Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort

Salvatore Tedesco, Marco Sica, Andrea Ancillao, Suzanne Timmons, John Barton, Brendan O'Flynn, Salvatore Tedesco, Marco Sica, Andrea Ancillao, Suzanne Timmons, John Barton, Brendan O'Flynn

Abstract

Background: Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings.

Objective: This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment.

Methods: Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered.

Results: For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%.

Conclusions: The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

Keywords: Fitbit; Garmin; aging; energy expenditure; fitness trackers; older adults; physical activity; sleep; wearable activity trackers; wristbands.

Conflict of interest statement

Conflicts of Interest: None declared.

©Salvatore Tedesco, Marco Sica, Andrea Ancillao, Suzanne Timmons, John Barton, Brendan O'Flynn. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 19.06.2019.

Figures

Figure 1
Figure 1
Placement of devices on a participant.
Figure 2
Figure 2
Mean absolute percentage error (MAPE) with standard deviation for each parameter and tracker. EE: energy expenditure; MVPA: moderate-to-vigorous physical activity; TST: total sleep time; WASO: wake after sleep onset.
Figure 3
Figure 3
Bland-Altman plots for steps.
Figure 4
Figure 4
Bland-Altman plots for energy expenditure.
Figure 5
Figure 5
Bland-Altman plots for moderate-to-vigorous physical activity.
Figure 6
Figure 6
Bland-Altman plots for sleep parameters. Top row: total sleep time; bottom row: wake after sleep onset.

References

    1. Kamišalić A, Fister I, Turkanović M, Karakatič S. Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors (Basel) 2018 May 25;18(6):1714. doi: 10.3390/s18061714.
    1. Alley S, Schoeppe S, Guertler D, Jennings C, Duncan MJ, Vandelanotte C. Interest and preferences for using advanced physical activity tracking devices: results of a national cross-sectional survey. BMJ Open. 2016 Dec 07;6(7):e011243. doi: 10.1136/bmjopen-2016-011243.
    1. Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012 Feb;12(2):2255–2283. doi: 10.3390/s120202255.
    1. Tedesco S, Urru A, Clifford A, O’Flynn B. Experimental validation of the Tyndall Portable Lower-limb Analysis System with wearable inertial sensors. Procedia Engineer. 2016;147:208–213. doi: 10.1016/j.proeng.2016.06.215.
    1. Fortune E, Lugade VA, Kaufman KR. Posture and movement classification: the comparison of tri-axial accelerometer numbers and anatomical placement. J Biomech Eng. 2014 May;136(5):051003. doi: 10.1115/1.4026230.
    1. Ancillao A, Tedesco S, Barton J, O'Flynn B. Indirect measurement of ground reaction forces and moments by means of wearable inertial sensors: a systematic review. Sensors (Basel) 2018 Aug 05;18(8):E2564. doi: 10.3390/s18082564.
    1. Xie J, Wen D, Liang L, Jia Y, Gao L, Lei J. Evaluating the validity of current mainstream wearable devices in fitness tracking under various physical activities: comparative study. JMIR Mhealth Uhealth. 2018 Apr 12;6(4):e94. doi: 10.2196/mhealth.9754.
    1. Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016 Mar;48(3):457–465. doi: 10.1249/MSS.0000000000000778.
    1. Tedesco S, Sica M, Garbay T, Barton J, O'Flynn B. A comprehensive comparison of commercial wrist-worn trackers in a young cohort in a lab-environment. IEEE PERCOM Workshops 2018; March 19-23, 2018; Athens, Greece. 2018. Mar 19,
    1. Malwade S, Abdul S, Uddin M, Nursetyo A, Fernandez-Luque L, Zhu X, Cilliers L, Wong C, Bamidis P, Li Y. Mobile and wearable technologies in healthcare for the ageing population. Comput Methods Programs Biomed. 2018 Jul;161:233–237. doi: 10.1016/j.cmpb.2018.04.026. doi: 10.1016/j.cmpb.2018.04.026.
    1. Ehn M, Eriksson LC, Åkerberg N, Johansson A. Activity monitors as support for older persons' physical activity in daily life: qualitative study of the users' experiences. JMIR Mhealth Uhealth. 2018 Feb 01;6(2):e34. doi: 10.2196/mhealth.8345.
    1. Puri A, Kim B, Nguyen O, Stolee P, Tung J, Lee J. User acceptance of wrist-worn activity trackers among community-dwelling older adults: mixed method study. JMIR Mhealth Uhealth. 2017 Nov 15;5(11):e173. doi: 10.2196/mhealth.8211.
    1. Tedesco S, Barton J, O'Flynn B. A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry. Sensors (Basel) 2017 Jun 03;17(6):1277. doi: 10.3390/s17061277.
    1. Phillips LJ, Petroski GF, Markis NE. A comparison of accelerometer accuracy in older adults. Res Gerontol Nurs. 2015 May;8(5):213–219. doi: 10.3928/19404921-20150429-03.
    1. Floegel TA, Florez-Pregonero A, Hekler EB, Buman MP. Validation of consumer-based hip and wrist activity monitors in older adults with varied ambulatory abilities. J Gerontol A Biol Sci Med Sci. 2017 Feb;72(2):229–236. doi: 10.1093/gerona/glw098.
    1. Straiton N, Alharbi M, Bauman A, Neubeck L, Gullick J, Bhindi R, Gallagher R. The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: a systematic review. Maturitas. 2018 Jun;112:85–93. doi: 10.1016/j.maturitas.2018.03.016.
    1. Paul SS, Tiedemann A, Hassett LM, Ramsay E, Kirkham C, Chagpar S, Sherrington C. Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults. BMJ Open Sport Exerc Med. 2015 Jul;1(1):e000013. doi: 10.1136/bmjsem-2015-000013.
    1. Alharbi M, Bauman A, Neubeck L, Gallagher R. Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population. Eur J Prev Cardiol. 2016 Sep;23(14):1476–1485. doi: 10.1177/2047487316634883.
    1. Boeselt T, Spielmanns M, Nell C, Storre JH, Windisch W, Magerhans L, Beutel B, Kenn K, Greulich T, Alter P, Vogelmeier C, Koczulla AR. Validity and usability of physical activity monitoring in patients with chronic obstructive pulmonary disease (COPD) PLoS One. 2016 Jun;11(6):e0157229. doi: 10.1371/journal.pone.0157229.
    1. Farina N, Lowry RG. The validity of consumer-level activity monitors in healthy older adults in free-living conditions. J Aging Phys Act. 2018 Jan 01;26(1):128–135. doi: 10.1123/japa.2016-0344.
    1. Burton E, Hill KD, Lautenschlager NT, Thøgersen-Ntoumani C, Lewin G, Boyle E, Howie E. Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people. BMC Geriatr. 2018 Dec 03;18(1):103. doi: 10.1186/s12877-018-0793-4.
    1. Grant PM, Dall PM, Mitchell SL, Granat MH. Activity-monitor accuracy in measuring step number and cadence in community-dwelling older adults. J Aging Phys Act. 2008 Apr;16(2):201–214.
    1. O'Connell S, ÓLaighin G, Quinlan LR. When a step is not a step! Specificity analysis of five physical activity monitors. PLoS One. 2017;12(1):e0169616. doi: 10.1371/journal.pone.0169616.
    1. An HS, Jones GC, Kang SK, Welk GJ, Lee JM. How valid are wearable physical activity trackers for measuring steps? Eur J Sport Sci. 2017 Apr;17(3):360–368. doi: 10.1080/17461391.2016.1255261.
    1. Imboden MT, Nelson NB, Kaminsky LA, Montoye AH. Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure. Br J Sports Med. 2018 Jul;52(13):844–850. doi: 10.1136/bjsports-2016-096990.
    1. Patterson M, Wang W, Ortiz A. Dietary and physical activity outcomes determine energy balance in US adults aged 50-74 years. J Aging Phys Act. 2018 Oct 01;26(4):561–569. doi: 10.1123/japa.2017-0304.
    1. Kim M, Yoshida H, Sasai H, Kojima N, Kim H. Association between objectively measured sleep quality and physical function among community-dwelling oldest old Japanese: A cross-sectional study. Geriatr Gerontol Int. 2015 Aug;15(8):1040–1048. doi: 10.1111/ggi.12396.
    1. Johnson ST, Thiel D, Al Sayah F, Mundt C, Qiu W, Buman MP, Vallance JK, Johnson JA. Objectively measured sleep and health-related quality of life in older adults with type 2 diabetes: a cross-sectional study from the Alberta's Caring for Diabetes Study. Sleep Health. 2017 Dec;3(2):102–106. doi: 10.1016/j.sleh.2016.12.002.
    1. Crouter SE, Kuffel E, Haas JD, Frongillo EA, Bassett DR Jr. Refined two-regression model for the ActiGraph accelerometer. Med Sci Sports Exerc. 2010 May;42(5):1029–1037. doi: 10.1249/MSS.0b013e3181c37458.
    1. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008 Jan;40(1):181–188. doi: 10.1249/mss.0b013e31815a51b3.
    1. Cole RJ, Kripke DF, Gruen W, Mullaney DJ, Gillin JC. Automatic sleep/wake identification from wrist activity. Sleep. 1992 Oct;15(5):461–469.
    1. Qin J, Barbour KE, Nevitt MC, Helmick CG, Hootman JM, Murphy LB, Cauley JA, Dunlop DD. Objectively measured physical activity and risk of knee osteoarthritis. Med Sci Sports Exerc. 2018 Dec;50(2):277–283. doi: 10.1249/MSS.0000000000001433.
    1. Song J, Semanik P, Sharma L, Chang RW, Hochberg MC, Mysiw WJ, Bathon JM, Eaton CB, Jackson R, Kwoh CK, Nevitt M, Dunlop DD. Assessing physical activity in persons with knee osteoarthritis using accelerometers: data from the osteoarthritis initiative. Arthritis Care Res (Hoboken) 2010 Dec;62(12):1724–1732. doi: 10.1002/acr.20305. doi: 10.1002/acr.20305.
    1. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016 Jun;15(2):155–163. doi: 10.1016/j.jcm.2016.02.012.
    1. Aspvik NP, Viken H, Zisko N, Ingebrigtsen JE, Wisløff U, Stensvold D. Are older adults physically active enough - a matter of assessment method? The Generation 100 Study. PLoS One. 2016;11(11):e0167012. doi: 10.1371/journal.pone.0167012.
    1. Marino M, Li Y, Rueschman MN, Winkelman JW, Ellenbogen JM, Solet JM, Dulin H, Berkman LF, Buxton OM. Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep. 2013 Nov 01;36(11):1747–1755. doi: 10.5665/sleep.3142.
    1. Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, Hamilton CB, Li LC. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth. 2018 Aug 09;6(8):e10527. doi: 10.2196/10527.
    1. Edwards BA, O'Driscoll DM, Ali A, Jordan AS, Trinder J, Malhotra A. Aging and sleep: physiology and pathophysiology. Semin Respir Crit Care Med. 2010 Oct;31(5):618–633. doi: 10.1055/s-0030-1265902.
    1. Lineberger MD, Carney CE, Edinger JD, Means MK. Defining insomnia: quantitative criteria for insomnia severity and frequency. Sleep. 2006 Apr;29(4):479–485.
    1. Kang SG, Kang JM, Ko KP, Park SC, Mariani S, Weng J. Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers. J Psychosom Res. 2017 Dec;97:38–44. doi: 10.1016/j.jpsychores.2017.03.009.
    1. Schneider PL, Crouter SE, Bassett DR. Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc. 2004 Feb;36(2):331–335. doi: 10.1249/01.MSS.0000113486.60548.E9.
    1. Tudor-Locke C, Sisson SB, Lee SM, Craig CL, Plotnikoff RC, Bauman A. Evaluation of quality of commercial pedometers. Can J Public Health. 2006;97(1):S10–S15. doi: 10.17269/cjph.97.1544.
    1. Schneider PL, Crouter SE, Lukajic O, Bassett DR. Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Med Sci Sports Exerc. 2003 Oct;35(10):1779–1784. doi: 10.1249/01.MSS.0000089342.96098.C4.
    1. McMahon SK, Lewis B, Oakes JM, Wyman JF, Guan W, Rothman AJ. Assessing the effects of interpersonal and intrapersonal behavior change strategies on physical activity in older adults: a factorial experiment. Ann Behav Med. 2017 Jun;51(3):376–390. doi: 10.1007/s12160-016-9863-z.

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