A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations

Jonathan M Peake, Graham Kerr, John P Sullivan, Jonathan M Peake, Graham Kerr, John P Sullivan

Abstract

The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.

Keywords: cognitive function; concussion; emotion; health; performance; sleep; stress.

Figures

Figure 1
Figure 1
Summary of current technologies for monitoring health/performance and targeted physical measurements.
Figure 2
Figure 2
Matrix to guide decision-making process for evaluating and selecting new technologies.
Figure 3
Figure 3
Classification of technologies based on whether they have been validated and/or used in research.

References

    1. Baron K. G., Duffecy J., Berendsen M. A., Cheung Mason I., Lattie E. G., Manalo N. C. (2017). Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Med. Rev. [Epub ahead of print]. 10.1016/j.smrv.2017.12.002
    1. Beurskens R., Steinberg F., Antoniewicz F., Wolff W., Granacher U. (2016). Neural correlates of dual-task walking: effects of cognitive versus motor interference in young adults. Neural Plast. 2016:8032180. 10.1155/2016/8032180
    1. Borges N. R., Driller M. W. (2016). Wearable lactate threshold predicting device is valid and reliable in runners. J. Strength Cond. Res. 30, 2212–2218. 10.1519/JSC.0000000000001307
    1. Brandmeyer T., Delorme A. (2013). Meditation and neurofeedback. Front. Psychol. 4:688. 10.3389/fpsyg.2013.00688
    1. Claassen J. A., Colier W. N., Jansen R. W. (2006). Reproducibility of cerebral blood volume measurements by near infrared spectroscopy in 16 healthy elderly subjects. Physiol. Meas. 27, 255–264. 10.1088/0967-3334/27/3/004
    1. Corbin-Berrigan L. A., Kowalski K., Faubert J., Christie B., Gagnon I. (2018). Three-dimensional multiple object tracking in the pediatric population: the NeuroTracker and its promising role in the management of mild traumatic brain injury. Neuroreport 29, 559–563. 10.1097/WNR.0000000000000988
    1. de Zambotti M., Claudatos S., Inkelis S., Colrain I. M., Baker F. C. (2015). Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiol. Int. 32, 1024–1028. 10.3109/07420528.2015.1054395
    1. de Zambotti M., Goldstone A., Claudatos S., Colrain I. M., Baker F. C. (2017a). A validation study of Fitbit Charge 2 compared with polysomnography in adults. Chronobiol. Int. 35, 465–476. 10.1080/07420528.2017.1413578
    1. de Zambotti M., Rosas L., Colrain I. M., Baker F. C. (2017b). The sleep of the ring: comparison of the OURA sleep tracker against polysomnography. Behav Sleep Med. [Epub ahead of print]. 10.1080/15402002.2017.1300587
    1. Düking P., Hotho A., Holmberg H. C., Fuss F. K., Sperlich B. (2016). Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Front. Physiol. 7:71. 10.3389/fphys.2016.00071
    1. Dupré D., Bland B., Bolster A., Morrison G., McKeown G. (2018). Dynamic model of athletes' emotions based on wearable devices, in Advances in Human Factors in Sports, Injury Prevention and Outdoor Recreation, ed Ahram T. (Cham: Springer International Publishing; ), 42–50.
    1. Fraser S. A., Dupuy O., Pouliot P., Lesage F., Bherer L. (2016). Comparable cerebral oxygenation patterns in younger and older adults during dual-task walking with increasing load. Front. Aging Neurosci. 8:240. 10.3389/fnagi.2016.00240
    1. Gabel V., Maire M., Reichert C. F., Chellappa S. L., Schmidt C., Hommes V., et al. . (2013). Effects of artificial dawn and morning blue light on daytime cognitive performance, well-being, cortisol and melatonin levels. Chronobiol. Int. 30, 988–997. 10.3109/07420528.2013.793196
    1. Galetta K. M., Barrett J., Allen M., Madda F., Delicata D., Tennant A. T., et al. . (2011). The King-Devick test as a determinant of head trauma and concussion in boxers and MMA fighters. Neurology 76, 1456–1462. 10.1212/WNL.0b013e31821184c9
    1. Gao W., Emaminejad S., Nyein H. Y. Y., Challa S., Chen K., Peck A., et al. . (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509–514. 10.1038/nature16521
    1. Geerdink M., Walbeek T. J., Beersma D. G., Hommes V., Gordijn M. C. (2016). Short blue light pulses (30 min) in the morning support a sleep-advancing protocol in a home setting. J. Biol. Rhythms 31, 483–497. 10.1177/0748730416657462
    1. Gruwez A., Libert W., Ameye L., Bruyneel M. (2017). Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals. Int. J. Med. Inform. 102, 87–92. 10.1016/j.ijmedinf.2017.03.008
    1. Halson S. L., Peake J. M., Sullivan J. P. (2016). Wearable technology for athletes: information overload and pseudoscience? Int. J. Sports Physiol. Perform. 11, 705–706. 10.1123/IJSPP.2016-0486
    1. Huppert T. J., Hoge R. D., Diamond S. G., Franceschini M. A., Boas D. A. (2006). A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage 29, 368–382. 10.1016/j.neuroimage.2005.08.065
    1. Hussain J. N., Mantri N., Cohen M. M. (2017). Working up a good sweat - the challenges of standardising sweat collection for metabolomics analysis. Clin. Biochem. Rev. 38, 13–34.
    1. Kang S. G., Kang J. M., Ko K. P., Park S. C., Mariani S., Weng J. (2017). Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers. J. Psychosom. Res. 97, 38–44. 10.1016/j.jpsychores.2017.03.009
    1. King D., Hume P., Gissane C., Clark T. (2015). Use of the King-Devick test for sideline concussion screening in junior rugby league. J. Neurol. Sci. 357, 75–79. 10.1016/j.jns.2015.06.069
    1. Kinnamon D., Ghanta R., Lin K. C., Muthukumar S., Prasad S. (2017). Portable biosensor for monitoring cortisol in low-volume perspired human sweat. Sci. Rep. 7:13312. 10.1038/s41598-017-13684-7
    1. Koh A., Kang D., Xue Y., Lee S., Pielak R. M., Kim J., et al. . (2016). A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat. Sci. Transl. Med. 8, 366ra165. 10.1126/scitranslmed.aaf2593
    1. Legault I., Faubert J. (2012). Perceptual-cognitive training improves biological motion perception: evidence for transferability of training in healthy aging. Neuroreport 23, 469–473. 10.1097/WNR.0b013e328353e48a
    1. Mandrick K., Chua Z., Causse M., Perrey S., Dehais F. (2016). Why a comprehensive understanding of mental workload through the measurement of neurovascular coupling is a key issue for neuroergonomics? Front. Hum. Neurosci. 10:250. 10.3389/fnhum.2016.00250
    1. Mantua J., Gravel N., Spencer R. M. (2016). Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography. Sensors 16:E646. 10.3390/s16050646
    1. Martín A., Sfer A. M., D'Urso Villar M. A., Barraza J. F. (2017). Position affects performance in multiple-object tracking in rugby union players. Front. Psychol. 8:1494. 10.3389/fpsyg.2017.01494
    1. McManus C. J., Collison J., Cooper C. E. (2018). Performance comparison of the MOXY and PortaMon near-infrared spectroscopy muscle oximeters at rest and during exercise. J. Biomed. Opt. 23, 1–14. 10.1117/1.JBO.23.1.015007
    1. Mehagnoul-Schipper D. J., van der Kallen B. F., Colier W. N., van der Sluijs M. C., van Erning L. J., Thijssen H. O., et al. . (2002). Simultaneous measurements of cerebral oxygenation changes during brain activation by near-infrared spectroscopy and functional magnetic resonance imaging in healthy young and elderly subjects. Hum. Brain Mapp. 16, 14–23. 10.1002/hbm.10026
    1. Mirelman A., Maidan I., Bernad-Elazari H., Nieuwhof F., Reelick M., Giladi N., et al. . (2014). Increased frontal brain activation during walking while dual tasking: an fNIRS study in healthy young adults. J. Neuroeng. Rehabil. 11:85. 10.1186/1743-0003-11-85
    1. Montgomery-Downs H. E., Insana S. P., Bond J. A. (2012). Movement toward a novel activity monitoring device. Sleep Breath. 16, 913–917. 10.1007/s11325-011-0585-y
    1. Moriguchi Y., Noda T., Nakayashiki K., Takata Y., Setoyama S., Kawasaki S., et al. . (2017). Validation of brain-derived signals in near-infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging. Hum. Brain Mapp. 38, 5274–5291. 10.1002/hbm.23734
    1. Nes B. M., Gutvik C. R., Lavie C. J., Nauman J., Wisløff U. (2017). Personalized activity intelligence (PAI) for prevention of cardiovascular disease and promotion of physical activity. Am. J. Med. 130, 328–336. 10.1016/j.amjmed.2016.09.031
    1. Parsons B., Magill T., Boucher A., Zhang M., Zogbo K., Bérubé S., et al. . (2016). Enhancing cognitive function using perceptual-cognitive training. Clin. EEG Neurosci. 47, 37–47. 10.1177/1550059414563746
    1. Patterson J., Amick R., Pandya P., Hankansson N., Jorgensen M. (2014a). Comparison of a mobile technology application with the balance error scoring system. Int. J. Athl. Ther. Train. 19, 4–7. 10.1123/ijatt.2013-0094
    1. Patterson J. A., Amick R. Z., Thummar T., Rogers M. E. (2014b). Validation of measures from the smartphone Sway Balance application: a pilot study. Int. J. Sports Phys. Ther. 9, 135–139.
    1. Piwek L., Ellis D. A., Andrews S., Joinson A. (2016). The rise of consumer health wearables: promises and barriers. PLoS Med. 13:e1001953. 10.1371/journal.pmed.1001953
    1. Puddy R., Wilkins N. (2011). Understanding Evidence Part 1: Best Available Research Evidence. A Guide to the Continuum of Evidence of Effectiveness. Atlanta, GA: Centers for Disease Control and Prevention.
    1. Pylyshyn Z. W., Storm R. W. (1988). Tracking multiple independent targets: evidence for a parallel tracking mechanism. Spat. Vis. 3, 179–197. 10.1163/156856888X00122
    1. Reynolds B. B., Patrie J., Henry E. J., Goodkin H. P., Broshek D. K., Wintermark M., et al. . (2016). Practice type effects on head impact in collegiate football. J. Neurosurg. 124, 501–510. 10.3171/2015.5.JNS15573
    1. Ryan T. E., Erickson M. L., Brizendine J. T., Young H. J., McCully K. K. (2012). Noninvasive evaluation of skeletal muscle mitochondrial capacity with near-infrared spectroscopy: correcting for blood volume changes. J. Appl. Physiol. 113, 175–183. 10.1152/japplphysiol.00319.2012
    1. Ryan T. E., Southern W. M., Reynolds M. A., McCully K. K. (2013). A cross-validation of near-infrared spectroscopy measurements of skeletal muscle oxidative capacity with phosphorus magnetic resonance spectroscopy. J. Appl. Physiol. 115, 1757–1766. 10.1152/japplphysiol.00835.2013
    1. Sato H., Yahata N., Funane T., Takizawa R., Katura T., Atsumori H., et al. . (2013). A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task. Neuroimage 83, 158–173. 10.1016/j.neuroimage.2013.06.043
    1. Sawka M. N., Friedl K. E. (2018). Emerging wearable physiological monitoring technologies and decision aids for health and performance. J. Appl. Physiol. 124, 430–431. 10.1152/japplphysiol.00964.2017
    1. Seidman D. H., Burlingame J., Yousif L. R., Donahue X. P., Krier J., Rayes L. J., et al. . (2015). Evaluation of the King-Devick test as a concussion screening tool in high school football players. J. Neurol. Sci. 356, 97–101. 10.1016/j.jns.2015.06.021
    1. Siegmund G. P., Guskiewicz K. M., Marshall S. W., DeMarco A. L., Bonin S. J. (2016). Laboratory validation of two wearable sensor systems for measuring head impact severity in football players. Ann. Biomed. Eng. 44, 1257–1274. 10.1007/s10439-015-1420-6
    1. Swartz E. E., Broglio S. P., Cook S. B., Cantu R. C., Ferrara M. S., Guskiewicz K. M., et al. . (2015). Early results of a helmetless-tackling intervention to decrease head impacts in football players. J. Athl. Train. 50, 1219–1222. 10.4085/1062-6050-51.1.06
    1. Van den Bulck J. (2015). Sleep apps and the quantified self: blessing or curse? J. Sleep Res. 24, 121–123. 10.1111/jsr.12270
    1. Villar R., Beltrame T., Hughson R. L. (2015). Validation of the Hexoskin wearable vest during lying, sitting, standing, and walking activities. Appl. Physiol. Nutr. Metab. 40, 1019–1024. 10.1139/apnm-2015-0140
    1. Viola A. U., James L. M., Schlangen L. J., Dijk D. J. (2008). Blue-enriched white light in the workplace improves self-reported alertness, performance and sleep quality. Scand. J. Work Environ. Health 34, 297–306. 10.5271/sjweh.1268
    1. Walsh D. V., Capo-Aponte J. E., Beltran T., Cole W. R., Ballard A., Dumayas J. Y. (2016). Assessment of the King-Devick(R) (KD) test for screening acute mTBI/concussion in warfighters. J. Neurol. Sci. 370, 305–309. 10.1016/j.jns.2016.09.014

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