A dazzling new array of wearable and other mobile technologies can provide a more complete clinical trial picture, increase efficiency, and reduce the burden on patients, says Ken Skodacek, part of the U.S. Food and Drug Administration’s (FDA) Clinical Trials Program and Payer Communication Task Force at the Center for Devices and Radiological Health (CDRH). However, he also cautioned collecting too much data or flawed data will undermine all those potential gains.
It can be hard to resist generating massive piles of data simply because the innovative technology is capable. Often, those conducting trials might understandably be “trying to get as much as possible out of the [technology] investment,” Skodacek told attendees of CTTI’s Mobile Technologies Event July 16 at FDA’s White Oak campus.
Optimizing study design can be a big ally, said Nina Mian, head of biomedical informatics, advanced analytics center, AztraZeneca. “Focus on the data you need to support primary and secondary endpoints” as opposed to collecting other information that’s not necessarily required for the specific trial. She acknowledged, however, that this can be a tougher call with it comes to exploratory students when it is often difficult to know up front what data might end up being valuable.
Conducting intelligent trial data collection isn’t just about saving money. Don’t squander “patient capital” by putting subjects through tasks that aren’t keys to trial success, said Brian Bot, principal scientist and community manager at Sage Bionetworks. If patients are over-taxed when it comes to utilizing a trial, the piece of hardware “can end up in a desk drawer” in frustration, he added. “You need to understand what they will or will not do,” Bot said.
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As an example, the panel looked at the value of GPS coordinates. In addition to raising privacy concerns from regulators and patients, a trial designer must think long and hard about whether seeking to obtain that kind of granular information is necessary to address primary and/or secondary endpoints. For example, in a trial where local weather and pollutants can trigger a respiratory response, GPS information could be extremely relevant. In this instance, there might be ways to track usage in a more macro manner without having to save specific GPS information on a patient-by-patient basis.
“That kind of data must be appropriately controlled,” Skodacek emphasized. Data variability is also a challenge, though its obviously not an issue unique to remote technologies. “Unexpected events” can sink a trial, he added. Example: A relatively healthier spouse might inadvertently put on the spouse patient’s wearable. Trial design and protocol can also help address this kind of problem if anticipated at the outset.
There are creative ways to tackle these and other types of mobile data collection challenges. Panelists used the example of a real trial with more than 100,000 patients where setting a specific time for data collection was helpful. By requesting data inflow at a particular day and moment, the trial was able to determine that some 93% of patients were actively engaged in the trial.
Author: Michael Causey