Digital Health Tech Review- ŌURA Ring

To kick off our series of sensor and digital health technology reviews we took a look at the ŌURA ring – a smart ring which claims to be ‘the most accurate, comfortable and beautiful wearable available’. The question is, in a wearables market that is full of big competitors and even bigger claims, can the ŌURA ring sustain being put through the ringer…..

As will be the case with all our reviews, we will be leveraging the evaluation framework for human performance measurement technologies published in Nature Digital Medicine. An overview of how these reviews will be conducted was published on our blog last week.

What is the ŌURA Ring and what does it do?

The ŌURA ring is a smart ring, designed to be worn on any finger (8 sizes available) and ‘help you get more restful sleep and perform better’. It strives to do this by collecting data about body temperature (negative thermistor sensor), heart rate [HR]/heart rate variability [HRV] (polysomnography) and activity/sleep tracking (tri-axial accelerometer and gyroscope). It is designed to be used in conjunction with a free android or iOS mobile app.

The ring provides you with metrics about sleep, activity and readiness.

The ŌURA Ring sleep, activity and readiness dashboard.

Who makes the ŌURA ring?

The ŌURA ring is a piece of wearable jewellery developed by ŌURA Health OyLtd, a health technology company founded by Kari Kivelä, Markku Koskela and Petteri Lahtela in Oulu, Finland in 2013. To date, the company has received upwards of $33 million in funding over four rounds and has an estimated annual revenue of approximately $2 million. Sports and TV fans might be interested to know that investors include the likes of Lance Armstrong, Shaquille O’Neal and Will Smith.

How much does it cost and where can I buy it?

Those interested in the ŌURA ring have a selection to choose from, ranging from the ‘Silver’ and ‘Black’ options (€314), to the ‘Stealth’ option (€419) to the diamond version (€1049). The ring is available internationally online, with no minimum or maximum order requirements. Importantly, the company will play import taxes for EU and USA customers only, while customers outside of this will have to fork out for the taxes themselves.

What is the science behind the ŌURA Ring?

Given the big claims by ŌURA that it is ‘most accurate, comfortable and beautiful wearable available, does the science live up to the hype?

There are currently three papers available which have evaluated the validity (ability to measure what it’s supposed to measure) of the ŌURA ring. One internal white paper on the accuracy of the HR/HRV and two papers (one peer-reviewed paper and one internal white paper) on the accuracy of the sleep/ sleep quality estimations

Firstly, the internal white paper comparing nocturnal HR and HRV to the gold-standard Echocardiogram [ECG] measurement:

  • Ten healthy adults wore the ŌURA ring and a Faros 360 ECG device (Mega Electronics, Kuopio, Finland) over-night. Individuals wore the smart ring on each hand.
  • The ŌURA ring software labelled inter-beat intervals to estimate HR and HRV, while the Kubios software was used to obtain the comparison reference HR and HRV.
  • Pearson correlation was then used to investigate the relationship between the two measures.
  • The authors reported ‘ŌURA Ring provided reliability as compared to ECG of (r = 0.999) and (r = 0.984) for HR and HRV, respectively’.

While the authors conclude that ‘The nocturnal resting HR and HRV numbers measured by the ring represent similar values to those derived from the ECG’, we have a couple of major issues with the methods used:

  1. This paper only focused on nocturnal HR and HRV, arguably the easiest time to quantify this, not taking into account the accuracy during bouts of activity throughout the day – this would likely reduce the accuracy of the estimates.
  2. This analysis is only based on periods where 50% (HR) and 30% (HRV) of the intervals were labelled as ‘normal’. However, they don’t provide information on how often the ŌURA ring labelled data as abnormal. To me this is essentially cherry-picking nice clean data for comparison, ignoring the ‘abnormal’ data.
  3. This is a statistical point, but one we cannot ignore. The authors conclude that the ring can provide ‘reliability as compared to ECG’. This is not true. What the authors did do, was show a high correlation between the ring’s estimations, but did not (as they have stated) demonstrate ‘reliability’ or indeed ‘agreement’ – that requires a completely different type of analysis.

In short, this internal paper showed that when they exclude unwanted ‘abnormal’ data, the ŌURA rings estimations of HR and HRV have a high correlation with the gold standard ECG; however, I would be intrigued to see what the accuracy of the measurement would be if they didn’t simply exclude unwanted data.

Secondly, an external peer-reviewed paper on the accuracy of the sleep/ sleep quality estimations published in the journal of Behavioural Sleep Medicine. This study was conducted by an external research group based in California and provides a decent unbiased analysis of the sleep estimation.

  • Forty-one healthy adolescents/ young adults wore the ŌURA ring in a single laboratory overnight.
  • Sleep data were recorded using the ŌURA ring and the gold-standard polysomnography [PSG].
  • The ŌURA ring metrics were compared to the PSG equivalent using Blant-Altman and epoch-by-epoch analysis.
  • The authors concluded that the ŌURA ring tended to underestimate Deep Sleep (approx. 20 min) and underestimate rapid-eye-movement (REM) sleep (approx. 17 min). Total sleep time and wake after sleep onset lay within the ≤30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively.
  • The ŌURA ring could detect sleep 96% of the time (excellent) but could only detect waking 52% of the time (poor). Additionally, agreement in detecting the stages of sleep (light, deep and REM sleep) was poor, ranging from 51-65%.

Interestingly, the authors conclusions about the ŌURA ring are relatively kind:

Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep-wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.”

However, we’d like to draw your attention to a few key aspects related to the results of this study:

  1. While the ŌURA ring was excellent at detecting sleep, the sophisticated flipping a coin method would have similar accuracy in detecting wake, based on this study.
  2. While there was an underestimation for light sleep (approx. 4 min), underestimate deep sleep (approx. 20 min) and underestimate REM (approx. 17 min), the Bland-Altman analysis highlights just how much the ring differed from the gold-standard measure. For example, the ŌURA ring overestimated deep-sleep by as much as ≈100 min and underestimated it by as much as ≈65 min. Seeing as the average deep sleep time was only 97 min, this shows a marked (and non-systematic) difference between the ŌURA and the gold-standard alternative. Similar findings are seen for REM sleep estimation.

In other words, the ŌURA ring can accurately detect sleep, but is pretty poor at detecting wake and the various different stages of sleep. Based on this, and contrary to the authors conclusions, I would argue that the ring is not very good at detecting sleep outcomes ‘beyond binary sleep-wake’.  

What happens to the raw data and can I access it?

A common trait that we see within the consumer wearables space is a lack of information related to the nature of their algorithms and whether raw sensor data can be obtained from the device. The ŌURA ring is no different, with no clear information provided about the methods used in the data analysis and if the user can in-fact access the raw sensor data. While the user can access the summary metrics through the mobile application, for research purposes, this may not be enough.

As with many consumer wearables, ŌURA are attempting to try to make sense of the data capture by the ring through a composite ‘Readiness’ score; a metric derived from sleep, activity balance, body temperature and resting heart rate. This is a common theme across the health and wellness tech sector, however we’ve yet to see anyone develop any magic score that is actually related to injury, illness or any other factor for that matter.

What about human factors?

With the statement that the ŌURA is the ‘most accurate, comfortable and beautiful wearable available’, one would assume that the ŌURA has undergone some rigorous user testing. While they do provide quite a detailed overview of their design process on their website, there is no objective published data on this.

Aside from this, the ring requires little interaction apart from the obvious wearing of it, synching the data through the mobile application and wirelessly charging the device. In fact, one of the most impressive aspects of the smart ring is the ability of the company to have shrunk this set of sensors into such a small device that boasts an impressive week-long battery life. It will be interesting to see where this technology brings us in the next 10 years.

Is it being used in any clinical trials?

As one of the biggest interests in our research group is essentially understanding how technology can be used to improve health and performance, we are always interested to know if the technology in question is being used in any clinical trials.

Currently, there have been no published clinical trials which have leveraged the ŌURA ring; however, there is one registered trial of 1060 participants currently underway seeking to develop a set of typologies of users of mobile sleep tracking technologies, and optimal sleep health educational strategies among users of mobile wearable technologies that track sleep.

The bottom line

The ŌURA ring boasts itself as the ‘most accurate, comfortable and beautiful wearable available’. While you may agree that it is indeed ‘comfortable’ and/or ‘beautiful’, based on this evaluation of the current evidence available, it is hard to fully agree with the statement that it is ‘accurate’ – especially if you are looking to get detailed information about the stages of sleep. The ring is not cheap, coming in at €314 – €1049, a substantial price to pay considering you could arguably get a wearable with similar accuracy, for substantially cheaper. However, if a ring-based wearable is more desirable than a wrist-worn one, then the ŌURA may be suitable for your needs – but only if you are looking for high-level activity, sleep and HR data. It will be fascinating to see the outcome of the ongoing clinical trial using the ŌURA ring, and see where the technology goes over the coming years.

If you have any questions about this Digital Health Tech review, the research conducted by our group or the Digital Health Technology Evaluation Framework, please contact us via the blog or twitter!

Louise Brennan, Dr. Alison Keogh & William Johnston

Digital Health Technology Reviews

With the rapid evolution of mobile sensing and computing technology, we have seen a surge in the amount of technology readily available at our fingertips. This has resulted in a surge in the number of companies producing both consumer and healthcare facing digitally enabled technologies.

These technologies span a wide range of use-cases; ranging from fitness and wellness, all the way through to healthcare.  Over the past decade, the world of human performance and behaviour measurement technology has rapidly moved forwards; starting with iconic wearables such as the Nike+, Fitbit and Google Glass. Since 2014, dubbed “The year of Wearable Technology” by some, we have seen a huge growth in the popularity of consumer wearable tech, with the introduction of the Apple Watch forcing numerous competitors to jump on the bandwagon.

With this, it is expected that the number of connected wearables will increase from 325 million in 2016 to 929 million by 20211, with the digital health consumer base expected to grow in parallel2.

While the increased availability of such devices has the potential for many positive outcomes in the spaces of health and fitness research and Digital Health, it also has the potential to result in huge confusion about what technologies are fit-for-purpose.

For example, the requirements of a piece of tech that simply need to detect the number of steps a recreational athlete takes during the day are massively different to the requirements needed if the user needs to detect if an individual has atrial fibrillation.

This led members of our research group and the Applied Research for Connected Health centre to develop a structured evaluation framework.

This framework allows for the:

“refining [of] the requirements for a specific application, and then evaluating the available devices against those requirements”

Over the coming year, members of our research group at the Insight Centre will leverage this framework to evaluate human performance measurement technologies, making these objective evaluations available online in the form of a blog post!

The structure of these blog posts will evolve over time, and will vary from evaluation-to-evaluation but will broadly focus on components of the evaluation framework.

A rough structure will be as follows:

  • What is the technology and what does it do?
  • Who makes the technology?
  • How much does it cost and where can I buy it?
  • What is the science behind the technology?
  • What happens to the data and can I access it?
  • What are the human factors associated with the technology?
  • Has it, or is it being used in any clinical trials?

1. Statista. Connected wearable devices worldwide 2016-2021. https://www.statista.com/statistics/487291/global-connected-wearable-devices/ (2018).

2. Berg Insights. mHealth and Home Monitoring8th Edition (Gothenburg, Sweden: Berg Insight, 2017).

3. Caulfield, B., Reginatto, B. and Slevin, P., 2019. Not all sensors are created equal: a framework for evaluating human performance measurement technologies. npj Digital Medicine2(1), p.7. 3.

Personal Sensing – What do we do?

Recent years have provided us with unprecedented access to a wide range of inexpensive sensing, aggregation and communication technologies that can transform society through provision of access to data relating to human behaviour and performance in health and sport.  However, this ever-advancing sensor web needs to be carefully matched to relevant data analytics and domain expertise to unlock the inherent value in the complex datasets that it produces. 

This research strand brings together expertise from life and clinical sciences, material science, computer science, and biomedical engineering to enhance the application of the sensor web to challenges in connected health and sport.  Whether dealing with chronic disease or performance enhancement in elite sport, a connected ecosystem is dependent on an efficient and unobtrusive approach to sensing, sharing and analyzing data to facilitate timely delivery of accurate information to all stakeholders in the process. 

In this strand of the Insight Centre for Data Analytics, a research centre spanning Dublin City University, NUI Galway, University College Cork, University College Dublin and other partner institutions, we address fundamental research activities related to optimizing the sensing, measurement and understanding of human behaviour and performance, and the implementation of feedback strategies that are designed to enhance it.  A further aim is to contribute to the expansion of sensing capabilities of the sensor web through development and validation of novel sensing technologies.

O’Brien Centre for Science, University College Dublin, Ireland

As part of our research, the personal sensing team in University College Dublin also conduct regular structured evaluations of health and wellness technology which are currently available on the market, with the view to keeping up-to-date with recent advances in the field, as well as ensuring that the technology which we may implement in our research is fit-for-purpose.

Over the coming months, we will be making our objective and structured reviews, conducted in accordance with the evaluation framework published in Nature Digital Medicine (Caulfield et al., 2019), available online through this blog post.

You can also follow us on twitter: @personalsensing, or contact us through this blog.

Visit the Insight Centre webpage for more details on what we do

Caulfield, B., Reginatto, B. and Slevin, P., 2019. Not all sensors are created equal: a framework for evaluating human performance measurement technologies. npj Digital Medicine2(1), p.7.