Systematic Review of the Validity and Reliability of Consumer-wearable Activity Trackers

• Review
• Open Admission
• Published: eighteen December 2015

Systematic review of the validity and reliability of consumer-wear activity trackers

International Journal of Behavioral Nutrition and Physical Activity volume 12, Article number:159 (2015) Cite this commodity

• 53k Accesses

• 658 Citations

• 425 Altmetric

• Metrics details

Abstract

Background

Consumer-vesture activity trackers are electronic devices used for monitoring fettle- and other health-related metrics. The purpose of this systematic review was to summarize the evidence for validity and reliability of popular consumer-vesture activity trackers (Fitbit and Jawbone) and their power to judge steps, distance, physical activity, free energy expenditure, and sleep.

Methods

Searches included merely full-length English language language studies published in PubMed, Embase, SPORTDiscus, and Google Scholar through July 31, 2015. Ii people reviewed and bathetic each included study.

Results

In total, 22 studies were included in the review (twenty 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 written report assessed distance for the Fitbit, finding an over-gauge at slower speeds and nether-estimate at faster speeds. Two field-based studies compared accelerometry-assessed physical activity to the trackers, with ane study finding college correlation (Spearman CC 0.86, Fitbit) while another report establish a broad range in correlation (intraclass CC 0.36–0.seventy, Fitbit and Jawbone). Using several different comparison measures (indirect and directly calorimetry, accelerometry, self-study), energy expenditure was more often under-estimated by either tracker. Total slumber 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 establish. Interdevice reliability was reported on 7 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 betwixt the devices was high.

Decision

This systematic review indicated higher validity of steps, few studies on distance and physical activity, and lower validity for free energy expenditure and slumber. The show reviewed indicated high interdevice reliability for steps, distance, energy expenditure, and sleep for sure Fitbit models. Equally new activity trackers and features are introduced to the market, documentation of the measurement properties can guide their use in enquiry settings.

Background

Consumer wearable devices are a popular and growing market for monitoring physical action, sleep, and other behaviors. The devices helped to abound what is known as the Quantified Self move, engaging those who wish to runway their ain personal information to optimize health behaviors [1]. A subset of consumer clothing devices used for monitoring concrete activeness- and fitness-related metrics are referred to every bit "action trackers" or "fitness trackers" [2]. Their popularity has risen as they have go more than affordable, unobtrusive, and useful in their awarding. An activity tracker tin can provide feedback and offering interactive behavior change tools via a mobile device, base station, or estimator for long-term tracking and information 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 exist 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, v].

A national United States (US) survey completed in 2012 indicated 69 % of adults tracked at least 1 wellness indicator for themselves, a family member, or friend using a tracking device (such as an activity tracker), paper tracking, or some other method [6]. From this survey, sixty % of adults reported tracking weight, diet, or practice. Those who tracked weight, nutrition, or do were similar past 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 to the lowest degree ane health behavior or condition, 21 % used some course of technology to runway the health information. Also among this group, 46 % indicated that tracking inverse 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 second stance, and 34 % indicated that it affected a decision nearly how to care for an illness or status.

Activity trackers are beingness used not only in the consumer market simply also in research studies. Physical activeness-related interventions are using activity trackers for self-monitoring, reinforcement, goal-setting, and measurement (examples amidst adults [4, 7–eleven] and youth [12]). Before more widespread apply of these trackers occurs in enquiry studies, for either intervention or measurement purposes, it is important to plant their validity and reliability.

The purpose of this review was to summarize the testify for validity and reliability of the most pop consumer-wearable activity trackers. Among a multifariousness of trackers on the market place, approximately 3.3 million sold betwixt April 2013 to March 2014, with 96 % made by Fitbit (67 %), Jawbone (18 %), and Nike (eleven %) [2]. Since Nike discontinued the auction of Fuelbands in 2014, our focus for this review was on activity trackers made by Fitbit and Jawbone. Before conducting the review, we searched visitor websites for documentation on the accurateness of measuring steps, distance, concrete activeness, energy expenditure, and sleep. The Fitbit company indicated that afterward multiple internal studies, they had "tuned the accurateness of the Fitbit tracker step counting functionality over hundreds of tests with multiple trunk types. All Fitbit trackers should exist 95–97 % accurate for pace counting when worn as recommended" [13]. However, 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 atmospheric condition can influence specific calculations, testing has shown UP to provide industry-leading accurateness 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, free energy expenditure, and slumber. For each study included in the review, we likewise abstracted 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 linguistic communication journals through July 31, 2015. No get-go date was imposed in the search. If a publication was available online first before print, we attempted to obtain a re-create; thus, some publications were officially published afterwards July 31, 2015 but were bachelor in the databases during our search period. Two separate searches were performed for the 2 activity trackers.

1. (one)

   (Fitbit) AND (validity OR validation OR validate OR comparison OR comparisons OR comparative OR reliability OR accuracy)

2. (2)

   (Jawbone) AND monitor AND (validity OR validation OR validate OR comparing 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, sixteen]) and conference proceedings (instance [17]). We likewise excluded studies focused on special populations, such equally stroke and traumatic encephalon injury [18], chronic obstructive pulmonary illness [nineteen], amputation [20], mental illness [21], or older adults in assisted living [22]. 1 study presented data on manifestly healthy older adults without mobility impairments and those of similar ages with reduced mobility; therefore, we reported merely on those without mobility impairments [23].

Abstraction and analysis

First, nosotros 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 brainchild tool used for this review was expanded from a tool initially created by De Vries et al. [24] to document study characteristics and measurement properties of the activity trackers. Specifically, we extracted information on the report population, protocol, statistical analysis, and results related to validity, reliability, and feasibility. We as well extracted any information provided by the studies on items entered into the activity tracker user account settings. A primary reviewer extracted details and a 2nd reviewer checked each entry. Discrepancies in coding were resolved by consensus. For any abstracted data that was missing from the publication, we attempted to contact at to the lowest degree 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 benchmark measure out of steps, altitude 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 activeness trackers included [25]:

• Intradevice reliability: test-retest results indicating consistency within the aforementioned tracker. This can exist conducted in the lab (such as on a shaker tabular array).

• Interdevice reliability: results indicating consistency across the same make/type of tracker measured at the same fourth dimension and worn in the same location. This can be assessed during activities performed in the laboratory or while complimentary-living.

Nosotros interpreted the correlation coefficients (CC) using the following ratings: 0- < 0.2 poor, 0.ii- < 0.iv fair, 0.4- < 0.6 moderate, 0.half dozen- < 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 wearing the trackers by participants.

Results

>Through the systematic search, 67 records were identified, 39 were screened, and 22 were included in the review that reported on the validity or reliability of whatever 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). Xx studies reported on at least ane type of Fitbit tracker [fifteen, 23, 29–46] and eight reported on at to the lowest degree one type of Jawbone tracker [thirty, 33, 35, 40, 42, 45, 47, 48].

Fitbit tracker

The Fitbit visitor (San Francisco, CA; https://www.fitbit.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 accelerometer and more recently an altimeter, heart charge per unit, and global positioning organisation (GPS) monitor. Using proprietary algorithms, data from measures collected along with information input by the user can judge steps, distance, physical activity, kilocalories, and sleep. Twenty-four hours-level information is summarized and available to the consumer. Minute-level data (called "intraday") requires more effort to obtain, such every bit through the Fitbit API [32], and tin be ready at intervals of one, 5, 10, 15, xx, or 60 min. Alternatively, data can exist extracted using third-political party service providers, such as Fitabase (Pocket-size Steps Labs LLC; https://www.fitabase.com), equally was used in the written report by Diaz et al. [15].

Table ane Fitbit and Jawbone activity tracker characteristics (searched May-July 2015)

Full size tabular array

The Fitbit I updated the Fitbit Ultra in 2012, which in turn updated the Fitbit Classic in 2011, and all three are shaped similarly every bit a clip. The Fitbit Zippo is teardrop-shaped and the Fitbit Flex is designed for the wrist. The following Fitbit trackers were explored for validity (Table 2):

1. (1)

   Archetype worn at the waist [29, 31, 39, 41] and not-dominant wrist [38];

2. (two)

   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. (3)

   Ane worn at the waist [fifteen, thirty, 32, 33, 35, 42, 43, 46], pants pocket [43], and ankle [46];

4. (4)

   Zip worn at the waist [thirty, 33, 35, 44]; and

5. (five)

   Flex worn on the wrist [xv, 30, 45].

Table ii Fitbit and Jawbone studies of interdevice reliability and validity (listed by author's concluding name and publication year)

Full size table

Reliability studies included the Classic worn at the waist [29] and non-ascendant 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 [xv, 43] and pants pocket [43]; and the Flex worn on the wrist [15].

Jawbone tracker

The Jawbone visitor (San Francisco, CA; https://jawbone.com) has offered at least half dozen activity trackers since 2011 (Table 1). Their trackers are worn at the wrist, with the exception of the Upwards Movement tracker to exist worn at the waist, pocket, or bra. The trackers contain a triaxial accelerometer, collecting information at 30 Hertz, and more recently bioelectrical impedance (for eye rate, respiration, and skin response), as well every bit both skin and ambient temperatures. Using proprietary algorithms, data from measures collected forth with data input past the user can judge steps, distance, physical action, kilocalories, and sleep. Currently, only day-level data is bachelor to the consumer.

The following ii Jawbone trackers, both designed for the wrist, were explored for validity (Table 2):

1. (i)

   Upwardly worn on the wrist [33, 35, forty, 42, 47, 48] and

2. (two)

   UP24 worn on the wrist [thirty, 45].

No Jawbone trackers were explored for reliability.

About one-half of the studies reported the data entered into the tracker user account [29, 33–35, 39, 41, 43], which was commonly historic period, gender, height, and weight. One study also reported inbound step length [34], some other report input handedness and smoking status [35], and some other study used effect markers to announce when an activeness started and ended [39]. A sleep written report indicated that they manually switched the band from agile to sleep mode in conjunction with lights on/off [48]. Other studies did non report what information were input into the user account [15, 23, 30–32, 36–38, 40, 42, 44–47].

Clarification of studies

Information drove was primarily conducted in the Usa, with one or two studies conducted in Australia [33], Canada [36, 43, 46], the Netherlands [32], Northern Ireland [44], Spain [23], and the Britain [42] (Table three). Studies unremarkably included an obviously salubrious sample and, where reported, almost all participants had a normal body mass index (BMI). Additionally, participants were > =xviii years and mostly younger to middle age, except for one report focusing exclusively on adults > =threescore years [41] and two studies on youth [37, 48]. Data were collected between 2010 [38] to 2015 [47].

Tabular array three Characteristics of studies included in the systematic review (listed by author'southward last name and publication year)

Full size table

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 whatever Fitbit tracker, validity was reported from 12 studies on steps [15, 23, 29, thirty, 33, 34, 36, 40, 42–44, 46], one written report on distance [43], two studies on concrete activity [33, 44], ten studies on energy expenditure [xv, 29, 31, 33–35, 39–41, 45], and 3 studies on sleep [33, 37, 38] (Tabular array 2). For any Jawbone tracker, validity was reported from four studies on steps [xxx, 33, forty, 42], nada 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 post-obit sections detail the validity results for each of the five measures.

Tabular array 4 Fitbit and Jawbone validity studies (listed past author'southward concluding proper name and publication yr)

Full size table

Validity for steps

The criterion measures for counting steps included comparisons against manual stride counting, either in-person [30, 36, 40] or with video recording [fifteen, 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], Torso Media SenseWear [33], and Opal sensors [42]). Hip-worn trackers by and large outperformed wrist-worn trackers for step accuracy [15, 23, 30, 40]. I report establish less error for the ankle-worn One compared to the waist-worn One [46].

For laboratory-based studies using pace counting equally the criterion [15, 23, 43], correlation with steps from the tracker was generally high (if reported, the mean correlations were > =0.80) for the Ultra (for most treadmill speeds [36]; for treadmill walking and elliptical but non for running or agility drills [xl]), One [30, 43], Cipher [30], and UP (for treadmill walking, running, and elliptical [40]) trackers. Still, several studies indicated that the One [15], Flex [15, thirty], Ultra (waist worn at slower walking speed (two km/h) and the pocket worn at faster speeds (> = 8 km/h)) [36]), and UP24 [thirty] under-estimated steps during treadmill walking and running.

For studies using accelerometry equally the criterion, correlation with tracker steps was also generally loftier (if reported, the mean correlations were > =0.80) for the Archetype [29], Ultra [29, 34], Nil [44], I [33], and UP [33] trackers. However, several studies indicated that the One [42], Flex [fifteen, 30], Upwardly [33](at boring walking speeds [42]), and UP24 [30] nether-estimated steps during treadmill walking and running. In contrast, in a study of 21 participants wearing the Ane for 2 days without restrictions, compared to an accelerometer the tracker generally over-counted steps for the One (mean accented divergence 779 steps/day) [33]. In one free-living study, the researcher wore both the Ultra and a Yamax pedometer while seated in a car driving on paved roads for about 20 min [36]. During this time no steps were recorded 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 equally the benchmark. Amidst xxx participants, they establish that the hip- and pocket-worn One generally over-estimated distance at the slower speeds (0.xc–1.33 one thousand/s), but under-estimated at faster speeds (one.78 chiliad/south) [43].

Validity for physical activity

The criterion measures for 2 studies exploring concrete action relied on other accelerometers (ActiGraph GT3X [44] and ActiGraph GT3X+ [33], both using Freedson et al. cutpoints [49], and Torso Media SenseWear [33]). Based on 42 participants wearing the Zip for 1 week during waking hours, moderate-to-vigorous concrete activity showed almost perfect correlation with an accelerometer (Spearman CC 0.86) [44]. However, in some other study of 21 participants wearing the Naught, I, and Upwards for 2 days without restrictions, compared to an accelerometer the trackers generally over-counted minutes of moderate-to-vigorous concrete activity (hateful absolute deviation 89.8, 58.6, eighteen.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 [fifteen, 29, 34, 35, 39, 40, 45], direct calorimetry [31], accelerometry (ActiGraph GT3X+ with a conversion equation [50] to estimate kilocalories [35] and BodyMedia SenseWear [33]), and self-reported data using a questionnaire [41]. Generally, regardless of the criterion used, energy expenditure was under-estimated for the Archetype [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 reported, they ranged widely [15, 29, 34, 35, 45]. A few studies indicated energy expenditure was over-estimated compared to indirect calorimetry: the Ultra during walking [forty], the Aught across a variety of laboratory-based activities [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 Upwardly [47, 48] over-estimated full sleep fourth dimension and sleep efficiency and under-estimated wake afterward slumber onset, resulting in high sensitivity and poor specificity. However, for the Ultra when using the sensitive mode setting, total sleep time and sleep efficiency were under-estimated and wake after sleep onset was over-estimated. In a study of 21 adults wearing the Ane and Upwardly for ii days without restrictions, compared to an accelerometer the trackers generally over-estimated fourth dimension in sleep (mean absolute difference 23.0, 22.0 min/day, respectively and intraclass CC 0.90, 0.85, respectively) [33].

Reliability

No study reported on the intradevice or interdevice reliability of the Jawbone, or the intradevice reliability of the Fitbit. Seven studies reported on the interdevice reliability of several Fitbit trackers (Tabular array 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], 2 studies were laboratory-based requiring monitoring with a PSG [37, 38], and one study was field-based [32]. For any Fitbit tracker, interdevice reliability was reported from 5 studies on steps [15, 29, 32, 36, 43], one written report on distance [43], no studies on concrete activeness, two studies on energy expenditure [15, 29], and two studies on sleep [37, 38]. The post-obit sections detail the reliability results for each of the five measures.

Table v Fitbit and Jawbone reliability studies (listed by author's terminal proper name and publication year)

Total size table

Reliability for steps

Comparing 2 different hip-worn trackers for 16 to 23 participants during treadmill walking and running, the intraclass CC was substantial to nearly 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 inside v % of each other [36]. In a field-based study of ten hip-worn Ultras all worn by the same person at the same fourth dimension for 8 days, the median intraclass CC was 0.90 for steps/infinitesimal, 1.00 for steps/hour, and one.00 for steps/twenty-four hours, and comparing across trackers, the maximum departure was only 3.3 % [32].

Comparison 3 hip-worn Ones worn by 23 participants during treadmill walking and running, the Pearson CC between the left and right hip, equally well every bit both right hips, was nigh perfect for steps (0.99 and 0.99, respectively) [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 treadmill and correlation for steps was near perfect when comparison beyond 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.ninety) [15].

Reliability for distance

In the only study of reliability assessment of distance, thirty participants wore three Ones on their hips and forepart pants pocket while walking or running at five different speeds on the treadmill and the correlation was almost perfect for distance measurements across trackers (intraclass CC 0.90–0.99) [43].

Reliability for free energy expenditure

Comparing ii different hip-worn trackers for 16–23 participants during treadmill walking and running, the intraclass CC was substantial to almost perfect for kilocalories expended for the Archetype (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, besides every bit both right hips, was almost perfect for kilocalories expended (0.97 and 0.96, respectively) [15]. These same participants wore ii Flex trackers on their wrists during treadmill walking and running that had almost perfect correlation for kilocalories expended (0.95) [15].

Reliability for slumber

Iii participants wore two Classics overnight and recorded almost perfect levels of understanding (96.v–99.i %) to classify whether the infinitesimal-level data was a sleep or wake infinitesimal [38]. Similarly, nine youth participants wore two Ultras on their wrist overnight, with data bachelor for seven participants (one pair did not record and one pair had pregnant discrepancies between readings) [37]. They found like readings for full 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 probable to study it than the field-based studies. For the lab measurements, Case et al. [30] indicated ane.4 % of data were missing from all tested trackers due to not properly setting them to record steps, Dannecker et al. [31] indicated incomplete data on two of 19 participants, and Gusmer et al. [34] excluded vi of 32 participants because ActiGraph stride counts were about one-half of the Ultra step counts (they note this is about likely an ActiGraph 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 (northward = 12) and corrupted PSG files (n = 2).

For a field-based study of 21 participants during 2 days of habiliment some data were lost: moderate-to-vigorous concrete action (n = seven due to information extraction of the One and the Zip (i.east., certain data were just available for a limited amount of time), n = 1 Cypher malfunction), steps (n = 1 Zip malfunction), free energy expenditure (n = one Aught malfunction), and slumber (n = 2 participant error for the One) [33]. In a second field-based report enrolling adults > =60 years of historic period, authors excluded v of 15 participants because they had difficulty with the Classic over the 10-day period (2 lost the tracker and 3 failed to plug it into the wireless base of operations to transmit data) [41]. In a separate 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 prove for validity and reliability of action trackers, identifying 22 studies published since 2012. While conducting this review, we learned how the trackers tin can exist set-upwardly to ameliorate upon off-the-shelf accuracy. Those testing and wearing the trackers are encouraged to consider several tips to potentially better the trackers' functioning (Table half-dozen).

Tabular array 6 Strategies to ameliorate the activity tracker accuracy for steps, altitude, physical action, energy expenditure, and sleep

Full size table

Validity and reliability

From this review, we found the validity (Fitbit and Jawbone) and interdevice reliability (Fitbit) of steps counts was generally loftier, particularly during laboratory-based treadmill tests. When errors were college, the direction tended to exist an nether-estimation of steps by the tracker compared to the criterion. This may be particularly problematic at wearisome walking speeds, like to findings when testing pedometers [51]. Specifically for steps, if the option is bachelor to set footstep 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 developed with slower gait speed. Only one study assessed the validity and reliability of distance walked, finding that while reliability was loftier, altitude was over-estimated at slower speeds and under-estimated at faster speeds [43].

Compared to other accelerometers, ane written report indicated that the trackers more often than not over-counted moderate-to-vigorous concrete activity, with some large differences plant (mean 0.iii, one.0, and i.5 h/day for the UP, One, and Nix, respectively) [33]. Withal, some other report indicated college agreement [44]. Information technology may exist that the cutpoints [49] used to define moderate-to-vigorous physical action in both studies were set too loftier, particularly for older or inactive adults. The reliability of physical activeness measurement has not been tested in any study.

From 10 developed studies, nosotros found that although interdevice reliability of energy expenditure was high, the validity of the tracker was lower. When reported, the CC generally ranged from moderate to substantial agreement. Across trackers, many studies indicated that the bias in mis-reporting was often an under-estimation of energy expended.

For sleep among youth and adults, despite high reliability, the trackers evaluated mostly over-estimated full slumber time [33, 37, 38, 47, 48], and when tested confronting 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 accelerometry, in which the devices are highly sensitive simply do not accurately notice periods of wake before and during sleep [52]. However, for one tracker the sensitive mode setting was tested, which under-estimated total sleep time and slumber efficiency and over-estimated wake after sleep onset [37]. Work is needed to improve the validity of slumber measurement with these trackers, particularly when using them for only ane 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 half dozen).

Feasibility

Vii of 22 studies reported on missing or lost data, ranging from approximately one.four to 22.ii % for laboratory-based studies and ten.6 to 33.iii % for field-based studies. Some of the lost data was attributable to the validation criterion measure and non the trackers, and other lost data were owing to researcher fault and not participant error. Even so, researchers should anticipate data loss based on these findings. Time to come studies should written report missing information and the reason for the loss. One study in this review [44] and others not included [4, viii, 19, 53] report relatively loftier acceptability in wearing the trackers. This blazon of data may help with agreement reasons for missing data in field-based studies, specially if they occur over long fourth dimension periods.

For the companies

Through this review, nosotros identified three recommendations manufacturers can contribute to heighten the use of the trackers for inquiry. First, the trackers incorporate firmware, defined equally an electronic component with embedded software to control the tracker. Firmware can be updated by the visitor at whatsoever time; when the tracker is synched, the new software is updated. These software changes can influence the measurement properties in either positive or negative ways, and can change what might have been previously confirmed or published. Firmware may prepare bugs or add features to the tracker, or it may alter how variables are calculated. Even so, many other changes take place, which the consumer cannot detect [54]. As an alternative, the company supporting ActiGraph accelerometers currently makes firmware updates bachelor to the public via their website, allowing researchers to assess those changes for bear on on the measurement properties of the accelerometer [55, 56]. A similar standard operating procedure would be a benign approach for researchers using these trackers.

2d, Jawbone UP3 and UP4 trackers include bioelectric impedance, with corresponding measures of heart rate and respiration, and both skin and ambience temperatures. Additionally, some of the newer Fitbit trackers include GPS (Surge) and optical centre rate sensors (Surge and Charge HR). With these enhancements, the companies seemingly have the tools to determine whether the tracker is being worn (due east.g., adherence) and whether it is existence worn by the same private (e.chiliad., one trunk authentication) [8]. Information technology would be beneficial if the companies derived an indicator of wear and made this bachelor on a minute-by-minute level, corresponding to other available data. Currently, neither the Jawbone nor Fitbit betoken the time worn, which could impact all metrics studied in this review.

3rd, the companies could allow access to more data that are collected. Now, the trackers provide users with simply a subset of data that is actually nerveless. The companies control the output available, making the day-level summary variables the easiest to obtain. For example, despite capturing GPS and middle charge per unit on two trackers, Fitbit currently limits the export of these total datasets. Furthermore, the resulting output is derived through proprietary algorithms that may change over time and with new features. In all likelihood, based on the operation of the trackers institute 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 past the trackers to calculate each output measure. For example, Jawbone indicates that height, weight, gender, historic period, and center rate, if available, are used to summate concrete activity [xiv].

Hereafter enquiry

In total, Fitbit offered at least 9 trackers since 2008 and Jawbone offered at to the lowest degree 6 trackers since 2011. Until we sympathise if the specifications within a visitor's family unit of trackers are similar, researchers should confirm the validity and reliability of new trackers. Moreover, an argument could be made to test whatever new tracker, even if the company confirms similar hardware and software processes. With time, the trackers offer more features through enhancements made to the trackers (Table ane). Each new tracker feature needs testing for reliability, validity, and usability. Specific types of activities should also exist tested, like 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 number of stair flights taken, heart rate, respiration, location via GPS technology, pare temperature, and ambience temperature.

Exploring the measurement properties of the trackers in a wide variety of populations would likewise be of import in both laboratory and field settings. Costless-living activities may better reflect the true accurateness of the tracker, because daily activities include a considerable corporeality of upper body motion that may or may not be accurately captured past the trackers [35]. Currently, the review only identified two studies that included children [37, 48]. Researchers mostly tested the trackers in eye-aged developed populations with normal BMI. Since studies of pedometers bespeak lower accuracy among participants with college BMI [57], it would be prudent to test various trackers types and locations amid 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], only this review found a variety of statistical methods practical to the data and interpreted slightly differently beyond studies. Those who behave future studies on the measurement properties of the trackers should be sure to initialize the tracker properly and signal in the publication how this was done and so others can replicate the process. Providing the specific tracker blazon, date purchased, and date tested would likewise be important.

Notably in that location were no reliability studies of any Jawbone tracker or the Fitbit Zip, and no intradevice reliability studies of whatever trackers. While more field-based studies are needed, the laboratory studies indicated high interdevice reliability for measuring steps, energy expenditure, and sleep. Only one report assessed altitude, also finding high interdevice reliability during treadmill walking and running [43]. It would exist ideal practice for all studies or programs to test the trackers for reliability before deploying them for either measurement or intervention.

While not reviewed hither, researchers should as well consider issues related to privacy and informed consent with activity trackers and smart phone applications [59, 60]. Since the trackers can measure out and store information for long periods of fourth dimension passively, providing informed consent takes on new meaning with the extended fourth dimension flow, locational data, and re-use of data in successive analyses. Users should likewise be aware that the companies access and utilize 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 bear on of the prior day's sleep and steps taken on cocky-reported mood past Jawbone users [63].

Limitations

Our review has several limitations. The literature on action trackers is quickly building and it is possible that studies were missed despite our all-time efforts. We encountered some challenges with comparing across studies, due to varying methods and reported results. The findings should exist viewed in calorie-free of the variety of study protocols and methodology.

When nosotros began the systematic review in autumn 2014, we were guided by the most recent market information available at that fourth dimension, indicating that Fitbit and Jawbone represented the majority of the consumer market place [2]. In June 2015, market share from the start quarter sales in 2015 indicated the top five vendors were Fitbit (34 %), Xiaomi (25 %), Garmin (6 %), Samsung (five %), and Jawbone (4 %) [64]. There is a built-in time lag between manufacturing and sale of activity trackers to use in the research laboratory and field. Thus, some action trackers that are currently available to consumers were not represented in this review, but should be considered as future studies accrue on new devices and brands.

Conclusions

This systematic review of 22 studies included assessments of five Fitbit and two Jawbone trackers, focusing on validity and reliability of steps, altitude, physical activity, energy expenditure, and slumber. No unmarried specific tracker had a consummate assessment across the v measures. This review also described several ways to improve the trackers' accuracy, offered recommendations to companies selling the trackers, and identified future areas of research. By and large, the review indicated higher validity of steps, fewer studies on distance and physical activeness, and lower validity for free energy expenditure and sleep. These studies also indicated high interdevice reliability for steps, free energy expenditure, and slumber for sure Fitbit models, but with no studies on the Jawbone. Every bit new activity trackers and features are introduced to the market place, documentation of the measurement properties can guide their use in research settings.

Abbreviations

BMI:

Body mass alphabetize

CC:

Correlation coefficient

GPS:

Global positioning system

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PSG:

Polysomnography

SD:

Standard departure

Us:

United states

References

1. Almalki M, Grayness G, Sanchez FM. The employ of self-quantification systems for personal health information: large data management activities and prospects. Wellness Data Science Systems. 2015;3(Suppl i HISA Big Information in Biomedicine and Healthcare 2013 Con):S1. doi: 10.1186/2047-2501-three-S1-S1.

2. Danova T. But iii.3 meg fettle trackers were sold in the US in the by year. Business Insider 2014. http://www.businessinsider.com/33-one thousand thousand-fettle-trackers-were-sold-in-the-usa-in-the-past-year-2014-5. Accessed March 2, 2015.

3. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle action monitors: A systematic content analysis. J Med Cyberspace Res. 2014;xvi(8):e192. doi:ten.2196/jmir.3469.

   Article  Google Scholar

4. Cadmus-Bertram LA, Marcus BH, Patterson RE, Parker BA, Morey BL. Randomized trial of a Fitbit-Based physical activity intervention for women. Am J Prev Med. 2015;49(iii):414–8.

   Article  Google Scholar

5. Michie Southward, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to assistance people change their concrete activity and salubrious eating behaviours: The CALO-RE taxonomy. Psych Wellness. 2011;26(11):1479–98.

   Commodity  Google Scholar

6. Play a joke on Southward, Duggan M. Tracking for Health. Pew Research Center, Pew Internet and American Life Projection. 2013. http://pewinternet.org/Reports/2013/Tracking-for-Health.aspx. Accessed October nine, 2015.

7. Bentley F, Tollmar K, Stephenson P, Levy L, Jones B, Robertson S, et al. Health mashups: Presenting statistical patterns between well-existence data and context in natural language to promote behavior change. ACM Trans Comput-Hum Interact. 2013;twenty(v):i–25.

   Article  Google Scholar

8. Kurti AN, Dallery J. Cyberspace-based contingency management increases walking in sedentary adults. J Appl Behav Anal. 2013;46(3):568–81.

   Article  Google Scholar

9. Washington WD, Banna KM, Gibson AL. Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. J Appl Behav Anal. 2014;47(two):231–45.

   Article  Google Scholar

10. Thompson WG, Kuhle CL, Koepp GA, McCrady-Spitzer SK, Levine JA. "Go4Life" exercise counseling, accelerometer feedback, and action levels in older people. Arch Gerontol Geriatr. 2014;58(three):314–9.

    Commodity  Google Scholar

11. Wang JB, Cadmus-Bertram LA, Natarajan L, White MM, Madanat H, Nichols JF, et al. Wear sensor/device (Fitbit One) and SMS text-messaging prompts to increase concrete activity in overweight and obese adults: A randomized controlled trial. Telemed J Eastward-Health. 2015;21(x):782–92.

    Article  Google Scholar

12. Hayes LB, Van Camp CM. Increasing physical activity of children during schoolhouse recess. J Appl Behav Anal. 2015;48(3):690–v.

    Commodity  Google Scholar

13. Fitbit Inc. How accurate are Fitbit trackers? 2015. http://assist.fitbit.com/articles/en_US/Help_article/How-accurate-are-Fitbit-trackers. Accessed June 16, 2015.

14. Jawbone. Jawbone Upwards: Activity Data Bug. 2015. https://help.jawbone.com/articles/en_US/PKB_Article/activity-data-bug-up. Accessed June 16, 2015.

15. Diaz KM, Krupka DJ, Chang MJ, Peacock J, Ma Y, Goldsmith J, et al. Fitbit: An authentic and reliable device for wireless physical activity tracking. Intl J Cardiol. 2015;185:138–xl.

    Article  Google Scholar

16. Klassen TD, Eng JJ, Chan C, Hassall Z, Lim S, Louie R, et al. Step count monitor for individuals post-stroke: Accuracy of the Fitbit One. Stroke. 2014;45(12):e261.

    Google Scholar

17. Perez-Macias JM, Jimison H, Korhonen I, Pavel Grand. Comparative cess of sleep quality estimates using home monitoring applied science. Briefing proceedings: Almanac International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Club Annual Conference. 2014; 2014:4979–82. doi: x.1109/embc.2014.6944742.

18. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS. Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther. 2014;94(2):222–nine.

    Article  Google Scholar

19. Vooijs M, Alpay LL, Snoeck-Stroband JB, Beerthuizen T, Siemonsma PC, Abbink JJ, et al. Validity and usability of low-cost accelerometers for net-based cocky-monitoring of physical activeness in patients with chronic obstructive pulmonary affliction. Interactive J Med Res. 2014;3(4):e14. doi:10.2196/ijmr.3056.

    Commodity  Google Scholar

20. Albert MV, Deeny Southward, McCarthy C, Valentin J, Jayaraman A. Monitoring daily office in persons with transfemoral amputations using a commercial activeness monitor: A feasibility report. PM & R: J Inj Funct Rehabil. 2014;6(12):1120–vii. doi:10.1016/j.pmrj.2014.06.006.

    Article  Google Scholar

21. Naslund JA, Aschbrenner KA, Barre LK, Bartels SJ. Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. Telemed J E-Health. 2015;21(3):213–half-dozen.

    Article  Google Scholar

22. Phillips LJ, Petroski GF, Markis NE. A comparison of accelerometer accurateness in older adults. Res Gerontol Nursing. 2015: 1–vii. doi:10.3928/19404921-20150429-03.

23. Lauritzen J, Munoz A, Luis Sevillano J, Civit A. The usefulness of activeness trackers in elderly with reduced mobility: A case study. Stud Health Technol Inform. 2013;192:759–62.

    Google Scholar

24. De Vries SI, Van Hirtum HW, Bakker I, Hopman-Rock G, Hirasing RA, Van Mechelen Westward. Validity and reproducibility of move sensors in youth: A systematic update. Med Sci Sports Exerc. 2009;41(4):818–27.

    Article  Google Scholar

25. Higgins PA, Straub AJ. Understanding the error of our ways: Mapping the concepts of validity and reliability. Nurs Outlook. 2006;54(1):23–ix.

    Article  Google Scholar

26. Landis J, Koch One thousand. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.

    CAS  Article  Google Scholar

27. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Med. 2009;6(7):e1000100. doi:x.1371/journal.pmed.1000100.

    Article  Google Scholar

28. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA argument. PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097.

    Commodity  Google Scholar

29. Adam Noah J, Spierer DK, Gu J, Bronner South. Comparison of steps and free energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. J Med Eng Tech. 2013;37(7):456–62.

    CAS  Article  Google Scholar

30. 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–half dozen.

    CAS  Article  Google Scholar

31. Dannecker KL, Sazonova NA, Melanson EL, Sazonov ES, Browning RC. A comparison of energy expenditure estimation of several physical activity monitors. Med Sci Sports Exerc. 2013;45(xi):2105–12.

    Article  Google Scholar

32. Dontje ML, de Groot Yard, Lengton RR, van der Schans CP, Krijnen WP. Measuring steps with the Fitbit activity tracker: An inter-device reliability written report. J Med Eng Tech. 2015;39(5):286–ninety.

    Commodity  Google Scholar

33. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: A cross-sectional study. Intl J Behav Nutr Phys Act. 2015;12:42. doi:10.1186/s12966-015-0201-9.

    Article  Google Scholar

34. Gusmer R, Bosch T, Watkins A, Ostrem J, Dengel D. Comparison of Fitbit Ultra to ActiGraph GT1M for assessment of physical action in young adults during treadmill walking. Open Sports Med J. 2014;8:11–5.

    Article  Google Scholar

35. Lee JM, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med Sci Sports Exerc. 2014;46(9):1840–8.

    Article  Google Scholar

36. Mammen Thousand, Gardiner S, Senthinathan A, McClemont Fifty, Stone M, Faulkner G. Is this bit fit? Measuring the quality of the FitBit step-counter. Wellness Fit J Tin can. 2012;5(4):30–nine.

    Google Scholar

37. Meltzer LJ, Hiruma LS, Avis K, Montgomery-Downs H, Valentin J. Comparison of a commercial accelerometer with polysomnography and actigraphy in children and adolescents. Sleep. 2015;38(8):1323–30.

    Google Scholar

38. Montgomery-Downs HE, Insana SP, Bail JA. Move toward a novel activity monitoring device. Sleep Breath. 2012;16(3):913–7.

    Article  Google Scholar

39. Sasaki JE, Hickey A, Mavilia M, Tedesco J, John D, Kozey Keadle South, et al. Validation of the Fitbit wireless activity tracker for prediction of energy expenditure. J Phys Act Health. 2015;12:149–54.

    Article  Google Scholar

40. Stackpool CM, Porcari JP, Mikat RP, Gillette C, Foster C. The accuracy of various activeness trackers in estimating steps taken and free energy expenditure. J Fit Res. 2014;iii(three):32–48.

    Google Scholar

41. Stahl ST, Insana SP. Caloric expenditure cess among older adults: Criterion validity of a novel accelerometry device. J Health Psych. 2014;19(11):1382–7.

    Article  Google Scholar

42. Tempest FA, Heller BW, Mazza C. Stride detection and activity recognition accuracy of seven concrete activity monitors. PLoS I. 2015;10(3):e0118723. doi:10.1371/journal.pone.0118723.

    Article  Google Scholar

43. Takacs J, Pollock CL, Guenther JR, Bahar M, Napier C, Chase MA. Validation of the Fitbit One activity monitor device during treadmill walking. J Sci Med Sport. 2014;17(v):496–500.

    Article  Google Scholar

44. Tully MA, McBride C, Heron L, Hunter RF. The validation of Fibit Zip concrete activity monitor equally a measure of free-living physical activity. BMC Res Notes. 2014;vii:952. doi:ten.1186/1756-0500-7-952.

    Article  Google Scholar

45. Bai Y, Welk GJ, Nam YH, Lee JA, Lee JM, Kim Y et al. Comparison of consumer and research monitors under semistructured settings. Med Sci Sports Exercise. 2015, in press. doi:10.1249/MSS.0000000000000727.

46. Simpson LA, Eng JJ, Klassen TD, Lim SB, Louie DR, Parappilly B, et al. Capturing step counts at slow walking speeds in older adults: Comparing of ankle and waist placement of measuring device. J Rehabil Med. 2015;47(nine):830–five.

    Article  Google Scholar

47. de Zambotti Yard, Claudatos S, Inkelis Due south, Colrain IM, Baker FC. Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiol Intl. 2015;32(7):1024–8.

    Commodity  Google Scholar

48. de Zambotti M, Baker FC, Colrain IM. Validation of sleep-tracking engineering science compared with polysomnography in adolescents. Sleep. 2015;38(9):1461–viii.

    Google Scholar

49. Freedson PS, Melanson E, Sirard J. Calibration of the information science and applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(v):777–81.

    CAS  Article  Google Scholar

50. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;fourteen(5):411–6.

    Article  Google Scholar

51. Crouter SE, Schneider PL, Karabulut M, Bassett Jr DR. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med Sci Sports Exerc. 2003;35(8):1455–60.

    Commodity  Google Scholar

52. Meltzer LJ, Montgomery-Downs HE, Insana SP, Walsh CM. Apply of actigraphy for assessment in pediatric sleep research. Slumber Med Rev. 2012;sixteen(v):463–75.

    Commodity  Google Scholar

53. Shih P, Han Chiliad, Poole E, Rosson M, Carroll J. Utilise and adoption challenges of wearable action trackers. 2015. iConference 2015 Proceedings. https://www.ethics.illinois.edu/handle/2142/73649. Accessed June sixteen, 2015.

54. Fitbit I. A brief look into how the Fitbit algorithms work. 2009.

55. John D, Freedson P. ActiGraph and Actical physical activeness monitors: A peek under the hood. Med Sci Sport Exerc. 2012;44(1 Suppl 1):S86–9.

    Article  Google Scholar

56. John D, Sasaki J, Hickey A, Mavilia G, Freedson PS. ActiGraph activity monitors: "The firmware event". Med Sci Sport Exerc. 2014;46(4):834–9.

    Article  Google Scholar

57. Crouter SE, Schneider PL, Bassett Jr DR. Spring-levered versus piezo-electric pedometer accurateness in overweight and obese adults. Med Sci Sport Exerc. 2005;37(10):1673–nine.

    Article  Google Scholar

58. Welk GJ, McClain J, Ainsworth BE. Protocols for evaluating equivalency of accelerometry-based activity monitors. Med Sci Sport Exerc. 2012;44(one Suppl 1):S39–49.

    Commodity  Google Scholar

59. Male monarch Ac, Glanz M, Patrick Yard. Technologies to measure out and modify physical activity and eating environments. Am J Prev Med. 2015;48(5):630–viii.

    Article  Google Scholar

60. de Montjoye YA, Hidalgo CA, Verleysen M, Blondel VD. Unique in the Crowd: The privacy bounds of homo mobility. Sci Rep. 2013;iii:1376. doi:10.1038/srep01376.

    Article  Google Scholar

61. Health Information Exploration Project. Personal Information for the Public Adept: New Opportunities to Enrish Understanding of Individual and Population Wellness. 2014. http://www.rwjf.org/content/dam/farm/reports/reports/2014/rwjf411080. Accessed October 9, 2015. Calit2, UC Irvine and UC San Diego.

62. Fitbit Inc. Weathering the weather. 2015. https://www.fitbit.com/weathermap. Accessed October 9, 2015.

63. Mohan Due south. The Jawbone Blog: What makes people happy? We take the data. 2015. https://jawbone.com/blog/what-makes-people-happy/. Accessed October nine, 2015.

64. IDC. Wearable Marketplace Remained Strong in the Outset Quarter Despite the Awaiting Debut of the Apple Watch, Says IDC. Printing release from IDC on June 3, 2015. Based on the "IDC Worldwide Quarterly Wearable Tracker, June 2, 2015". 2015. http://www.idc.com/getdoc.jsp?containerId=prUS25658315. Accessed Oct nine, 2015.

Download references

Acknowledgment

We thank Sonia Grego, Sara Satinsky, and the anonymous reviewers for comments on earlier drafts of this newspaper. We also thank the authors of the reviewed studies for responding to our requests for farther information and clarification. This work was supported, in part, by RTI International through the RTI University Scholars Programme and iSHARE. The content is solely the responsibility of the authors and does non necessarily represent the official views of RTI International.

Author information

Affiliations

1. Section of Epidemiology, Gillings Schoolhouse of Global Public Health, University of N Carolina—Chapel Loma, 137 East Franklin Street, Suite 306, Chapel Hill, 27514, NC, Us

   Kelly R. Evenson & Michelle M. Goto

2. RTI International, Inquiry Triangle Park, NC, USA

   Kelly R. Evenson & Robert D. Furberg

Corresponding author

Correspondence to Kelly R. Evenson.

Additional information

Competing interests

The authors declare that they accept no competing interests.

Authors' contributions

KRE developed the aims of the report, helped conduct the literature review, coded all articles, contacted authors for missing data, and drafted the paper. All remaining authors provided disquisitional feedback on several earlier drafts of the paper. MMG also conducted the last literature review and coded all articles. All authors read and approved the concluding manuscript.

Additional file

Additional file one:

Flow of commodity selection using the PRISMA schematic (Liberati et al., 2009 [ 27 ]; Moher et al., 2009 [ 28 ]). (PDF 62 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution four.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted utilise, distribution, and reproduction in any medium, provided you requite appropriate credit to the original writer(s) and the source, provide a link to the Artistic Commons license, and bespeak if changes were made. The Artistic Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zilch/1.0/) applies to the data fabricated bachelor in this article, unless otherwise stated.

Reprints and Permissions

About this article

Cite this article

Evenson, K.R., Goto, Grand.M. & Furberg, R.D. Systematic review of the validity and reliability of consumer-article of clothing activity trackers. Int J Behav Nutr Phys Act 12, 159 (2015). https://doi.org/ten.1186/s12966-015-0314-1

Download citation

• Received: 05 Baronial 2015

• Accepted: 04 Dec 2015

• Published: 18 December 2015

• DOI : https://doi.org/10.1186/s12966-015-0314-1

Keywords

• Distance
• Energy expenditure
• Fitbit
• Intervention
• Jawbone
• Measurement
• Physical action
• Sleep
• Steps
• Walking

miltenbergervotearome.blogspot.com

Source: https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-015-0314-1