Health-Tech Reports / Wearables / September 25, 2016

Consumer health wearables: Who benefits from it?

As health and fitness wearables continue to increase in popularity, questions remain on the evidence behind it and who actually benefits from it

by Leona Tan

  •  Health and fitness wearables are on the rise, with device owners likely to be young and healthy individuals with disposable incomes.
  • To date, systematic reviews have found some evidence for increasing physical activity, and clinimetric validity for measuring Parkinson’s disease symptoms. Several reviews have examined the use of wearable technology in monitoring mobility related activities in older adults, but the evidence was largely inconsistent with a lack of real-world testing.
  • There is emerging research into the use of wearables in identifying depression symptoms as well as sleep and cardiac functioning. However, many of these applications are still in early phases of development and testing.
  • Limited evidence reveals a clear need for further research in real-world settings. It is also unknown if such devices are feasible and accessible to the underserved population as data on participants’ socioeconomic status is often not reported.
  • As these wearables continue to rise in popularity and use, questions remain on the evidence for health outcomes as well as feasibility and accessibility for at-risk populations including the underserved.

The wearable technology industry is experiencing considerable growth, with one in six consumers in the United States currently using some form of wearable technology [1]. These devices are increasingly used for health and fitness purposes, as reflected in predicted sales increases from 19 million in 2016 to 110 million in 2018 [2].

Despite industry growth, claims of actual improvement in health outcomes may be premature, as the evidence for health wearables is currently unclear. To date, questions remain on the evidence behind health wearables, and who actually benefits from it. This article takes a closer look at the research to date for consumer health wearables and implications for future research and development.

Wearable device owners tend to be young individuals with disposable incomes, who already lead healthy lifestyles

Consumer health wearables are typically worn on the head, body, or wrist of the user. Most of these devices are used to track or monitor physical activity, sleep patterns, heart rate, muscle activity, as well as other behaviors and physiological symptoms in real-time [3, 4]. A 2014 Nielsen report on consumer wearables found fitness bands to be the most popular wearable devices (61%), followed by smart watches (45%) and mHealth (mobile health) devices (17%) [1]. The report also found that most wearable tech owners tend to be young males (18-34 years), followed by older females (35-54 years old). Majority of device owners were from upper middle class backgrounds, with 29% earning over $100,000 annually. In regards to the general health of users, another recent consumer report found that fitness wearable device owners already led healthy lifestyles, who purchased wearables with the intention of tracking their fitness progress [2]. But are these devices simply an experimental tool for those who can afford it, or can it actually lead to better health outcomes? Additionally, can it benefit those who need it most?

What science knows so far

The research for health wearables to date is limited to a handful of systematic reviews. These reviews have found promising evidence from recent studies for: 1) increasing physical activity 2) monitoring mobility related activities in older adults, and 3) clinimetric validity in measuring Parkinson’s disease symptoms.

  1. Promising evidence for increasing physical activity, but no better than traditional interventions

A 2007 systematic review on the use of pedometers found significant increases in physical activity as well as significant decreases in body mass index and blood pressure [5]. However, most studies comprise small samples sizes and were examined over a relatively short period of time. A more recent review in 2015 of higher quality research studies examined the efficacy and feasibility of wearable devices (electronic activity monitory systems – EAMS) that monitor physical activity and provide feedback to the user [6]. Wearables examined included commercial devices such as Gruve, PAM activity monitor, and Fitbit as well as other devices available through distributors. The review found some evidence suggesting that these wearables could increase physical activity and decrease weight significantly, however there was insufficient evidence to show that it was more effective than traditional face-to-face interventions. This suggests that wearables are a helpful tracking tool to motivate users that could supplement, but not replace face-to-face physical activity interventions. It is also not known if these findings could be applied to those at risk. Study participants were not from a severely obese population and socio-economic status (SES) was not reported, thus feasibility and efficacy for at-risk populations could not be determined.

  1. Monitoring mobility in the elderly – Inconsistent evidence and lack of real-world testing

Other that fitness trackers, wearable devices for the elderly population to monitor their mobility and to prevent falls from occurring is also becoming increasingly available.  Falls in older people are a major public health concern as it can have severe consequences such as injury or even death [7]. Two recent systematic reviews on commercially available fall detection wearable devices – mostly accelerometers – found that majority of identified studies used convenience samples of healthy young adults and tested the efficacy of the devices within laboratory settings [8, 9]. Elderly participants in real-world settings were lacking in these studies, and there was insufficient evidence for these devices to reduce fall rates. Inadequate sensitivity and specificity also increased the likelihood of false alarm rates alerted by such devices. Other methodological issues included the lack of standardization, which made comparisons across studies difficult [9].

Only one review examined studies of movement sensor technologies in real-world settings [10]. The review identified three studies in institutionalized older adults that used wearable sensors attached to a patient’s thigh or foot. Two of the studies demonstrated some evidence of reducing fall rates, however the devices were not feasible for confused patients and patients who wanted more freedom of movement. These observational studies were also of lower quality, using a pre-post design with no control group. Only one study used a clustered randomized controlled trial design of over 300 patients but did not find a difference in fall rates. An older review of wearable mobility monitoring from 2008 drew similar conclusions, despite reviewing studies dating back to 2001 [11]. This highlights a crucial need to evaluate wearable technology in real-world settings using elderly instead of healthy young participants. None of the reviews reported the cost of these devices nor income earnings of participants, calling into question the affordability of these devices.

  1. Measuring Parkinson’s disease symptoms – clinically valid wearables are available, but further testing is needed

There is emerging research on the ability of wearable technology to detect more subtle movements that might not otherwise be detected by traditional interventions [12]. A 2016 systematic review on clinical validation of monitoring technologies to measure Parkinson’s disease (PD) symptoms identified 22 commercially available wearable devices, out of which 6 were recommended for assessing PD [12]. The recommendation was based on the successful fulfilment of three criteria: 1) used in the assessment of PD symptoms 2) used in published studies by people other than the developers and 3) successful reported clinimetric testing. The 6 recommended devices that were clinically valid for measuring PD symptoms were Mobility Lab System, Physilog®, StepWatch 3 (SAM), TriTrac RT3, McRoberts Dynaport, and Axivity (AX3). Follow up research was recommended by the authors to ensure quality and clinical suitability variables were considered in future studies.

Research in real-world settings is needed, with a focus on clinical populations and the underserved

There are a number of other studies investigating the effectiveness of wearable technology to identify other symptoms and diagnose illnesses. Some of these include depression symptoms [13], sleep apnea [14], as well as sleep and cardiac functioning [15]. However, similar to the reviews mentioned above, many of these applications are still in the early phases of development and testing [3]. There is currently insufficient data to determine the effectiveness of these devices in improving health outcomes within clinical populations and those at-risk.

Whille the literature and research on health wearables is gradually building, it is important to note that technology reflected in most recent publications can be easily outdated due to the rapid development of new technologies [4]. For instance, Apple Watch – one of the most popular wearable brands – was only recently released in 2015 and is not reflected in many of the studies mentioned above.

Reviews of the current research on wearables highlight the need for developers to work alongside researchers to design clinically valid devices that can be tested within various clinical populations. Future research should also collect and report more background information on participants’ health status, cost effectiveness, as well as SES to determine feasiblity and accessibility of these devices. There is also a need to focus on at risk populations, including the underserved. Researchers should consider what the limitations and needs are for this group as well as take steps to bridge the inequality gap to develop effective technologies for those most in need.

References

  1. References
    1. Nielsen’s Connected Life Report. 2014 7 September 2016]; Available from: http://www.nielsen.com/us/en/insights/news/2014/tech-styles-are-consumers-really-interested-in-wearing-tech-on-their-sleeves.html.
    2. Juniper Research. Smart Wearable Devices. Fitness, Healthcare, Entertainment & Enterprise 2013-2018. 2013; Available from: http://www.juniperresearch.com/reports/Smart_Wearable_Devices.
    3. Reynolds, G. Activity Trackers May Undermine Weight Loss Efforts. The New York Times 2016 20 September 2016; Available from: http://www.nytimes.com/2016/09/27/well/activity-trackers-may-undermine-weight-loss-efforts.html?_r=1.
    4. Piwek, L., et al., The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Medicine, 2016. 13(2): p. e1001953.
    5. Evenson, K.R., M.M. Goto, and R.D. Furberg, Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act, 2015. 12: p. 159.
    6. Bravata, D.M., et al., Using pedometers to increase physical activity and improve health: A systematic review. JAMA, 2007. 298(19): p. 2296-2304.
    7. Lewis, Z.H., et al., Using an electronic activity monitor system as an intervention modality: A systematic review. BMC Public Health, 2015. 15: p. 585.
    8. Centers for Disease Control and Prevention, N.C.f.I.P.a.C. Important Facts about Falls. 2016 20 September 2016]; Available from: http://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html.
    9. Chaudhuri, S., H. Thompson, and G. Demiris, Fall detection devices and their use with older adults: a systematic review. J Geriatr Phys Ther, 2014. 37(4): p. 178-96.
    10. Schwickert, L., et al., Fall detection with body-worn sensors : a systematic review. Z Gerontol Geriatr, 2013. 46(8): p. 706-19.
    11. Kosse, N.M., et al., Sensor technologies aiming at fall prevention in institutionalized old adults: a synthesis of current knowledge. Int J Med Inform, 2013. 82(9): p. 743-52.
    12. de Bruin, E.D., et al., Wearable systems for monitoring mobility-related activities in older people: a systematic review. Clinical Rehabilitation, 2008. 22(10-11): p. 878-895.
    13. Godinho, C., et al., A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease. J Neuroeng Rehabil, 2016. 13: p. 24.
    14. McCall, W.V., A rest-activity biomarker to predict response to SSRIs in major depressive disorder. Journal of Psychiatric Research, 2015. 64: p. 19-22.
    15. Harrington, J., et al., An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea. Sleep and Breathing, 2013. 17(3): p. 1071-1078.
    16. de Zambotti, M., et al., Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiology & Behavior, 2016. 158: p. 143-149.

 



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