Validating the Commercially Available Garmin Fenix 5x Wrist-Worn Optical Sensor for Aerobic Capacity

Main Article Content

James C Anderson, Jr.
Trent Chisenall
Blake Tolbert
Justin Ruffner
Paul N. Whitehead
Ryan T. Conners


Recreational exercisers continue to take a greater interest in monitoring their personal fitness levels. One of the more notable measurements that are monitored and estimated by wrist-worn tracking devices is maximum aerobic capacity (VO2max), which is currently the accepted measure of cardiorespiratory fitness. Traditional methods of obtaining VO2max present expensive barriers, whereas new wearable technology, such as of the Garmin Fenix 5x (GF5) provides a more cost-effective alternative. PURPOSE: To determine the validity of the GF5 VO2max estimation capabilities against the ParvoMedics TrueOne 2400 (PMT) metabolic measurement system in recreational runners. METHODS: Twenty-five recreational runners (17 male and 8 female) ages 18-55 participated in this study. Participants underwent two testing sessions: one consisting of the Bruce Protocol utilizing the PMT, while the other test incorporated the GF5 using the Garmin outdoor protocol. Both testing sessions were conducted within a few days of each other, with a minimum of 24 hours rest between sessions. RESULTS: The mean VO2max values for the PMT trial (49.1 ± 8.4 mL/kg/min) and estimation for the GF5 trial (47 ± 6.0 mL/kg/min) were found to be significantly different (t = 2.21, p = 0.037).   CONCLUSION: The average difference between the GF5 estimation and the PMT was 2.16 ml/kg/min.  Therefore, the watch is not as accurate compared to a PMT for obtaining VO2max.  However, although not statically significant, the proximity of scores to the PMT shows that the GF5 can be an option for a person seeking an affordable and easily available method of determining VO2max.  


Download data is not yet available.

Article Details

How to Cite
Anderson, J. C., Chisenall, T., Tolbert, B., Ruffner, J., Whitehead, P. N., & Conners, R. T. (2019). Validating the Commercially Available Garmin Fenix 5x Wrist-Worn Optical Sensor for Aerobic Capacity. International Journal for Innovation Education and Research, 7(1), 147-158.


Altini, M., Penders, J., & Amft, O. (2016). Estimating oxygen uptake during nonsteady-state activities and transitions using wearable sensors. IEEE Journal of Biomedical and Health Informatics, 20(2), 469-475. American Thoracic Society. (2003). ATS/ACCP statement on cardiopulmonary exercise testing. American Journal of Respiratory and Critical Care Medicine, 167(2), 211 - 277. Balady, G. J., Arena, R., Sietsema, K., Myers, J., Coke, L., Fletcher, G. F., ... & Keteyian, S. J. (2010). Clinician’s guide to cardiopulmonary exercise testing in adults: a scientific statement from the American Heart Association. Circulation, 122(2), 191-225. Beltrame, T., Amelard, R., Wong, A., & Hughson, R. L. (2017). Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living. Scientific Reports, 7, 45738.

Crouter, S. E., Antezak, A., Hudak, J. R., DellaValle, D. M., Haas, J. D. (2006). Accuracy

and reliability of the ParvoMedics TrueOne 2400 and MedGraphics VO2000

metabolic systems. European Journal of Applied Physiology, 98(2), 139-151.

Demers, L. M., Heest, J. V., Lasley, B. L., de Souza, M. J. (2003). Luteal phase

deficiency in recreational runners: Evidence for a hypometabolic state. The Journal of

Clinical Endocrinology & Metabolism, 88(1), 337-346. de Zambotti, M., Godino, J. G., Baker, F. C., Cheung, J., Patrick, K., & Colrain, I. M. (2016). The boom in wearable technology: Cause for alarm or just what is needed to better understand sleep? Sleep, 39(9), 1761-1762. Díaz, V., Benito, P. J., Peinado, A. B., Álvarez, M., Martín, C., Di Salvo, V., ... & Calderón, F. J. (2008). Validation of a new portable metabolic system during an incremental running test. Journal of Sports Science & Medicine, 7(4), 532. Elliot, C. A., Hamlin, M. J., & Lizamore, C. A. (2017). Validity and reliability of the Hexoskin® wearable biometric vest during maximal aerobic power testing in elite cyclists. Journal of Strength and Conditioning Research. Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 159. Firstbeat Technologies. (2017). Automated fitness level (VO2max) estimation with heart rate and speed data. Retrieved from Firstbeat Technologies. (2014). Stress and recovery analysis method based on 24-hour heart rate variability. Retrieved from 24-hour-heart-rate-variability-firstbeat-white-paper-2/.pdf. Firstbeat Technologies. (2015). Recovery analysis for athletic training based on heart rate variability. Retrieved from Franklin, B. A., Whaley, E. T., Howley, & Balady, G. J. (6th). (2000). ACSM’s Guidelines for

Exercise Testing and Prescription. Philadelphia: Lippincott Williams & Wilkins. Galy, O., Manetta, J., Coste, O., Maimoun, L., Chamari, K., & Hue, O. (2003). Maximal oxygen uptake and power of lower limbs during a competitive season in triathletes. Scandinavian Journal of Medicine & Science in Sports, 13(3), 185-193. Garmin. (2017). Garmin Fenix 5x: Owner’s Manual. Retrieved from

Gastin, P. B., McLean, O., Spittle, M., & Breed, R. V. (2013). Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology. Journal of Science and Medicine in Sport, 16(6), 589-593. Jacks, D. E., Topp, R., & Moore, J. B. (2012). Prediction of VO2 peak using a sub-maximal bench step test in children (Revised*). Clinical Kinesiology (Online), 66(3), 74. Kolla, B. P., Mansukhani, S., & Mansukhani, M. P. (2016). Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices, 13(5), 497-506.

Kraft, G. L., Dow, M. (2018). Validation of the polar fitness test. International Journal for

Innovation Education and Research, 6(1), 27-34.

Kraft, G. L., Roberts, R. A. (2017). Validation of the Garmin forerunner 920XT fitness

watch VO2peak test. International Journal for Innovation Education and Research, 5(2),

-67. Lee, J., & Finkelstein, J. (2015). Consumer sleep tracking devices: a critical review. Digital Healthcare Empowering Europeans: Proceedings of MIE2015, 210, 458. Leth, S., Hansen, J., Nielsen, O. W., & Dinesen, B. (2017). Evaluation of commercial self- monitoring devices for clinical purposes: Results from the future patient trial, phase I. Sensors, 17(1), 211. Malek, M. H., Housh, T. J., Berger, D. E., Coburn, J. W., & Beck, T. W. (2005). A new non— exercise-based Vo2max prediction equation for aerobically trained men. The Journal of Strength & Conditioning Research, 19(3), 559-565. Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154-1175. Morris, C. K., Myers, J., Froelicher, V. F., Kawaguchi, T., Ueshima, K., & Hideg, A. (1993). Nomogram based on metabolic equivalents and age for assessing aerobic exercise capacity in men. Journal of the American College of Cardiology, 22(1), 175-182. Novacheck, T. F. (1998). The biomechanics of running. Gait & posture, 7(1), 77-95. Owens, S., & Gutin, B. (1999). Exercise testing of the child with obesity. Pediatric Cardiology, 20(1), 79-83.Park, S., & Jayaraman, S. (2003). Enhancing the quality of life through wearable technology. IEEE Engineering in Medicine and Biology Magazine, 22(3), 41-48. Perret, C., & Mueller, G. (2006). Validation of a new portable ergospirometric device (Oxycon Mobile®) during exercise. International Journal of Sports Medicine, 27(05), 363-367. Pollock, M. L., Gaesser, G. A., Butcher, J. D., Després, J. P., Dishman, R. K., Franklin, B. A., & Garber, C. E. (1998). ACSM position stand: The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Medicine & Science in Sports & Exercise, 30(6), 975-991.

Riebe, D., Ehrman, J. K., Liguori, G., Magal, M. (10th). (2018). ACSM’s guidelines for

exercise testing and prescription. Philadelphia: Wolters Kluwer. Scherr, D., Kastner, P., Kollmann, A., Hallas, A., Auer, J., Krappinger, H., ... & Schreier, G. (2009). Effect of home-based telemonitoring using mobile phone technology on the outcome of heart failure patients after an episode of acute decompensation: Randomized controlled trial. Journal of Medical Internet research, 11(3). Strohrmann, C., Rossi, M., Arnrich, B., & Troster, G. (2012). A data-driven approach to kinematic analysis in running using wearable technology. Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks, 118-123. Tanaka, H., Bassett Jr, D. R., Swensen, T. C., & Sampedro, R. M. (1993). Aerobic and anaerobic power characteristics of competitive cyclists in the United States Cycling Federation. International Journal of Sports Medicine, 14(6), 334-338. Taylor, H. L., Buskirk, E., & Henschel, A. (1955). Maximal oxygen intake as an objective measure of cardio-respiratory performance. Journal of applied physiology, 8(1), 73-80. Washington, R. L., Bricker, J. T., Alpert, B. S., Daniels, S. R., Deckelbaum, R. J., Fisher, E. A., ... & Marx, G. R. (1994). Guidelines for exercise testing in the pediatric age group. From the committee on atherosclerosis and hypertension in children, council on cardiovascular disease in the young, the American Heart Association. Circulation, 90(4), 2166-2179. Whyte, G., Lumley, S., George, K., Gates, P., Sharma, S., Prasad, K., & McKenna, W. J., (2000). Physiological profile and predictors of cycling performance in ultra-endurance triathletes. Journal of Sports Medicine and Physical Fitness, 40(2), 103. Winterhalter, C. A., Teverovsky, J., Wilson, P., Slade, J., Horowitz, W., Tierney, E., & Sharma, V. (2005). Development of electronic textiles to support networks, communications, and medical applications in future US Military protective clothing systems. IEEE Transactions on Information Technology in Biomedicine, 9(3), 402-406. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Cambridge, MA: Morgan Kaufmann.