146 – Wearable Technology & Patient Monitoring (S10E11)

Imagine this for a second. You're running a clinical
trial, but instead of just grabbing data points
every few weeks, you've got this constant stream
of info, real time, how a patient's actually
responding to a new treatment. What kind of insights
could that unlock? Seriously. That's a great
place to start. Really frames it perfectly, because
that's exactly what we're diving into today.
How these wearable devices are starting to make
that continuous monitoring a real thing in clinical
trials. Yeah, it feels like a game changer. It
really could be. It's changing how we track responses.
So for this deep dive, we've sifted through quite
a bit transcripts, book excerpts on the whole
drug development pipeline. Clinical trials, how
drugs move in the body. Pharmacokinetics. Exactly.
Regulations, manufacturing the works. And our
mission really is to pull out the key nuggets.
How is this wearable tech actually being integrated?
How's the data used? And crucially, how does
it potentially improve things for patients in
these studies? We want to give you that shortcut,
you know, a solid grasp without getting totally
bogged down. Makes sense. So let's kick off with
the why. Why do we even need this continuous
monitoring? What was wrong with the old way?
Well, the traditional way, it was really about
snapshots, wasn't it? Intermittent, yeah. Exactly.
A patient visits the clinic maybe once a month,
maybe less, get some measurements, answer questions.
But that's just one moment. Precisely. It misses
everything that happens between those visits.
And our bodies, patient responses, they aren't
static. They fluctuate. So those occasional checks,
they often just couldn't capture the real dynamics
of how a drug was working over time. Or maybe
not working. It's like trying to understand a
movie by only watching five random minutes. Good
analogy, yeah. You missed the plot, the character
development. Yeah. All the important stuff. So
wearables promise to fill those gaps. Give us
that continuous flow. What kind of data are we
actually talking about here? It's more than just
step counting, I assume. Oh, absolutely. Way
beyond basic step counting now. The tech has
really leaped forward. We're talking continuous
heart rate monitoring and a heart rate variability
that tells you about nervous system activity,
stress, detailed activity levels, not just steps,
but intensity, duration. Sleep patterns are huge.
Great. Quality of sleep, not just hours. Exactly.
How long? Sleep cycles, disturbances. Skin temperature
is another one. And things are moving towards
continuous glucose monitoring, other biomarkers.
It's expanding fast. Wow. So you get this much,
much richer, more holistic view of the patient's
experience. That's the idea. You start seeing
trends, patterns emerge over time that you'd
completely miss otherwise. And connecting that
back to the drug. Right. continuous stream lets
researchers see the body's response in, well,
in much finer detail, instead of one blood pressure
reading at the clinic. You see it across the
whole day? Yeah, how it changes, how the treatment
might be influencing those changes day in, day
out, that level of granularity. It's potentially
invaluable for understanding the real impact.
Okay, so the potential is clear. We've got this
firehose of data coming in, but how does it actually...
fit into the rigid structure of a clinical trial.
It can't be just plug and play, right? No, definitely
not. That's a really critical point. Just having
the data isn't the solution. It needs to be integrated
properly, thoughtfully. What does that involve?
Several key things. First, the wearables themselves.
They need validation. Meaning? Rigorous testing
to make sure they're accurate, reliable over
time. You can't base decisions on faulty data.
Makes sense. They also need to be user friendly.
Patients aren't all tech wizards. So easy to
wear, easy to manage. Right, minimize the burden.
Then there's the whole data infrastructure. Secure
collections, secure storage. managing these huge
volumes of data, privacy is paramount. Absolutely.
And finally, you need the tools, the analytical
methods, to actually make sense of this continuous
stream, to pull out the meaningful signals from
the noise. So it's a whole ecosystem that needs
to function smoothly from the patient's wrist,
essentially, all the way to the data analysis
and where are we seeing the benefits across all
phases of trials. Even early on yeah potentially
across the board in those very early phase one
trials where safety is the main focus uh -huh
wearable data can offer much more sensitive red
flags for side effects maybe subtle but persistent
changes in heart rate patterns or sleep quality
things you might miss with just Monthly check
-ins. Precisely. So you get earlier warnings,
potentially allowing quicker action to protect
patients, than later in efficacy trials. Phase
two, phase three. Right. The continuous data
gives a more dynamic picture of whether the drug
is actually working. Think about, say, a trial
for a pain drug. Continuous activity tracking
might show a patient becoming consistently more
mobile over time. That's objective evidence that
complements them, saying, yeah, my pain feels
better. That's powerful. objective data alongside
this objective report. Can you give another concrete
example? How might this data show a positive
response? Sure. Let's take a trial for sleep
medication. Traditionally, you might use sleep
diaries, maybe an overnight lab study, which
is pretty artificial. Right. Not their normal
environment. With a wearable, you track sleep
duration, stages deep sleep, REM sleep awakenings,
night after night in their own bed. Much more
realistic. If the treatment works, you'd hope
to see maybe consistently longer sleep, more
deep sleep, fewer wakeups, all captured objectively.
It paints a much clearer, more ecologically valid
picture of the impact. That really highlights
the difference. OK. What about the flip side?
How does this help spot problems or if a treatment
just isn't cutting it? This is where that continuous
baseline is so valuable. The patient's normal.
Exactly. If you see significant sustained deviations
from their usual activity levels or heart rate
patterns, that could signal a problem. Well,
maybe a steady decrease in activity for someone
being treated for depression. That might suggest,
unfortunately, that the treatment isn't helping
with energy or motivation. or maybe persistent
weird changes in vital signs captured by the
wearables. That could be an early warning of
an adverse effect that needs checking out fast.
So earlier detection of things going wrong or
just not going right. Yes. And that allows for
quicker adjustments, maybe change the dose, maybe
even stop the drug if needed. It really prioritizes
patient safety. And this fits with that broader
push in drug development we saw mentioned about
using pharmacodynamic endpoints, biomarkers,
even preclinical stage, the animal studies, to
get a fuller picture of a drug's effects right
from the start. So it's about building that comprehensive
understanding from lab bench to bedside and now
continuously at the bedside or on the wrist.
Well put, yes. This really sounds like it could
push us towards more personalized medicine even
within the trial itself, tailoring treatment
based on this real -time feedback. That's one
of the most exciting prospects, I think. The
potential is huge. Continuous monitoring means
we could maybe move beyond the standard dose
for everyone. How so? Well, imagine a wearable
consistently shows a patient's heart rate spikes
after a certain dose. The trial team sees that
pattern. And maybe considers... tweaking the
dose down for that specific patient. Exactly.
Or, conversely, if someone's physiological markers
just aren't budging at the standard dose, maybe
an increase could be considered guided by their
individual data. Fine -tuning in real time. It
holds that promise, yeah. More effective treatments,
potentially with fewer side effects because they're
better matched to the individual's response.
That feels like a significant step forward. And
the safety aspect you mentioned earlier, detection.
That's fundamental. Absolutely fundamental. Thinking
back to some of the material on drug safety testing.
And non -climical stuff. Yeah. A key goal there
is always finding that safe starting dose, understanding
the toxicity profile. Wearables in the clinical
phase give us this incredibly granular way to
monitor for those potential toxicities in humans.
And, with more precision, it can even give insights
into whether toxicities are reversible, which
is crucial information now potentially tracked
continuously in people. It can't be all smooth
sailing. Yeah. What are the bumps in the road?
The challenges? Oh, there are definitely challenges.
Big ones. Data quality is number one. Garbage
in, garbage out. Pretty much. The devices have
to be accurate and reliable. Consistent. Then
there's data security and patient privacy. We're
talking sensitive health data. Hugely important.
Needs robust protection. Absolutely critical.
And then there's the user aspect. Not everyone's
comfortable with tech. Right. The digital divide
or just varying levels of tech savviness. Exactly.
So the devices, the apps, the interfaces they
need to be super user friendly, accessible, not
burdensome. Otherwise, you risk excluding people
or getting poor data because they can't use it
properly. Those are really practical, significant
hurdles to clear. Now, one other thread we looked
at was related to OPRND. Right, the pharmaceutical
quality and manufacturing side. Yeah, often focused
on things like chemical synthesis, process optimization.
Seems a bit different from patient monitoring.
Is there a connection? It might seem different
on the surface, but I think there's a really
interesting underlying connection. Well, yeah.
Think about what drives OPR and D. It's about
gathering the best data on reaction yields, impurities.
process parameters to optimize the outcome, right?
To make the manufacturing process better, more
efficient, more reliable. Okay. Data -driven
optimization. Exactly. And isn't that fundamentally
what we're talking about with wearables and trials?
Gathering better, more comprehensive data. To
optimize the understanding of the treatment and
ultimately patient outcomes. Precisely. The meticulous
attention to detail in optimizing a chemical
step or ensuring process control. That same kind
of rigorous data -focused approach is needed
to successfully integrate wearables. Both are
striving for greater precision, deeper understanding,
through better data. That's a great parallel.
It links the lab bench and manufacturing floor
with the clinic through that common thread of
data -driven improvement. It really ties it together.
Yeah, I think so. So as we kind of wrap up this
deep dive, what are the main takeaways on wearables
and patient monitoring in clinical trials, if
you had to boil it down? Sure. I'd say the main
thing is wearable tech is a genuinely powerful
new tool for continuous patient monitoring. It's
not hype. This real -time continuous data gives
us a much, much richer understanding of treatment
response compared to the old ways. More comprehensive.
Definitely. And this has real potential to boost
patient safety through early detection, enable
more personalized treatments, and ultimately
hopefully lead to better outcomes. But with caveats.
Absolutely. Challenges around data quality, security,
usability, they're real and need careful management.
But overall, it's a transformative shift towards
more informative, efficient. and patient -focused
trials. That sums it up brilliantly. It's been
fascinating really to see how this tech is reshaping
such a core part of getting new medicines developed.
The level of detail is just amazing. It is pretty
remarkable. So hopefully for everyone listening,
this deep dive has given you a solid handle on
this really dynamic field, a good foundation.
Hope so. And to leave you with something to chew
on. As wearables become even more embedded, not
just in trials, but in everyday health care.
Think about this. How might this constant flow
of personal health data fundamentally change
how we even think about diseases and how we develop
therapies in the future? What are the implications
of the ethical, practical, scientific as we head
towards a future awash in continuous health data?
Something to ponder.