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R E V I E W Open Access
Systematic review of the validity and
reliability of consumer-wearable activity
trackers
Kelly R. Evenson
1,2*
, Michelle M. Goto
1
and Robert D. Furberg
2
Abstract
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.
Keywords: Distance, Energy expenditure, Fitbit, Intervention, Jawbone, Measurement, Physical activity, Sleep, Steps,
Walking
* Correspondence: kelly_evenson@unc.edu
1
Department of Epidemiology, Gillings School of Global Public Health,
University of North Carolina—Chapel Hill, 137 East Franklin Street, Suite 306,
Chapel Hill 27514NC, USA
2
RTI International, Research Triangle Park, NC, USA
© 2015 Evenson et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Evenson et al. International Journal of Behavioral Nutrition
and Physical Activity (2015) 12:159
DOI 10.1186/s12966-015-0314-1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
Consumer wearable devices are a popular and growing
market for monitoring physical activity, sleep, and other
behaviors. The devices helped to grow what is known as
the Quantified Self movement, engaging those who wish
to track their own personal data to optimize health be-
haviors [1]. A subset of consumer wearable devices used
for monitoring physical activity- and fitness-related met-
rics are referred to as “activity trackers”or “fitness
trackers”[2]. Their popularity has risen as they have
become more affordable, unobtrusive, and useful in their
application. An activity tracker can provide feedback and
offer interactive behavior change tools via a mobile device,
base station, or computer for long-term tracking and data
storage [3, 4]. The trackers enable self-monitoring towards
daily or longer-term goals (such as a goal to walk a certain
distance over time) and can be used to compare against
one’s peers or a broader community of users, both of
which are advantageous mediators to increasing walking
and overall physical activity [3, 5].
A national United States (US) survey completed in
2012 indicated 69 % of adults tracked at least one health
indicator for themselves, a family member, or friend
using a tracking device (such as an activity tracker),
paper tracking, or another method [6]. From this survey,
60 % of adults reported tracking weight, diet, or exercise.
Those who tracked weight, diet, or exercise were similar
by gender, but more likely to be non-Hispanic White or
African American, older, and have at least a college degree
compared to Hispanics, younger ages, and those with less
than a college degree, respectively. Among those who
tracked at least one health behavior or condition, 21 %
used some form of technology to track the health data.
Also among this group, 46 % indicated that tracking chan-
ged their overall approach to maintaining their health or
the health of the person they cared for, 40 % indicated that
it led them to ask a doctor new questions or obtain a sec-
ond opinion, and 34 % indicated that it affected a decision
about how to treat an illness or condition.
Activity trackers are being used not only in the con-
sumer market but also in research studies. Physical
activity-related interventions are using activity trackers
for self-monitoring, reinforcement, goal-setting, and
measurement (examples among adults [4, 7–11] and
youth [12]). Before more widespread use of these
trackers occurs in research studies, for either interven-
tion or measurement purposes, it is important to estab-
lish their validity and reliability.
The purpose of this review was to summarize the evi-
dence for validity and reliability of the most popular
consumer-wearable activity trackers. Among a variety of
trackers on the market, approximately 3.3 million sold
between April 2013 to March 2014, with 96 % made by
Fitbit (67 %), Jawbone (18 %), and Nike (11 %) [2]. Since
Nike discontinued the sale of Fuelbands in 2014, our
focus for this review was on activity trackers made by
Fitbit and Jawbone. Before conducting the review, we
searched company websites for documentation on the
accuracy of measuring steps, distance, physical activity,
energy expenditure, and sleep. The Fitbit company indi-
cated that after multiple internal studies, they had
“tuned the accuracy of the Fitbit tracker step counting
functionality over hundreds of tests with multiple body
types. All Fitbit trackers should be 95–97 % accurate for
step counting when worn as recommended”[13]. How-
ever, no other information was provided to document
the accuracy of steps, nor the other measures we
reviewed. The Jawbone company indicated that “while
variations in user, terrain, and activity conditions can in-
fluence specific calculations, testing has shown UP to
provide industry-leading accuracy in tracking activity
and sleep”[14]. Similarly, no other details were provided
of how accuracy was determined. Therefore, we focused
our search on the ability of these trackers to estimate
steps, distance, physical activity, energy expenditure, and
sleep. For each study included in the review, we also ab-
stracted information on the tracker’s feasibility of use.
Methods
Literature search
Searches of PubMed, Embase, and SPORTDiscus were
conducted to include only full-length studies published
in English language journals through July 31, 2015. No
start date was imposed in the search. If a publication
was available online first before print, we attempted to
obtain a copy; thus, some publications were officially
published after July 31, 2015 but were available in the
databases during our search period. Two separate
searches were performed for the two activity trackers.
(1)(Fitbit) AND (validity OR validation OR validate OR
comparison OR comparisons OR comparative OR
reliability OR accuracy)
(2)(Jawbone) AND monitor AND (validity OR
validation OR validate OR comparison OR
comparisons OR comparative OR reliability OR
accuracy)
The term “monitor”was added to the Jawbone search
to reduce the number of dental-related articles retrieved.
In addition, we reviewed Google Scholar similarly (same
search terms, dates, only English language journals) and
the reference lists of included studies for publications
missed by the searches. We excluded abstracts (examples
[15, 16]) and conference proceedings (example [17]). We
also excluded studies focused on special populations, such
as stroke and traumatic brain injury [18], chronic ob-
structive pulmonary disease [19], amputation [20], mental
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 2 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
illness [21], or older adults in assisted living [22]. One
study presented data on apparently healthy older
adults without mobility impairments and those of simi-
lar ages with reduced mobility; therefore, we reported
only on those without mobility impairments [23].
Abstraction and analysis
First, we documented descriptive information on the
activity trackers (models, release date, placement, size,
weight, and cost) through internet searches conducted
from May-July 2015. Second, an abstraction tool used
for this review was expanded from a tool initially cre-
ated by De Vries et al. [24] to document study charac-
teristics and measurement properties of the activity
trackers. Specifically, we extracted information on the
study population, protocol, statistical analysis, and re-
sults related to validity, reliability, and feasibility. We
also extracted any information provided by the studies
on items entered into the activity tracker user account
settings. A primary reviewer extracted details and a
second reviewer checked each entry. Discrepancies in
coding were resolved by consensus. For any abstracted
information that was missing from the publication, we
attempted to contact at least one author to obtain the
information. Summary tables were created from the
abstracted information.
Validity of the activity trackers included [25]:
–Criterion validity: comparing the trackers to a
criterion measure of steps, distance traveled,
physical activity, energy expenditure, and sleep.
–Construct validity: comparing the trackers to other
constructs that should track or correlate positively
(convergent validity) or negatively (divergent
validity).
Reliability of the activity trackers included [25]:
–Intradevice reliability: test-retest results indicating
consistency within the same tracker. This can be
conducted in the lab (such as on a shaker table).
–Interdevice reliability: results indicating consistency
across the same brand/type of tracker measured at
the same time and worn in the same location. This
can be assessed during activities performed in the
laboratory or while free-living.
We interpreted the correlation coefficients (CC) using
the following ratings: 0- < 0.2 poor, 0.2- < 0.4 fair, 0.4- <
0.6 moderate, 0.6- < 0.8 substantial, and 0.8- < 1.0 almost
perfect [26]. Feasibility assessment included how much
missing or lost data occurred and any feedback on wear-
ing the trackers by participants.
Results
>Through the systematic search, 67 records were identi-
fied, 39 were screened, and 22 were included in the re-
view that reported on the validity or reliability of any
Fitbit or Jawbone tracker. The Preferred Reporting Items
for Systematic Reviews and Meta-Analyses (PRISMA)
[27, 28] figure displays the detailed results from the
search (Additional file 1). Twenty studies reported on at
least one type of Fitbit tracker [15, 23, 29–46] and eight
reported on at least one type of Jawbone tracker [30, 33,
35, 40, 42, 45, 47, 48].
Fitbit tracker
The Fitbit company (San Francisco, CA; https://www.fit-
bit.com) has offered at least nine activity trackers since
2008 (Table 1). Depending on the type of activity tracker,
the company recommends wearing them at the waist,
wrist, pocket, or bra. The trackers contain a triaxial ac-
celerometer and more recently an altimeter, heart rate,
and global positioning system (GPS) monitor. Using pro-
prietary algorithms, data from measures collected along
with information input by the user can estimate steps,
distance, physical activity, kilocalories, and sleep. Day-
level data is summarized and available to the consumer.
Minute-level data (called “intraday”) requires more effort
to obtain, such as through the Fitbit API [32], and can
be set at intervals of 1, 5, 10, 15, 20, or 60 min. Alterna-
tively, data can be extracted using third-party service
providers, such as Fitabase (Small Steps Labs LLC;
https://www.fitabase.com), as was used in the study by
Diaz et al. [15].
The Fitbit One updated the Fitbit Ultra in 2012, which
in turn updated the Fitbit Classic in 2011, and all three
are shaped similarly as a clip. The Fitbit Zip is teardrop-
shaped and the Fitbit Flex is designed for the wrist. The
following Fitbit trackers were explored for validity
(Table 2):
(1)Classic worn at the waist [29,31,39,41] and non-
dominant wrist [38];
(2)Ultra worn at the waist/hip [23,29,34,36,40],
pants pocket [32,36], dominant-handed wrist [23],
non-dominant wrist [37], shirt collar [36], and bra
[36];
(3)One worn at the waist [15,30,32,33,35,42,43,46],
pants pocket [43], and ankle [46];
(4)Zip worn at the waist [30,33,35,44]; and
(5)Flex worn on the wrist [15,30,45].
Reliability studies included the Classic worn at the waist
[29] and non-dominant wrist [38]; the Ultra worn at the
waist/hip [29, 36], pants pocket [32], and non-dominant
wrist [37]; the One worn at the waist [15, 43] and pants
pocket [43]; and the Flex worn on the wrist [15].
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 3 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 1 Fitbit and Jawbone activity tracker characteristics (searched May-July 2015)
Tracker Released date Selected measures Placement Size (cm) Weight (g) Cost (US$) Discontinuation
Fitbit
Fitbit Classic (also
referred to as the
"original Fitbit" or
"Fitbit Tracker")
September 2008 Steps, distance, calories,
sleep
Waist, pocket, bra 5.5(h) × 1.9(w) × 1.4(d) 11 Not available Winter 2012: discontinued
Fitbit Ultra October 2011
(new hardware
upgrade to the Classic)
Steps, distance, calories,
sleep, altimeter
Waist, pocket, bra,
wrist (requires Ultra
sleep band)
5.5(h) × 1.9(w) × 1.4(d) 11 Not available August 2012: discontinued
Fitbit One September 2012
(update to the Ultra)
Steps, distance, calories,
active minutes, sleep, altimeter
Waist, pocket, bra 4.8(h) × 1.9(w) × 1.0(d) 9 99.95
Fitbit Zip May 2013 Steps, distance, calories,
active minutes
Waist, pocket, bra 3.6(h) × 2.9(w) × 1.0(d) 8 59.95
Fitbit Flex May 2013 Steps, distance, calories,
active minutes, sleep
Wrist Small: 14.0–17.6(c) × 1.4(w) 13 99.95
Large: 16.1–20.9(c) × 1.4(w) 15
Fitbit Force October 2013 Steps, distance, calories,
active minutes, sleep,
altimeter
Wrist Small: 14.0–17.6(c) × 1.9(w) 31 Not available February 2014: recalled by
company because of skin
reactions to the band
Large: 16.1–20.9(c) × 1.9(w)
Fitbit Charge November 2014 Steps, distance, calories, active
minutes, altimeter, sleep
Wrist Small: 14.0–17.0(c) × 2.1(w) 23 129.95
Large: 16.1–20.0(c) × 2.1(w)
Extra Large: 19.8–23.0(c) × 2.1(w)
Fitbit Surge January 2015 Steps, distance, calories,
active minutes, altimeter,
sleep, heart rate, GPS
Wrist Small: 14.0–16.0(c) × 3.4(w) 77 249.95
Large: 16.0–19.8(c) × 3.4(w)
Extra Large: 19.8–22.6(c) × 3.4(w)
Small: 14.0–17.0(c) × 2.1(w)
Fitbit Charge HR January 2015 Steps, distance, calories,
active minutes, altimeter,
sleep, heart rate
Wrist Large: 16.1–19.4(c) × 2.1(w) 23 149.95
Extra Large: 19.4–23.0(c) × 2.1(w)
Jawbone
Jawbone UP November 2011 Steps, calories, distance
(app), sleep
Wrist Small: 14.0–15.5 19 99.99 December 2011: company
provided refunds because the
band had trouble holding a
charge and synching to the
band hardware
Medium: 15.5–18.0 21
Large: 18.0–20.0 23
Jawbone UP24 November 2013 Steps, calories, distance
(app), sleep
Wrist Small: 5.2(w) × 3.5(h) (inner);
6.6(w) × 5.0(h) (outer)
19 129.99 July 2015: no longer for sale
on the company's website
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 4 of 22
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Table 1 Fitbit and Jawbone activity tracker characteristics (searched May-July 2015) (Continued)
Medium: 6.3(w) × 4.0(h) (inner);
7.6(w) × 5.4(h) (outer)
22
Large: 6.9(w) × 4.3(h) (inner);
8.1(w) × 5.6(h) (outer)
23
Jawbone UP MOVE November 2014 Steps, calories, distance
(app), sleep
Waist, pocket, bra,
wrist (requires
separate wrist strap)
2.8(diameter) × 1.0(d) 7 49.99
Jawbone UP2 April 2015 Steps, calories, distance
(app), sleep
Wrist 14.0–19.0(c) × 1.2(w) 25 99.99
Jawbone UP3 November 2014 Steps, calories, distance (app),
sleep, bioimpedance (heart
rate, respiration, galvanic skin
response), skin and ambient
temperature
Wrist 14.0–19.0(c) × 1.2(w) 29 179.99
Jawbone UP4 July 2015 Steps, calories, distance (app),
sleep, bioimpedance (heart rate,
respiration, galvanic skin response),
skin and ambient temperature
Wrist 14.0–19.0(c) × 1.2(w) 29 199.99
Abbreviations: ccircumference, ddepth, GPS global positioning system, hheight, wwidth
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 5 of 22
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Table 2 Fitbit and Jawbone studies of interdevice reliability and validity (listed by author's last name and publication year)
Interdevice reliability Validity
Motion sensor Steps Distance Physical
activity
Energy
expenditure
Sleep Steps Distance Physical activity Energy expenditure Sleep
Fitbit
Fitbit Classic (also
referred to as the
"original Fitbit" or
"Fitbit Tracker")
Adam Noah 2013 [29] Adam Noah
2013 [29]
Montgomery-
Downs 2012
[38]
Adam Noah 2013
[29]
Adam Noah 2013 [29];
Dannecker 2013 [31]:
Sasaki 2015 [39];
Stahl 2014 [41]
Montgomery-
Downs 2012 [38]
Fitbit Ultra Adam Noah 2013 [29];
Dontje 2015 [32];
Mammen 2012 [36]
Adam Noah
2013 [29]
Meltzer 2015
[37]
Adam Noah 2013 [29];
Gusmer 2014 [34];
Lauritzen 2013 [23];
Mammen 2012 [36];
Stackpool 2014 [40]
Adam Noah 2013 [29];
Gusmer 2014 [34];
Stackpool 2014 [40]
Meltzer 2015 [37]
Fitbit One Diaz 2015 [15];
Takacs 2014 [43]
Takacs
2014
Diaz 2015
[15]
Case 2015 [30]; Diaz
2015 [15]; Ferguson
2015 [33]; Simpson
2015 [46]; Storm
2015 [42]; Takacs
2014 [43]
Takacs 2014 [43] Ferguson 2015 [33] Diaz 2015 [15];
Ferguson 2015 [33];
Lee 2014 [35]
Ferguson 2015 [33]
Fitbit Zip Case 2015 [30];
Ferguson 2015 [33];
Tully 2014 [44]
Ferguson 2015 [33];
Tully 2014 [44]
Ferguson 2015 [33];
Lee 2014 [35]
Fitbit Flex Diaz 2015 [15] Diaz 2015
[15]
Case 2015 [30];
Diaz 2015 [15]
Bai 2015 [45];
Diaz 2015 [15]
Jawbone
Jawbone UP Ferguson 2015 [33];
Stackpool 2014 [40];
Storm 2014 [42]
Ferguson 2015 [33] Ferguson 2015 [33];
Lee 2014 [35];
Stackpool 2014 [40]
de Zambotti 2015a
[47]; de Zambotti
2015b; Ferguson
2015
Jawbone UP24 Case 2015 [30] Bai 2015 [45]
We found no studies for the Fitbit Force, Surge, Charge, or Charge HR, or the Jawbone UP MOVE, UP2, UP3, or UP4
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 6 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Jawbone tracker
The Jawbone company (San Francisco, CA; https://jaw-
bone.com) has offered at least six activity trackers since
2011 (Table 1). Their trackers are worn at the wrist, with
the exception of the UP MOVE tracker to be worn at
the waist, pocket, or bra. The trackers contain a triaxial
accelerometer, collecting data at 30 Hertz, and more re-
cently bioelectrical impedance (for heart rate, respir-
ation, and skin response), as well as both skin and
ambient temperatures. Using proprietary algorithms,
data from measures collected along with information in-
put by the user can estimate steps, distance, physical ac-
tivity, kilocalories, and sleep. Currently, only day-level
data is available to the consumer.
The following two Jawbone trackers, both designed for
the wrist, were explored for validity (Table 2):
(1)UP worn on the wrist [33,35,40,42,47,48] and
(2)UP24 worn on the wrist [30,45].
No Jawbone trackers were explored for reliability.
About half of the studies reported the data entered
into the tracker user account [29, 33–35, 39, 41, 43],
which was usually age, gender, height, and weight. One
study also reported entering stride length [34], another
study input handedness and smoking status [35], and an-
other study used event markers to denote when an activ-
ity started and ended [39]. A sleep study indicated that
they manually switched the band from active to sleep
mode in conjunction with lights on/off [48]. Other stud-
ies did not report what data were input into the user ac-
count [15, 23, 30–32, 36–38, 40, 42, 44–47].
Description of studies
Data collection was primarily conducted in the US,
with one or two studies conducted in Australia [33],
Canada [36, 43, 46], the Netherlands [32], Northern
Ireland [44], Spain [23], and the United Kingdom [42]
(Table 3). Studies usually included an apparently
healthy sample and, where reported, almost all partici-
pants had a normal body mass index (BMI). Addition-
ally, participants were > =18 years and mostly younger
to middle age, except for one study focusing exclu-
sively on adults > =60 years [41] and two studies on
youth [37, 48]. Data were collected between 2010 [38]
to 2015 [47].
Validity
All but one study (21/22) explored the validity of at least
one type of activity tracker (Table 4). Sample sizes of the
studies ranged from six [23] to 65 [48]. For any Fitbit
tracker, validity was reported from 12 studies on steps
[15, 23, 29, 30, 33, 34, 36, 40, 42–44, 46], one study on
distance [43], two studies on physical activity [33, 44],
ten studies on energy expenditure [15, 29, 31, 33–35,
39–41, 45], and three studies on sleep [33, 37, 38]
(Table 2). For any Jawbone tracker, validity was reported
from four studies on steps [30, 33, 40, 42], zero studies
on distance, one study on physical activity [33], three
studies on energy expenditure [33, 35, 40], and three
studies on sleep [33, 47, 48]. The following sections de-
tail the validity results for each of the five measures.
Validity for steps
The criterion measures for counting steps included com-
parisons against manual step counting, either in-person
[30, 36, 40] or with video recording [15, 23, 43, 46], or
steps recorded by pedometers (Yamax CW-700 [44]) or
accelerometers (Actical [29], ActiGraph GT1M [34],
ActiGraph GT3X [44], ActiGraph GT3X+ [33], Body
Media SenseWear [33], and Opal sensors [42]). Hip-
worn trackers generally outperformed wrist-worn
trackers for step accuracy [15, 23, 30, 40]. One study
found less error for the ankle-worn One compared to
the waist-worn One [46].
For laboratory-based studies using step counting as
the criterion [15, 23, 43], correlation with steps from the
tracker was generally high (if reported, the mean correla-
tions were > =0.80) for the Ultra (for most treadmill
speeds [36]; for treadmill walking and elliptical but not
for running or agility drills [40]), One [30, 43], Zip [30],
and UP (for treadmill walking, running, and elliptical
[40]) trackers. However, several studies indicated that
the One [15], Flex [15, 30], Ultra (waist worn at slower
walking speed (2 km/h) and the pocket worn at faster
speeds (> = 8 km/h)) [36]), and UP24 [30] under-
estimated steps during treadmill walking and running.
For studies using accelerometry as the criterion, cor-
relation with tracker steps was also generally high (if re-
ported, the mean correlations were > =0.80) for the
Classic [29], Ultra [29, 34], Zip [44], One [33], and UP
[33] trackers. However, several studies indicated that the
One [42], Flex [15, 30], UP [33](at slow walking speeds
[42]), and UP24 [30] under-estimated steps during tread-
mill walking and running. In contrast, in a study of 21
participants wearing the One for 2 days without restric-
tions, compared to an accelerometer the tracker gener-
ally over-counted steps for the One (mean absolute
difference 779 steps/day) [33]. In one free-living study,
the researcher wore both the Ultra and a Yamax pedom-
eter while seated in a car driving on paved roads for
about 20 min [36]. During this time no steps were re-
corded for the Ultra, while the pedometer recorded
three steps.
Validity for distance
Only one study explored the validity of distance walked
using the treadmill distance as the criterion. Among 30
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 7 of 22
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Table 3 Characteristics of studies included in the systematic review (listed by author's last name and publication year)
Author (year) Location of lab
or recruitment
area
Sample size (for
validity and
reliability studies)
Mean age
(SD), range
Mean body mass index
(SD), range in kilograms/
meters squared
Data
collection
year(s)
Inclusion criteria
Adam Noah
(2013) [29]
Northeastern
university, US
16 and 23 (V and R) 26.7 (7.6) Not reported 2011-2012 Apparently healthy participants, had to participate in moderate to vigorous physical
activity based on the International Physical Activity Questionnaire (> = 150 minutes/
week of moderate intensity or > =75 minutes/week of vigorous intensity)
Bai (2015)
[45]
Ames, Iowa, US 52 (V) 18–65 24.0, 17.6–39.9 2014 Apparently healthy adults with no major surgeries in the past year
Case (2015)
[30]
Philadelphia,
Pennsylvania,
US
14 (V) 28.1 (6.2) 22.7 (1.5) 2014 Apparently healthy adults
Dannecker
(2013) [31]
Fort Collins and
Denver,
Colorado, US
19 (V) 26.9 (6.6) 25.1 (4.6) 2010 Apparently healthy adults, inactive to moderately active (<6 hours/week of exercise)
de Zambotti
(2015a) [47]
San Francisco,
California, US
28 (V) 50.1 (3.9) 24.6 (3.6) 2014–
2015
Perimenopausal women
de Zambotti
(2015b) [48]
San Francisco,
California, US
65 (V) 15.8 (2.5) 21.2 (3.5) 2014 Apparently healthy without sleep disorders
Diaz (2015)
[15]
New York City,
New York, US
23 (V and R) 20–54 19.6–29.9 2013–
2014
Apparently healthy
Dontje
(2015)[32]
Groningen, The
Netherlands
1 (R) 46 Not reported 2012 Not reported
Ferguson
(2015) [33]
Adelaide, South
Australia
21 (V) 32.8 (10.2),
20–59
27.3 (3.2) male; 25.5 (5.2)
female
2013 Apparently healthy
Gusmer
(2014) [34]
Minneapolis,
Minnesota, US
32 (V) 21.1 (1.7), 18–
29
Not reported 2012 Apparently healthy
Lauritzen
(2013) [23]
Seville, Spain 6 (V) 35.3 (6.5), 24–
45
Not reported not
reported
Not reporting on sample with reduced mobility and no results on older sample with
normal mobility
Lee (2014)
[35]
Ames, Iowa, US 60 (V) 24.2 (4.7)
female; 28.6
(6.4) male
24.3 (2.6), 19.528.0 male;
21.8 (2.7), 18.1–31.2 female
2013 No major disease and nonsmokers
Mammen
(2012) [36]
Toronto, Canada 10 (V)and 1 (R) 23.0 (1.2), 20–
25
21.4 (1.9) 2011–
2012
Healthy young adults
Meltzer
(2015) [37]
Birmingham,
Alabama, US
63 (V) and 9 (R) 9.7 (4.6), 3–17 Not reported 2012–
2013
Sample referred to clinic for sleep disordered breathing; results of polysomnography
indicated: 61 % none, 23 % mild, 16 % moderate to severe
Montgomery-
Downs (2012)
[38]
Morgantown,
West Virginia,
US
24 (V) and 3 (R) 26.1, 19–41 Not reported 2010 Healthy adults, no sleep disorders
Sasaki (2015)
[39]
Amherst,
Massachusetts,
US
20 (V) 24.1 (4.5) 23.9 (2.9) 2011–
2012
Apparently healthy
Simpson
(2015) [46]
Vancouver,
Canada
42 (V) 73 (6.9) 26.1 (4.6) 2014 > = 65 years, able to walk unassisted
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 8 of 22
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Table 3 Characteristics of studies included in the systematic review (listed by author's last name and publication year) (Continued)
Stackpool
(2014) [40]
LaCrosse,
Wisconsin, US
20 (V) 18–44 Not reported 2013 Healthy volunteers; all were recreationally active (2–5 hours/week)
Stahl (2014)
[41]
Morgantown,
West Virginia,
US
10 (V) 63.8 (3.2), 60–
68
24.5 (4.2) 2011 None noted; on average participants reported 3 chronic health conditions, no
functional limitations, and rated their health as "good"
Storm (2015)
[42]
Sheffield, United
Kingdom
16 (V) 28.9 (2.7) 23.5 (2.3) 2013 No reported impairment or morbidity that could interfere with physical activity
assessment
Takacs (2014)
[43]
Vancouver,
Canada
30 (V and R) 29.6 (5.7) 22.7 (3.0) 2013 Able to walk on a treadmill for 30 min; no neurological, cognitive or musculoskeletal
disorders
Tully (2014)
[44]
Belfast, Northern
Ireland
42 (V) 43 Not reported 2013 Apparently healthy staff of Queen's University Belfast
Abbreviations: Rreliability sample size, SD standard deviation, US United States, Vvalidity sample size
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 9 of 22
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Table 4 Fitbit and Jawbone validity studies (listed by author's last name and publication year)
Sample characteristics Tracker wearing protocol Measurements Validity results
Author (year) n% female Activity Lab/
field
Validity criterion (measure
assessed)
Type Placement Measures
Adam Noah
et. al (2013) [29]
16 38 6 min each of treadmill walking
(3.5 mph), walking with incline
(3.5 mph at 5 %), jogging (5.5
mph), and stair stepping (30.5
centimeter step at 96 beats/min)
Lab Two Actical accelerometers
(steps), indirect calorimetry using
K4b2 Cosmed (EE)
Ultra
(Fitbit)
Waist (one
on each
side)
Steps/min,
kilocalories/
min
Fitbit Ultra vs. Actical ICC:
average 0.94, range 0.80–0.99
(steps); Fitbit Ultra vs. Cosmed
ICC: average 0.77, range 0.58-0.87
(kilocalories)
23 43 Classic
(Fitbit)
Waist (one
on each
side)
Steps/min,
kilocalories/
min
Fitbit vs. Actical ICC: average 0.93,
range 0.82–0.98 (steps); Fitbit vs.
Cosmed ICC: average 0.74, range
0.18-0.72 (kilocalories)
Bai et. al
(2015) [45]
52 46 20 min sedentary, 25 min
treadmill at self-selected speed,
25 min resistance exercise
Lab Indirect calorimetry using
Oxycon Mobile (EE)
Flex
(Fitbit)
Left wrist Kilocalories/
80- min trial
Overestimated overall EE by 20.4
kilocalories; Pearson CC 0.78; overall
mean absolute error 16.8 %
UP24
(Jawbone)
Right wrist Underestimated overall EE by 23.1
kilocalories; Pearson CC 0.77; overall
mean absolute error 18.2 %
Case et. al
(2015) [30]
14 71 Treadmill walking at 3.0 mph for
500 and 1500 steps, each done
twice
Lab Tally counter (steps) One
(Fitbit)
Waist Steps/trial 500 step trial (n= 27 observations)
mean 498.6 (SD 3.7); 1500 step trial
(n= 26 observations) mean 1497.0
(SD 10.7)
Zip
(Fitbit)
Waist Steps/trial 500 step trial (n= 27 observations)
mean 498.6 (SD 10.8); 1500 step
trial (n= 27 observations) mean
1498.4 (SD 10.4)
Flex
(Fitbit)
Wrist Steps/trial 500 step trial (n= 28 observations)
mean 465.4 (SD 92.1); 1500 step
trial (n= 28 observations) mean
1378.0 (SD 142.7)
UP24
(Jawbone)
Wrist Steps/trial 500 step trial (n=28observations)
mean 477.5 (SD 102.1); 1500 step
trial (n= 28 observations) mean
1477.0 (SD 174.4)
Dannecker et. al
(2013) [31]
19 (16 with
Fitbit data)
47 (from n= 19) Resting, supine, sitting, standing,
free living activity, and 6 random
activities out of 8 (walking (2.5
mph, 3.5 mph, or 2.5 mph with
2.5 % grade), stepping,
sweeping, cycling (75 watts),
standing, sitting
Lab 4 h stay in whole room
calorimeter (EE)
Classic
(Fitbit)
Belt at
anterior
superior
iliac spine
Total EE
during the 3.5-
h period while
in the room
calorimeter
(omitted first
30 minutes)
Root-mean-square error of
tracker 28.7 % or 143 kilocalories;
root-mean-square error of tracker
after labeling activities 12.9 % or
64 kilocalories
de Zambotti
et. al (2015a) [47]
28 100 One nights sleep (n= 10), 2
nights sleep (n=18)
Lab Polysomnography (sleep) UP
(Jawbone)
Non
dominant
wrist
TST, sleep
onset latency,
WASO
Overestimated TST by 26.6 ±
35.3 min (p< 0.001) and sleep
onset latency by 5.2 ± 9.6 min (p
= 0.005); underestimated WASO
by 31.2 ± 32.3 min (p< 0.001)
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 10 of 22
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Table 4 Fitbit and Jawbone validity studies (listed by author's last name and publication year) (Continued)
de Zambotti et.
al (2015b) [48]
65 43 One nights sleep Lab Polysomnography (sleep) UP
(Jawbone)
Non
dominant
wrist
TST, sleep
efficiency,
sleep onset
latency,
WASO
Overestimated TST by 10.0 min
(p< 0.001), sleep efficiency by
1.9 % (p< 0.001), and sleep onset
latency by 1.3 min (p= 0.33);
underestimated WASO by
10.6 min (p< .001)
Diaz et. al
(2015) [15]
23 57 6 min each of treadmill walking
(1.9 mph, 3.0 mph, 4.0 mph)
and jogging (5.2 mph)
Lab Counting from a video recording
(steps), indirect calorimetry using
Ultima CPX (EE)
One
(Fitbit)
2 on right
hip, 1 on
left hip
Steps/min,
kilocalories/
min
Pearson CC 0.97–0.99 and mean
difference −3.1 to −0.3 (steps);
Pearson CC 0.86-0.87 (kilocalories)
and mean difference −0.8 to 0.4
kilocalories
Flex
(Fitbit)
1 on each
wrist
Steps/min,
kilocalories/
min
Pearson CC 0.77-0.85 and mean
difference −26.3 to −2.9 (steps);
Pearson CC 0.88 and mean
difference −0.2 to 2.6 (kilocalories)
One
(Fitbit)
Right hip Steps/day,
MVPA min/
day,
kilocalories/
day, sleep
min/day
Pearson CC 0.99 (steps), 0.91
(MVPA), 0.76 (kilocalories), 0.92
(sleep); ICC 0.95 (steps), 0.46
(MVPA), 0.55 (kilocalories), 0.90
(sleep); mean absolute difference
779 (steps), 58.6 (MVPA), 349
(kilocalories), 23.0 (sleep); range of
differences = −890 to 1849 (steps),
1.0 to 137.2 (MVPA), −1724 to −83
(kilocalories), 45 to 76 (sleep)
Zip
(Fitbit)
Right hip Steps/day,
MVPA min/
day,
kilocalories/
day
Pearson CC 0.99 (steps), 0.88
(MVPA), 0.81 (kilocalories); ICC
0.98 (steps), 0.36 (MVPA), 0.57
(kilocalories); mean absolute
difference 447 (steps), 89.8
(MVPA), 484 (kilocalories); range
of differences −970 to 1596
(steps), 10.0 to 157.2 (MVPA),
−1145 to 218 (kilocalories)
Ferguson et. al
(2015) [33]
21 52 48 h (including sleep, excluding
showering) of free-living condi-
tions, no activity restrictions/
guidelines
Field BodyMedia SenseWear model
MF (steps, physical activity, EE,
sleep); ActiGraph GT3X+ (steps,
physical activity)
UP
(Jawbone)
Left wrist Steps/day,
MVPA min/
day,
kilocalories/
day, sleep
min/day
Pearson CC 0.97 (steps), 0.81
(MVPA), 0.74 (kilocalories), 0.89
(sleep); ICC 0.97 (steps), 0.70
(MVPA), 0.27 (kilocalories), 0.85
(sleep); mean absolute difference
806 (steps), 18.0 (MVPA), 866
(kilocalories), 22.0 (sleep); range of
differences −1978 to 2252 (steps),
−4.7 to 96.5 (MVPA), −1937 to −94
(kilocalories), −31 to 132 (sleep)
Gusmer et. al
(2014) [34]
32 78 30-min phases of treadmill
walking at slow and brisk
speeds (±10 % of selfselected
comfortable walking speed)
Lab ActiGraph G1TM (steps), CPX
Ultima metabolic cart (EE)
Ultra
(Fitbit)
Right hip Steps/min,
kilocalories/
trial
Pearson CC: slow walk: 0.97
(steps: mean 105.3 ActiGraph vs.
105.9 Ultra), 0.69 (kilocalories:
mean 100.9 cart vs. 88.0 Ultra);
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 11 of 22
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Table 4 Fitbit and Jawbone validity studies (listed by author's last name and publication year) (Continued)
brisk walking: 0.996 (steps: mean
114.2 ActiGraph vs. 113.9 Ultra),
0.94 (kilocalories: mean 121.9 cart
vs.100.9 Ultra)
Lauritzen et. al
(2013) [23]
6 0 20-meter walk at participant's
normal pace
Lab Counting from a video recording
(steps)
Ultra
(Fitbit)
1 on belt/
pants
pocket on
dominant
leg, 1 on
wrist of
dominant
hand
Steps/20-min
trial
Hip error 2.9 % (SD 2.3 %); wrist
error 31.3 % (SD 30.7 %)
One
(Fitbit)
Waist Kilocalories/
trial
Mean absolute error 10.4 %;
Pearson CC 0.81; root-mean-
square error 40.1; did not fall in
90 % equivalence interval; sys-
tematic bias with slope −0.22
comparing One (x) to Oxycon (y);
Pearson CC to ActiGraph 0.80
Lee et. al (2014)
[35]
60 50 13 activities that were all 5 min
in length except for treadmill
(3 min each) totalling
69 minutes
Lab Oxycon Mobile (EE); ActiGraph
GT3X+ worn on hip, applied
Sasaki et al. 2011 [39] algorithm
(EE)
Zip
(Fitbit)
Waist Kilocalories/
trial
Mean absolute error 10.1 %;
Pearson CC 0.81; root-mean-square
error 40.8; fell within 90 % equiva-
lence interval from measured EE;
systematic bias with slope - 0.29
comparing Zip (x) to Oxycon (y);
Pearson CC to ActiGraph 0.77
UP
(Jawbone)
Left wrist Kilocalories/
trial
Mean absolute error 12.2 %;
Pearson CC 0.74; root-mean-
square error 45.8; did not fall in
90 % equivalence interval; no
systematic direction of bias with
slope - 0.03 comparing UP (x) to
Oxycon (y); Pearson CC to
ActiGraph 0.65
Mammen et. al
(2012) [36]
10 50 One min on the treadmill at
each of 8 speeds (4 walking
and 4 running)
Lab Manually count (steps) Ultra
(Fitbit)
Waist,
inside the
pants
pocket,
shirt collar
(men) or
bra
(women)
Steps/trial Waist-worn Ultra under counted
at 2 km/hour (31 steps/min; p<
0.05) but had similar counts at >
=3 km/hour. Pocket- worn Ultra
under counted during running
(10, 19, 34, 38 steps/min at 8, 9, 10,
and 11 km/hour, respectively; p<
0.05), but recorded similar counts
when walking (2, 3, 4.5, and 6 km/
hour). Similar counts across walk/
run trials for collar-(males) or bra-
(females) worn Ultras.
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 12 of 22
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Table 4 Fitbit and Jawbone validity studies (listed by author's last name and publication year) (Continued)
Meltzer et. al
(2015) [37]
63 51 One night's sleep Lab Polysomnography (sleep) Ultra
(Fitbit)
Non
dominant
wrist
TST, sleep
efficiency,
WASO
Normal mode overestimated TST
by 41 minutes and sleep efficiency
by 8 %, underestimated WASO by
32 minutes; 87 % sensitivity, 52 %
specificity, 84 % accuracy. Sensitive
mode underestimated TST by
105 minutes and sleep efficiency
by 21 % and overestimated WASO
by 106 minutes; 70 % sensitivity,
79 % specificity, 71 % accuracy.
Montgomery-
Downs et. al
(2012) [38]
24 40 One night's sleep Lab Polysomnography (sleep) Classic
(Fitbit)
Non
dominant
wrist
TST, sleep
efficiency
Polysomnography recorded
465.0 min (SD 48.4) with 79.5 %
sleep efficiency and 370.9 min
(SD 70.3) TST; Fitbit measured
94.0 % sleep efficiency and
438.0 min TST; Fitbit
overestimated sleep efficiency
compared to polysomnography
by 14.5 % (SD 10.7 %) and
overestimated TST by mean
67.1 min (SD 51.3).
Sasaki et. al
(2015) [39]
20 50 Visit 1: 6 min each of treadmill
walking (3.0 at 5 % and 4.0 at
5 %) and jogging (5.5 mph),
three trials; visit 2: 6 min each
of household activities (choice
from 3 activity routines)
Lab Oxycon Mobile (EE) Classic
(Fitbit)
Belt around
waist in
line with
the anterior
axillary line
Total EE (rest
plus activity)
Pearson CC 0.86; systematic
underestimation of EE by the
Fitbit with a mean bias of −4.5 ±
1.0 kcals/6 min; for 6 of 15
activities the Fitbit significantly
underestimated EE (stairs, cycling,
laundry, raking, treadmill 3.0 mph
with 5 % grade, treadmill 4.0
mph with 5 % grade) and 1 of 15
activities the Fitbit significantly
overestimated EE (carrying
groceries)
Simpson et. al
(2015) [46]
42 74 8 trials of walking 15 meters
(self selected speed and 0.3-
0.9 m/s at 0.1 increments)
Lab Counting from a video recording
(steps)
One
(Fitbit)
Right waist,
right ankle
Steps/trial % error: 0.3 m/s: ankle 14.5, waist
98.4; 0.4 m/s: ankle 5.9, waist 82.0;
0.5 m/s: ankle 4.1, waist 40.4;
0.6 m/s: ankle 3.2, waist 21.6;
0.7 m/s: ankle 2.5, waist
10.5;0.8 m/s: ankle 2.8, waist 7.0;
0.9 m/s: ankle 2.8, waist 5.6; Bland
Altman mean difference −0.4 to
5.7 steps for ankle and 1.4 to 48.0
for waist
Stackpool et. al
(2014) [40]
20 50 20 min each of: treadmill
walking, treadmill running,
elliptical cross-training, agility-
related exercises
Lab Manually counting (steps);
Oxycon Mobile (EE)
Ultra
(Fitbit)
Hip Steps and
kilocalories
for each 20-
min bout
Pearson CC: treadmill walking
(0.99 steps, 0.24 kilocalories),
treadmill running (0.44 steps, 0.63
kilocalories), elliptical (0.99 steps,
0.47 kilocalories), agility (0.47
steps, 0.67 kilocalories)
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 13 of 22
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Table 4 Fitbit and Jawbone validity studies (listed by author's last name and publication year) (Continued)
UP
(Jawbone)
Wrist Steps and
kilocalories
for each 20-
min bout
Pearson CC: treadmill walking
(0.98 steps, 0.87 kilocalories),
treadmill running (0.98 steps, 0.69
kilocalories), elliptical (0.99 steps,
0.40 kilocalories), agility (0.34
steps, 0.65 kilocalories)
Stahl and
Insana (2014)
[41]
10 30 During waking hours for 10
consecutive days
Field Self-reported estimation of
expended kilocalories/week from
CHAMPS questionnaire (EE).
Note: kilocalories/week divided
by 7 to obtain kilocalories/day;
then basal metabolic rate was
added to the kilocalories/day.
Classic
(Fitbit)
Waist Kilocalories/
day
Pearson CC 0.61; Fitbit
underestimated by a mean of
195.0 kilocalories/day; 70 % of
participant's data were within 1
SD and 100 % were within 2 SD
Storm et. al
(2015) [42]
16 38 11-min walking protocol
(included indoor and outdoor
walking and steps) repeated at
self-selected natural, slow, and
fast speeds
Lab OPAL sensors placed on each
ankle (steps)
One
(Fitbit)
Left waist Steps/11-min
trial
1.1 % self-selected walk, 1.0 %
fast walk; limits of agreement 15
± 35 steps; under estimated for
slow walk (−25 mean steps), self-
selected walk (−12 mean steps),
fast walk (−9 mean steps)
UP
(Jawbone)
Right wrist Steps/11-min
trial
Mean absolute error 10.1 % slow
walk, 2.5 % self-selected walk,
2.1 % fast walk; limits of agree-
ment 16 ± 135; under estimated
for slow walk (−35 mean steps),
self-selected walk (−4 mean
steps), fast walk (−9 mean steps)
Takacs et. al
(2014) [43]
30 50 5 min each of treadmill walking
(0.90, 1.12, 1.33, 1.54, 1.78
meters/second)
Lab Motion capture system and
manually counting (steps);
treadmill output (distance)
One
(Fitbit)
1 right hip,
1 left hip, 1
in front
pocket of
the
dominant
leg
Steps/trial,
distance/trial
Steps: no significant difference
(p> 0.05) between observed and
One step counts at any of the 3
locations, ICC 0.97-1.00, relative
error <1.3 %. Distance: significant
differences between observed
and One distance, ICC 0.0-0.05,
relative error 5.0-39.6 %.
Tully et. al
(2014) [44]
42 60 7 days of free-living wear ex-
cluding water activities and
sleep
Field ActiGraph GT3X and Yamax
CW700 pedometer (steps,
physical activity)
Zip
(Fitbit)
Right waist Steps/day,
MVPA min/
day
Spearman CC: 0.91 (ActiGraph
steps), 0.86 (ActiGraph MVPA),
0.91 (Yamax steps)
Abbreviations: CC correlation coefficient, CHAMPS Community Healthy Activities Model Program for Seniors, EE energy expenditure, ICC intraclass correlation coefficient, km kilometers, mmeters, m/s meters/second,
min minute, mph miles per hour, MVPA moderate to vigorous physical activity, SD standard deviation, TST total sleep time, WASO wake after sleep onset
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 14 of 22
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participants, they found that the hip- and pocket-worn
One generally over-estimated distance at the slower
speeds (0.90–1.33 m/s), but under-estimated at faster
speeds (1.78 m/s) [43].
Validity for physical activity
The criterion measures for two studies exploring phys-
ical activity relied on other accelerometers (ActiGraph
GT3X [44] and ActiGraph GT3X+ [33], both using
Freedson et al. cutpoints [49], and Body Media Sense-
Wear [33]). Based on 42 participants wearing the Zip
for 1 week during waking hours, moderate-to-vigorous
physical activity showed almost perfect correlation
with an accelerometer (Spearman CC 0.86) [44]. How-
ever, in another study of 21 participants wearing the
Zip, One, and UP for 2 days without restrictions, com-
pared to an accelerometer the trackers generally over-
counted minutes of moderate-to-vigorous physical
activity (mean absolute difference 89.8, 58.6, 18.0 min/
day, respectively and intraclass CC 0.36, 0.46, 0.70,
respectively) [33].
Validity for energy expenditure
The criterion measures for energy expenditure assessed
in kilocalories was indirect calorimetry [15, 29, 34, 35,
39, 40, 45], direct calorimetry [31], accelerometry (Acti-
Graph GT3X+ with a conversion equation [50] to esti-
mate kilocalories [35] and BodyMedia SenseWear [33]),
and self-reported data using a questionnaire [41]. Gener-
ally, regardless of the criterion used, energy expenditure
was under-estimated for the Classic [29, 31, 39, 41], One
[33, 35], Flex, Ultra [29, 34] (for running, elliptical, and
agility drills [40]), Zip [33, 35], UP [33, 35](for agility
drills [40]), and UP24 [45]. When correlations were re-
ported, they ranged widely [15, 29, 34, 35, 45]. A few
studies indicated energy expenditure was over-estimated
compared to indirect calorimetry: the Ultra during walk-
ing [40], the Zip across a variety of laboratory-based ac-
tivities [35], the Flex during several combined activities
(sedentary, aerobic, and resistance exercises) [45], and
the UP during running [40].
Validity for sleep
Five studies explored the validity of sleep measures,
four using polysomnography (PSG) [37, 38, 47, 48] and
the other using the BodyMedia SenseWear device [33]
as the criterion. Compared to PSG, the Classic [38],
Ultra [37], and UP [47, 48] over-estimated total sleep
time and sleep efficiency and under-estimated wake
after sleep onset, resulting in high sensitivity and poor
specificity. However, for the Ultra when using the sen-
sitive mode setting, total sleep time and sleep effi-
ciency were under-estimated and wake after sleep
onset was over-estimated. In a study of 21 adults
wearing the One and UP for 2 days without restric-
tions, compared to an accelerometer the trackers gen-
erally over-estimated time in sleep (mean absolute
difference 23.0, 22.0 min/day, respectively and intra-
class CC 0.90, 0.85, respectively) [33].
Reliability
No study reported on the intradevice or interdevice reli-
ability of the Jawbone, or the intradevice reliability of the
Fitbit. Seven studies reported on the interdevice reliabil-
ity of several Fitbit trackers (Table 5), with sample sizes
ranging from one [32, 36] to 30 [43]. Four studies were
laboratory-based focusing solely on locomotion on the
treadmill [15, 29, 36, 43], two studies were laboratory-
based requiring monitoring with a PSG [37, 38], and one
study was field-based [32]. For any Fitbit tracker, inter-
device reliability was reported from five studies on steps
[15, 29, 32, 36, 43], one study on distance [43], no stud-
ies on physical activity, two studies on energy expend-
iture [15, 29], and two studies on sleep [37, 38]. The
following sections detail the reliability results for each of
the five measures.
Reliability for steps
Comparing two different hip-worn trackers for 16 to 23
participants during treadmill walking and running, the
intraclass CC was substantial to almost perfect for steps
taken for the Classic (range 0.86–0.91) and the Ultra
(range 0.76–0.99) [29]. In another study, during six
treadmill walking trials of 20 steps by one researcher,
three hip-worn Ultras were compared and all trackers
read within 5 % of each other [36]. In a field-based study
of 10 hip-worn Ultras all worn by the same person at
the same time for 8 days, the median intraclass CC was
0.90 for steps/minute, 1.00 for steps/hour, and 1.00 for
steps/day, and comparing across trackers, the maximum
difference was only 3.3 % [32].
Comparing three hip-worn Ones worn by 23 partici-
pants during treadmill walking and running, the Pearson
CC between the left and right hip, as well as both right
hips, was almost perfect for steps (0.99 and 0.99, re-
spectively) [15]. In another study, 30 participants wore
three Ones on their hips and front pants pocket while
walking or running at five different speeds on the tread-
mill and correlation for steps was almost perfect when
comparing across trackers (intraclass CC 0.95–1.00)
[43]. Lastly, comparing two wrist-worn Flex trackers
worn by 23 participants during treadmill walking and
running, the Pearson CC between the left and right wrist
was almost perfect for steps (0.90) [15].
Reliability for distance
In the only study of reliability assessment of distance, 30
participants wore three Ones on their hips and front
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 15 of 22
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Table 5 Fitbit and Jawbone reliability studies (listed by author's last name and publication year)
Sample
characteristics
Tracker wearing protocol Measurements Interdevice reliability results
Author (year) n% female Activity Lab/
field
Type Placement Measures
Adam Noah et.
al (2013) [29]
16 38 Treadmill walking (3.5 mph), walking
with incline (3.5 mph at 5 %), jogging
(5.5 mph), and stair stepping (30.5
centimeter step at 96 beats/min)
Lab Ultra
(Fitbit)
Waist (1 on each side) Steps/min,
kilocalories/min
ICC comparing 2 different devices worn at once:
range 0.76-0.99 (steps), range 0.91-0.97 (kilocalories)
23 43 Classic
(Fitbit)
Waist (1 on each side) Steps/min,
kilocalories/min
Comparing 2 different devices worn at once:
ICC = average 0.88, range 0.86-0.91 (steps);
average 0.87, range 0.74-0.92 (kilocalories)
Diaz (2015) [15] 23 57 6 min each of treadmill walking
(1.9 mph, 3.0 mph, 4.0 mph) and
jogging (5.2 mph)
Lab One
(Fitbit)
2 on right hip, 1 on left hip Steps/min,
kilocalories/min
Pearson CC left and right hips: 0.99 (steps),
0.97 (kilocalories); Pearson CC two right hip devices:
0.99 (steps), 0.96 (kilocalories)
Flex
(Fitbit)
1 on each wrist Steps/min,
kilocalories/min
Pearson CC left and right wrists: 0.90 (steps),
0.95 (kilocalories)
Dontje (2015)
[32]
1 0 8 consecutive days excluding sleep
and water-based activities
Field Ultra
(Fitbit)
5 over left pants pocket, 5 over
right pants pocket
Steps/min,
steps/hour,
steps/day
10 devices collected movement (yes vs no) across
minutes (98 %); two-way median ICC of absolute
agreement 0.90 (steps/min), 1.00 (steps/hour), 1.00
(steps/day); concordance CC 0.90 (steps/min), 1.00
(steps/hour), 0.99 (steps/day); from Bland-Altman
plots 95 % of the measures were within the
boundaries of 28 steps above and below the mean
difference; maximum difference for all devices was 3.3 %
Mammen (2012)
[36]
1 0 6 trials were performed while the
researcher wore the devices and
walked 20 steps
Lab Ultra
(Fitbit)
3 trials on right hip, 3 trials on
left hip
Steps/trial All trackers were within +/−5 % of each other
Meltzer (2015)
[37]
9 Not
reported
1 night's sleep Lab Ultra
(Fitbit)
2 on nondominant wrist TST, sleep
efficiency
Among n = 7: no differences between trackers for TST
(468.7 vs. 471.1 min normal mode; 300.4 vs. 289.9 min
sensitive mode) or sleep efficiency (92.9 % vs. 93.3 %
normal mode; 59.4 % vs. 57.4 % sensitive mode)
Montgomery-
Downs (2012)
[38]
3 Not
reported
1 night's sleep Lab Classic
(Fitbit)
2 on nondominant wrist Sleep vs. wake 3 participant's recorded 96.5 %, 99.1 %, and 97.6 %
agreement at 1-minute epochs
Takacs (2014)
[43]
30 50 5 min each of treadmill walking (0.90,
1.12, 1.33, 1.54, 1.78 meters/second)
Lab One
(Fitbit)
1 on the waist at each hip, 1 in
front pocket of the dominant
leg
Steps/trial,
distance/trial
Across 5 treadmill speeds ICC: range 0.95-1.00 (steps),
range 0.90-0.99 (distance)
Abbreviations: CC correlation coefficient, EE energy expenditure, ICC intraclass correlation coefficient, min minute, mph miles per hour, TST total sleep time
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 16 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
pants pocket while walking or running at five different
speeds on the treadmill and the correlation was almost
perfect for distance measurements across trackers (intra-
class CC 0.90–0.99) [43].
Reliability for energy expenditure
Comparing two different hip-worn trackers for 16–23
participants during treadmill walking and running, the
intraclass CC was substantial to almost perfect for kilo-
calories expended for the Classic (range 0.74–0.92) and
the Ultra (range 0.91–0.97) [29]. Comparing three hip-
worn Ones worn by 23 participants during treadmill
walking and running, the Pearson CC between the left
and right hip, as well as both right hips, was almost per-
fect for kilocalories expended (0.97 and 0.96, respect-
ively) [15]. These same participants wore two Flex
trackers on their wrists during treadmill walking and
running that had almost perfect correlation for kilocalo-
ries expended (0.95) [15].
Reliability for sleep
Three participants wore two Classics overnight and re-
corded almost perfect levels of agreement (96.5–99.1 %)
to classify whether the minute-level data was a sleep or
wake minute [38]. Similarly, nine youth participants
wore two Ultras on their wrist overnight, with data avail-
able for seven participants (one pair did not record and
one pair had significant discrepancies between readings)
[37]. They found similar readings for total sleep time
and sleep efficiency for either the normal or sensitive
mode.
Feasibility
Feasibility assessment was abstracted for the 22 studies
in this review. In total, seven of 18 studies reported on
missing or lost data, with the lab-based studies less likely
to report it than the field-based studies. For the lab mea-
surements, Case et al. [30] indicated 1.4 % of data were
missing from all tested trackers due to not properly set-
ting them to record steps, Dannecker et al. [31] indi-
cated incomplete data on two of 19 participants, and
Gusmer et al. [34] excluded six of 32 participants be-
cause ActiGraph step counts were about half of the
Ultra step counts (they note this is most likely an Acti-
Graph failure). For one night of recording in the sleep
laboratory, Meltzer et al. [37] reported missing data for
14 of 63 participants to assess validity, due to data not
recording for the Ultra (n= 12) and corrupted PSG files
(n= 2).
For a field-based study of 21 participants during 2 days
of wear some data were lost: moderate-to-vigorous phys-
ical activity (n= 7 due to data extraction of the One and
the Zip (i.e., certain data were only available for a limited
amount of time), n= 1 Zip malfunction), steps (n= 1 Zip
malfunction), energy expenditure (n= 1 Zip malfunc-
tion), and sleep (n= 2 participant error for the One)
[33]. In a second field-based study enrolling adults >
=60 years of age, authors excluded five of 15 participants
because they had difficulty with the Classic over the 10-
day period (two lost the tracker and three failed to plug
it into the wireless base to transmit data) [41]. In a sep-
arate field-based study, the Zip was worn over 1 week
and five of 47 participants had at least some missing
data [44].
Discussion
This review summarized the evidence for validity and re-
liability of activity trackers, identifying 22 studies pub-
lished since 2012. While conducting this review, we
learned how the trackers can be set-up to improve upon
off-the-shelf accuracy. Those testing and wearing the
trackers are encouraged to consider several tips to po-
tentially improve the trackers’performance (Table 6).
Validity and reliability
From this review, we found the validity (Fitbit and Jaw-
bone) and interdevice reliability (Fitbit) of steps counts
was generally high, particularly during laboratory-based
treadmill tests. When errors were higher, the direction
tended to be an under-estimation of steps by the tracker
compared to the criterion. This may be particularly
problematic at slow walking speeds, similar to findings
when testing pedometers [51]. Specifically for steps, if
the option is available to set stride length, this should
improve accuracy (Table 6). Hip-worn trackers generally
performed better at counting steps than trackers worn
elsewhere on the body, although Mammen et al. [36]
suggests moving the placement from the hip if being
worn by an older adult with slower gait speed. Only one
study assessed the validity and reliability of distance
walked, finding that while reliability was high, distance
was over-estimated at slower speeds and under-
estimated at faster speeds [43].
Compared to other accelerometers, one study indi-
cated that the trackers generally over-counted moderate-
to-vigorous physical activity, with some large differences
found (mean 0.3, 1.0, and 1.5 h/day for the UP, One, and
Zip, respectively) [33]. However, another study indicated
higher agreement [44]. It may be that the cutpoints [49]
used to define moderate-to-vigorous physical activity in
both studies were set too high, particularly for older or
inactive adults. The reliability of physical activity meas-
urement has not been tested in any study.
From 10 adult studies, we found that although interde-
vice reliability of energy expenditure was high, the valid-
ity of the tracker was lower. When reported, the CC
generally ranged from moderate to substantial agree-
ment. Across trackers, many studies indicated that the
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 17 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 6 Strategies to improve the activity tracker accuracy for steps, distance, physical activity, energy expenditure, and sleep
Instruction Explanation Web Links: accessed 10/14/2015
Wear the tracker in the same position
each day
While wearing the activity tracker in the same position daily may be obvious for the wrist-based
trackers, those worn on a pocket, bra, or hip could vary in accuracy depending on location. Trackers
are more accurate when worn close to the body
a
. For free-living research studies, the wearing loca-
tion should be standardized and communicated to participants.
a
http://help.fitbit.com/articles/en_US/Help_article/
How-do-I-wear-my-Zip/
Enter your details and sync At initial set-up, users should accurately enter height, weight, gender, and age into the application
and sync it to the tracker. For example, these characteristics, as well as heart rate if available, are
used by the Fitbit to calculate energy expenditure
b
. Related to this, if body weight meaningfully
changes, then updating the tracker with the new weight would be important.
b
http://help.fitbit.com/articles/en_US/Help_article/
How-does-Fitbit-know-how-many-calories-I-ve-
burned
For wrist-worn trackers, indicate if wearing
it on the dominant or non-dominant side
In the software set-up, indicate if possible whether the wrist-worn tracker is being worn on the dom-
inant or non-dominant hand. For Jawbone, trackers worn on the non-dominant wrist may be more
accurate
c
, probably because the non-dominant hand is less active than the dominant hand, so it
provides a better representation of overall body movement. Fitbit indicates that using the non-
dominant hand setting increases sensitivity of step counting and can be used if the tracker is under
counting steps
d
.
c
https://jawbone.com/up/faq
d
http://help.fitbit.com/articles/en_US/Help_article/
How-accurate-is-my-Surge
Calibrate stride length Calibrating stride length may improve distance measures. In our review, only one study indicated
that this was performed [34]. Fitbit indicates a default stride length is used otherwise, based on
height and gender
e
. Jawbone also provides information for calibration
f
.
d
http://help.fitbit.com/articles/en_US/Help_article/
How-do-I-measure-and-adjust-my-stride-length
e
https://help.jawbone.com/articles/en_US/
PKB_Article/424
Use add-on features and obtain updates Using add-on features and obtaining updates might become more important since future iterations
of algorithms to calculate physical activity or energy expenditure may use new features, such as
heart rate and respiration. For example, Fitbit indicates that trackers with heart rate better recognize
“active minutes”for physical activities that do not incorporate stepping, such as weight lifting or
rowing
e
.
f
https://help.fitbit.com/articles/en_US/Help_article/
What-are-very-active-minutes/
Add more information via the diary or
journal function
Providing information to the tracker on the specific physical activity being performed can help the
tracker learn what activities look like for the individual. This is particularly important if the algorithms
used by the activity tracker rely on machine learning techniques.
Interact with the sleep mode settings Interacting with the sleep mode settings may help the tracker learn if the user is sleeping, napping,
or awake. Fitbit indicates that the normal mode counts significant movements as being awake and
is appropriate for most users, while the sensitive setting will record nearly all movements as time
awake
f
.
g
http://help.fitbit.com/articles/en_US/Help_article/
Sleep-tracking-FAQs#Whatisthedifference
These options may not be available for all trackers that were reviewed
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 18 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
bias in mis-reporting was often an under-estimation of
energy expended.
For sleep among youth and adults, despite high reli-
ability, the trackers evaluated generally over-estimated
total sleep time [33, 37, 38, 47, 48], and when tested
against PSG the trackers over-estimated sleep efficiency
and under-estimated wake after sleep onset [37, 38, 47,
48]. These findings are similar to other studies of accel-
erometry, in which the devices are highly sensitive but
do not accurately detect periods of wake before and dur-
ing sleep [52]. However, for one tracker the sensitive
mode setting was tested, which under-estimated total
sleep time and sleep efficiency and over-estimated wake
after sleep onset [37]. Work is needed to improve the
validity of sleep measurement with these trackers, par-
ticularly when using them for only one or two nights of
testing [38]. It may be that newer trackers will perform
better if they “learn”when the person is asleep, awake,
or napping (Table 6).
Feasibility
Seven of 22 studies reported on missing or lost data,
ranging from approximately 1.4 to 22.2 % for laboratory-
based studies and 10.6 to 33.3 % for field-based studies.
Some of the lost data was attributable to the validation
criterion measure and not the trackers, and other lost
data were attributable to researcher error and not par-
ticipant error. Even so, researchers should anticipate
data loss based on these findings. Future studies should
report missing data and the reason for the loss. One
study in this review [44] and others not included [4, 8,
19, 53] report relatively high acceptability in wearing the
trackers. This type of information may help with under-
standing reasons for missing data in field-based studies,
particularly if they occur over long time periods.
For the companies
Through this review, we identified three recommenda-
tions manufacturers can contribute to enhance the use
of the trackers for research. First, the trackers contain
firmware, defined as an electronic component with em-
bedded software to control the tracker. Firmware can be
updated by the company at any time; when the tracker
is synched, the new software is updated. These software
changes can influence the measurement properties in ei-
ther positive or negative ways, and can change what
might have been previously confirmed or published.
Firmware may fix bugs or add features to the tracker, or
it may change how variables are calculated. However,
many other changes take place, which the consumer
cannot detect [54]. As an alternative, the company sup-
porting ActiGraph accelerometers currently makes firm-
ware updates available to the public via their website,
allowing researchers to assess those changes for impact
on the measurement properties of the accelerometer [55,
56]. A similar standard operating procedure would be a
beneficial approach for researchers using these trackers.
Second, Jawbone UP3 and UP4 trackers include bio-
electric impedance, with corresponding measures of
heart rate and respiration, and both skin and ambient
temperatures. Additionally, some of the newer Fitbit
trackers include GPS (Surge) and optical heart rate sen-
sors (Surge and Charge HR). With these enhancements,
the companies seemingly have the tools to determine
whether the tracker is being worn (e.g., adherence) and
whether it is being worn by the same individual (e.g.,
one body authentication) [8]. It would be beneficial if
the companies derived an indicator of wear and made
this available on a minute-by-minute level, corresponding
to other available data. Currently, neither the Jawbone nor
Fitbit indicate the time worn, which could impact all met-
rics studied in this review.
Third, the companies could allow access to more data
that are collected. At present, the trackers provide users
with only a subset of data that is actually collected. The
companies control the output available, making the day-
level summary variables the easiest to obtain. For example,
despite capturing GPS and heart rate on two trackers, Fit-
bit currently limits the export of these full datasets. Fur-
thermore, the resulting output is derived through
proprietary algorithms that may change over time and
with new features. In all likelihood, based on the perform-
ance of the trackers found in this review, these algorithms
are supported through machine learning techniques. At a
minimum, it would be helpful for companies to reveal
what pieces of data are being used by the trackers to cal-
culate each output measure. For example, Jawbone indi-
cates that height, weight, gender, age, and heart rate, if
available, are used to calculate physical activity [14].
Future research
In total, Fitbit offered at least 9 trackers since 2008 and
Jawbone offered at least 6 trackers since 2011. Until we
understand if the specifications within a company’s family
of trackers are similar, researchers should confirm the val-
idity and reliability of new trackers. Moreover, an argu-
ment could be made to test any new tracker, even if the
company confirms similar hardware and software pro-
cesses. With time, the trackers offer more features
through enhancements made to the trackers (Table 1).
Each new tracker feature needs testing for reliability, valid-
ity, and usability. Specific types of activities should also be
tested, similar to the study by Sasaki et al. [39]. While this
review focused on steps, distance, physical activity, energy
expenditure, and sleep, other features to test include num-
ber of stair flights taken, heart rate, respiration, location
via GPS technology, skin temperature, and ambient
temperature.
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 19 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Exploring the measurement properties of the trackers
in a wide variety of populations would also be important
in both laboratory and field settings. Free-living activities
may better reflect the true accuracy of the tracker, be-
cause daily activities include a considerable amount of
upper body movement that may or may not be accur-
ately captured by the trackers [35]. Currently, the review
only identified two studies that included children [37,
48]. Researchers mostly tested the trackers in middle-
aged adult populations with normal BMI. Since studies
of pedometers indicate lower accuracy among partici-
pants with higher BMI [57], it would be prudent to test
various trackers types and locations among participants
with higher BMI [43].
Moreover, with the proliferation of trackers, researchers
would benefit from an evidence-based position statement
on the properties necessary to consider a tracker valid and
reliable [38]. Guidance on equivalency of accelerometers
exists [58], but this review found a variety of statistical
methods applied to the data and interpreted slightly differ-
ently across studies. Those who conduct future studies on
the measurement properties of the trackers should be sure
to initialize the tracker properly and indicate in the publi-
cation how this was done so others can replicate the
process. Providing the specific tracker type, date pur-
chased, and date tested would also be important.
Notably there were no reliability studies of any Jawbone
tracker or the Fitbit Zip, and no intradevice reliability
studies of any trackers. While more field-based studies are
needed, the laboratory studies indicated high interdevice
reliability for measuring steps, energy expenditure, and
sleep. Only one study assessed distance, also finding high
interdevice reliability during treadmill walking and run-
ning [43]. It would be ideal practice for all studies or pro-
grams to test the trackers for reliability before deploying
them for either measurement or intervention.
While not reviewed here, researchers should also con-
sider issues related to privacy and informed consent with
activity trackers and smart phone applications [59, 60].
Since the trackers can measure and store data for long
periods of time passively, providing informed consent
takes on new meaning with the extended time period,
locational information, and re-use of data in successive
analyses. Users should also be aware that the companies
access and use the data that are entered and collected
[61]. Recent examples include an indication of the states
with the most steps by Fitbit users [62] and the impact
of the prior day’s sleep and steps taken on self-reported
mood by Jawbone users [63].
Limitations
Our review has several limitations. The literature on ac-
tivity trackers is rapidly building and it is possible that
studies were missed despite our best efforts. We
encountered some challenges with comparing across
studies, due to varying methods and reported results.
The findings should be viewed in light of the variety of
study protocols and methodology.
When we began the systematic review in fall 2014, we
were guided by the most recent market data available at
that time, indicating that Fitbit and Jawbone represented
the majority of the consumer market [2]. In June 2015,
market share from the first quarter sales in 2015 indi-
cated the top five vendors were Fitbit (34 %), Xiaomi
(25 %), Garmin (6 %), Samsung (5 %), and Jawbone (4 %)
[64]. There is a built-in time lag between manufacturing
and sale of activity trackers to use in the research labora-
tory and field. Thus, some activity trackers that are cur-
rently available to consumers were not represented in
this review, but should be considered as future studies
accumulate on new devices and brands.
Conclusions
This systematic review of 22 studies included assess-
ments of five Fitbit and two Jawbone trackers, focusing
on validity and reliability of steps, distance, physical ac-
tivity, energy expenditure, and sleep. No single specific
tracker had a complete assessment across the five mea-
sures. This review also described several ways to im-
prove the trackers’accuracy, offered recommendations
to companies selling the trackers, and identified future
areas of research. Generally, the review indicated higher
validity of steps, fewer studies on distance and physical
activity, and lower validity for energy expenditure and
sleep. These studies also indicated high interdevice reli-
ability for steps, energy expenditure, and sleep for cer-
tain Fitbit models, but with no studies on the Jawbone.
As new activity trackers and features are introduced to
the market, documentation of the measurement proper-
ties can guide their use in research settings.
Additional file
Additional file 1: Flow of article selection using the PRISMA
schematic (Liberati et al., 2009 [27]; Moher et al., 2009 [28]).
(PDF 62 kb)
Abbreviations
BMI: Body mass index; CC: Correlation coefficient; GPS: Global positioning
system; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-
Analyses; PSG: Polysomnography; SD: Standard deviation; US: United States.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
KRE developed the aims of the study, helped conduct the literature review,
coded all articles, contacted authors for missing information, and drafted the
paper. All remaining authors provided critical feedback on several earlier
drafts of the paper. MMG also conducted the final literature review and
coded all articles. All authors read and approved the final manuscript.
Evenson et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:159 Page 20 of 22
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Acknowledgment
We thank Sonia Grego, Sara Satinsky, and the anonymous reviewers for
comments on earlier drafts of this paper. We also thank the authors of the
reviewed studies for responding to our requests for further information and
clarification. This work was supported, in part, by RTI International through
the RTI University Scholars Program and iSHARE. The content is solely the
responsibility of the authors and does not necessarily represent the official
views of RTI International.
Received: 5 August 2015 Accepted: 4 December 2015
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