Case 1:19-cv-11586-FDS Document 343-13 Filed 03/02/22 Page 1 of 7
`Case 1:19-cv-11586-FDS Document 343-13 Filed 03/02/22 Page 1 of 7
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`Case 1:19-cv-11586-FDS Document 343-13 Filed 03/02/22 Page 2 of 7
`Case 1:19-cv-11586-FDS Document 343-13 Filed 03/02/22 Page 2 of 7
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`
`Validity of Cardiorespiratory Fitness Measured
`with Fitbit Compared to VOomax
`
`KATHARINE KLEPIN!, DAVID WING’, MICHAEL HIGGINS’, JEANNE NICHOLS!”, and JOB G. GODINO??
`‘School ofExercise and Nutritional Sciences, San Diego State University, San Diego, CA; *Exercise and Physical Activity
`Resource Center, University ofCalifornia, San Diego, San Diego, CA; and *Centerfor Wireless and Population Health Systems,
`University of California, San Diego, San Diego, CA
`
`ABSTRACT
`
`KLEPIN, K., D. WING, M. HIGGINS,J. NICHOLS,and J. G. GODINO.Validity of Cardiorespiratory Fitness Measured with Fitbit Com-
`pared to VOjnax. Med. Sci. Sports Exerc., Vol. 51, No. 11, pp. 2251-2256, 2019. Purpose: Cardiorespiratoryfitness (CRF), broadly defined
`as the body’s ability to utilize oxygen, is a well-established prognostic marker of health, but it is not routinely measured. This may be due to
`the difficulty of acquiring high-quality CRF measures. The purpose of this study was to independently determine the validity of the Fitbit Charge
`2’s measure ofCRF (Fitbit CRF). Methods: Sixty-five healthy adults between the ages of 18 and 45 yr (55% female, 45% male) were recruited to
`undergo gold standard VOomax testing and wear a Fitbit Charge 2 continuously for 1 wk during which they were instructed to complete a qual-
`ifying outdoor run to derive the Fitbit CRF (units: mL-kg!-min™). This measure was compared with VOomax theasures (units: mL-kg™ min”)
`epochedat 15 and 60 s. Results: Bland—Altmananalyses revealed that Fitbit CRF hada positive bias of 1.59 mL-kg| min| compared withlab-
`oratory data epoched at 15 s and 0.30 mL-kg‘min| compared with data epochedat 60 s (1 = 60). F statistics (2.09; 0.08) and P values (0.133;
`0.926) from Bradley—Blackwoodtests for the concordance of Fitbit CRF with 15- and 60-s laboratorydata, respectively, support the null rypoth-
`esis of equal means and variances, indicating there is concordance between the two measures. Mean absolute percentage error was less than 10%
`for each comparison. Conclusions: The Fitbit Charge 2 provides an acceptable level of validity when measuring CRF in young,healthy, and fit
`adults whoare able to run. Further research is required to determineifit is a potentially usefiil tool in clinical practice and epidemiological research
`to quantify, categorize, and longitudinally track risk for adverse outcomes. Key Words: CARDIORESPIRATORY FITNESS, VOomaxs
`GRADED EXERCISE TEST, ACTIVITY TRACKER
`
`he epidemics of obesity, diabetes, and cardiovascular
`disease are now globalin scale (1), and boththeir inci-
`dence and prevalence are expected to increase as a re-
`sult of the aging of the population and an exacerbation of
`health disparities (2). The risk for these common, complex
`chronic diseases and their associated comorbidities can be sub-
`stantially reduced through improvements in cardiorespiratory
`fitness (CRF) (3,4). Cardiorespiratory fitness, broadly defined
`as the body’s ability to transport, absorb, and utilize oxygen is
`a well-established prognostic marker of health (3-6). In fact,
`
`Address for correspondence: Job G. Godino, Ph.D., 9500 Gilman Drive 0811,
`La Jolla, CA 92093; E-mail: jgodino@ucsd.edu.
`Submitted for publication October 2018.
`Accepted for publication May 2019.
`0195-9131/19/5111-2251/0
`MEDICINE & SCIENCEIN SPORTS & EXERCISE@
`Copyright © 2019 The Authors). Published by Wolters Kluwer Health, Inc.
`on behalf of the American College of Sports Medicine. This is an open-
`access article distributed under
`the terms of the Creative Commons
`Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND),
`where it is permissible to download and share the work provided it is properly
`cited. The work cannot be changed in any way or used commercially without
`permission from the journal.
`DOE 10.1249/MSS.000000000000204 1
`
`there is increasing epidemiological and clinical evidence that
`suggests that CRF may be a stronger predictor of all-cause
`mortality than other chronic disease risk factors, such as
`smoking, hypertension, high cholesterol, and type 2 diabetes
`(4,7,8). Although CRF has been shown to significantly improve
`the reclassification ofrisk for adverse outcomes (9-12), itis not
`routinely measured (3).
`This may be due, at least in part, to the difficulty of acquir-
`ing high-quality CRF measures. The “gold standard” measure
`of CRF is maximal oxygen uptake, or VOomax, which is
`assessed during a graded exercise test typically conducted on
`a treadmill or cycling ergometer (3, 13,14). This requires indi-
`viduals to wear a face-mask that enables the measurement of
`breath-by-breath volume and fractional composition of in-
`spired and expired gases. This type of CRF test not only re-
`quires substantial engagement by the individual being tested
`but also significant expertise, time, and cost to implement,
`making it impractical in most clinical and epidemiological
`contexts. A somewhat less burdensome measure of CRF can
`be derived from a 12-min run test (also known as a “Cooper
`Test”), which requires individuals to run as far as possible
`for up to 12 min ona flat course (15,16). VOomax is then es-
`timated from the total distance traveled according to well-
`established age- and sex-based population norms (15,16).
`Although this test requires less expertise, time, and cost to
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`conduct than a VOomax test, it too may not be feasible to
`widely undertake.
`Recent advances in microtechnology, data processing, wire-
`less communication, and battery capacity have resulted in the
`proliferation of low-cost, noninvasive wearable devices that
`seamlessly integrate with the wearer’s smartphone and can
`be used to measure multiple health-related signals in a
`free-living environment (17). One such device is the Fitbit
`Charge 2, a low-cost wrist-worn activity tracker (Fitbit
`Inc., San Francisco, CA, https://www.fitbit.com/charge2).
`Among other things,
`it contains a triaxial accelerometer,
`an optical heart rate monitor, and an altimeter. When linked
`with the GPS sensor on a wearer’s smartphone during an
`outdoor run on flat terrain at a comfortable pace that lasts
`at least 10 min, Fitbit will utilize the wearer’s heart rate
`and pace during the run, along with their resting heart rate,
`age, sex, and weightto calculate an estimate of CRF (the exact
`algorithm used is proprietary and currently unknown). Like
`the aforementioned 12-min runtest, this methodrelies heavily
`on a structured run of a known duration, suggesting a great
`deal of face validity. However, the test validity of the Fitbit
`Charge 2’s measure of CRF has not been investigated to date.
`In the present study, we assessed thetest validity ofthe Fitbit
`Charge 2’s measure of CRF by comparing it with VO>,,,. mea-
`sured during a graded exercise test conducted on a treadmill
`using state-of-the-science equipment. This study represents a
`logical step toward being able to make an informed decision
`about whether or not the Fitbit Charge 2’s measure of CRF
`could be used within clinical practice and epidemiological re-
`search. Given that CRF is a very informative markerof overall
`health, the potential to accurately and cheaply measure it via a
`consumer-level wearable in a free-living environment has im-
`portant implications for its widespread adoption.
`
`METHODS
`
`Participants. Potential participants were recruited via a
`combination of print (e.g., flyers) and digital (e.g., email) ad-
`vertisements. Eligible participants were adults age 18 to
`45yr, free from chronic diseases or injuries that would impede
`the completion of a graded-exercise test to volitional fatigue
`and at least three outdoor runs of 15 min or more, owned a
`smartphone capable of running the Fitbit application and
`pairing to the Fitbit Charge 2 with GPS enabled, and spoke
`English. Potential participants were excluded ifthey answered
`affirmatively to one or more questions in the American Col-
`lege of Sports Medicine’s Physical Activity Readiness
`Questionnaire (18), indicated that they could not run contin-
`uously for at least 15 min without stopping, or indicated
`they were pregnant.
`Procedures and measures. All study procedures were
`approved by the University of California, San Diego Institu-
`tional Review Board (approval number 161732). All partici-
`pants provided written informed consent and attended two
`in-personstudy visits at the Exercise and Physical Activity Re-
`source Center (EPARC).
`
`During the first visit, participants self-reported sex and age,
`and EPARCstaffmeasured participants’ weight(to the nearest
`0.1 kg) and height (to the nearest 0.1 cm) using a calibrated
`digital scale and stadiometer (Seca, Chino, CA). Both weight
`and height were measured with participants wearinglightweight
`clothes but without shoes, and two separate measurements were
`averaged (if weight or height measurements differed by more
`than 1%, then a third measure was taken and the average of
`the two measures that differed by less than 0.02 kg or 0.05 cm,
`respectively, was taken). Body mass index was calculated as
`weight in kilograms divided by height in square meters.
`Participants then completed a maximal graded exercise test
`on a Quinton Q-Stress treadmill (Mortara, Milwaukee, WD
`that was calibrated monthly for accuracy of speed and grade.
`The maximal graded exercise test protocol began with a
`warm-up at a self-selected pace on the treadmill for 5 to
`10 min. During the warm-up, EPARCstaff explained how to
`use the Borg RPE and reminded participants that they were ex-
`pected to achieve their maximallevel of exertion. Participants
`were then equipped with a breath mask that covers the nose
`and mouth (KORR Medical Technologies, Salt Lake City,
`UT), and a Bluetooth enabled heart rate monitor worn on the
`chest (Garmin, Olathe, KS). The preprogrammed treadmill
`protocol began with participants running at 5 mph (5.0 mph)
`with 0% incline for 3 min (13,19-21). The workload was then
`increased approximately 0.75 METs every minute (13,19-21).
`This was achieved via an increase in speed (0.5 mph-min') for
`the first 2 min, and an increase in incline by 1.5% every min-
`ute thereafter (13,19-21). RPE was assessed during the final
`10 s of each minute, and the protocol continued until the par-
`ticipant signaled to stop (Le., indication of volitional fatigue)
`(13,19-21). Upon indication of volitional fatigue, the tread-
`mill was immediately slowed to 2.0 mph, and participants
`were encouraged to walk until completely recovered. Breath
`by breath oxygen uptake (VO) was continuously measured
`using an indirect calorimeter (COSMED,Trentino, Italy) that
`was calibrated for gas volumeand fractional composition im-
`mediately (i.¢., less than 30 min) before the start of the maxi-
`mal graded exercise test protocol. At present, there is no
`consensus on the length of the epoch to use when averaging
`breath-by-breath level VO, data, but there is evidence that
`void of steady state VO, consumption, shorter epochs are
`more likely to elicit higher values (15,20,22). The extent to
`whichhigher values are more accurate remains unclear. There-
`fore, to present a range of epochslikely to be used, VO} data
`were averaged into 15- and 60-s epochs, and the largest value
`recorded during these epochs was identified as VOomax in
`analyses (1.e., 15-s CRF and 60-s CRF) (15,20,22). Use of in-
`direct calorimetry is the gold standard method for assessing
`CRF (G,13,19-21).
`EPARCstaff also downloaded the Fitbit application onto
`participants’ smartphone and logged into a study-specific
`Fitbit account that was created using a unique username and
`password (Le., the participant was not identified), and paired
`each participant’s phone to a study provided Fitbit Charge 2.
`The study-specific account was then populated with each
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`participant’s age, sex, handedness, and measured height and
`weight. EPARC staff explained how to properly wear the
`Fitbit Charge 2 and use it for GPS tracked outdoor runs. Par-
`ticipants were instructed to complete at
`least
`three GPS
`tracked outdoor runs on flat terrain at a comfortable pace
`lasting at least 15 min over the following week. They were
`also instructed to wear the device continuously except while
`swimming or bathing. A pamphlet detailing this information
`was provided to each participant. After the establishment of
`a resting heart rate and a qualifying run, Fitbit utilized a par-
`ticipant’s heart rate and pace during the run, along with their
`resting heart rate, age, sex, and weight to calculate an esti-
`mate of CRF. The exact algorithm used is proprietary and
`currently unknown.
`Durmg the second visit, which occurred approximately
`1 wkafter the first, EPARCstaff manually recorded partict-
`pants’ CRF ascalculated by Fitbit (.e., Fitbit CRF). The Fitbit
`Charge 2 was then unpaired from the participant’s phone, and
`the Fitbit account was closed. Participants were also asked to
`complete a widely utilized system usability scale questionnaire
`asking aboutthe mtuitiveness ofthe Fitbit Charge 2 and cor-
`responding smart phone application (23), and whether they
`believed that the device and application would be helpful in
`improving physical fitness. Questions were rated on a five-
`point Likert scale ranging from strongly disagree (1) to
`strongly agree (5). Scores were recalculated on a 0- to
`4-scale, summed, and multiplied by 2.5 to create a 100-point
`scale with higher scores indicating higher usability (23,24).
`As compensation for completion of the study, participants
`were given a feedback report about their VO2max,
`lactate
`threshold, and potential training zones.
`Statistical analysis. Demographic and anthropometric
`characteristics of the study sample were described using uni-
`variate descriptive statistics (.e., proportions and means and
`standard deviations). Test validity was described using Bland—
`Altman procedures to analyze the agreement of 15-s CRF and
`60-s CRF with Fitbit CRF. (25). Bradley—Blackwoodtests were
`used for a simultaneous analysis of the concordance between
`means and variances of the respective measures (26). Mean
`absolute percentage error was calculated as the average of
`absolute differences between the measures, divided by the
`relevant VOrmax- multiplied by 100. CRF measures were
`categorized according to age- and sex-based population
`norms defined as superior, excellent, good, fair, and poor
`(27). Categories were also collapsed into groups defined
`as superior or excellent, good, and fair or poor, because
`these categories are aligned with those used in risk stratifica-
`tion for all-cause mortality (6). The binary agreement between
`the aforementioned categories was analyzed using ¥° tests of
`independence. All statistical analyses were conducted using
`STATA 13.0 (StataCorp, College Station, TX).
`
`RESULTS
`
`From June 4, 2017, to December 4, 2017, 65 participants
`enrolled in the study. One participant experienced an
`
`equipment malfunction during the maximal graded exercise
`test and did not continue in the study. Another voluntarily
`dropped out before completing all measures. Three partici-
`pants did not complete a GPS tracked outdoor run that allowed
`for the calculation of Fitbit CRF. A total of 60 participants
`(27 male and 33 females) completed all study protocols
`and were included in data analyses. The mean (SD) age
`was 31.0 yr (7.3 yr), mean (SD) height was 169.5 cm
`(10.5 cm), mean (SD) weight was 70.2 kg (14.1 kg), and mean
`(SD) body mass index was 24.3 kgm* (3.3 kg-m*) (Table 1).
`Figure | shows that when compared to [5-s CRF, Fitbit
`CRFhada positive mean bias of 1.59 mL-kg‘min‘ with up-
`per and lowerlimits of 13.28 and —10.10, respectively. Com-
`pared with 60-s CRF, Fitbit CRF had a positive mean bias of
`0.30 mL-kg‘min! with upper and lowerlimits of 11.96
`and —11.36, respectively. For each comparison, the F statistic
`(15-s CRF vs Fitbit CRF = 2.09; 60-s CRF vs Fitbit
`CRF = 0.08) and corresponding P value (15-s CRF vs Fitbit
`CRF = 0.133; 60-s CRF vs Fitbit CRF = 0.926) of the
`Bradley—Blackwoodtest supports the null hypothesis of equal
`means and variances indicating that there is concordance be-
`tween measures regardless of the epoch used in the gold stan-
`dard. The Bland—Altmanplots also revealed two observations
`that fell outside the limits of agreement (3.3%) within each
`comparison. The mean absolute percentage error was nearly
`equal when Fitbit CRF was compared with 15-s CRF and
`60-s CRF, with values of 9.41% and 9.14%, respectively.
`Figure 2 showsthat Fitbit CRF correctly classified category
`of fitness 70.00% (42/60) of the time when compared with
`both 15-s CRF and 60-s CRF. These estimates improved when
`categories are binned as superior or excellent, good, fair or
`poor, with 91.70% (55/60) with both 15-sCRF and 60-s
`CRF. For each comparison,the 7 statistic (15 ¢ CRF vs Fitbit
`CRF = 66.93; 60-s CRF vs Fitbit CRF = 64.33) and correspond-
`ing P value (both <0.001) reject the null hypothesis of indepen-
`dence, indicating an association between the measures.
`Three participants who completed all of the physical as-
`sessments did not complete the system usability scale, thus
`data from 57 participants were analyzed. The mean (SD)
`score in reference to the Fitbit Charge 2 was 79.8 (15.1),
`
`TABLE 1. Participant characteristics.
`
`Age (yt)
`18 to 25
`13 (21.7)
`26 to 30
`20 (33.3)
`31 to 35
`9 (15.0)
`36 to 40
`9 (15.0)
`41 to 45
`9 (15.0)
`Sex
`Female
`Male
`Height (cm), mean (SD)
`Weight (kg), mean (SD)
`Body mass index (kg-m7’), mean (SD)
`CRF
`15-s CRF (mL-kg"!-min=!), mean (SD)
`60-s CRF (mL-kg™-min7'), mean (SD)
`Fitbit CRF (mL-kg7!-min='), mean (SD)
`“Values are numbers (percentages) unless otherwise specified.
`
`33 (55)
`27 (45)
`169.51 (11.03)
`70.29 (15.28)
`24
`
`48.9 (8.2)
`47.6 (8.1)
`47.3 (8.1)
`
`
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`VALIDITY OF CRF MEASURED BYFITBIT VERSUS VOzmax
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`FIGURE 1—Bland-—Altmanplots showing the agreement Fitbit CRF with (A) 15-s CRF and (B) 60-s CRF. Unit of measure, mL-kg‘min.
`
`and the mean (SD) score in reference to the corresponding
`smartphone application was 80.9 (12.5). These scores corre-
`spond to an adjective rating of “excellent” acceptability (24).
`Additionally, when asked if information from the Fitbit
`Charge 2 and corresponding application would motivate them
`to be more active over the long-term, most participants agreed
`(mean [SD], 4.2 [0.87]).
`
`DISCUSSION
`
`Regardless of the epoch used, there was a significant as-
`sociation between Fitbit CRF and VO>max although agree-
`ment
`improved when 60-s epochs were used in the
`laboratory-based measure. With an average bias of only
`0.3 mL-kg'-min! over minute level epochs and a mean ab-
`solute error less than 10%, the Fitbit Charge 2 provides an
`acceptable level of validity when measuring CRF. As such,
`it appears that the Fitbit Charge 2 offers many ofthe benefits
`implicit in submaximal field testing (i.ec., lower cost, less
`risk of injury, etc.). Additionally, because the device can
`be worn over long periods, there is an added opportunity
`for free-living, longitudinal tracking of CRF.
`The aim ofthis study wasto assess the test validity of the
`Although specific VO2max Values can be useful for targeted
`Fitbit Charge 2’s measure of CRF when compared with the
`current gold standard measures ofVOomax assessed using indi-
`physical training, their clinical and epidemiological use is
`magnified when used forrisk stratification. It is here that Fitbit
`rect calorimetry in a healthy population. By collecting breath-
`CRF mayhave an important impact. The y° analysis indicated
`by-breath data and averaging across multiple possible epochs,
`statistically significant high categorical agreement (70.0%)
`we were able to examine this agreementat several potentially
`whenfive levels of fitness were utilized. When further col-
`meaningful levels. Specifically, we analyzed the validity of
`lapsed to three categories, more in-line with the risk stratifica-
`Fitbit CRF against “true” maximal capacity whichis likely ob-
`tion proposed by Blair et al. (6), agreement was high (91.7%).
`served when small changes in oxygen uptake are averaged
`Importantly, these findings are perhaps unsurprising given that
`over short epochs(e.g., 15s), and also against longer epochs
`such a large proportion of the sample in the present study was
`(e.g., 60 s) like those utilized for generating predictive algo-
`
`rithms in commonly utilized field assessments of CRF. classified as havingafitness level that was either superior or
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`FIGURE 2—Categorical comparisonof Fitbit CRF with (A) 15-s CRF and (B) 60-s CRF. Unit of measure, mL-kg‘minplaced into age- and sex-based
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`2254—Official Joumal of the American College of Sports Medicine
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`excellent. Before strong conclusions about these findings can
`be made, additional research including populations with low
`levels of fitness and chronic diseases are necessary to more ro-
`bustly determine ifFitbit CRF can be used to quantify, catego-
`rize, and longitudinally track risk for adverse outcomes.
`Results from participants’ responses on the usability and
`acceptability of the Fitbit Charge 2 and corresponding
`smartphone application are promising for the prospect of
`widespread adoption in free-living populations. Specifically,
`participants found both the device and smartphone application
`easy to use and potentially helpful in regard to motivating
`healthy levels of physical activity. If the results of this study
`are replicated in more clinically relevant populations (i.c.,
`those with low fitness levels and chronic disease), then Fitbit
`may provide a platform for relatively inexpensive collection
`of large-scale, longitudinal data regarding CRF.
`Although the data gathered in this study are promising, the
`findings should be considered withinits limitations. First, the
`majority of participants had a high fitness level and were able
`to run. Further research is needed to determine if Fitbit CRF
`can be accurately derived when individuals transition from
`running to walking, or while walking throughout the entirety
`ofan assessment. Additionally, we recruited a relatively young
`sample that likely had a high level of familiarity and comfort
`with mobile technology in general, and smartphone-based ap-
`plications in particular. Additional research is necessary to de-
`termine if the Fitbit Charge 2 provides valid measures of CRF
`in a heterogeneous sample with lower overall fitness, greater
`age, existing disease, and less confidence in the use of mobile
`technology. An additional limitationis that participants may
`
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`CONCLUSIONS
`
`The Fitbit Charge 2’s measure of CRF offers an acceptably
`valid estimate of VOxmax in a young, healthy, and fit popula-
`tion of adults who were able to run. This free-living measure
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`relevant populations.
`
`The authors thank Dr. Linda Hill for her generous support of this
`project. The authors also thank all of the staff of the Exercise and
`Physical Activity Resource Center (EPARC) and the participants
`for their contributions.
`The authors acknowledge funding support for the publication of this
`workfrom the Mobilize Center, a NationalInstitutes of Health (NIH) Big
`Data to Knowledge Center of Excellence supported by NIH grant
`U54EB020405.
`The authors have no conflicts of interest to report. The results of the
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`VALIDITY OF CRF MEASURED BYFITBIT VERSUS VOzmax
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