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Clinical data from wearables: Quality, regulatory and privacy concerns

There's little doubt that wearables and smartphones have had a huge impact on clinical trials and will only continue to do so in the future. However, there are still major concerns around the quality, regulatory and privacy implications of acquiring data from these new technologies.

We asked five experts working in clintech what they see as the biggest challenges with mHealth and how they can be overcome.

Dan Sfera, BS, MBA (@TheRealDanSfera), The Clinical Trials Guru, DSCS CRO

“I see this as an inevitable evolution on data collection in clinical research. From a quality perspective, the data can be trusted as it comes “straight from the source” without any outside influence. This is especially true for the patient reported outcomes, but will also allow sponsors to collect real-time adverse event data.

From a regulatory perspective, we may get to the point where these apps and/or devices will need to undergo calibrations in order to be standardized and provide uniform results. It wouldn’t surprise me at all if we started seeing trials conducted as proof of concept for the applications and devices themselves prior to having them utilized in actual clinical trials.”

 

Julianne Hull, CEO at WenStar Enterprises

“We have spent many years ensuring that every data point used in a clinical trial can be tracked back to its source. If there are changes in data then it is expected there is a full audit trail to include date, time, previous and new data and who changed the data.

How can we ensure the equivalent integrity of data from wearables? Does it matter if there are minor errors, minor gaps? What will it take to allow regulators to accept that the data is acceptable even with some small errors in non-critical data? What is the cost versus the excitement about wearables – measuring heart rate, blood pressure, sleep patterns, when one exercises or eats, all the time?

Even with human error, being able to have data in real time straight form the subject’s wearable will increase the accessibility to data and over time decrease the cost of getting this data. Where can we use this data to support NDAs? Initially probably in observational post-marketing trials, then long term why not phase three trials to support NDAs?”

 

Bruce Hellman, CEO and Co-Founder, uMotif

“As with everything in clinical trials, it's essential to think about the patient first when capturing smartphone or wearable data.  So the patient-centric approach we always adopt is 'Don't take it, unless I know I've given it'. Being clear with trial participants about what data you're collecting from their devices is essential to meet with privacy and data protection needs and ensuring patients are engaged in the study.

In terms of quality, it's a case of looking at each source of data through the correct lens. Most commercial wearable devices provide useful engagement and indicative data, but are certainly not exact. 2000 steps today is certainly fewer than 7000 yesterday, but it's by no means an exact figure. On the other hand, data from a CE Marked device is of much higher quality. capturing the provenance of the data and its route into a study dataset is therefore important, so researchers can treat each source according to its quality and validity.  Not all data is equal!”  

 

Michelle Petersen (@shelleypetersen), Founder, Healthinnovations

“One of the largest problems relating to the privacy and regulation of mobile devices are those people marketing unapproved medical apps and wearables (low-risk devices) as medical devices.

The language within the sphere of mHealth is quite clear, a medical device is any device or linked mobile app which claims to diagnose, treat, prevent or monitor disease and injury under the supervision of a physician.  Thus, medical devices need to be approved by a federal body before they are used on consumers.  A wearable, health-based app or fitness tracker is known as a ‘low-risk device’ which is designed for use in the gym to specifically measure heart rate and other biometrics as opposed to, say, repetition or defects and does not need approval...

The implication of medical-grade data, despite no regulatory oversight, is what is causing most of the confusion with regards to mHealth. If pharma marketed their drugs the way these unpoliced accounts market unapproved low-risk devices for medical applications, they would be fined heavily.

Therefore, the question is not what makes a medical device a medical device, rather, it is who makes a medical device a medical device. Only the policing of those accounts making or implying unfounded claims will start to innovate mHealth, and it will be the laying down of these clear boundaries married with strong legal terminology which will begin another evolution of this currently ‘device-fluid’ industry.”

 

Yerramalli Subramaniam, Co-Founder and CTO, CliniOps

“One of the key regulatory challenges of collecting data from a patient’s smartphones in either provisioned or BYOD model is the patient authenticity - is the data from the patient or someone else? Over the last few years, the smartphone industry has made big strides in user authentication with technologies such as fingerprint (touch-ID), iris scanner, facial recognition and others. Most of these technologies are mature and follow the general precept of just authenticating the user instead of giving access to raw data of underlying modality, thus keeping the user’s biometric data safe.

However, security frameworks and algorithms for such technologies are quite diverse and regulatory agencies could help the industry by providing guidance in FIPS/ENISA which could then be adopted by healthcare and the life sciences industry.”

 

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