Accuracy of Wearable Trackers for Measuring Moderate- to Vigorous-Intensity Physical Activity: A Systematic Review and Meta-Analysis

in Journal for the Measurement of Physical Behaviour
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  • 1 University of Wisconsin–Madison

Background: The evidence base regarding validity of wearable fitness trackers for assessment and/or modification of physical activity behavior is evolving. Accurate assessment of moderate- to vigorous-intensity physical activity (MVPA) is important for measuring adherence to physical activity guidelines in the United States and abroad. Therefore, this systematic review synthesizes the state of the validation literature regarding wearable trackers and MVPA. Methods: A systematic search of the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted through October 2019 (PROSPERO registration number: CRD42018103808). Studies were eligible if they reported on the validity of MVPA and used devices from Fitbit, Apple, or Garmin released in 2012 or later or available on the market at the time of review. A meta-analysis was conducted on the correlation measures comparing wearables with the ActiGraph. Results: Twenty-two studies met the inclusion criteria; all used a Fitbit device; one included a Garmin model and no Apple-device studies were found. Moderate to high correlations (.7–.9) were found between MVPA from the wearable tracker versus criterion measure (ActiGraph n = 14). Considerable heterogeneity was seen with respect to the specific definition of MVPA for the criterion device, the statistical techniques used to assess validity, and the correlations between wearable trackers and ActiGraph across studies. Conclusions: There is a need for standardization of validation methods and reporting outcomes in individual studies to allow for comparability across the evidence base. Despite the different methods utilized within studies, nearly all concluded that wearable trackers are valid for measuring MVPA.

Accurate assessment of physical activity remains challenging. Wearable fitness trackers are ubiquitous among consumers and represent new opportunities for measurement (Kaewkannate & Kim, 2016; Lunney, Cunningham, & Eastin, 2016). Compared with research-grade devices like the ActiGraph, consumer-based wearable fitness trackers feature user-friendly applications, social features, and integrated communications across apps, which aim to promote physical activity and reduce sedentary behavior. However, these devices are most useful if they provide valid measures of activity. Within a decade, consumer devices have evolved from a simple waist-worn pedometer to integrated devices used in the home, at workplace wellness programs, in research studies, and at the individual level to help construct healthier, more active lifestyles (Block et al., 2017; Mobbs, Phan, Maharaj, & Rao, 2016; Yavelberg, Zaharieva, Cinar, Riddell, & Jamnik, 2018; Zhang, McClean, Ko, Morgan, & Schmitz, 2017).

The validity of wearable fitness trackers—including Apple Watches, Fitbits, and Garmins—has been assessed for steps, energy expenditure, moderate- to vigorous-intensity physical activity (MVPA), sedentary time, and sleep (Dooley, Golaszewski, & Bartholomew, 2017; Evenson, Goto, & Furberg, 2015; Floegel, Florez-Pregonero, Hekler, & Buman, 2017; Henriksen et al., 2018; Huang, Xu, Yu, & Shull, 2016; Jo, Lewis, Directo, Kim, & Dolezal, 2016; Kang et al., 2017; Mantua, Gravel, & Spencer, 2016; Price et al., 2017; Roos, Taube, Beeler, & Wyss, 2017; Shcherbina et al., 2017; Toth et al., 2018; Treacy et al., 2017; Woodman, Crouter, Bassett, Fitzhugh, & Boyer, 2017). Published reviews regarding the validity of the steps feature comprise the majority of the evidence base for wearable fitness trackers (Adam Noah, Spierer, Gu, & Bronner, 2013; An, Jones, Kang, Welk, & Lee, 2017; Chandrasekar, Hensor, Mackie, Backhouse, & Harris, 2018; Treacy et al., 2017; Ummels, Beekman, Theunissen, Braun, & Beurskens, 2018). In 2015, Evenson et al. synthesized the findings of 22 published studies on the validity and reliability of wearable fitness trackers (including Fitbit and Jawbone) and the specific abilities of the devices to measure steps, distance, physical activity, energy expenditure, and sleep. At the time of Evenson’s review, most studies assessed the validity of steps or energy expenditure, and only two studies assessed validity of intensities and minutes of physical activity (Evenson et al., 2015). Other systematic reviews have assessed the validity of specific applications of wearable fitness trackers, such as Coughlin and Stewart (2016), on the use of wearables in promoting physical activity (Coughlin & Stewart, 2016; O’Driscoll et al., 2018).

Publications on the validity of wearable trackers for physical activity assessment have used many different strategies, devices, and criteria for validation. To date, no systematic review has addressed minutes of activity (e.g., MVPA). This is a crucial gap because most physical activity guidelines, including those of the United States and the World Health Organization are expressed in minutes, not steps (Garriguet & Colley, 2014; Physical Activity Guidelines Advisory Committee, 2018; Physical Activity Guidelines Advisory Committee, 2009). Thus, MVPA is one of the most relevant metrics for consumers and researchers. Therefore, our purpose was to systematically review validation studies published since 2012 using consumer-based wearable activity trackers to measure MVPA. We selected 2012 as the starting date to cover relevant literature since the Evenson et al. paper. Specifically, we aimed to (a) describe how MVPA is assessed against criterion measurements and (b) report the populations studied, the magnitude of agreement, and the statistical analyses used to define the validity of the wearable fitness trackers.

Methods

Search Strategy and Study Eligibility

A systematic review of the literature in the PubMed, Scopus, SPORTDiscus, and Cochrane Library databases was conducted in May 2018 and updated in October 2019. PROSPERO guidelines were followed (PROSPERO registration number: CRD42018103808). The search strategy was developed by identifying terminology used in previous studies (Evenson et al., 2015). The search terms were (Fitbit OR Apple [monitor OR watch OR tracker] OR Garmin) AND (validity OR validation OR validate OR comparison OR comparative OR reliability OR accuracy). The term “monitor OR watch OR tracker” needed to be added to differentiate Apple brand trackers from fruit-related literature. Searches were limited to peer-reviewed articles that were available in the English language. There was no restriction on the study population (general or targeted). One author was contacted by authors to confirm device specifics not available in the manuscript (Phillips, Petroski, & Markis, 2015). PRISMA guidelines were followed for composing and reviewing this manuscript.

Eligible Devices

This review focused on devices manufactured by Fitbit, Garmin, or Apple, the core market leaders most often used in research studies. Other brands in the marketplace (e.g., Jawbone) were not included due to not being available for purchase at the time of the review. We did not include some of the earliest tracker models. However, the release cycle of fitness trackers is substantially faster than the pace of academic research and publishing, meaning that current models of trackers will have no peer-reviewed validation data. We therefore included device models released in 2012 or later or on the market in May 2018. The latter criterion allowed for the inclusion of a few longstanding models, such as the Fitbit Zip, that were released prior to 2012 but still on the market at the time of the literature search. Devices eligible for this review were the Fitbit Alta, Alta HR, Blaze, Charge, Charge HR, Flex, Flex 2, One, Ionic, Surge, Versa, and Zip. Garmin Vivoactive, Vivofit 2, Vivofit 3, Vivofit 4, Vivofit Jr, Vivofit Jr 2, Vivosmart, and Vivosmart HR and Apple Watch Series 1, Sport Series 1, Edition—Series 1, Series 2, Edition—Series 2, Series 3, Edition—Series 3, and Hermes. Of these eligible Fitbit, Garmin, and Apple models, validation data for MVPA minutes were only available for five Fitbit models: The Fitbit Charge HR, Fitbit Charge 2, Fitbit Zip, Fitbit Flex, and Fitbit One.

Validation Outcomes

Studies were eligible for inclusion if they reported on the validity of at least one of the eligible devices with respect to minutes of moderate to vigorous minutes of physical activity. Eligible studies needed to have direct comparison of MVPA from both the wearable device and the criterion measure. There were differences in the way authors chose to abstract this from the wearable fitness device, such as active (corresponding to moderate intensity) or very active (corresponding to vigorous intensity) minutes from the Fitbit, use of Fitabase database downloads, while other models/devices may have had different definitions (“Help Article: What are active minutes?” 2015; Huberty et al., 2017). Specifically, we included articles that included intensities comparable with exercise. Articles that reported validations of only steps, energy expenditure, heart rate, and so forth, were not included. The initial search strategy was not specific to the validation outcome; rather these were determined during the article selection process.

Article Selection

After removal of duplicates, a three-phase process was used to screen the articles identified during the search. Articles that targeted special populations (e.g., children or hospitalized heart failure patients) were included throughout the selection process. The first phase of screening was based on titles only; the second phase included abstracts, and the third phase included the full-text articles. For each round, two independent reviewers assessed each article, and a third reviewer resolved any discrepancies.

Data Extraction

The following information was abstracted from the studies: study population, study age group, proportion of men, device used, outcomes assessed, criterion used, study setting (free living or lab), type of analysis for validity, author’s definition of MVPA, and summary of findings. One reviewer extracted the data with the other two reviewers checking the information for accuracy.

Quality Assessment

A modified version of the Downs and Black checklist was used to assess quality of the included studies (Downs & Black, 1998). The full Downs and Black checklist contains 27 items that assess three domains of reporting validity, internal, and external validity. We used a modified version consisting of 15 items for a maximum score of 15 points. Since all the studies included in this review are observational, we followed the methods used by Prince et al. and Warburton et al. using only the 15-item version of the checklist (Prince et al., 2008; Warburton, Charlesworth, Ivey, Nettlefold, & Bredin, 2010).

Meta-Analysis

Correlations (Pearson’s r or Spearman’s ρ) between the wearable fitness tracker and criterion measures of MVPA were obtained from each study. Fisher’s r to Z transformation was applied prior to analysis. Random-effects models were used to estimate the average effects and the variance of the effects across studies. The I2 statistic was used to quantify the heterogeneity of effects. A 95% confidence interval for the average correlation, and a 95% prediction interval for the correlation in a future study were calculated. Subgroup analyses based on correlation (Pearson or Spearman), age (adults or children), health (healthy adults or nonhealthy adults), location (lab based or free living), and wear location (right hip or other hip) were performed to assess potential sources of heterogeneous effects. Analyses were performed using the meta package in R studio (version 1.2.5042; R studio, Boston, MA).

Results

The literature review identified 1,527 unique titles from the four databases (Figure 1; Moher, Liberati, Tetzlaff, Altman, & The PRISMA Group, 2009). After excluding irrelevant articles, 283 papers remained. Those 283 abstracts were read by two different reviewers, a process which identified 122 articles for full-text review. Articles were excluded if they were not a validation study, if there was not a full-text manuscript (e.g., conference abstract), or if the study used a fitness tracker other than a Garmin, Fitbit, or Apple device. After full-text review, 22 unique articles focusing on the validity of MVPA were identified.

Figure 1
Figure 1

—Flow diagram of article selection process. MVPA = moderate- to vigorous-intensity physical activity. Adapted from “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” by D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, and The PRISMA Group, 2009, PLoS Medicine, 6(6), p. e1000097. Copyright 2009 by xxxx. For more information visit www.prisma-statement.org.

Citation: Journal for the Measurement of Physical Behaviour 3, 4; 10.1123/jmpb.2019-0072

Table 1 summarizes relevant study characteristics including wearable tracker model(s), the definition of MVPA, and the type of statistical analysis. Studies included healthy (n = 17) and disease-specific (n = 5) populations. Studies included children and adolescents between ages 4 and 13 years (n = 7), adults with mean ages between 28 and 43 years (n = 11), and older populations with mean ages between 62 and 66 years (n = 5). One study was conducted among pregnant women. Studies were typically very good quality, with a mean score of 12.6 (range 8–15) on the modified Downs and Black checklist. All studies received maximum scores for description of participant characteristics and study interventions.

Table 1

 Characteristics of the Validation Studies Included in this Review (n = 22a)

Author (year)NPopulation%MaleWearable device(s) testedCriterion measure and locationWear time requirement and cut pointsHow MVPA defined
Lab-based studies
 Byun et al. (2018)a28Healthy children54%Fitbit FlexDirect observationN/AScale of 0–3 for sedentary to vigorous activity (60-s sampling): 0 (sedentary), 1 (light), 2 (moderate), and 3 (vigorous)
 Byun et al. (2018)a2759%
 Imboden et al. (2017)30Healthy adults50%Fitbit FlexAG GT3X+ (right hip)MVPA ≥ 3 METs or 2,691+ cpmTime spent in “active minutes”
 Kang, Kim, Byun, Suk, & Lee, (2019)43Healthy children58%Fitbit Charge HRAG GT3X+ (both wrists)Hand-weight exercise = 2.1 METs

Moderate = 2.7–3.5 METs
Scale of 0–3 for sedentary to vigorous activity (60-s sampling): 0 (sedentary), 1 (light), 2 (moderate), and 3 (vigorous)
 Liang, & Getchell (2018)35Healthy children

Healthy adults
60%

70%
Fitbit ZipActicalModerate = 3–6 METs

Vigorous > 6 METs
Total minutes spent in moderate and vigorous intensity
 Sushames et al. (2016)25Healthy adults52%Fitbit FlexAG GT3X+ (preferred hip)Moderate = 1,952–5,724 cpm Vigorous = 5,725–9,498 cpm

Very vigorous = 9,498+ cpm
Minutes in moderate and vigorous exercise (as obtained by the Fitbit website)
Free-living studies
 Alharbi et al. (2016)48Adults w/ congestive heart disease and their families52%Fitbit FlexAG GT3X (unspecified hip)Sedentary = 0–100 cpm

Light = 101–2,020 cpm

Moderate = 2,021–5,999 cpm Vigorous = 6,000+ cpm

MVPA = 2,021+ cpm
Fitbit: sedentary = 1 MET, moderate = 3+ METs, vigorous = 6+ METs
 Brewer et al. (2017)53Healthy adults17%Fitbit FlexAG GT3X+ (right hip)MVPA = 1,952+ cpmTime spent in “active minutes”
 Collins, Yang, Trentadue, Gong, & Losina (2019)15Older adults w/ osteoarthritis33%Fitbit Charge2AG GT3X+ (nondominant wrist, unspecified hip)Valid day ≥ 10 hr; valid week ≥ 4 days

MVPA: vector magnitude ≥ 1,924 cpm in bouts of ≥10 min
Fitbit: MVPA > 3 METs, bouts ≥ 10 min allowing two grace minutes where steps was greater than threshold
 Degroote, De Bourdeaudhuij, Verloigne, Poppe, & Crombez (2018)36Healthy adults50%Fitbit ChargeAG GT3X+ (right hip)Valid day was 10 hr of wear

Sedentary = 0–99 cpm

Light = 100–1,951 cpm

Moderate = 1,952–5,723 cpm

Vigorous = 5,724+ cpm
Fitbit variable:

“active minutes”
 Dominick et al. (2016)19Healthy adults21%Fitbit FlexAG GT3X (dominant hip)Sedentary = 0 (0–199 cpm)

Light = 1 (200–2,690 cpm)

Moderate = 2 (2,691–6,166 cpm)

Vigorous = 3 (6,167+ cpm)
Sedentary = 0

Light = 1

Moderate = 2

Vigorous = 3
 Ferguson et al. (2015)21Healthy adults48%Fitbit One

Fitbit Zip
AG GT3X+ (right hip)Sedentary < 200 cpm

Light = 200–2,690 cpm

Moderate = 2,691–6,166 cpm

Vigorous = 6,167+ cpm
One: sum of the very and moderate active minutes

Zip: sum of the vigorous and moderate active minutes
 Gomersall et al. (2016)29Healthy adults52%Fitbit OneAG GT3X+ (right hip)Sedentary < 100 cpm

MVPA = 2,020+ cpm
Time spent in “very active minutes”
 Hui et al. (2018)12Adult stroke survivors58%Fitbit OneActical (nonparetic ankle)Metabolic:

Sedentary = 1 MET

Light < 3 METs

Moderate = 3–6 METs

Vigorous > 6 METs
Time spent in sedentary, light active, fairly active, and very active

No specific thresholds for activity intensity were provided
 Mooses, Oja, Reisberg, Vilo, & Kull (2018)147Healthy children50%Fitbit ZipAG GT3X-BT (unspecified hip)MVPA = 2,296+ cpmFitbit’s categories of “fairly active” was moderate and “very activity” was vigorous intensity
 Redenius, Kim, & Byun (2019)65Healthy adults28%Fitbit FlexAG GT3X+ (dominant hip)Freedson: MVPA = 1,952+ cpm

Troiano: MVPA = 2,020+ cpm

VM3: MVPA = 2,691+ cpm
Downloaded from Fitabase, minute by minute; used the Fitbit algorithm of 2 = moderate PA, 3 = vigorous PA
 Rosenberger et al. (2016)40Healthy adults48%Fitbit OneAG GT3X+ (right hip)MVPA = 1,580+ cpmMinutes in moderate plus vigorous physical activity
 Schneider and Chau (2016)25 35 27Healthy adolescents (three cohorts)48%

47%

40%
Fitbit ZipAG GT3X (unspecified hip)MVPA ≥ 4 METs

Cohorts 1 + 3 MVPA: 2,058 cpm

Cohort 2: 2,220 cpm
Total time in “active” and “very active
 St-Laurent, Mony, Mathieu, & Ruchat (2018)16Pregnant women0%Fitbit Zip

Fitbit Flex
AG GT3X (unspecified hip)Valid wear of 10 hr for 4 days

Moderate = 1,952–5,724 cpm Vigorous = 5,725 cpm
Not reported
 Sushames et al. (2016)25Healthy adults52%Fitbit FlexAG GT3X+ (preferred hip)Moderate = 1,952–5,724 cpm

Vigorous = 5,725–9,498 cpm

Very vigorous = 9,498+ cpm
Minutes in moderate and vigorous exercise (as obtained by the Fitbit website)
 Tedesco et al. (2019)20Older, healthy adults45%Fitbit Charge2, Garmin vivosmartHR+AG GT9X-BT (dominant hip)Valid day ≥ 10 hr

MVPA = 2,020+ cpm
Not reported
 Van Blarigan et al. (2017)25Adults w/ prostate cancer100%Fitbit OneAGGT3X+ (right hip)Light = 100–2,019 cpm

Moderate = 5,725–9,498 cpm

Vigorous = 9,498+ cpm
Light <3 METs, moderate 3–5.9 METs, and vigorous ≥6 METs
 Voss et al. (2017)30Children w/ congenital heart disease47%Fitbit Charge HRAG GT3X+ (right hip)MVPA = 2,296+ cpmFairly + very active minutes

Note. AG = ActiGraph; MET = metabolic equivalent of task; cpm = counts per minute; HR = heart rate; MVPA = moderate- to vigorous-intensity physical activity; N/A = not applicable; PA = physical activity; VM3 = composite vector magnitude (VM) of activity counts from all three (3) axes from the accelerometer.

aTwo separate publications in different journals, but near identical results, methods, and study.

Criterion assessments included multiple ActiGraph models, direct observation, Actical accelerometer, or indirect calorimetry (converting energy expenditure to physical activity intensity for comparison with wearable fitness device data). Of the 20 studies using the ActiGraph, 13 used the GT3X+, five used the GT3X, two used the Bluetooth-enabled GT3X-BT, one used the GT9X, and one used the GT9X-BT. The ActiGraph was not always worn on the right side on a belt. Of the 15 studies (68%) that had a sample size fewer than 40, four (18%) had fewer than 20 subjects.

A variety of statistical techniques were used to assess the validity between wearable fitness trackers and the criterion measure (Table 2). The most frequently used statistical assessments were Bland–Altman plots (n = 15) and Pearson or Spearman correlation (n = 14). Other approaches were mean absolute percent error (n = 8), regression (n = 8), and t tests for mean differences (n = 10). Papers typically used multiple statistical approaches with an average of 4.8 statistical tests per manuscript and a maximum of eight.

Table 2

List of Statistical Analyses Used Within and Across Studies

StudyCorrelation (r/rho)ICCRegressionKappaMean absolute percentage errorMean relative errorMean absolute differenceSensitivitySpecificityPPVBland–AltmanCoefficient of variationROC-AUCt testsRM ANOVAEquivalence testing
Alharbi et al. (2016)        
Brewer et al. (2017)            
Byun et al. (2018)          
Byun et al. (2018)         
Collins et al. (2019)              
Degroote et al. (2018)             
Dominick et al. (2016)             
Ferguson et al. (2015)            
Gomersall et al. (2016)           
Hui et al. (2018)             
Imboden et al. (2017)           
Kang et al. (2019)        
Liang, & Getchell (2018)             a 
Mooses et al. (2018)              
Redenius et al. (2019)          b 
Rosenberger et al. (2016)              
Schneider and Chau (2016)            
St-Laurent et al. (2018)            
Sushames et al. (2016)           
Tedesco et al. (2019)           
Van Blarigan et al. (2017)               
Voss et al. (2017)            

Note. ICC = intraclass correlation coefficient; ANOVA = analysis of variance; RM = repeated measures; AOC-ROC = area under the curve—receiver operator curve; PPV = positive predictive value.

aChi-square goodness of fit. bOne-way ANOVA.

For studies that utilized correlation, Table 3 shows the type of correlation (Spearman, Pearson, and intraclass correlation coefficient) and the strength of association reported. In studies focused on healthy children, the correlation values for the trackers against the criterion measure ranged between .7 (Schneider & Chau, 2016) and .9 when using direct observation as the criterion (Byun, Lee, Kim, & Brusseau, 2018). One study examined validity in a clinical-based sample of children; Voss et al. found an intraclass correlation coefficient of less than .7 in a sample of children with congenital heart disease (Voss, Gardner, Dean, & Harris, 2017). Overall, authors reported high correlations between the Fitbit Zip and the ActiGraph and concluded that the Fitbit Flex was accurate for sedentary behavior and for total physical activity in children.

Table 3

Magnitude of Association Observed in Studies that Reported Correlations

Author (year)MetricCorrelation magnitude
Studies comparing Fitbit with ActiGraph
 Alharbi et al. (2016)Moderate

Vigorous

MVPA
Moderate: r = .76

Vigorous: r = .19

MVPA: r = .74

(also available in paper: results stratified by sex and for cardiac patients only)
 Brewer et al. (2017)MVPA and “active minutes”MVPA: r = .70

“active minutes”: r = .66
 Byun et al. (2018)MVPA and total PA

(two algorithms used)
Pate: MVPA: r = .59, total PA: r = .56

Evenson: MVPA: r = .58, total PA: r = .49
 Degroote et al. (2018)MVPAρ = .56, ICC = .66
 Dominick et al. (2016)Moderate and vigorousModerate: r = .43

Vigorous: r = .80
 Ferguson et al. (2015)MVPAFitbit Zip: r = .88, ICC = .36

Fitbit One: r = .91, ICC = .46
 Gomersall et al. (2016)MVPAr/ρ = .80, ICC = .72
 Imboden et al. (2017)Fitbit’s “active minutes”r = .10
 Kang et al. (2019)MVPADominant vs. nondominant wrist ICC = .77

Crouter: ICC = .73

Chandler: ICC = .28
 Mooses et al.(2018)MVPAClass time: ρ = .24

PE lesson: ρ = .72

Recess: ρ = .56
 Redenius et al. (2019)MVPAFreedson: r = .66, ρ = .71

Troiano: r = .65, ρ = .69

VM3: r = .76, ρ = .79
 Schneider and Chau (2016)MVPACohort 1: r = .67

Cohort 2: r = .79

Cohort 3: r = .94
 Sushames et al. (2016)Specific modalities of PAWalking on incline: −0.06, 0.21, 0.02

Jogging: 0.58, 0.38, 0.52

Stair stepping: 0.69, 0.43, 0.72
 Van Blarigan et al. (2017)MVPA, moderate, and vigorousMVPA: r = .85

Vigorous: ρ = .65

Moderate r = .70
 Voss et al. (2017)MVPAFull sample: r = .54, ICC = .66

Boys: r = .72, ICC = .82

Girls: r = .38, ICC = .49

Age < 13 years: r = .41, ICC = .52

Age > 13 years: r = .70, ICC = .78
Studies comparing Fitbit with Actical
 Hui et al. (2018)Moderate and vigorousModerate: r = .90, .91, .83

Vigorous: r = .86
Studies comparing Fitbit with Garmin
 Tedesco et al. (2019)MVPAICC = .96
Studies not using correlations
 Collins et al. (2019)Pairwise comparisons of MVPAFitbit underestimated MVPA by 5 min
 Liang, & Getchell (2018)Chi-square goodness of fitFitbit indicated more time in moderate/vigorous activity
 Rosenberger et al. (2016)Bland–AltmanNo systematic bias found in MVPA
 St-Laurent et al. (2018)Bland–AltmanNo systematic bias found in MVPA

Note. Bold indicates statistical significance. Correlations were not reported by the following studies: Collins et al. (2019), Liang, & Getchell (2018), Rosenberger et al. (2016), and St-Laurent et al. (2018). ICC = intraclass correlation coefficient; MVPA = moderate- to vigorous-intensity physical activity; PA = physical activity; PE = physical education.

Like children’s populations, validation studies among adults were conducted in both clinical and healthy populations with generally moderate to high (.5–.9) correlations observed. In a study of adults with congestive heart failure and their families, authors found correlations of .7 between the Fitbit Flex and the ActiGraph, with lower correlations for women (.6) than men (.8) (Alharbi, Bauman, Neubeck, & Gallagher, 2016). In studies using the Fitbit Flex against the ActiGraph as the criterion measure, “active minutes” was correlated with the criterion measure measured at .7 (Brewer, Swanson, & Ortiz, 2017). Other studies including the Fitbit Flex found underestimations of both active minutes (Imboden, Nelson, Kaminsky, & Montoye, 2017) and time in sedentary and light activity (Dominick, Winfree, Pohlig, & Papas, 2016), while other studies found that the Fitbit was more accurate for specifically studied moderate and vigorous intensity activities of jogging and stair stepping while underestimating steps (Sushames, Edwards, Thompson, McDermott, & Gebel, 2016). For those studies that used the Fitbit One or the Fitbit Zip against the ActiGraph, MVPA correlations ranged from .7 to .9 (Ferguson, Rowlands, Olds, & Maher, 2015; Gomersall et al., 2016) while another study reported high error rates with MVPA measurement between 52% and 92% (Rosenberger, Buman, Haskell, McConnell, & Carstensen, 2016).

The results of the meta-analysis showed a mean correlation of .74 (.66, .81) across all studies assessing MVPA and using correlation analyses. High heterogeneity was noted across studies with an I2 of 43% (p = .05). To further explore the heterogeneity across studies, several moderators were investigated as described in the methods. None of these moderators were statistically significant predictors showing meaningful heterogeneity across the selected strata. The 95% prediction interval for correlation analyses was found to be .39–.91, which is shown in Figure 2.

Figure 2
Figure 2

—Forest plot of included studies used for meta-analysis (n = 12) (Byun, Kim, Brusseau, 2018). COR = correlation; CI = confidence interval; PI = prediction interval.

Citation: Journal for the Measurement of Physical Behaviour 3, 4; 10.1123/jmpb.2019-0072

Discussion

The most used device manufacturer was the Fitbit, with ActiGraph as the most common criterion device. Across different populations using different wearable devices, moderate to high correlations were found between MVPA on the wearable and the criterion assessment. While Fitbit is a propriety company, some researchers relied on “active” minutes or “very active” minutes, where others used the categorical definitions provided from a Fitabase download. While the correlations were mostly moderate to high across studies, the definition of “sufficient” correlation depends heavily on the context and the required level of precision for a specific use. Consumers will have different expectations for what is useful compared with a researcher hoping to reply on a precise, physiological measurement.

Each study differed with respect to the metric used to report agreement between the Fitbit and the criterion measure. These analytic/methodological differences made it very hard to compare across studies. As seen in Table 1, there were several different ActiLife cut points applied to adult populations leading to different definitions of MVPA based on counts per minute. Authors differed with respect to what they considered “good” validity. For example, two studies that found a correlation of .8 have reported both “excellent” and “adequate” agreement between devices (Alharbi et al., 2016; Van Blarigan et al., 2017). Furthermore, correlation analyses were the most frequently utilized statistical technique, which is not the most appropriate technique for assessing validity. Correlation analyses are highly influenced by the sample selection process. A recent publication by Welk et al. has recommended standardized protocols for future validation studies including valid, appropriate statistical techniques (Welk et al., 2019).

Statistical validity was assessed using various techniques to either describe the associations between the devices (such as correlation analyses), to describe the differences between the devices, such as mean or relative percentage error, or choosing to report the bias between devices, such as using a Bland–Altman plot. The many different statistical techniques are highlighted in Table 2. Only one article (Rosenberger et al., 2016) used equivalence testing, which has the potential to establish the unbiased accuracy of the measurement. While equivalence testing has the capacity to examine the unbiasedness between measurements, the method is infrequently used with most authors using correlations. Even within correlation analyses alone, Table 3 shows the many ways authors chose to compare MVPA with other measures. While Pearson correlations were more common, many studies have sample sizes of fewer than 40 (n = 15) and the required tests for normality were not always explicitly stated in the methods section of those publications. These small sample sizes are especially important to consider regarding the statistical analyses, where the normal distribution assumption is probably unsatisfied and thus, a Pearson correlation or Bland–Altman analyses requiring a 95% confidence interval constructed on a normal distribution will influence the results.

Studies that reported a systematic bias appeared to find that wearables tended to overestimate (vs. underestimate) MVPA relative to the ActiGraph. Possible reasons include that the Fitbit overestimates activity based on algorithmic decisions made within the device/the data processing. However, it is possible that wrist-worn accelerometry overestimates activity and that perhaps the issue is the comparison between a wrist-worn consumer’s wearable versus a hip-worn research-grade device but this issue should persist across all wrist-worn devices. However, as Table 1 shows, not every validation study followed the same protocols for ActiGraph wear with some devices worn on the wrist, some on the dominant hip, some on the right hip and others unspecified. Finally, it is possible that participants in free-living studies might have better adherence to the easy to wear commercial wearables versus the more cumbersome ActiGraph. While this is unlikely, it is a possible explanation for the overreporting found in many of the studies.

While this review provides context and description of the current state of the validation evidence for wearable fitness trackers, there are still several areas for future investigation and improvement. Beyond MVPA, wearable fitness trackers can also measure light-intensity activity as well as sedentary behaviors, which contribute to energy balance and health outcomes (Rosenberger et al., 2019). While a few studies in our review have included measures of light physical activity or sedentary behavior, a preliminary review of the literature shows only three systematic reviews on this topic including one review on the health benefits associated with light-intensity physical activity (Fuezeki, Engeroff, & Banzer, 2017). Two reviews that focus on sedentary behavior examine the validity of device-based measures and the role of motion sensing technology in relationship to physical activity and sedentary behavior (Gierisch et al., 2015; Heesch, Hill, Aguilar-Farias, van Uffelen, & Pavey, 2018). While this is not a systematic review of the literature, future work should examine the independent and combined contributions of wearable fitness trackers beyond MVPA including light-intensity physical activity and sedentary behavior.

We used the modified Downs and Black checklist to assess the methodological quality of the included studies. The included studies were generally of high quality, with a mean score of 12.6 out of 15 items for validity. The risk of bias in individual studies with respect to recruitment strategies is difficult to assess especially for individual validation studies. One potential source of bias is the use of different cut points for MVPA or the highly variable definitions across devices when it comes to intensities of activity. These across-study differences are captured in Table 1.

The meta-analysis provided an average correlation across the included studies, but also showed a high level of variation across studies. This variation is to be expected with the high variability of the methodological choices represented in Table 1, as well as the use of correlation analyses. As previously mentioned, correlation is not ideal for assessing validity especially when the analysis is very sensitive to both the population sampling strategy and the inherent variation of MVPA within the studied population. We chose five meaningful moderators to explain some of the underlying variability, but those moderators did not explain the heterogeneity across studies. Previous studies have shown imperfect agreement between different methods of activity assessment, such as Prince et al. showing a mean agreement of 0.8 between self-reported and device-based measurements (Prince et al., 2008). However, our meta-analysis shows that there is a consistent, positive, moderate (0.74) relationship between the criterion measure and the wearable fitness tracker. Determining if a correlation of .74, on average, is sufficient for measuring MVPA certainly depends on the context. At a population level, this may be appropriate; however, if these devices are used for an individual’s measurement of performance that level of association may be inadequate. The heterogeneity observed in these included studies is not easily explained; hence future research is warranted to better understand the relationship between these two measures, ideally using standardized validation protocols to enhance comparability.

Future work needs to include population subgroups, such as the very young, the very old, those with mobility limitations or gait difference, and those with other behavioral patterns related to chronic health or disability. This focus will become increasingly important as our population ages and more wearables are on market targeted toward children. Additionally, there is a need for standardization in reporting of outcomes (Montoye, Moore, Bowles, Korycinski, & Pfeiffer, 2018; Welk, 2019; Welk et al., 2019). As previously mentioned, there was high dissimilarity across studies, which makes aggregating results challenging. Future work should consider the use of multiple approaches to increase the comparability of results across papers, such as included the mean absolute percentage error and a Bland–Altman plot for example. Finally, future work should focus on non-Fitbit trackers as well. The state of the evidence has well-represented Fitbit, and other manufacturers need to be included to provide a complete picture for researchers and consumers alike.

This systematic review has both strengths and limitations. Our review focuses on MVPA, which is the intensity of activity that forms the basis for the national and international physical activity guidelines—but which is an area that has not been addressed by previous reviews. Additionally, our search criteria examined a wide distribution of study types, examining populations across the lifespan as well as healthy and chronic disease populations. However, there were a relatively small number of papers found despite the wide net. Consequently, summarizing the results across studies was challenging due to the wide variety of analytic methods used, criterion definitions, and locations of the criterion. We also were able to conduct a meta-analysis to examine the mean correlation across included studies, even though the correlation analyses are not the strongest assessment of validity. Finally, due to the pace of technology versus science, several papers reported on tracker models that are no longer on the market. This technology pace is a consideration to make for future research as well as consumers evaluating the appropriate wearable device. Nevertheless, this review reports on the state of the evidence regarding validation studies in wearable fitness trackers and assessing MVPA, showing the relatively high (.74) correlations between the criterion measures and these consumer-based devices. Standardization of validation methods and reporting outcomes in individual studies is necessary to allow for comparability across the evidence base.

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If the inline PDF is not rendering correctly, you can download the PDF file here.

Gorzelitz, Farber, and Cadmus-Bertram are with the Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, USA. Gangnon is with the Department of Population Health Sciences; Department of Biostatistics and Medical Informatics; and Department of Statistics, University of Wisconsin–Madison, Madison, WI, USA.

Cadmus-Bertram (lisa.bertram@wisc.edu) is corresponding author.
  • View in gallery

    —Flow diagram of article selection process. MVPA = moderate- to vigorous-intensity physical activity. Adapted from “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” by D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, and The PRISMA Group, 2009, PLoS Medicine, 6(6), p. e1000097. Copyright 2009 by xxxx. For more information visit www.prisma-statement.org.

  • View in gallery

    —Forest plot of included studies used for meta-analysis (n = 12) (Byun, Kim, Brusseau, 2018). COR = correlation; CI = confidence interval; PI = prediction interval.

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  • Ummels, D., Beekman, E., Theunissen, K., Braun, S., & Beurskens, A.J. (2018). Counting steps in activities of daily living in people with a chronic disease using nine commercially available fitness trackers: Cross-sectional validity study. JMIR mHealth uHealth, 6(4), e70. PubMed ID: 29610110 doi:10.2196/mhealth.8524

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  • Welk, G.J., Bai, Y., Lee, J.-M., Godino, J., Saint-Maurice, P.F., & Carr, L. (2019). Standardizing analytic methods and reporting in activity monitor validation studies. Medicine & Science in Sports & Exercise, 51(8), 1767. PubMed ID: 30913159 doi:10.1249/MSS.0000000000001966

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