RESEARCH ARTICLE
Comparison of wrist-worn Fitbit Flex and
waist-worn ActiGraph for measuring steps in
free-living adults
Anne H. Y. Chu1*, Sheryl H. X. Ng1, Mahsa Paknezhad2, Alvaro Gauterin2, David Koh1,3,
Michael S. Brown4, Falk Mu¨ller-Riemenschneider1,5
1 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore,
2 Department of Computer Science, School of Computing, National University of Singapore, Singapore,
Singapore, 3 PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link,
Gadong, Brunei Darussalam, 4 Department of Electrical Engineering and Computer Science, Lassonde
School of Engineering, York University, Toronto, Ontario, Canada, 5 Institute of Social Medicine,
Epidemiology and Health Economics, Charite´ University Medical Centre Berlin, Berlin, Germany
* anne.chu@u.nus.edu, anne.chu.hy@gmail.com
Abstract
Introduction
Accelerometers are commonly used to assess physical activity. Consumer activity trackers
have become increasingly popular today, such as the Fitbit. This study aimed to compare
the average number of steps per day using the wrist-worn Fitbit Flex and waist-worn Acti-
Graph (wGT3X-BT) in free-living conditions.
Methods
104 adult participants (n = 35 males; n = 69 females) were asked to wear a Fitbit Flex and an
ActiGraph concurrently for 7 days. Daily step counts were used to classify inactive (<10,000
steps) and active (10,000 steps) days, which is one of the commonly used physical activity
guidelines to maintain health. Proportion of agreement between physical activity categoriza-
tions from ActiGraph and Fitbit Flex was assessed. Statistical analyses included Spear-
man’s rho, intraclass correlation (ICC), median absolute percentage error (MAPE), Kappa
statistics, and Bland-Altman plots. Analyses were performed among all participants, by
each step-defined daily physical activity category and gender.
Results
The median average steps/day recorded by Fitbit Flex and ActiGraph were 10193 and
8812, respectively. Strong positive correlations and agreement were found for all partici-
pants, both genders, as well as daily physical activity categories (Spearman’s rho: 0.76–
0.91; ICC: 0.73–0.87). The MAPE was: 15.5% (95% confidence interval [CI]: 5.8–28.1%) for
overall steps, 16.9% (6.8–30.3%) vs. 15.1% (4.5–27.3%) in males and females, and 20.4%
(8.7–35.9%) vs. 9.6% (1.0–18.4%) during inactive days and active days. Bland-Altman plot
indicated a median overestimation of 1300 steps/day by the Fitbit Flex in all participants.
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
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OPEN ACCESS
Citation: Chu AHY, Ng SHX, Paknezhad M,
Gauterin A, Koh D, Brown MS, et al. (2017)
Comparison of wrist-worn Fitbit Flex and waist-
worn ActiGraph for measuring steps in free-living
adults. PLoS ONE 12(2): e0172535. doi:10.1371/
journal.pone.0172535
Editor: Maciej Buchowski, Vanderbilt University,
UNITED STATES
Received: August 18, 2016
Accepted: February 6, 2017
Published: February 24, 2017
Copyright: © 2017 Chu 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.
Data Availability Statement: Due to ethical
restrictions set by the National University of
Singapore Institutional Review Board, study data
cannot be made publicly available. Requests for
data may be sent to Anne Chu (email: anne.chu@u.
nus.edu), Falk Mu¨ller-Riemenschneider (email:
ephmf@nus.edu.sg) or the National University of
Singapore Institutional Review Board (email:
irb@nus.edu.sg).
Fitbit Flex and ActiGraph respectively classified 51.5% and 37.5% of the days as active
(Kappa: 0.66).
Conclusions
There were high correlations and agreement in steps between Fitbit Flex and ActiGraph.
However, findings suggested discrepancies in steps between devices. This imposed a chal-
lenge that needs to be considered when using Fibit Flex in research and health promotion
programs.
Introduction
New wearable technologies have helped raise individual self-awareness about physical activity
behavior. Among all the functionalities that a range of wearable devices have, step counting is
the most fundamental and consistently found feature. Step counts have been proposed as a
health indicator for population studies [1], and even community-based health-promotion pro-
grams [2]. The 10,000 steps/day guideline is one of the commonly used physical activity indices
[3]. Various government/professional organizations around the world have used the 10,000
daily steps recommendation as an index of high physical activity level. This daily step-based rec-
ommendation has been endorsed by the World Health Organization (WHO), National Heart
Association of Australia, US Centers for Disease Control and Prevention, and American Heart
Association to improve overall health. For healthy adults, it appears that this guideline is a real-
istic estimate of an appropriate daily physical activity level [4, 5]. It was suggested that those
achieving the goal of 10,000 steps per day were more likely to meet physical activity guidelines
as compared to those with lower step counts [2]. Furthermore, health promotion programs that
included a daily step goal were reportedly more successful in increasing physical activity than
those without this component [6]. The use of step data (usually as steps/day) is a simple means
of reflecting habitual physical activity pattern, and this approach has become acceptable to
many researchers and practitioners [1, 6]. Moreover, walking activity has been reported as a
prevalent form of leisure-time physical activity and a functional task in the daily lives [7].
Among all the accelerometers commonly used in research, the ActiGraph (Pensacola, FL,
USA) is well-validated and has been extensively used for assessing physical activity under free-
living conditions [8–11]; The ActiGraph accelerometers use algorithms to quantify and con-
textualize the resultant acceleration signals of human motion. They have shown high accuracy
for moderate-to-high walking speed stepping in the laboratory (compared to direct observa-
tions, ICC: 0.72–0.99) and under free-living conditions (compared to the Yamax Digiwalker,
ICC: 0.90) [12]. The ActiGraph has been used in large-scale epidemiological studies such as
the US National Health and Nutrition Examination Survey (NHANES) [13], and the Women’s
Health Study (WHS) [14].
Recently, consumer-based activity trackers (e.g. Fitbit, Jawbone UP, LUMOback, Nike
+ Fuelband, Omron Walking Style Pro pedometer, etc.) and in-built accelerometers in smart-
phones have become increasingly popular [15, 16]. It was forecasted that the smart wearables
market could reach 170 million units by 2017 [17]. Fitbit (San Francisco, CA, USA) is one of
the most commonly used brands amongst the consumer-based activity trackers. As of 2015,
Fitbit had reached 9.5 million active users [18]. Among their products, the wrist-worn Fitbit
Flex has become popular in recent years either for aesthetic reasons or wearing comfort. The
Fitbit Flex is sleek and displays only LED with a tap screen. Users are able to monitor and
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
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Funding: This research was supported by a grant
WBS: R-608-000-117-646 from the National
University of Singapore.
Competing interests: The authors have declared
that no competing interests exist.
access data on the number of steps, sleep quality, and other personal metrics through the Fitbit
dashboard. This could be useful for targeted physical activity interventions designed to achieve
healthy behaviors. It was suggested that wrist-worn accelerometers allowed for monitoring of
low-intensity activities, and were associated with considerable increases in wearing compliance
and data quality [19].
A number of studies have validated wireless consumer-based monitors of different brands
in measuring step counts and energy expenditure [16, 20–23]. A recent systematic review con-
cluded high validity for the Fitbit Classic, One and Zip compared to accelerometry-based step
counts (particularly in laboratory settings) [24]. It was further highlighted that more field-
based studies are needed. Evaluation of the trackers in assessing free-living physical activity
(non-controlled environment outside a lab setting) is particularly important, as the results are
more likely to reflect usual day-to-day activities. To date, sample sizes of studies on the Fitbit
Flex validity under free-living conditions have been relatively small (ranging from 14 to 25 par-
ticipants) and based on young adults [16, 25–27]. Of note, one similar study was limited by a
small sample size of one adult only [28]. However, despite the high correlation between activity
trackers, these studies generally showed that Fitbit Flex has measurement limitations regarding
the overestimation and underestimation of activity levels compared with the reference device,
depending on different study settings and types of activity [26, 27].
Given these considerations and highlighted gaps, this study aimed to make standardized
comparisons based on step counts from the consumer-oriented Fitbit Flex and the research-
grade ActiGraph wGT3X-BT. Differences in levels and types of physical activity between
males and females have been reported [29, 30]. It was reported that more males than females
tended to practise sports (e.g. soccer, basketball, etc.), whereas females were more likely to
engage in yoga, dancing, aerobics, etc. [31]. Because these differences may influence their accu-
racy in measurement, we further performed gender specific analysis. Hence, the objectives of
this study were:
1. To compare free-living steps/day recorded by the Fitbit Flex and the ActiGraph wGT3X-BT
accelerometers in all participants, by each step-defined daily physical activity category and
gender.
2. To compare the agreement between devices in classifying participants’ step-defined daily
physical activity categories.
Materials and methods
Study design and participants
This was a cross-sectional study. The present study was a part of a previously published study
[32], whereby a convenience sample of 107 employees who completed both ActiGraph and Fit-
bit Flex measures were included. Participants from a large public University and a hospital in
Singapore were recruited between February 2014 and June 2014. Individuals were residing in
Singapore and were of various ethnicities (Chinese, Malay, Indian and others). Participants
were invited to take part in this study through mass e-mailing. Individuals who indicated inter-
est were approached and interviewed by the researcher.
The inclusion criteria were:
1. Males and females aged 21 to 65 years
2. Either students or working adults
3. Absence of physical disabilities or illness that would create abnormal gait patterns.
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
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The study was approved by the National University of Singapore Institutional Review
Board (NUS-IRB Ref No.: B-14-021). Participants provided their written informed consent to
participate in this study.
Procedure
The goals and procedures of the study were explained to each participant by the researcher via
face-to-face interview. Participants’ information on gender, age, education level, height and
weight were self-reported. Instructions were given to the participants by trained personnel on
how to put on a wrist-worn Fitbit Flex and a waist-worn ActiGraph concurrently for 7 days.
Instruction manuals on the proper use of the ActiGraph and Fitbit Flex were also given to par-
ticipants for additional guidance. Participants were instructed that the devices had to be worn
for at least 10 hours/day, and could be removed at night depending on their comfort level.
They were asked to complete a daily time sheet to record each wearing day when both devices
were worn while maintaining their normal activities. Information required on the time sheet
comprised of the dates they started and stopped wearing the devices.
ActiGraph wGT3X-BT
The ActiGraph™ wGT3X-BT monitor (ActiGraph, LLC, Pensacola, Florida, USA) is a triaxial
accelerometer (Dimensions: 4.6cm x 3.3cm x 1.5cm; weight: 19 grams) worn on the waist
using an elastic belt to secure above the right hip bone for quantifying the amount and fre-
quency of human movements. The monitor was initialized at a sample rate of 30Hz to record
activities for free-living conditions. Participants were instructed to wear the ActiGraph for
7-day. They were allowed to remove the ActiGraph only while bathing or immersing the body
in water. ActiGraph data were downloaded using ActiLife 6 software (ActiGraph, LLC, Pensa-
cola, FL, USA) by the researchers upon collection of the devices. Downloaded data were inte-
grated into 60-sec epochs.
Fitbit Flex
Fitbit FlexTM (Dimensions: 22.2cm x 6.0cm x 6.0cm; weight: 100 grams) is a wrist-worn wear-
able wireless sensor with a triaxial accelerometer that records physical activity throughout the
day. It can sync with a smartphone application/computer. Participants were instructed to wear
the Fitbit Flex on their non-dominant wrist, for the same duration as the ActiGraph (up to
7-day) concurrently. In general, Fitbit Flex requires the creation of individual user accounts to
download stored data using a Web-based software application. However, for the purpose of
our study, anonymous user accounts were created by the study team which could only be
accessed by the researchers. Steps data were therefore stored on the devices, and the minute-
by-minute Fitbit Flex data were downloaded at the end of each participant’s wearing period by
the study team.
Data reduction
For wear time validation, because the ActiGraph accelerometer is an established device to mea-
sure physical activity with many validation studies determining their accuracy [33, 34], valid
wear time determined by the ActiGraph was regarded as the reference. A detailed description
of the procedures on ActiGraph wear time validation and removal of sleep time can be found
elsewhere [32]. Then, a valid day was defined as having an accumulation of 1500 steps/day
with 10 hours/day restricted only to common wear time based on both ActiGraph and Fitbit
Flex. The 1500 steps/day criterion was based on a previous research conducted by Tudor-
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
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Locke et al. comparing accelerometers positioned at different locations under free-living con-
ditions [35]. All participants with 4 valid days of data were included in the analysis. Addi-
tionally, wear time was also verified based on the daily time sheets.
Statistical analysis
All statistical procedures were performed using SPSS software (version 20.0). The significance
level was set at P<0.05. Descriptive characteristics were presented as mean (standard devia-
tion; SD) or median (interquartile range; IQR). Shapiro-Wilk test was used to determine
whether the data was normally distributed. Differences in the characteristics between genders
were detected by non-parametric tests. Mann-Whitney U test (for continuous variables), chi-
squared test (for categorical variables) and Fisher’s exact test (for categorical variables with
cells having an expected frequency of five or less) were used.
Analyses of the relationship between ActiGraph and Fitbit Flex were performed across: all
participants, by each category of step-defined daily physical activity, and gender. Because there
could be potential within-subject variations, comparison of step counts for the magnitude of
relationship between the two devices was done on a day-to-day basis. Spearman’s correlation
coefficient (rho) and intraclass correlation coefficient (ICC) were used to assess correlation
and agreement, respectively in steps between ActiGraph and Fitbit Flex. An ICC value of
0.75 implied excellent, 0.60–0.74 good, 0.40–0.59 fair and <0.40 poor agreement [36].
Median of absolute percentage error (MAPE) between devices was calculated: (absolute error/
observed steps) × 100%. The difference in MAPE by each category of step-defined daily physi-
cal activity and gender was compared using Mann-Whitney U test. ActiGraph derived steps/
day was used to classify two step-defined activity categories for the assessments of Spearman’s
rho and ICC. The classification of days into two step-defined activity categories was adapted
based on previous studies: valid days with a cumulative of 10,000 steps/day were considered
as active days, and <10,000 steps/day were inactive days [5, 37, 38]. As for the Bland-Altman
analysis, a non-parametric approach was adopted since the differences between the two devices
were non-normally distributed. Bland-Altman plots were presented as median, 10th and 90th
percentiles to display variance around differences between two devices. Proportion of agree-
ment in achievement of 10,000 steps per day produced by ActiGraph and Fitbit Flex was
assessed using Kappa.
Results
Out of 107 recruited participants, 104 were included because they met the wear time criteria
and provided 682 days of data. Table 1 shows participants’ sociodemographic characteristics of
the study. Participants had a median age of 31.0 years (IQR: 26.0–42.8), predominantly female
(66.3%), and had a university degree (74.0%). On average, 6.6 valid wear days were recorded
per participant and there was no significant difference between males and females. The Acti-
Graph and Fitbit Flex steps were significantly higher in males than females (P = 0.03 and 0.01
for ActiGraph and Fitbit Flex, respectively).
Fitbit Flex recorded a significantly higher (P < 0.001) number of daily step counts than that
from the ActiGraph across all participants, by gender and each category of step-defined daily
physical activity (Table 2). Males reflect significantly higher daily step counts from Fitbit Flex
(P = 0.01) and ActiGraph (P = 0.028) compared to females.
The magnitude of the correlation and agreement in step counts between ActiGraph and Fit-
bit Flex were assessed (Table 2). Good to excellent significant positive correlations and agree-
ment were shown in all participants, by gender and category of step-defined daily physical
activity. Table 3 shows the number of days that were misclassified as active or inactive
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
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according to the Fitbit Flex. The proportion of overall agreement of devices in classifying days
as active or inactive was estimated, reporting a kappa of 0.66, indicating a moderate agreement
(Table 3).
Fig 1 shows the MAPE in number of steps between the two devices. Significant differences
in the MAPE of step counts were found between devices across step-defined physical activity
categories (P<0.001), but not for gender (P = 0.17).
Figs 2 and 3A–3D present Bland-Altman plots on the median of differences, and the 10th
and 90th percentiles between steps/day obtained from Fitbit Flex and ActiGraph. The bias
(median difference) is 1300 steps/day for all participants. In general, the Fitbit Flex overesti-
mated steps/day relative to ActiGraph (median differences range: 1166–1509 steps/day by gen-
der and 1280–1312 by step-defined physical activity categories).
Discussion
This study focused on the direct comparison of steps obtained from the Fitbit Flex and Acti-
Graph. The results show positive correlations and agreement in step counts of free-living
adults as measured by the waist-worn ActiGraph and wrist-worn Fitbit Flex activity monitors.
At the same time, overestimation of step counts and classification as active days by Fitbit Flex
were found. This may have important public health implications if consumers or participants
of health promotion programs are identified as being active when in fact they are not.
Table 1. Characteristics of study population.
All (n = 104)
Males (n = 35)
Females (n = 69)
P-valuea
Age (Med; IQR)
31.0; 26.0–42.8
33.0; 27.0–50.0
30.0; 25.5–40.5
0.05
Height, cm (Med; IQR)
163.0; 157.0–169.8
170.0; 168.0–175.0
160.0; 155.0–163.0
<0.001
Weight, kg (Med; IQR)
60.0; 53.0–69.9
65.0; 60.0–80.0
56.6; 50.0–66.0
<0.001
BMI (Med; IQR)
22.6; 20.3–25.5
23.1; 20.8–25.8
22.1; 20.2–25.1
0.3
Education, n (%)
0.01
Secondary
7 (6.8)
0 (0)
7 (10.2)
Technical school/diploma
20 (19.2)
3 (8.6)
17 (24.6)
University
77 (74.0)
32 (91.4)
45 (65.2)
Organization, n (%)
0.51
Public university
70 (67.3)
24 (68.6)
46 (66.7)
University hospital
34 (32.7)
11 (31.4)
23 (33.3)
0.92
Valid wearing day/week (M±SD)
6.6 ± 0.9
6.6 ± 1.0
6.5 ± 0.9
BMI, body mass index; IQR, interquartile range; M, mean; Med, median; SD standard deviation.
a Test of signiﬁcant difference between males and females.
doi:10.1371/journal.pone.0172535.t001
Table 2. Comparison, relative agreement and median of absolute error in step counts between ActiGraph and Fitbit Flex: all participants, by gen-
der and category of step-defined daily physical activity.
Step count/day
All (682 days)
Males (229 days)
Females (453 days)
Inactive (426 days)
Active (256 days)
Fitbit Flex (Med; IQR)
10193; 7490–12898a
11030; 7604–14838a
9992; 7397–12509a
8235; 6267–10003a
14075; 11948–16864a
ActiGraph (Med; IQR)
8812; 6152–11471a
9409; 6268–12897a
8599; 6053–11118a
6856; 4982–8465a
12716; 11112–14505a
Spearman’s rho
0.89*
0.91*
0.87*
0.76*
0.76*
ICC (95% CI)
0.85 (0.58–0.93)
0.87 (0.56–0.94)
0.83 (0.56–0.92)
0.73 (0.68–0.77)
0.82 (0.77–0.85)
CI, conﬁdence interval; IQR, interquartile range.
a ActiGraph and Fitbit Flex estimates are signiﬁcantly different (P < 0.05).
* P < 0.01
doi:10.1371/journal.pone.0172535.t002
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Recently, a number of studies have investigated the accuracy of various consumer-based
physical activity trackers, recognizing the role they may play in physical activity promotion.
For instance, Case et al. [16], Storm et al. [20], and Diaz et al. [21] have validated consumer
wearables for measuring steps. However, to date very few studies have investigated the accu-
racy of these monitors under free-living conditions [24]. This is highly important because the
accuracy of devices may differ considerably in day-to-day life as compared to under highly
controlled and short protocols of activities. Recently, several studies have been conducted with
regard to this important research question [25–27]. Dierker et al. [25] assessed the validity of
Fitbit Flex among 17 college-aged adults and found that although the steps measured by Fitbit
Flex (9596 ± 2361 steps) were higher than the ActiGraph GT3X+ (7766 ± 2388 steps), the dif-
ference was not statistically significant (P = 0.052). However, the authors instructed the partici-
pants to remove the devices while they were exercising over the 7-day monitoring period;
hence it is possible that not all free-living movements have been captured as in the present
Table 3. Agreement between ActiGraph and Fitbit Flex for categorizing step-defined daily physical
activity.
No. of days (%)a
ActiGraph
Fitbit Flex
Inactive
Active
Inactive
320 (46.9)
11 (1.6)
Active
106 (15.5)
245 (35.9)
Total
426 (62.5)
256 (37.5)
Kappa (95% CI)
0.66 (0.61–0.71)
a Physical activity categories are based on ActiGraph daily step counts: inactive <10,000 steps/day and
active 10,000 steps/day [5].
doi:10.1371/journal.pone.0172535.t003
Fig 1. MAPE (%) between ActiGraph and Fitbit Flex. Error bars indicate IQR of MAPE. MAPE, median absolute percentage error.
doi:10.1371/journal.pone.0172535.g001
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study. In another study by Dominick et al. [26], the Fitbit Flex registered a total of 10286 ±
3760 free-living steps/day as compared to the ActiGraph of 9639 ± 3456 steps/day (albeit no
significant difference was found between devices) among 19 participants. In contrast, Sus-
hames et al. [27] reported a larger absolute difference of over 3000 steps (47.0%) in free-living
steps between Fitbit Flex and ActiGraph among 25 adults, of which the Fitbit Flex has underes-
timated step counts. The reason for this underestimation from Fitbit Flex is unclear, but it
could be related to the variability in participants’ movements or undercounting of steps by the
Fitbit Flex.
Different study settings and reference methods could contribute to the discrepancies in out-
comes. Kooiman et al. [39] assessed the validity of Fitbit Flex over 1 day in a smaller sample of
free-living adults and found high agreements in steps with the activPAL. They found a notice-
ably smaller mean absolute percentage difference of 3.7% against the activPAL [39]. In accor-
dance with our findings, another recent study comparing Fitbit Flex and ActiGraph on 48
cardiac patients (mean age: 65.5 years), in which high correlations and a difference in step
counts of 1038 steps/day in the total population over 4 days of monitoring period were
reported. Thus, comparing findings among different populations can provide an implication
Fig 2. Bland-Altman plot of differences between waist-worn ActiGraph and wrist-worn Fitbit Flex against the mean
according to all participants. The solid line represents median of the differences between devices, dotted lines are 10th and
90th percentiles of the differences.
doi:10.1371/journal.pone.0172535.g002
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of how reproducible and valid this device is. It was also noted that the overestimation in step
counts by the Fitbit Flex in this study resulted in a considerable misclassification of days as
being active, which may have important public health implications. As shown in our analysis,
the differences in steps between Fitbit Flex and ActiGraph were larger on inactive days as com-
pared to active days.
Hypothetically, as most lifestyle activities include movements at the wrist, people might
have performed movements such as hand waving that could be identified as potential false pos-
itive events/steps by Fitbit Flex. It was apparent that wrist-movements could reflect arm/
Fig 3. Bland-Altman plots of differences between waist-worn ActiGraph and wrist-worn Fitbit Flex against the mean according to: (A) Males, (B)
Females, (C) Inactive days, and (D) Active days. The solid lines represent median of the differences between devices, dotted lines are 10th and 90th
percentiles of the differences.
doi:10.1371/journal.pone.0172535.g003
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forearm motions with a relatively small mass (while sitting), or they could be classified as step
counts (while walking or running) [40]. Tudor-Locke et al. [35] found a large difference even
using the same ActiGraph device placed between different attachment sites. They further
reported that the difference between mean steps from the wrist and waist was 2558 steps under
free-living conditions, with a higher average step counts on the wrist [35]. In line with this,
Hilderbrand et al. [41] found a 200% higher step activity from the wrist-worn GENEActiv
than the waist-worn ActiGraph in some adults. These observations suggest room for further
progress, since recent studies reported using wrist-worn monitors resulted in improved wear-
ing compliance due to comfort issues and without having the need to remove them intermit-
tently [42]. Ultimately, prolonged wear time would improve data quality as the issue of
missing data due to non-compliance could be minimized.
Strengths
Despite the growing body of evidence, this study expands substantially on previous studies. Most
importantly, as highlighted earlier, the comparison of the devices was done under free-living
conditions for estimation of unstructured lifestyle activities. Secondly, the relationship between
these devices were assessed for 7-day of wearing protocol. Thirdly, this study was conducted
among a relatively large sample of adults. Fourthly, the performance of the devices was compared
across different subgroups (males vs. females and step-defined physical activity categories).
Limitations
This study may have limited generalizability as participants were predominantly females, rela-
tively young and healthy. Furthermore, the use of ActiGraph as the reference instrument has
its drawbacks. It is possible that the difference in steps between devices could be attributable to
not only the Fitbit Flex, but also the ActiGraph, which is not the gold standard for measuring
step counts [43]. However, the ActiGraph has been shown to be a valid tool to assess step
count (as compared with the Omron pedometer and Yamax Digiwalker [11, 12]), and it is
practical for use in epidemiological studies [44]. Careful consideration should also be given to
the effects of movement artefact and signal noise due to the use of devices that are not attached
directly to the skin (i.e. Fitbit Flex worn on a wrist-band and ActiGraph on a waist-belt),
which might have affected the devices’ functionality to accurately measure step count. Being
limited to only step count data, there was no indication as to whether the activities performed
were of light-, moderate- or vigorous-intensity level. In general, step counts from accelerome-
ters of different attachment sites (i.e. wrist- and waist-worn) might not be ideal for a direct
comparison; nonetheless, results of this study were more likely to reflect the performances of
these devices in real-world practice.
Conclusions
Positive correlation and agreement in step counts were found between wrist-worn Fitbit Flex
and waist-worn ActiGraph in free living adults, which is consistent with the existing evidence
mainly from laboratory studies. However, a considerable overestimation of Fitbit Flex was
noted, which resulted in substantial misclassification by Fitbit Flex when applying common
step count recommendations. This can have important practical implications for the use of
these devices by researchers, practitioners and health promoters, which often use the achieve-
ment of certain step count goals or increases in step counts as desired outcomes. Evidence pre-
sented in this paper adds to the existing literature on the validity of consumer devices for
physical activity monitoring and these cautionary limitations should be considered in the
design of study data collection and health promotion strategies.
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
10 / 13
Acknowledgments
We thank our colleagues and participants for their involvement in this study.
Author Contributions
Conceptualization: AC FMR.
Data curation: AC FMR AG.
Formal analysis: AC FMR SN AG.
Funding acquisition: FMR.
Investigation: AC FMR.
Methodology: AC FMR SN.
Project administration: AC FMR.
Resources: AC FMR MSB MP AG.
Software: AC FMR SN AG MSB.
Supervision: FMR DK.
Validation: AC FMR.
Visualization: AC.
Writing – original draft: AC FMR SN DK.
Writing – review & editing: AC FMR SN DK.
References
1.
Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults.
Med Sci Sports Exerc. 2009; 41(7):1384–91. doi: 10.1249/MSS.0b013e318199885c PMID: 19516163
2.
Shephard RJ, Tudor-Locke C. The objective monitoring of physical activity: Contributions of accelero-
metry to epidemiology, exercise science and rehabilitation. Cham: Springer; 2016.
3.
Tudor-Locke C, Craig CL, Brown WJ, Clemes SA, De Cocker K, Giles-Corti B, et al. How many steps/
day are enough? For adults. Int J Behav Nutr Phys Act. 2011; 8:79. doi: 10.1186/1479-5868-8-79
PMID: 21798015
4.
Hardman AE, Stensel DJ. Physical activity and health: The evidence explained. 2nd ed. London: Rout-
ledge Taylor and Francis Group; 2009.
5.
Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for
public health. Sports Med. 2004; 34(1):1–8. PMID: 14715035
6.
Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to
increase physical activity and improve health: a systematic review. JAMA. 2007; 298(19):2296–304.
doi: 10.1001/jama.298.19.2296 PMID: 18029834
7.
Tudor-Locke C, Ham SA. Walking behaviors reported in the American Time Use Survey 2003–2005.
JAMA. 2008; 5(5):633–47.
8.
Calabr MA, Lee JM, Saint-Maurice PF, Yoo H, Welk GJ. Validity of physical activity monitors for assess-
ing lower intensity activity in adults. Int J Behav Nutr Phys Act. 2014; 11(1):1–9.
9.
Aadland E, Ylvisåker E. Reliability of the Actigraph GT3X+ accelerometer in adults under free-living
conditions. PloS One. 2015; 10(8):e0134606. doi: 10.1371/journal.pone.0134606 PMID: 26274586
10.
Freedson P, Bowles HR, Troiano R, Haskell W. Assessment of physical activity using wearable moni-
tors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc. 2012; 44(1
Suppl 1):S1–4.
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
11 / 13
11.
Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. 24 Hours of sleep, seden-
tary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016; 48(3):457–
65. doi: 10.1249/MSS.0000000000000778 PMID: 26484953
12.
Lee JA, Williams SM, Brown DD, Laurson KR. Concurrent validation of the Actigraph GT3X+, Polar
Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-
based and free-living settings. J Sports Sci. 2015; 33(10):991–1000. doi: 10.1080/02640414.2014.
981848 PMID: 25517396
13.
Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United
States measured by accelerometer. Med Sci Sports Exerc. 2008; 40(1):181–8. doi: 10.1249/mss.
0b013e31815a51b3 PMID: 18091006
14.
Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiologic
studies: issues and challenges. Br J Sports Med. 2014; 48(3):197–201. doi: 10.1136/bjsports-2013-
093154 PMID: 24297837
15.
Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J
Med Internet Res. 2012; 14(6):e161. doi: 10.2196/jmir.2171 PMID: 23171838
16.
Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable
devices for tracking physical activity data. JAMA. 2015; 313(6):625–6. doi: 10.1001/jama.2014.17841
PMID: 25668268
17.
Ranck J. The wearable computing market: a global analysis. Gigaom Pro. 2012.
18.
Smart wearables—Statista Dossier. Fitbit—number of active users 2012–2015. 2015. Available: http://
www.statista.com/statistics/472600/fitbit-active-users/.
19.
Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity
research. Br J Sports Med. 2014; 48(13):1019–23. doi: 10.1136/bjsports-2014-093546 PMID:
24782483
20.
Storm FA, Heller BW, Mazza C. Step detection and activity recognition accuracy of seven physical
activity monitors. PloS One. 2015; 10(3):e0118723. doi: 10.1371/journal.pone.0118723 PMID:
25789630
21.
Diaz KM, Krupka DJ, Chang MJ, Peacock J, Ma Y, Goldsmith J, et al. Fitbit(R): An accurate and reliable
device for wireless physical activity tracking. Int J Cardiol. 2015; 185:138–40. doi: 10.1016/j.ijcard.2015.
03.038 PMID: 25795203
22.
Nelson MB, Kaminsky LA, Dickin DC, Montoye AH. Validity of consumer-based physical activity moni-
tors for specific activity types. Med Sci Sports Exerc. 2016.
23.
Schneider M, Chau L. Validation of the Fitbit Zip for monitoring physical activity among free-living ado-
lescents. BMC Res Notes. 2016; 9(1):448. doi: 10.1186/s13104-016-2253-6 PMID: 27655477
24.
Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wear-
able activity trackers. Int J Behav Nutr Phys Act. 2015; 12(1):159.
25.
Dierker K, Smith B. Comparison between four personal activity monitors and the Actigraph GT3X+ to
measure daily steps. Med Sci Sports Exerc. 2014; 46(5):792.
26.
Dominick G, Winfree K, Pohlig R, Papas M. Physical activity assessment between consumer-and
research-grade accelerometers: a comparative study in free-living conditions. JMIR MHealth UHealth.
2016; 4(3):e110. doi: 10.2196/mhealth.6281 PMID: 27644334
27.
Sushames A, Edwards A, Thompson F, McDermott R, Gebel K. Validity and reliability of fitbit flex for
step count, moderate to vigorous physical activity and activity energy expenditure. PloS One. 2016; 11
(9):e0161224. doi: 10.1371/journal.pone.0161224 PMID: 27589592
28.
Dontje ML, de Groot M, Lengton RR, van der Schans CP, Krijnen WP. Measuring steps with the Fitbit
activity tracker: an inter-device reliability study. J Med Eng Technol. 2015; 39(5):286–90. doi: 10.3109/
03091902.2015.1050125 PMID: 26017748
29.
Abel T, Graf N, Niemann S. Gender bias in the assessment of physical activity in population studies.
Soz Praventivmed. 2001; 46(4):268–72. PMID: 11582854
30.
Marquez DX, McAuley E. Gender and acculturation influences on physical activity in Latino adults. Ann
Behav Med. 2006; 31(2):138–44. doi: 10.1207/s15324796abm3102_5 PMID: 16542128
31.
Australian Bureau of Statistics. Women’s participation in sport and physical activity. Canberra, ACT;
2006.
32.
Chu AHY, Ng SH, Koh D, Mu¨ller-Riemenschneider F. Reliability and validity of the self-and interviewer-
administered versions of the Global Physical Activity Questionnaire (GPAQ). PloS One. 2015; 10(9):
e0136944. doi: 10.1371/journal.pone.0136944 PMID: 26327457
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
12 / 13
33.
Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classi-
fication algorithm. Med Sci Sports Exerc. 2011; 43(2):357–64. doi: 10.1249/MSS.0b013e3181ed61a3
PMID: 20581716
34.
Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med
Sport. 2011; 14(5):411–6. doi: 10.1016/j.jsams.2011.04.003 PMID: 21616714
35.
Tudor-Locke C, Barreira TV, Schuna JMJ. Comparison of step outputs for waist and wrist accelerome-
ter attachment sites. Med Sci Sports Exerc. 2015; 47(4):839–42. doi: 10.1249/MSS.
0000000000000476 PMID: 25121517
36.
Fleiss JL, Levin B, Paik MC. The measurement of interrater agreement—Statistical methods for rates
and proportions. 1981; 2:212–236.
37.
Tudor-Locke C. Steps to better cardiovascular health: How many steps does it take to achieve good
health and how confident are we in this number? Curr Cardiovasc Risk Rep. 2010; 4(4):271–6. doi: 10.
1007/s12170-010-0109-5 PMID: 20672110
38.
Barriera TV, Tudor-Locke C, Champagne CM, Broyles ST, Johnson WD, Katzmarzyk PT. Comparison
of GT3X accelerometer and YAMAX pedometer steps/day in a free-living sample of overweight and
obese adults. J Phys Act Health. 2013; 10(2):263–70. PMID: 22821951
39.
Kooiman TJ, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and
validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil. 2015; 7:24. doi: 10.1186/
s13102-015-0018-5 PMID: 26464801
40.
Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary
behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013; 45(5):964. doi: 10.
1249/MSS.0b013e31827f0d9c PMID: 23247702
41.
Hildebrand M, Van Hees VT, Hansen BH, Ekelund U. Age-group comparability of raw accelerometer
output from wrist-and hip-worn monitors. Med Sci Sports Exerc. 2014; 46(9):1816–24. doi: 10.1249/
MSS.0000000000000289 PMID: 24887173
42.
Tudor-Locke C, Barreira T, Schuna J, Mire E, Chaput J-P, Fogelholm M, et al. Improving wear time
compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood
Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act. 2015; 12(1):11.
43.
Welk G. Physical activity assessments for health-related research. Champaign, IL: Human Kinetics;
2002.
44.
John D, Freedson P. Actigraph and actical physical activity monitors: a peek under the hood. Med Sci
Sports Exerc. 2012; 44(1 Suppl 1):S86–S9.
Comparison of Fitbit Flex and ActiGraph for steps in free-living adults
PLOS ONE | DOI:10.1371/journal.pone.0172535
February 24, 2017
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Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults.
Chu, Anne H Y,Ng, Sheryl H X,Paknezhad, Mahsa,Gauterin, Alvaro,Koh, David,Brown, Michael S,Müller-Riemenschneider, Falk