| Research Article |
Open Access |
|
| Validity of a Multi-Sensor Armband for Estimating Energy Expenditure
during Eighteen Different Activities |
| Paige Dudley1, David R Bassett1*, Dinesh John2 and Scott E Crouter3 |
| 1University of Tennessee, Knoxville, USA |
| 2Northeastern University, Boston, USA |
| 3University of Massachusetts, Boston, USA |
| *Corresponding author: |
David R Bassett
Department of Kinesiology,
Recreation, and Sport Studies
The University of Tennessee
1914 Andy Holt
Ave, Knoxville
TN 37996-2700, USA
Tel: 865-974-8766
Fax: 865-974-8981
E-mail: djohn1@kin.umass.edu |
|
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| Received July 13, 2012; Accepted August 27, 2012; Published August 29, 2012 |
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| Citation: Dudley P, Bassett DR, John D, Crouter SE (2012) Validity of a Multi-
Sensor Armband for Estimating Energy Expenditure during Eighteen Different
Activities. J Obes Wt Loss Ther 2:146. doi:10.4172/2165-7904.1000146 |
| |
| Copyright: © 2012 Dudley P, et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
| |
| Abstract |
| |
| Purpose: To examine the validity of an armband physical activity monitor in estimating energy expenditure (EE)
over a wide range of physical activities. |
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| Methods: 68 participants (mean age=39.5 ± 13.0 yrs) performed one of three routines consisting of six activities
(approximately 10 min each) while wearing the armband and the Cosmed K4b2 portable metabolic unit. Routine 1
(n=25) involved indoor home-based activities, routine 2 (n=22) involved miscellaneous activities, and routine 3 (n=21)
involved outdoor aerobic activities. |
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| Results: Mean differences between the EE values in METs (criterion minus estimated) are as follows. Routine
1: watching TV (-0.1), reading (-0.1), laundry (0.1), ironing (-1.3), light cleaning (-0.4), and aerobics (0.4). Routine
2: driving (-0.6), Frisbee golf (-0.9), grass trimming (-0.5), gardening (-1.5), moving dirt with a wheelbarrow (-0.1),
loading and unloading boxes (0.1); Routine 3: sidewalk walking (-1.0), track walking (-0.8), walking with a bag (-0.6),
tennis (1.6), track running (2.2), and road running (2.1). The armband significantly overestimated EE during several
light-to-moderate intensity activities such as driving (by 74%), ironing (by 70%), gardening (by 55%), light cleaning
(by 15%), Frisbee golf (by 24%), and sidewalk walking (by 26%) (P<0.05). The arm band significantly underestimated
high intensity activities including tennis (by 20%), and track or road running (by 20%). |
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| Conclusion: Although the armband provided mean EE estimates within 16% of the criterion for nine of the
18 activities, predictions for several activities were significantly different from the criterion. The armband prediction
algorithms could be refined to increase the accuracy of EE estimations. |
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| Keywords |
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| Energy expenditure; Indirect calorimetry; SW armband;
Accelerometer |
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| Abbreviations |
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| PA: Physical Activity; EE: Energy Expenditure;
DLW: Doubly Labeled Water; TDEE: Total Daily Energy Expenditure;
PAEE: Physical Activity Energy Expenditure; REE: Resting Energy
Expenditure; HR: Heart Rate; SW: Sense Wear; IC: Indirect Calorimetry |
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| Introduction |
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| Physical activity (PA) assessment, including the frequency, intensity,
and duration of bouts, as well as the associated energy expenditure
(EE), is challenging. Traditionally, survey instruments have been used
to assess physical activity, but their accuracy is limited by individuals’
ability to recall and report the characteristics of physical activity bouts
performed [1]. Doubly labeled water (DLW) is considered by many to
be the gold standard of assessing total daily energy expenditure (TDEE).
Unfortunately, the DLW method is laboratory-based, cost-prohibitive,
cannot give details regarding the frequency, intensity, and duration of
bouts. Furthermore, in order to yield information on physical activity
EE (PAEE), resting EE (REE) must be measured, or at least estimated. |
| |
| Objective PA monitors including heart rate (HR) monitors,
pedometers, and accelerometers are used in research, but they also
have limitations. For example, HR predictions of EE are influenced
by non-PA related factors like emotional stimuli, body temperature,
fatigue, and caffeine. In addition, HR predictions of EE are less valid
for light intensity PA than moderate-to-vigorous PA [2]. Pedometers
are small and inexpensive, but provide little information on intensity.
Conversely, accelerometers provide information on frequency,
intensity, and duration of activity bouts [3-5]. However, neither
pedometers nor accelerometers (using single regression equations) are
valid for estimating EE across a wide range of activities [6,7]. |
| |
| Recently, devices that use multi-sensor approaches, such as the SenseWear (SW) armband (BodyMedia Inc., Pittsburg, PA),
have been developed. The armband contains an accelerometer, in
addition to physiological sensors that measure near body and ambient
temperature, heat flux, and galvanic skin response to determine EE.
Although the SW armband has been tested in several validation studies,
newer generations of the armband and software have been released
that contain modifications of algorithms to estimate EE. The results
of previous validation studies may vary, due to the use of different
software versions or armband models. The latest version of the SW
armband is the SW Pro 3, which uses “pattern recognition” algorithms
to convert the sensor signals to estimates of energy expenditure. |
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| Previous studies of the SW armband validity have focused mainly
on laboratory-based activities such as bicycling, treadmill running, or
arm ergometry, and only a few have addressed common daily activities
such as occupational tasks, housework, or over-ground walking [8-11].
Thus, the purpose of this study was to assess the validity of the SW
Pro3 Armband and its software (version 6.1) in estimating EE in adults
(18-65 years of age) across a wide range of activities, using indirect
calorimetry (IC) as the criterion measure. |
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| Materials and Methods |
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| Participants |
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| 68 participants (30 male, 38 female) from the University of
Tennessee campus and surrounding Knoxville community volunteered
to participate in the study. Informed consent was obtained from all
participants and the Institutional Review Board of the University of
Tennessee-Knoxville approved the study. Participants completed a
brief health history questionnaire to determine their eligibility for
inclusion in the study. Potential participants were excluded from the
study if they reported any contraindications, such as medications
for seizures or heart conditions, chest pain, or cardiovascular events.
Participants had their height and weight measured in light clothing
without shoes. Testing occurred on campus, at the participant’s
home, or at the investigator’s home. All participant data were stored
on a password-protected computer with confidential identification
numbers used for all participants’ files. |
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| Protocol |
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| Routines: Participants performed 1 of 3 routines, each of which
contained six different physical activities. |
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| Indoor Home-Based (Routine 1): Watching Television, Reading a
Book, Doing Laundry, Ironing, Light Cleaning, Aerobics. |
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| Miscellaneous (Routine 2): Driving a car, Frisbee Golf, Grass
Trimming, Gardening, Moving Dirt with Wheelbarrow, Loading and
Unloading 6.8 kg (15 lb) boxes. |
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| Outdoor aerobic (Routine 3): Walking (self-paced on a sidewalk
course), Walking (self-paced on a track), Walking with a 6.8 kg (15 lb)
bag, Singles Tennis, Running (self-paced on a track), Running (selfpaced
on a sidewalk course). |
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| For Routine 1, doing laundry included a combination of gathering
clothes, loading the washing machine and/or drier, folding clothes, and
putting clothes away. Ironing included setting up the ironing board,
filling the iron with water, and the actual ironing of clothes. Light
cleaning included wiping off countertops or other surfaces, dusting,
straightening shelves, putting away small items, and other small
tasks. Aerobics was done using the same 10-minute segment from a
commercial exercise video, for all participants. The intermediate-level
aerobics activities included both upper and lower body movements. |
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| For Routine 2, participants drove their own vehicles through a
residential neighborhood. Frisbee golf consisted of aiming for selected
targets and then walking to retrieve the Frisbee and continuing this
procedure until 10 minutes had elapsed. Grass trimming required the
use of an electric string trimmer around gardens, bushes, and trees.
Gardening was a combination of planting small bulbs and laying bricks
at garden edges with a small spade tool. This activity did not include the
carrying of these bricks any distance. Moving dirt with a wheelbarrow
included use of a large shovel to load the wheelbarrow, walking with the
loaded wheelbarrow, and emptying its contents. For the final activity,
participants were asked to walk with a 6.8 kg box in their arms, set it
down, pick it up, and carry it to another location. |
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| For Routine 3, both walking and running activities were self-paced.
Distance was recorded to determine speed for each subject in these
activities. The sidewalk course was the same for all participants during
self-paced walking and running. This course included sidewalks,
crosswalks, and slightly hilly terrain. The 6.8 kg (15 lb) bag was an overthe-
shoulder laptop computer case. |
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| The number of participants varied for routine 1 (n=25), routine 2
(n=22), and routine 3 (n=21). No participant performed more than one
routine. For all routines, each activity was performed for 10 minutes
with a 3 to 5 minute break between activities. A 10-minute seated rest
period was included before the start of each routine, to estimate the
resting energy expenditure. During the rest period and each of the six
physical activity bouts within a single routine, subjects wore the SW
Pro-3 Armband (BodyMedia Inc.), the Cosmed K4b2 (Cosmed, Rome,
Italy), and three other activity monitors as part of a larger study. The
weight of all the equipment (2.0 kg) was added to the subject’s body
mass prior to testing, and this value was used as the subject’s total body
mass. |
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| Indirect calorimetry |
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| The Cosmed K4b2 portable metabolic system was used as the
criterion measure in the present study. The Cosmed K4b2 is a breathby-
breath gas analysis system consisting of a facemask, analyzer unit,
and battery in a harness system. Before testing each subject, the unit was
warmed up for around 30 to 40 minutes and then calibrated according
to the manufacturer’s instructions. Calibration of instrument included
four parts: room air calibration, reference gas calibration (16.03% O2
and 3.98% CO2), turbine flow-meter calibration with a 3.0 L syringe
(Hans-Rudolph), and CO2/O2 analyzer delay calibration with the
participant wearing the face mask. The analyzer unit was programmed
with each participant’s data, the relative humidity, and the barometric
pressure. For each participant, a disposable gel-seal was placed on the
facemask to prevent air leaks, and the facemask was secured with a
headpiece. Before testing began, the facemask was checked to see if it
was airtight and devoid of any leaks. To reduce analyzer drift caused by
extreme temperatures, the outdoor routines were not performed when
the temperature was below 50°F (10°C) [12]. After testing, the Cosmed
data were downloaded and analyzed by accompanying software
(version 7.5a). After each testing session, the memory of the analyzing
unit was cleared and its battery recharged. |
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| SW Pro3 armband |
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| The SW Pro3 Armband is a small (85.3 mm × 53.4 mm × 19.5 mm,
wt=0.79 kg) water resistant device; it was worn on the back of the right
upper arm secured by an adjustable Velcro strap. An accompanying
watch was worn on the right wrist and displayed current measurements.
The armband was placed on the arm 10 minutes before testing to
allow sensors to adjust to skin temperature. The unit does not require
calibration and is battery operated. |
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| Before use, the armband was configured for the participant
using a USB port and cable with the accompanying BodyMedia
software (version 6.1). The participant’s gender, birth date, height,
weight, handedness, and smoking status were entered. During the
configuration, the armband was synchronized with the computer clock,
portable digital clock (used to record real time), and display watch. All
start and stop times of activities were recorded both in real time as well
as the display time on the Cosmed to allow minute-by-minute data
comparison of the two methods. The SW collects data from different
sensors on the armband including a biaxial accelerometer and sensors
to monitor heat flux, skin temperature, near body temperature, and
galvanic skin response and stores them in memory. After the routine was
completed, armband data were downloaded and saved to a computer,
and the armband’s memory was cleared for the next use. Proprietary
algorithms were used to analyze the raw data to yield output measures
including time spent at different intensities (moderate, vigorous, and
very vigorous), number of steps taken, and energy expenditure (in
METS and kcal/min). |
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| Data and Statistical Analysis |
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| The Cosmed K4b2 collected breath-by-breath data, but after
downloading, data were averaged over 1-minute intervals. The SW
collected data in 1-minute periods. For the Cosmed K4b2 data, the
software converted absolute VO2 values to relative values (adjusted for
body mass) and then to MET values for each activity. For the SW data,
proprietary algorithms and specific subject configuration produced
average MET level data. All Cosmed and SW data were exported to
Excel software. For both instruments, the MET values were averaged
over the last five minutes of each activity (excluding the final minute).
These averages for each activity were used in all statistical analyses. |
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| Statistical analyses were performed using SPSS (version 15.0) for
Windows (SPSS Inc, Chicago, IL, USA). Repeated measures ANOVAs
(method x activity) were used to compare the Cosmed MET values and
SW predicted MET values for each routine. If significant differences
were detected, post-hoc paired samples t-tests were used to locate the
differences. Significance was defined as p<0.05. To show individual
variability in the difference scores (Cosmed minus SW), modified
Bland-Altman plots were constructed showing the mean bias and 95%
prediction intervals [13]. |
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| Results |
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| Participant characteristics are presented in table 1. Complete data
were obtained on all participants, with the exception of one participant on whom we did not obtain SW resting EE data, and one participant on
whom we did not obtain SW or Cosmed data for singles tennis. Data
from these two participants were excluded from the analyses. |
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|
Table 1: Characteristics of study participants (n=68). |
|
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| For all three routines, analyses showed a significant method x
activity interaction (p<0.001). Post-hoc with an adjusted alpha-level
of 0.01 to control for Type I error showed that the SW significantly
overestimated EE during ironing and light cleaning in Routine 1
(p<0.01) (Table 2). For Routine 2, analyses revealed that the SW
significantly overestimated EE during driving, Frisbee golf, and
gardening (p<0.01) (Table 3). For Routine 3, analyses revealed that the
SW significantly underestimated tennis, track run, and road run while
and overestimated road walk (p<0.01) (Table 4). Figure 1 displays the
mean MET values by both methods for all physical activities, in order
of increasing intensity. |
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Table 2: Cosmed EE measures and Sensewear (SW) EE estimates in Routine 1. |
|
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Table 3: Cosmed EE measures and SenseWear (SW) EE estimates for Routine 2. |
|
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Table 4: Cosmed EE measures and Sensewear (SW) EE estimates for Routine 3. |
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Figure 1: Mean of MET Values by both Methods for all Physical Activities. |
|
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| Modified Bland-Altman plots were constructed to show the
difference between the two methods in average EE estimations (Figure
2). Figures 2A-2C show differences for the three routines individually,
and figure 2D shows all the data combined. Combining all routines in
figure 2D, it was evident that the SW underestimated EE to a greater
degree at higher intensities, and there was a significant correlation
between the difference scores and activity intensity (r=0.70, p<0.01). |
|
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|
Figure 2: (A-D) Bland-Altman plots displaying differences (Cosmed-SenseWear METs) for rates of energy expenditure. Solid lines represent the mean difference
of the observations and dashed lines mark the 95% confidence interval for observations. |
|
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| Discussion |
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| The goal of this study was to examine the validity of the SW Pro3
armband for estimating EE in adults, during field-based activities. This is the first study to test this armband model and its accompanying
software (version 6.1) with these types of activities, in a group with a
wide age range. Of the eighteen activities tested, the SW was found to
provide valid estimates of EE in nine activities: watching TV, reading,
doing laundry, aerobics, moving dirt with a wheelbarrow, loading
and unloading 6.8 kg boxes, walking (track), and walking with a 6.8
kg laptop computer bag. Because the SW is promoted as a useful tool
to assess EE in daily life, the errors seen in several other activities are
a cause for concern [14]. Though our ability to make comparisons to
previous studies is limited due to differences in armband models or
software versions, our results are generally consistent with previous
studies. |
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| To our knowledge, the only other study to test the same SW
armband model and software version is that of Dwyer et al. [15].
Although the primary purpose of their study was to compare the
SW EE estimates in cystic fibrosis patients versus healthy controls,
the researchers also examined the validity of the SW to estimate EE
(compared to IC) during treadmill walking. Using a graded treadmill
walking protocol, they found that the SW EE underestimations
increased at higher intensities, similar to our results. |
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| The present study was also compared to those that have used
previous software versions. The most similar studies to ours in terms
of methodology are those by Arvidsson et al. [16,17] that investigated
the validity of the SW in children using the SW Pro2 and software
version 5.1. The children performed 14 physical activities such as
basketball, jumping on a trampoline, playing games on a cell phone,
and walking and running at different speeds. Results showed that the SW significantly underestimated EE in most activities, with the degree
of underestimation increasing as the intensity increased [16]. Similarly,
we noted instances of both over- and under-estimation, and we also
observed that the SW underestimated more at higher intensities. For all
activities, their study found a positive correlation of r=0.58 (p<0.001)
[16] between difference scores of METS and the intensity of the
activities, whereas our overall correlation was r=0.70 (p<0.01). |
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| Our study used a sidewalk course for walking and running, and the
over- and under-estimations by the SW were seen with intermittent, as
well as continuous, walking and running. The sidewalk course included
crosswalks, hills, and pedestrian traffic, yet the results showed the same
magnitude of over- and under-estimations as on the track. Although
previous authors have suggested these inaccuracies were due to the
use of adult-specific algorithms in children, our results indicate that
the errors persist in an adult population and might be due to the type
of activity [16]. In one sense, it is encouraging that the SW remained
consistent in its estimates, even if under- and over-estimations exist,
because it would seem that adjustments to the algorithms could
improve the estimation of EE in walking and running. |
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| The results of our study are also similar to those seen in a study
by Galvani [8] that used the SW Pro2 armband. Although their study
examined only 8 women, the general categories of activities (occupation,
housework, recreation, and conditioning) were similar to our study.
For instance, light cleaning could be classified as “housework”, and
carrying a notebook computer bag to “occupation”. As with many
of our activities, their study noted significant (p<0.001) differences
between the SW and Cosmed for all PA categories [8]. However, no statistical details regarding specific activities were provided. A positive
correlation between error scores and intensity was noted, with the SW
tending to underestimate more at moderate and vigorous intensities.
For all activities, the 95% CI of the error scores in their study was -5.07
to 4.85 METS whereas our study showed a smaller 95% CI of -2.8 to 3.0
METS. Overall, our results confirmed findings from previous studies
[8,15,17,18] that found a greater underestimation of EE as activity
intensity increased. |
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| Continuous updating of the proprietary algorithms has occurred
since the introduction of the SW Armband. The development of new
algorithms is mentioned in several SW studies including those of
Fruin and Walberg-Rankin [19], Jakicic et al. [9] and Cole et al. [18].
Fruin and Walkberg-Rankin tested the first armband model and the
accompanying introductory software version. Young adults were
required to perform 40-minute of stationary cycling at 60% VO2 peak,
and a 30-minute treadmill test at three intensities: 80.5 m/min (0% grade), 107.3 m/min (0% grade), and 107.3 m/min (5% grade). After
initial analysis with accompanying software and its general algorithms,
the data were sent to BodyMedia, Inc. with contextual information
about what activities were being performed, for a second analysis. The
results indicated no significant differences in EE estimates between SW
and IC during cycle ergometry. However, the SW overestimated EE
during walking at 0% grade by 14-38% (p<0.02), and underestimated
EE during walking at 5% grade by 22% EE (p<0.002). The SW provided
greater EE estimates with increased treadmill speeds, but not with
increased percent grades. |
| |
| In a laboratory-based study by Jakicic et al. [9], young adults
performed 20-30 min bouts of increasing intensity on four exercise
modes: walking, cycling, stepping, and arm ergometry. The first analysis
of data used general algorithms of software version 3.2 and showed the
SW significantly (p<0.001) underestimated EE in walking, cycling, and
stepping and significantly (p<0.001) overestimated EE during arm
ergometry. After sending data to BodyMedia, Inc. with contextual
information such as exercise mode, exercise-specific algorithms
were used to perform a second analysis of data. The exercise-specific
algorithms showed no significant differences between SW and IC EE
estimates in any of the tested activities. |
| |
| The study of Cole et al. [18] prompted additional software
modifications. Unlike the healthy subjects used in prior studies,
this study used cardiac patients to test the SW and three software
versions. Participants performed 8-min bouts of treadmill walking,
recumbent stepping, arm cranking, and rowing with individualized
intensities. Using software version 2.2, the SW significantly (p<0.01)
underestimated EE during treadmill and rowing activities, but did not
result in significant between-method differences during the other two
activities. Using software version 4.0, no significant differences were
found for EE estimates by the SW and IC for any activity. However,
significant biases for stepping and arm ergometry persisted in this
software version. The SW showed a clear tendency to underestimate
EE in these two activities. For rowing and treadmill exercise, betweenmethod
variance increased with increasing EE. Given the unique
population, BodyMedia developed cardiac-specific algorithms based
on a portion of this data, and this software version was tested on the
remaining participants’ data. Using this software, SW accuracy was
further improved with no significant differences for EE estimates in
any activity. The between-method variance was reduced compared to
previous software versions. These results and those of other studies
indicate the improvements made by software modifications [9,18,19].
However, the multiple versions of software highlight the difficulty in
directly comparing results among several studies. |
| |
| The present study has both strengths and limitations. One of its
strengths lies in the types of activities selected. Our activities focused
on those that are common in daily life as opposed to those that are
confined to a laboratory. Our finding that common lifestyle activities
like ironing and light cleaning were not accurately measured indicates
that more field-based research and software updates are needed.
In addition, other studies used clinical populations such as obese
individuals or cardiac patients [18,20,21], our study used healthy
individuals with a broad age range, so the results should be broadly
applicable. The current study also has limitations. The findings are
limited to adults only, and we did not obtain data that would allow us to
test the accuracy of algorithms used to predict EE in youth. In addition,
since none of the subjects performed all 12 activities, our ability to
compare results between routines was limited. Future studies might
consider using all subjects for all activities to circumvent this problem. |
| |
| Given future modifications of SW algorithms and improved
accuracy, the SW could be useful for a variety of applications. The
device is simple to use and unobtrusive, which enhances its feasibility
for clinical interventions. For example, a recent study by Polzien et al.
[22] highlighted the application of the SW in a weight loss study. In
that study, continuous use of the SW armband and software to track EE
and record dietary intake, along with standard behavioral counseling,
produced significantly greater weight loss (p<0.05) than counseling
alone. Regardless of any inaccuracies that may have occurred in
estimating EE with the SW, weight loss was clearly improved by the SW
concept. These results bode well for future weight loss interventions,
and suggest that the SW may be useful as a motivational tool. |
| |
| Although not the focus of our study, the SW can be used to estimate
an individual’s TDEE. Traditionally, dietitians have calculated TDEE
by first estimating resting metabolic rate, then adjusting it by a certain
factor based on self-reported physical activity levels [23]. In contrast, the
SW uses a multi-sensor approach, individual characteristics, and other
information to calculate TDEE. Although our results indicate that the
SW is not valid for specific physical activities, the TDEE estimates are
still likely to be an improvement over standard prediction equations
and self-report. For instance, Johannsen et al. tested two versions of the
device: the SW Pro 3 armband and SW Mini against the criterion DLW
method over 14 consecutive days [24]. The mean value for the SW Pro
3 armband was within 112 kcal/day of the criterion, and the mean value
for the SW Mini was within 22 kcal/day of the criterion. The mean (+
SD) absolute percent errors were similar for the two monitors (SWA:
8.1 + 6.8% error, Mini: 8.3 + 6.5% error). |
| |
| Conclusion |
| |
| This study assessed the validity of the SW in estimating energy
expenditure over a wide range of activities. Compared to IC, significant
differences in average MET levels by the SW were found for several
activities. There was a tendency for EE to be significantly overestimated
at light-to-moderate intensities, and underestimated at higher
intensities. Future studies are needed to confirm our results and to
determine whether possible modifications to proprietary algorithms
will improve SW accuracy in field-based activities. |
| |
| Acknowledgements |
| |
| We would like to acknowledge the assistance of Ms. Cary Springer (University
of Tennessee Statistical Consulting Services) in analyzing the data, and Ms.
Pamela Andrews in assisting with laboratory procedures. Supported by NIH grant
R21 CA122430-01. |
| |
|
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