocol were used in the
analysis. To use the value of steady-state of each protocol,
the data of the last two minutes (34 minutes) for all
activities were used in data analysis. For the EE of each
wearable watches, EE per minute was calculated by
dividing actual swimming time by minutes.
The descriptive statistics of 78 participants by protocol
including resting are shown in Table 2. Oxygen uptake,
metabolic equivalents, heart rate, and participative RPE
were gradually increased as the speed increased.
Criterion measurement instruments
Criterion lap counts and stroke counts were directly
counted by the research assistant. The lap counts were
swimming back and forth once in a 50m pool (unit: lap
counts/ bout) and stroke counts were one stroke was
considered to be completed when one arm was rotated (unit:
stroke counts/ bout). Bout means individual speed of
swimming consisting of four minutes. The portable
respiratory gas analyzer (K4b2, Cosmed, Italy) and a
swimming snorkel (Aqua Trainer Snorkel, Cosmed, Italy)
was used as the criterion measure of EE. Cosmed K4b2 is
a light weight (925g) measuring instrument worn on the back,
which allows real-time monitoring of oxygen intake, carbon
dioxide emissions, and ventilation rate using the method. The
Aqua Trainer Snorkel is connected to the K4b2 analyzer and
used for real-time gas analysis during swimming. There
liability of both criterion measurement instruments has been
verified by many researchers (Eisenmann, Brisko, Shadrick,
& Welsh, 2003; McLaughlin, King, Howley, Bassett, &
Ainsworth, 2001; Baldarietal., 2013; Keskinenetal., 2003).
The portable gas analyzer was calibrated according to its
manual before data collection.
Accuracy of Swimming Wearable Watches
83
Wearable watches
Among the top five best-selling (international data
corporation; IDC, 2017) wearable watches (Xiaomi, Apple,
Fitbit, Samsung, Garmin), two swim activity monitors, the
Apple Watch S2 and Garmin Finex 3HR. Apple has had a
swimming recognition function since 2016. Apple Watch S2
(Apple Inc, Cupertino, CA, USA), which was used in the
present study, provides information such as activity
metabolism, EE, heart rate, stroke count, lap count, travel
distance, total time, and pace during swimming (Apple, 2017).
Garmin was established in 1989 and specialized in
global positioning system (GPS). It began to produce
exercise-related wearables in 2003, and a swimming-related
function was first installed on the Forerunner 910XT in
2012. The Garmin Finex 3HR (Garmin, Ltd, Schaffhausen,
Switzerland), which was used in the present study,
provides information such as EE, stroke count, travel
distance, exercise duration, average pace, maximum pace,
average speed, top speed, and SWOLF (swim golf) etc.
(Garmin, 2017).
In the present study, lap counts, stroke counts, and EE
from the Apple and Garmin were evaluated with the
criterion measures. But Garmin does not present lap count
information, lap counts were used to calculated by dividing
the total distance traveled in four minutes by 50m.
Others measures
Height was measured with an accuracy of 0.1 cm using
an extensimeter (Jenix, DS-102, Korea). Weight and body
fat percentage were measured to 0.1 kg and 0.1% in light
clothing using a body composition analyzer (Biospace,
InBody 720, Korea). The measured height and weight
information was entered into the user information field of
each wearable watches and the portable respiratory gas
analyzer before the experiment. Heart rate was measured
using a waterproof heart rate chest strap (Polar Electro Oy,
Polar V800, Finland), and the subjective exercise intensity
was measured on the Borg scale immediately after each
protocol.
The analyses were conducted using SPSS ver. 22.0 and
Medcale ver. 14.0, and the specific data processing
methods were as follows.
Means and standard deviations were calculated of
physical characteristics and all relevant data. Two-way
analysis of variance (ANOVA) was used to test the effects
of swimming by speed and the interaction effect between
test instruments, and the p hoc test was performed using
the Bonferroni test. The levels of error observed on the
wearable watches were calculated into mean absolute
percent error (MAPE %) based on the criterion measure:
[|measured valued observed in wearable devices criterion
measures | / criterion measures × 100%].
Mean differencewearable watches using Bland &
Altman plot. In addition, to assess measurement agreement
between criterion measures and the measurements made by
the Apple and Garmin devices, a 95% equivalence test was
performed. For wearable watches to be considered
equivalent to the criterion with 95% precision, equivalence
zone was determined as ± 10% of the criterion mean (Lee,
Kim and Welk, 2014) this. All statistical significance
levels were set at α=.05.
Descriptive statistics of criterion measures of swimming
by speed and lap counts, stroke counts, and EE provided
by Apple and Garmin devices are shown in Table 3. The
results of a two-way ANOVA on the effects of swimming
by speed and the interaction effect between test
instruments showed no statistically significant interaction
effect for lap counts and stroke counts (lap counts: F =
1.359, p = .211, stroke counts: F = .145, p =. 997), but
statistically significant interaction effect (F = 9.395, p <
.001) was found for EE. The post hoc test showed no
significant difference between Garmin and the criteria at
all speeds, but Apple was overestimating compared to the
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Mihyun Lee et al.
N
Device
Raw Score
MAPE (%)
Mean
SD
Mean
SD
Lap counts/bout
0.4m/s
41
Criterion
4.00
.00
Apple Watch S2
3.85
.57
6.10
13.44
Garmin Finex 3HR
3.38
1.05
20.63
22.57
0.6m/s
71
Criterion
5.97
.23
Apple Watch S2
6.00
.17
0.47
2.78
Garmin Finex 3HR
5.60
.81
8.89
12.07
0.8m/s
67
Criterion
7.94
.58
Apple Watch S2
8.04
.66
0.93
4.52
Garmin Finex 3HR
7.69
.97
5.89
10.19
1.0m/s
52
Criterion
9.94
.74
Apple Watch S2
9.88
.90
1.35
4.86
Garmin Finex 3HR
9.78
.95
3.34
7.00
1.2m/s
10
Criterion
12.00
.47
Apple Watch S2
11.78
.67
1.67
3.51
Garmin Finex 3HR
12.00
.53
0
0
Stroke count/bout
0.4m/s
44
Criterion
57.71
14.82
Apple Watch S2
56.30
17.00
9.33
11.95
Garmin Finex 3HR
63.53
18.25
17.59
20.76
0.6m/s
71
Criterion
70.75
13.40
Apple Watch S2
70.10
12.80
6.52
8.18
Garmin Finex 3HR*
76.69
12.11
9.01
12.77
0.8m/s
63
Criterion
86.11
16.02
Apple Watch S2
87.66
17.04
6.67
6.08
Garmin Finex 3HR
90.93
15.04
7.65
6.43
1.0m/s
51
Criterion
109.00
21.43
Apple Watch S2
108.51
23.39
6.23
7.06
Garmin Finex 3HR
114.06
17.85
7.95
8.51
1.2m/s
10
Criterion
117.32
17.20
Apple Watch S2
117.40
10.44
6.81
2.46
Garmin Finex 3HR
120.60
10.61
6.81
3.37
Energy expenditure (kcal/min)
0.4m/s
33
Criterion
3.78
1.00
Apple Watch S2
9.48
2.11
151.66
55.16
Garmin Finex 3HR
3.49
1.30
32.74
24.95
0.6m/s
60
Criterion
5.79
1.89
Apple Watch S2
10.88
2.24
100.09
55.25
Garmin Finex 3HR
6.28
1.74
30.50
30.23
0.8m/s
59
Criterion
8.06
2.22
Apple Watch S2
12.13
2.52
61.26
34.79
Garmin Finex 3HR
8.43
1.96
24.82
20.48
1.0m/s
41
Criterion
10.59
2.71
Apple Watch S2
13.03
2.56
32.70
22.51
Garmin Finex 3HR
10.97
1.91
18.94
18.70
1.2m/s
9
Criterion
14.53
2.46
Apple Watch S2
14.41
2.72
17.71
9.33
Garmin Finex 3HR
14.86
3.48
17.93
15.67
MAPE (%): mean absolute percentage error, Bout means individual speed of swimming consisting of four minutes.
Table 3.
Descriptive statistics of criteria and wearable watches
Accuracy of Swimming Wearable Watches
85
Figure 1.
Mean absolute percentage error (MAPE; %) for
wearable watches based on intensity: (A) Lab counts, (B)
Stroke counts, (C) Energy expenditure.
Apple: Apple Watch S2, Garmin: Garmin Finex HR
criteria and Garmin (p < .001).
Figure 1 shows criterion measures by swimming speeds
compared to the MAPE of lap counts, stroke counts, and
EE provided by Apple and Garmin. For lap counts and
stroke counts, the MAPE of Apple was within 10% (lap
counts: 0.5-6.1%, stroke counts: 6.2-9.3%) while that of
Gamin was about 21% (lap counts: 0-20.6%, stroke counts:
6.8-17.6%), and both devices showed higher error rates
when speed was slower. On the other hand, Apple
overestimated EE at all speeds in that the MAPE at the
speed of 0.4 m/s-1.0 m/s were vigorous (32.70%-151.66%)
but the MAPE (17.93%) became lower at the speed of
1.2m/s. Garmin showed MAPE of 17.9%-32.7%, and both
wearable watches showed a tendency of gradually
decreasing the MAPE as the intensity of exercise
increased.
The 95% confidence interval of mean difference limits
was determined with a Bland-Altman plot created by using
criterion measures and averages and average differences of
lap counts, stroke counts, and EE provided by the Apple
and Garmin devices (Figure 2). For lap counts, Apple
(Mean difference = 0, Low LoA-Upper LoA: -0.8-0.8)
showed a confidence interval with a smaller deviation than
Garmin (Mean difference = -0.3, Low LoA-Upper LoA:
-2.0 - 1.4). For the stroke counts, Apple (Mean difference
= -0.4, Low LoA-Upper LoA: -16.5 - 15.7) also showed a
confidence interval with a smaller deviation than Garmin
(Mean difference = 4.9, Low LoA-Upper LoA: 21.6-
-11.8). For EE, however, Garmin (Mean difference = 0.3,
Low LoA-Upper LoA: -4.1 - 4.7) showed a confidence
interval with a smaller deviation than Apple (Mean
difference = 4.2, Low LoA-Upper LoA: -0.7 9.0).
To test measurement agreement between criterion
measures and the measurements of the Apple and Garmin
a 95% equivalence test was performed Figure 3. Lap
counts were within the equivalence limits (criterion
measure ± 10%) except for 0.4 m/s for Garmin and the
stroke counts was within the equivalence limits except for
Garmin at 0.4 m/s and 0.6m/s.
In the case of Apple, EE was within the equivalence
zone (90% CI = 13.08-15.93) at 1.2 m/s, but it was not
within the equivalence zone at other speeds and
overestimated the EE compared to the criteria. In the case
of Garmin, EE was within the equivalence zone at all
speeds except for 1.2 m/s at which it was outside the
equivalence zone.
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Mihyun Lee et al.
A. Lap counts
B. Stroke counts
C. Energy expenditure
Figure 2.
Bland & Altman plot for wearable watches: (A) Lab counts, (B) Stroke counts, (C) Energy expenditure.
Apple : Apple Watch S2, Garmin : Garmin Finex HR
Accuracy of Swimming Wearable Watches
87
Figure 3.
Equivalence test (95% CI) for wearable watches :
(A) Lab counts, (B) Stroke counts, (C) Energy expenditure.
Apple : Apple Watch S2, Garmin : Garmin Finex HR, Bout
means individual speed of swimming consisting of four
minutes.
Swimming is a representative aerobic exercise and many
international organizations related to health recommend
swimming as an exercise to improve and prevent
hypertension and cardiovascular diseases. Owing to
technological development, wearable watch have been
recently released with which feedback on the amount of
exercise can be checked in real time during swimming.
The goal of ordinary swimmers is health promotion and
weight control, and objective evaluation of the amount of
physical activities can be useful for achieving such a goal.
Until now however, research has been lacking on the
accuracy of the stroke counts, lap counts, and EE at
different speeds, which are the main indicators of the
function of wearable swimming recognition devices.
The accuracy of swimming related data (lap count,
stroke count, EE) by both Apple and Garmin wearable
watches used in the present study was improved as the
intensity of exercise was increased. In addition, the
accuracy of the information was high for lap count and the
stroke count while the accuracy of EE varied depending on
the speed and the type of wearable watches. Recently,
wearable devices are being used as tools to maintain health
by measuring personal activities and increasing motivation
to participate in physical activities (Chowdhury, Western,
Nightingale, Peacock, & Thompson, 2017). Accordingly,
the necessity of research on the accuracy of wearable
devices that provide information on various physical
activities is increasing. The present study evaluated the
accuracy of swimming information provided by wearable
watches while swimming. The significance of the findings
from academic and practical perspectives based on the
results of the present study and other related studies can be
comparatively analyzed as follows.
Dooley et al., (2017) evaluated the accuracy of EE on
treadmill activities measured by Fitbit, Apple, and Garmin
devices by exercise intensity. The results were consistent
with the findings of the present study in that the accuracy
improved as the exercise intensity increased and all three
wearable devices overestimated compared to the criterion
test instrument. The findings of the study by Wallen et al.
(2016) who evaluated EE measured by wearables such as
Apple Watch, Fitbit Charge HR, Samsung Gear S, and
Mio Alpha, however, is in contrast to the findings of the
present study in that all test instruments in their study
tended to underestimate. Because Wallen et al. (2016)
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Mihyun Lee et al.
analyzed the results of 58-minute circuit training (sitting,
standing, treadmill exercise stress test, cycle exercise)
without differentiating exercise intensity, it is impossible to
interpret which part of the protocol caused the difference.
In a study (Chowdhury et al. 2017) evaluating the EE
accuracy of wearable devices (Microsoft Band, Apple
Watch, Fitbit Charge HR, Jawbone UP 24, Bodymedia
Armband, Actiheart) in a laboratory and in daily life, all
devices were reported to underestimate compared to the
criterion in a 24-hour daily life setting. However, in a
laboratory environment, the measurement by Apple was
similar to the criterion EE and the error rate decreased in
vigorous intensity activities such as jogging. In summary,
the accuracy of EE so far measured by commercialized
wearable devices has varied depending on the research
design, and the accuracy increased when the intensity of
the activity increased.
Apple Watch disclosed on its home page that lap count,
travel distance, and EE are estimated during indoor
swimming based on an accelerometer (Apple, 2017).
Among studies that used accelerometers to predict the
amount of swimming, Mooney et al., (2015) investigated
the trend of using sensor technology during the training of
elite swimmers, and found that the most frequent location
of sensors during swimming was the wrist and the waist.
In addition, Nordsborg et al., (2014) investigated the
relationship between reference oxygen uptake and the size
of the triaxial accelerometer worn on the wrist, waist, and
ankle. They found the strength of correlation in the order
of wrist (r = 0.77), ankle (r = 0.73), and waist (r = 0.46)
and were able to confirm the possibility of using wearable
devices for the prediction of swimming EE.
The research protocol of Nordsborg et al., (2014) used
the speed of 1.6 m/s for elite swimmer and 1.3 m/s for
general participants, which was faster than the speed
employed in the protocol of the present study. Ganzevles
et al., (2017) compared the reliability and validity of lap
time and the stroke count by attaching an accelerometer to
the back of swimming athletes and comparing the
measurements to video recordings. They found the errors
for lap time and the stroke count to be ±2% and ±1%,
respectively, and reported that accelerometers can be
reliably used as a means to measure the amount of
exercise during swimming, which partially agrees with the
findings of the present study. Both studies (Nordsborg et
al., 2014; Ganzevles et al., 2017) conducted accuracy test
at higher speeds than the protocol of the present study
because the research protocol of Ganzevles et al. (2017)
also used elite swimmers who could swim freestyle at a
speed of 1.32 m/s. A review on protocols used in previous
studies related to EE in swimming showed that Montpetiti
et al., (1988) used 1.0 - 1.25 m/s speed protocol and
presented swimming EE using speed. Barbosa et al.,
(2006) tested differences in EE by the swimming style and
the study protocol was also performed at 1.0 m/s - 1.6 m/s.
Other studies on swimming EE mostly employed elite
swimmers and relatively high swimming speed (Craig &
Pendergast, 1979; Zamparo et al., 2005). The reason for
low accuracy at light intensity activities appears to be
attributable to differences in swimming skills of the
participants, the characteristics of the instrument (such as
accelerometer) installed on wearable devices, and the
limitations in the internal algorithms.
Mooney et al., (2017) who evaluated the accuracy of
wearable devices in the recognition of swimming motion
stated that even though the accuracy of distance estimation
was accurate, the accuracy of stroke speed, stroke length,
and average speed was affected by lap time and the stroke
count, which makes it appropriate for general swimmers to
use it as a reference value but accuracy needs to be
improved to use them to improve performance.
In conclusion, the accuracy of the Apple was higher
than that of the Garmin in lap count and the stroke count,
but the performance of both wearable watches was similar
if lower speed was excluded. The accuracy of the
evaluation of EE of Apple was lower than that of Garmin
and the accuracy was worse at lower speed, which
indicates the necessity of continuous improvement in the
algorithm of swimming EE of wearable devices.
Accordingly, the information on EE of wearable watches
while swimming should be selectively used according to
the situation.
Accuracy of Swimming Wearable Watches
89
This study has some limitations. First, the accuracy of
swimming EEs was limited to freestyle. Because the
protocol was selected with consideration given to the
maximum freestyle speed of the participants for 50 m,
accuracy for speeds faster than 1.2 m/s were not evaluated
because there was no participant who could perform at a
speed higher than this. In previous studies, participants
were elite swimmers which might influence high accuracy
of estimating energy expenditure. Future studies are needed
using recreational speeds. In addition, wearable watches
were limited to two types to minimize discomfort to
participants. Considering the fact that wearable watches,
which provide swimming information, are continuously
developing owing to recent technological development
device types, speed, and swimming skills (elite vs.
recreational swimmers) is needed in future studies.
This is the first study to investigate the accuracy of
estimating energy expenditures from two commercially
available wearable watches (Garmin and Apple) of
freestyle swimming on swimmers. The error rate of lap
counting and stroke counts at various swimming speed
were within 10% for Apple and about 20% for Garmin.
The criterion measurements and a 95% equivalence test
showed that the lap counts and the strokes counts recorded
by Apple were within the equivalence zone for all of the
exercise intensities measured. Bland-Altman plot showed
confidence intervals with relatively small deviations in lap
counts and the stroke counts for Apple, and EE for
Garmin. But the error rate of estimating energy
expenditure was higher for Apple than for Garmin. The
EEs of most swimming speeds from Apple were outside
the equivalence zone except for at 1.2 m/s and were
overestimated compared to the criteria. On the other hand,
EEs of various swimming speeds from Garmin were within
the equivalence zone except for 1.2 m/s. In conclusion,
Apple and Garmin wearable watches accurately measure
lap counts and stroke counts. However, the accuracy of
estimating EE are poor at slow to medium swimming
speed. Further improvement is needed to estimate energy
expenditure of swimming at various speed.
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... Figure 1 depicts the PRISMA flow diagram for identifying, screening, and checking eligi- bility, which then helps to determine inclusion of the articles. A total of 18 articles [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] were included in the qualitative synthesis. studies which report on the use of swimming wearables streaming data to provide realtime feedback. ...
... Figure 1 depicts the PRISMA flow diagram for identifying, screening, and checking eligibility, which then helps to determine inclusion of the articles. A total of 18 articles [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] were included in the qualitative synthesis. ...
... From the 18 articles included for qualitative synthesis, eight (44.4%) [21,[23][24][25]30,31,34,38] assessed wearables exclusively in front crawl, six (33.3%) [26,27,29,32,33,35] assessed wearables exclusively in all four swim strokes, two assessed wearables for the tumble turn in front crawl (11.1%) [36,37], one just assessed wearables in breaststroke (5.5%) [22], and another one assessed wearables in butterfly stroke (5.5%) [28]. Overall, 177 swimmers were recruited in all studies (103 males, 62 females, and 12 that the authors failed to note the sex of the participants). ...
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... 8 Hence, a more affordable, convenient to be used in water activities, applicable in real life and validated device, Garmin Fitness Forerunner 935 was selected to evaluate the VO 2 max among the swimmer. 9 Recently, many wearable watches have emerged in monitoring physical activity functions as well as in clinical and research settings. 10 Previous studies on wearable watches were limited to the validity of energy expenditure, stroke counts, swimming speed, swimming style recognition, lap counting 10,11 as well as the measurement of VO 2 max on the land-based settings. ...
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... 8 Hence, a more affordable, convenient to be used in water activities, applicable in real life and validated device, Garmin Fitness Forerunner 935 was selected to evaluate the VO 2 max among the swimmer. 9 Recently, many wearable watches have emerged in monitoring physical activity functions as well as in clinical and research settings. 10 Previous studies on wearable watches were limited to the validity of energy expenditure, stroke counts, swimming speed, swimming style recognition, lap counting 10,11 as well as the measurement of VO 2 max on the land-based settings. ...
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Maximal aerobic capacity (VO 2 max) is one of the important factors that influence swimming performance. Currently, the Garmin Forerunner Fitness Watch 935 used to measure VO 2 max are expensive, require skilled-trained personnel, not feasible for large-scale use, and land-based, which will not be accurate in measuring water-based activity. In order to measure the swimming performance, there is a need for an affordable, feasible, and reliable device. Therefore, the current study aimed to examine the intra-rater reliability of Garmin Forerunner Fitness Watch 935 accuracy in measuring the VO 2 max among collegiate swimmers during the 200m swimming task. The VO 2 max measurement of 10 collegiate swimmers was taken with Garmin Forerunner for two trials. The intra-class correlation coefficient (ICC), standard error of measurements (SEMs), and Bland-Altman plot was used in the current study to establish the inter-rater reliability measurement. The intra-rater reliability of Garmin Forerunner showed high reliability and accuracy with an intra-class correlation coefficient (2,1) of 0.869 and standard error of measurements of 0.231 ml/kg/min. Further, the results were strengthened with Bland-Altman plot showed an acceptable agreement between the two trials. The Garmin Forerunner would be a simple, objective and useful device to be used by physiotherapists, trainers and other sports-related disciplines to assess and improve the swimming performance by targeting the heart rate and VO 2 max.
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Background Physical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown. Objective The objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities. MethodsA total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE). ResultsFor the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P
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Background: Consumer-wearable activity trackers are electronic devices used for monitoring fitness- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-wearable activity trackers (Fitbit and Jawbone) and their ability to estimate steps, distance, physical activity, energy expenditure, and sleep. Methods: Searches included only full-length English language studies published in PubMed, Embase, SPORTDiscus, and Google Scholar through July 31, 2015. Two people reviewed and abstracted each included study. Results: In total, 22 studies were included in the review (20 on adults, 2 on youth). For laboratory-based studies using step counting or accelerometer steps, the correlation with tracker-assessed steps was high for both Fitbit and Jawbone (Pearson or intraclass correlation coefficients (CC) > =0.80). Only one study assessed distance for the Fitbit, finding an over-estimate at slower speeds and under-estimate at faster speeds. Two field-based studies compared accelerometry-assessed physical activity to the trackers, with one study finding higher correlation (Spearman CC 0.86, Fitbit) while another study found a wide range in correlation (intraclass CC 0.36-0.70, Fitbit and Jawbone). Using several different comparison measures (indirect and direct calorimetry, accelerometry, self-report), energy expenditure was more often under-estimated by either tracker. Total sleep time and sleep efficiency were over-estimated and wake after sleep onset was under-estimated comparing metrics from polysomnography to either tracker using a normal mode setting. No studies of intradevice reliability were found. Interdevice reliability was reported on seven studies using the Fitbit, but none for the Jawbone. Walking- and running-based Fitbit trials indicated consistently high interdevice reliability for steps (Pearson and intraclass CC 0.76-1.00), distance (intraclass CC 0.90-0.99), and energy expenditure (Pearson and intraclass CC 0.71-0.97). When wearing two Fitbits while sleeping, consistency between the devices was high. Conclusion: This systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for energy expenditure and sleep. The evidence reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for certain Fitbit models. As new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in research settings.
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This paper explores the application of video-based methods for the analysis of competitive swimming performance. A systematic search of the existing literature was conducted using the following keywords: swim*, performance, analysis, quantitative, qualitative, camera , video on studies published in the last five years, in the electronic databases ISI Web of Knowledge, PubMed, Science Direct, Scopus and SPORT discus. Of the 384 number of records initially identified, 30 articles were fully reviewed and their outcome measures were analysed and categorised according to (i) the processes involved, (ii) the application of video for technical analysis of swimming performance and (iii) emerging advances in video technology. Results showed that video is one of the most common methods used to gather data for analysing performance in swimming. The process of using video in aquatic settings is complex, with little consensus amongst coaches regarding a best-practice approach, potentially hindering usage and effectiveness. Different methodologies were assessed and recommendations for coaches, sport scientists and clinicians are provided. Video is an extremely versatile tool. In addition to providing a visual record, it can be used for qualitative and quantitative analysis and is used in both training and competition settings. Cameras can be positioned to gather images both above and below the water. Ongoing advances in automation of video processing techniques and the integration of video with other analysis tools suggest that video analysis will continue to remain central to the preparation of elite swimmers.
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Background Heart rate (HR) monitors are valuable devices for fitness-orientated individuals. There has been a vast influx of optical sensing blood flow monitors claiming to provide accurate HR during physical activities. These monitors are worn on the arm and wrist to detect HR with photoplethysmography (PPG) techniques. Little is known about the validity of these wearable activity trackers. Aim Validate the Scosche Rhythm (SR), Mio Alpha (MA), Fitbit Charge HR (FH), Basis Peak (BP), Microsoft Band (MB), and TomTom Runner Cardio (TT) wireless HR monitors. Methods 50 volunteers (males: n=32, age 19–43 years; females: n=18, age 19–38 years) participated. All monitors were worn simultaneously in a randomised configuration. The Polar RS400 HR chest strap was the criterion measure. A treadmill protocol of one 30 min bout of continuous walking and running at 3.2, 4.8, 6.4, 8.0, and 9.6 km/h (5 min at each protocol speed) with HR manually recorded every minute was completed. Results For group comparisons, the mean absolute percentage error values were: 3.3%, 3.6%, 4.0%, 4.6%, 4.8% and 6.2% for TT, BP, RH, MA, MB and FH, respectively. Pearson product-moment correlation coefficient (r) was observed: r=0.959 (TT), r=0.956 (MB), r=0.954 (BP), r=0.933 (FH), r=0.930 (RH) and r=0.929 (MA). Results from 95% equivalency testing showed monitors were found to be equivalent to those of the criterion HR (±10% equivalence zone: 98.15–119.96). Conclusions The results demonstrate that the wearable activity trackers provide an accurate measurement of HR during walking and running activities.
Article
Aim. The purpose of this study was to compare the Cosmed K4b(2) portable gas analysis system with the Cosmed Quark b(2) metabolic cart. Methods. Twenty-one subjects attended one testing session that consisted of duplicate measurements of gas volumes and concentrations using both Cosmed gas analysis systems at 3 treadmill work rates; 1) 80 m.min(-1), 0 % grade, 2) 80 m.min(-1), 5 % grade, and 3) 80 m.min(-1), 10% grade. Subjects walked for 3 min at each rate with one of the gas analysis systems attached to the facemask. The order of the procedures was randomized so that one system was used during both phases (1st or 2nd) of each work rate. Results. The results indicated that oxygen consumption (VO2) was significantly higher in the K4b(2) compared to the Quark at 80 m.min(-1), 0 % grade (14.3+/-1.2 vs 13.6+/-1.2 ml.kg(-1).min(-1), respectively), (p<0.01). The fractional concentration of oxygen in expired air was also significantly lower in the K4b(2) at 80 m.min(-1), 0 % grade and 80 m.min(-1), 10 % grade (p<0.05). There were no significant differences between systems for minute ventilation or carbon dioxide production. Despite the small mean bias in mean VO2 values (0.5-1.0 ml.kg(-1).min(-1) higher) in the K4b(2), all individual values were within the limits of agreement (mean difference+/-2 SD) as determined by the Bland-Altman technique. Conclusion. The findings show a minimal bias in respiratory and metabolic parameters during bi-pedal locomotor activities at low to moderate exercise intensities in the two gas analysis systems.