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"Wearable Wars: Evolution in Sleep Assessment" Douglas Kirsch (02.10.2021)

February 26, 2021

"Wearable Wars: Evolution in Sleep Assessment" Douglas Kirsch (02.10.2021)

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  • 00:17Alright, hi everybody,
  • 00:18I am Lauren Tobias. I would
  • 00:20like to welcome you to our Yale
  • 00:22Sleep Seminar this afternoon.
  • 00:24I'll make a few brief announcements
  • 00:26before I turn it over to Larry Epstein
  • 00:29to introduce our speaker today first.
  • 00:31Please do take a moment to
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  • 00:36credit for attendance,
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  • 00:48If you have questions
  • 00:49during the presentation,
  • 00:50please make root use of the chat room
  • 00:53throughout the hour and we will invite
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  • 00:57ask questions allowed at the end.
  • 00:58We will have recorded versions of these
  • 01:01lectures available online within two
  • 01:03weeks at the link provided in the chat.
  • 01:05And please feel free to share
  • 01:07it announcements for our weekly
  • 01:08lecture series to anyone that
  • 01:10you think may be interested,
  • 01:11or contact Debbie Lovejoy to
  • 01:13be added to our email list.
  • 01:15And then before I introduce today's speaker,
  • 01:17I just want to let you know that
  • 01:19next week stock is going to be
  • 01:22given by Doctor Francesca Facco,
  • 01:23who is an associate professor of obstetrics,
  • 01:25gynecology and reproductive Sciences
  • 01:26at the University of Pittsburgh,
  • 01:28and she's going to be speaking
  • 01:30about sleep and pregnancy.
  • 01:31So please make a note of that
  • 01:33and join us next week as well.
  • 01:36So with that I will turn it
  • 01:37over to Larry Epstein.
  • 01:39Thank you, it's my pleasure
  • 01:41today to introduce Doctor Doug.
  • 01:42Curses are speaker doctor.
  • 01:44Kirsch is a good friend is and is
  • 01:46well known to many of us with him
  • 01:48having spent a long time in the
  • 01:51Boston area tended medical school at
  • 01:52the University of Massachusetts to
  • 01:54this neurology training in Rochester
  • 01:55and completed a sleep fellowship
  • 01:57at the University of Michigan.
  • 01:58He was a member of the Brigham
  • 02:00program for many years.
  • 02:02First sleep health centers and at
  • 02:03Brigham and Women's Faulkner Hospital.
  • 02:05He's currently the director of the
  • 02:07Sleep Program for Atrium Health,
  • 02:08the largest healthcare system
  • 02:10in North and South Carolina.
  • 02:11And a clinical professor at
  • 02:13UNC School of Medicine.
  • 02:15He is familiar to many of you.
  • 02:17As an educator,
  • 02:18running many Jimmy courses,
  • 02:19and having served as the program
  • 02:21chair for the Annual Sleep Meeting
  • 02:23for the American Academy of Sleep
  • 02:25Medicine and Sleep Research Society.
  • 02:27He's also served on the Board of
  • 02:29directors of the ASM and is a past president.
  • 02:33He's long held an interest in emerging
  • 02:36sleep technologies and help has
  • 02:38helped direct the ASM's valuation
  • 02:40and policy towards new technology.
  • 02:42Appropriately,
  • 02:42his topic today is wearable Wars
  • 02:45evolution in sleep assessment.
  • 02:48Thanks for that kind introduction Larry,
  • 02:51and I'd like to say coming from the
  • 02:54Boston area, I can not think of
  • 02:56many times at which Harvard and Yale
  • 02:59have actually done things together.
  • 03:01This seems like a novel in different world,
  • 03:04so maybe while Covid has brought
  • 03:07a lot of terrible things upon us,
  • 03:09maybe joint Harvard Yale,
  • 03:11things may be a bonus.
  • 03:13Covid park. So with that,
  • 03:15let me start by telling you a
  • 03:17little bit about this.
  • 03:22The world of sleep obviously has gone
  • 03:24through a lot of changes over the years.
  • 03:27For a long time, sleep problems
  • 03:30were really not medical problem,
  • 03:32but then overtime they became an
  • 03:35ultra specialized medical problem.
  • 03:37And they became specialized.
  • 03:38But then testable in the home.
  • 03:41And then Lastly,
  • 03:42your mobile phone can be asleep tester.
  • 03:44But the reality is this isn't the timeline.
  • 03:47This is the timeline right?
  • 03:49That the speed of this change has been rapid.
  • 03:55We are all comfortable with seeing this
  • 03:57kind of image in lab polysomnographers
  • 03:59he is and remains the gold standard
  • 04:02for us to assess patients sleeping
  • 04:04problem in this particular case,
  • 04:06you're seeing a patient who has pretty
  • 04:08significant obstructive sleep apnea.
  • 04:10This is a two minute respiratory page
  • 04:12in the bottom and you can see they
  • 04:15have a prolonged apnea with a longer
  • 04:17saturation on the tailing behind it.
  • 04:23More and more comfortable we have
  • 04:25gotten with home based testing,
  • 04:27and so here's an example of a
  • 04:29person who has obstructive sleep
  • 04:30apnea in the home based setting.
  • 04:33A lot less leads,
  • 04:34but the diagnosis still pretty clear.
  • 04:39It starts to get trickier though in the home,
  • 04:42sometimes the subtleties of
  • 04:44sleep apnea can be missed.
  • 04:45It can be harder without some of
  • 04:47the things that were comfortable
  • 04:49with all those, EG leads.
  • 04:51These kinds of events that may or
  • 04:53may not be events, but we can't tell.
  • 04:56We don't have arousals.
  • 04:57We have no way of kind of knowing
  • 04:59in this particular type of home
  • 05:02based environment what's going on.
  • 05:04And at the same time,
  • 05:06here's a 5 minute page of a home based test.
  • 05:09Is that a four and a half minute apnea?
  • 05:13Probably not right.
  • 05:14That person is.
  • 05:16Clearly removed the nasal flow
  • 05:18for a period of time and then
  • 05:20put it back into place.
  • 05:21On the other hand,
  • 05:22somebody who's not well trained
  • 05:24in this wouldn't recognize that
  • 05:25a four and a half million Afghan
  • 05:28may not be a realistic thing.
  • 05:30Home base tests aren't perfect,
  • 05:31but we are increasingly comfortable
  • 05:33with some of the areas in
  • 05:34which they have faults.
  • 05:38So moving then into the world in which we
  • 05:41are going to have to become increasingly
  • 05:44comfortable with is is this sort of
  • 05:48idea of consumer sleep technology.
  • 05:50So consumers like technology are
  • 05:52really non prescriptive devices
  • 05:54that are directly marketed to
  • 05:56consumers that make an assertion to
  • 05:58perform sleep monitoring tracking.
  • 06:00Or sleep related interventions
  • 06:02and these can take different.
  • 06:05Sir forms that are apps which
  • 06:07obviously we're all comfortable with.
  • 06:09This device programs that
  • 06:10run on our mobile devices.
  • 06:12There are wearables which
  • 06:13are things that people wear.
  • 06:15Obviously the consumer wears
  • 06:17during their sleep period to both
  • 06:19potentially assess their sleep
  • 06:20in that particular situation.
  • 06:22And then there are near Obols,
  • 06:24which are devices that are in the
  • 06:27environment that track people sleep.
  • 06:30So let's start with what these
  • 06:32devices often started with,
  • 06:33which is that measured activity,
  • 06:35and so this is a study from 2015 that says,
  • 06:39broadly,
  • 06:39that a whole bunch of different
  • 06:41activity trackers that their
  • 06:43reliability and validity of trackers
  • 06:45for measuring step count is good.
  • 06:47However,
  • 06:47research in real life did
  • 06:49not necessarily intervene,
  • 06:50and so this is one of my
  • 06:52favorite articles I've read,
  • 06:53which was from the well known
  • 06:55scientific Journal Vanity Fair,
  • 06:56where it says that he was wearing a
  • 06:59Fitbit and jawbone at the same time.
  • 07:01In the Fitbit said I had 7000 steps,
  • 07:03Jawbone had me 2000 steps,
  • 07:05which was a bit a discrepancy,
  • 07:07and he says it's somewhat dismaying to
  • 07:09check my daily graph on the Jawbone app.
  • 07:11And behold,
  • 07:12a few skyscrapers spikes of activity
  • 07:14between wide plateaus is suspended animation.
  • 07:16By comparison,
  • 07:16my Fitbit chart looks downright jazzy.
  • 07:19And as he say,
  • 07:21further testing is clearly indicated.
  • 07:24And so,
  • 07:25here were some more scientific
  • 07:27based looks at some of these
  • 07:29devices and the there were accuracy
  • 07:31issues in terms of well you can
  • 07:34get 100 steps for eating cereal.
  • 07:35You're getting 1000 steps by shaving
  • 07:38and brushing your teeth, right?
  • 07:39That movement and steps are
  • 07:41not the same thing,
  • 07:43and so recognizing there
  • 07:44are some challenges here.
  • 07:49So in the early world of using
  • 07:51wearable technologies they were using
  • 07:53mostly micromovements and heart rate.
  • 07:56And the accuracy with which they
  • 07:57tracked sleep was somewhat contentious,
  • 07:59and so why is that?
  • 08:01Well, because not everybody
  • 08:02sleeps in the same way.
  • 08:04People who move a lot during
  • 08:06sleep may be considered awake,
  • 08:07but they may actually be sleeping.
  • 08:09People who sit for long periods
  • 08:11of time but aren't sleeping may
  • 08:13be considered to be sleeping.
  • 08:15And there are settings within
  • 08:16devices whether you're in
  • 08:18regular mode or sensitive mode,
  • 08:19that will actually change the results.
  • 08:22So that makes this whole process a
  • 08:24little bit concerning when you are
  • 08:26a clinician trying to look at this.
  • 08:30So. How is the difference is right?
  • 08:33Two, broadly speaking,
  • 08:34we're all comfortable with the lab.
  • 08:36We hook him up with all these wires.
  • 08:38All these things.
  • 08:39We look at a bunch of funny looking waves
  • 08:42and we have a scoring mechanism that
  • 08:45gets us into different stages of sleep.
  • 08:47However, these wearable devices,
  • 08:49for instance, you get some amount of motions,
  • 08:52amount, heart rate.
  • 08:53There's a black box algorithm that sort
  • 08:56of spits out some amount of information.
  • 08:59And I'll thank Kathy Goldstein
  • 09:01for this image.
  • 09:02I highly recommend this article
  • 09:03that she wrote about the
  • 09:05topic we're discussing today.
  • 09:08So what came of this one, 2015?
  • 09:10There was a review and you can
  • 09:12see the devices that they kind
  • 09:14of reviewed and more or less.
  • 09:16I love this discussion.
  • 09:17The review identified a critical lack
  • 09:19of basic information about the devices.
  • 09:215 out of 6 devices provided no supporting
  • 09:23information on their sensor accuracy
  • 09:25and four out of 6 devices provided
  • 09:27no information on their output.
  • 09:28Metric accuracy so broadly in 2015,
  • 09:30which is not that long ago.
  • 09:32Five years ago there was really
  • 09:34no data about these things.
  • 09:36But then we start moving
  • 09:38in on gathering data,
  • 09:40so this is data from 2012.
  • 09:43Looking at an early Fitbit and
  • 09:45measuring it against an active graph
  • 09:48and there were some differences
  • 09:50there versus Poly sonography.
  • 09:52And that they found that the Fitbit
  • 09:54overestimated the time participants were
  • 09:56asleep by about 67 minutes on average.
  • 09:58So about an hour missed.
  • 10:00Compared to the polysomnogram.
  • 10:04You can see it a few years later,
  • 10:07again 2015.
  • 10:08This is when this data start taking
  • 10:10off and you can see differences
  • 10:12in children and adolescents.
  • 10:14So in normal mode there's
  • 10:16an overestimation of sleep
  • 10:17insensitive mode for the Fitbit,
  • 10:19there is an underestimation of sleep
  • 10:21and again this is compared to PSG,
  • 10:24which is considered articles standard.
  • 10:29Jawbone company that doesn't exist and
  • 10:31went out of business in 2016, 2017.
  • 10:34This was early data about
  • 10:36that device and it said, oh,
  • 10:39this device is pretty decent agreement
  • 10:41with Poly, some Poly sonography.
  • 10:43However, there were published in 2015,
  • 10:46at which time the Jawbone Up,
  • 10:48which was the device that they tested,
  • 10:50was now three generations behind where they
  • 10:53were actually the consumers were using,
  • 10:55so the IT was great that they had
  • 10:57tested it against polysomnography.
  • 10:59But was the new device is better?
  • 11:02Worse, different hard to know the
  • 11:05speed of consumer technology changes
  • 11:07done it much faster rate than that
  • 11:09of the scientific technology.
  • 11:11And so this is what you can see.
  • 11:14Is that in 22,008 to 2015 there
  • 11:16were at least nine tracker from
  • 11:19Fitbit and six trackers from Jawbone
  • 11:21in a short period of time,
  • 11:23and that speed of change has continued.
  • 11:28What you can see, though,
  • 11:29is that the data around these devices
  • 11:32is growing rapidly, so you can see
  • 11:34where I was kind of pointing out,
  • 11:36and that inflection .20 fourteen
  • 11:382015 that there was not much data.
  • 11:40So this is a graph of the publications
  • 11:43that cover Fitbit and sleep in Pub Med,
  • 11:45and you can see it climbing rapidly.
  • 11:48There is an entire research library
  • 11:50devoted to Fitbit, and so there
  • 11:52are almost 700 publications as one.
  • 11:54I checked this the other day.
  • 11:57And so, recognizing that we're gathering more
  • 12:00and more information about these devices,
  • 12:02but the challenge is that the speed of
  • 12:05change in the consumer world is still
  • 12:07faster than that of the research world.
  • 12:12So as of 2021, what is Fitbit saying
  • 12:14about their sleep calculation?
  • 12:16They say Fitbit estimates your
  • 12:18sleep stages using a combination of
  • 12:20movement and heart rate patterns.
  • 12:22While you're sleeping,
  • 12:23your device tracks beat to beat
  • 12:24changes in your heart rate and
  • 12:26his heart rate variability,
  • 12:27which fluctuate as you transition between
  • 12:28light sleep, deep sleep and REM sleep.
  • 12:31And then they sort of use that data,
  • 12:34calculate sleep.
  • 12:34So let me show you a couple of cases.
  • 12:36These are cases that.
  • 12:39That were patients of mine.
  • 12:41So this is a 42 year old woman who
  • 12:43came to see me said needs more
  • 12:45sleep than the average person.
  • 12:47She's always been someone who's
  • 12:48just been kind of more tired.
  • 12:50If she doesn't get that 9 to
  • 12:5310 hours of sleep.
  • 12:54And so she got in a Fitbit fairly early,
  • 12:58self tracks or sleep and she sees this.
  • 13:01Now again, this is early Fitbit data,
  • 13:04but pointing out the fact
  • 13:05that her sleep is fragmented,
  • 13:07that she's considered in restless time
  • 13:09for about 220 out of this 200 minutes
  • 13:12out of this under 7 hours of recording time.
  • 13:16Well,
  • 13:16she comes with a sleep doctor.
  • 13:18It turns out she snores and has
  • 13:20restless sleep and she's not a
  • 13:22particularly overweight person,
  • 13:23but she gets a home sleep apnea test done.
  • 13:27Finds that she has moderate sleep
  • 13:29apnea and she was placed on CPAP
  • 13:31and is sleeping much better.
  • 13:33Case 254 year old presents to the
  • 13:35clinic with Insomnia state she
  • 13:37doesn't sleep well during the night,
  • 13:39even with medication.
  • 13:40She's using ambient somewhere between
  • 13:4110 and 20 milligrams nightly.
  • 13:43She often will say that she just
  • 13:46doesn't sleep at all at night now.
  • 13:49And she too is tracking her sleep
  • 13:51with a Fitbit. And this is her.
  • 13:53Her Fitbit data,
  • 13:54which shows almost no restless time.
  • 13:56Now it's not a particularly long
  • 13:58period of time.
  • 13:59It's about five hours of recording,
  • 14:01but it shows she barely moved and
  • 14:03suggests he's in fact asleep now.
  • 14:05Is she sleeping? She not asleep?
  • 14:07The argument being is very hard to
  • 14:10stay that still for five or six hours,
  • 14:12and so the suggestion is she's
  • 14:14probably asleep,
  • 14:15which is common in patients who
  • 14:17have insomnia.
  • 14:17But she was unwilling to admit
  • 14:19this could represent sleep.
  • 14:21That there was something wrong with her.
  • 14:282015 timeframe Chris Winter, who's a
  • 14:30known sleep doc down the Virginia area,
  • 14:32decides to do something for the Huffington
  • 14:35Post and sort of straps a whole bunch
  • 14:39of different devices to himself.
  • 14:41And you can see he kind of
  • 14:43lines them all up together.
  • 14:44Kind of post the data as they look at
  • 14:47sort of each of the devices in sequence,
  • 14:49comparing them all against
  • 14:51polysomnogram at the top.
  • 14:52And you can see the differences
  • 14:54in what that data looks like.
  • 14:56That different different
  • 14:57pieces of data lined up.
  • 14:59You can see the basis Health Tracker
  • 15:01was one that looked a little
  • 15:02bit more like what we would have
  • 15:04expected compared the polysomnogram,
  • 15:06but each device has its own data.
  • 15:09That is what I would say not quite right.
  • 15:12And So what he says here is with one
  • 15:14only one night of data collected,
  • 15:16I'm reluctant to declare winners and losers.
  • 15:18But more purely sleep mounting perspective,
  • 15:20the basis seem to distinguish self
  • 15:23from her perspective and accuracy.
  • 15:25And so does that mean anything?
  • 15:27Now it was a one night study of a bunch of
  • 15:30things put together back five years ago,
  • 15:32but the point being is that there's an
  • 15:34enormous level of variance in how these
  • 15:36different devices track and look at data.
  • 15:38And they are all for the most
  • 15:41part done in a black blocksworld.
  • 15:44So Fitbit now has over that 5-6 ten years
  • 15:47gathered an enormous amount of data,
  • 15:49and they can push out data about some
  • 15:52of the things that they have, right?
  • 15:54So this is average results based
  • 15:57on millions of nights.
  • 15:59So why do you like the data or not
  • 16:01in terms of how they interpreted the
  • 16:03fact is they have so many users and
  • 16:06so many nights compared to in some
  • 16:08ways we have in our laboratories that
  • 16:10regardless of how right or wrong it is,
  • 16:12there is an enormous amount
  • 16:13of information there,
  • 16:14and it's potentially helpful,
  • 16:15and so you can kind of see that the
  • 16:18average Fitbit user is sleeping
  • 16:20for better or worse what they say,
  • 16:226 hours and 30 minutes a night.
  • 16:24This is their average bedtime and wake time.
  • 16:26Their average light sleep,
  • 16:27deep sleep, and REM sleep.
  • 16:31You can see the power of large data blocks,
  • 16:34right? So this is useless data,
  • 16:36But it's interesting, right?
  • 16:37So this is Jawbone published at this point,
  • 16:40when there was an earthquake in the Bay Area,
  • 16:43and you can kind of see how what happens to
  • 16:46each of the users in their different area.
  • 16:48The earthquake was kind of based
  • 16:50out of that Napa Sonoma area.
  • 16:52You can see the biggest spike in wakefulness
  • 16:55there and the further away you got from
  • 16:58from this epicenter of the earthquake
  • 17:00could see fewer and fewer people woke up.
  • 17:03And so this is not surprising, right?
  • 17:05Earthquake happens.
  • 17:06Some number of people are actually going
  • 17:09to wake up from it and again shows
  • 17:11you the fact that all this data is is
  • 17:14effectively somewhat useful potentially.
  • 17:15But right now it's not necessary.
  • 17:17Being used in that way.
  • 17:20So Fitbit continues to evolve overtime.
  • 17:22You saw kind of some of the basic data
  • 17:24forms that I've shown you in clinic today.
  • 17:26You're seeing things that look much
  • 17:28more like this in the upper right
  • 17:30corner where there is REM sleep
  • 17:32and light sleep and deep sleep.
  • 17:34For better or worse,
  • 17:35in patients will come in and have these
  • 17:37discussions about you know how right or not,
  • 17:39right?
  • 17:39These things are,
  • 17:40and I will tell them that it's hard
  • 17:42to know what to make of the data from
  • 17:44the Fitbit because it hasn't been
  • 17:46well compared to Poly sonography,
  • 17:47but it's worth saying that it's
  • 17:49important to recognize,
  • 17:50you know when the points of start
  • 17:51and end arc as if nothing else.
  • 17:53You can get a sense better than sleep diary,
  • 17:56perhaps of when there's sort of
  • 17:57trying to get to bed and when
  • 17:59they're trying to get up,
  • 18:00and that is a valuable piece of information,
  • 18:03particularly people who are
  • 18:04tracking their sleep regularly.
  • 18:08Now what's new in your Fitbit is
  • 18:11oxygen measurement and this has
  • 18:13been known to be coming for awhile.
  • 18:15And it's not 100% clear how
  • 18:17this fits in with sleep apnea.
  • 18:19Yet, as presently Academy I went
  • 18:21to meet with the Fitbit folks
  • 18:23and I will tell you I was. Um?
  • 18:25Surprised and amazed at how far
  • 18:27ahead where they thought they were.
  • 18:29They were that they had already
  • 18:31been planning for.
  • 18:32So how to think about sleep apnea and
  • 18:34how to think about oxygen measurement?
  • 18:36Then there really their quest to get
  • 18:38their users to be as healthy as possible.
  • 18:42And so this sort of oxygen measurement
  • 18:43was the next step in their process,
  • 18:45and you can see that they have what
  • 18:48they are considering oxygen variation.
  • 18:50So small variations is consistency.
  • 18:53Big variations is inconsistency.
  • 18:54And this is the kind of chart
  • 18:57that people are getting.
  • 18:59Now when you look at what
  • 19:01a Fitbit actually says,
  • 19:02it says Nope,
  • 19:03your Fitbit device is not intended
  • 19:04for medical purposes and consult your
  • 19:06health care professional about any
  • 19:08questions or health issues you may have.
  • 19:10So as of yet they have not crossed the
  • 19:12line into being a diagnostic device.
  • 19:14They are still in any assessment mode.
  • 19:18And so they answer some questions
  • 19:20about oxygen testing, but they say,
  • 19:22broadly speaking as a practical,
  • 19:24met practical way of thinking about this.
  • 19:28You get oxygen to reports but
  • 19:30but how does it get reported?
  • 19:33What does that variability mean? How much?
  • 19:36How much variability is abnormal?
  • 19:38And what does that mean in terms
  • 19:40of how likely you are to actually
  • 19:42need to test someone who comes in
  • 19:45with a variable oxygen report?
  • 19:46And then what happens as you think
  • 19:48about this in the longer term, right?
  • 19:50So night after night they're
  • 19:51having these reports?
  • 19:52What if those variable report those
  • 19:53variability reports are actually variable?
  • 19:55That some nights are better
  • 19:56and some nights are worse?
  • 19:57And what do you make of that
  • 19:59right that the challenge?
  • 20:00Here is that there's increasing
  • 20:02amounts of data,
  • 20:03but not very easy ways of taking that
  • 20:04data into a useful reporting structure.
  • 20:07And that was part of what we
  • 20:09actually talked to them about,
  • 20:10is it would be helpful to have
  • 20:13some a way of assessing that.
  • 20:15And affectively,
  • 20:16how does this variability compare?
  • 20:18In fact, to sleep apnea, right?
  • 20:19They know,
  • 20:20probably on the back end a
  • 20:22little bit about what they,
  • 20:24what their oxygen devices showing they
  • 20:26don't necessarily at this point know
  • 20:28whether it results in sleep apnea or not,
  • 20:30but at some point I suspect
  • 20:32that will be made more clear.
  • 20:34I suspect research is going on
  • 20:36to that level at this point.
  • 20:38But these devices are currently in
  • 20:40what I would consider a pre assessment
  • 20:42mode right there, they're not.
  • 20:43They don't want to be the
  • 20:45diagnostic device as of yet,
  • 20:47but they want to raise the question
  • 20:49they want people to think about
  • 20:50getting the test done that is going
  • 20:52to be more conclusive,
  • 20:54and so they've wanted.
  • 20:55At least they went when we met with them.
  • 20:58As the ASM said, hey,
  • 20:59we want to work with you guys.
  • 21:01We don't want to replace you guys.
  • 21:07I showed you a bunch of data from 2015.
  • 21:09Let me show you a little
  • 21:10bit of the more recent data.
  • 21:12So this is a meta analysis done
  • 21:16in 2019 and looking at sort
  • 21:19of Fitbits of various models.
  • 21:23And more or less what they said
  • 21:24is that the Fitbit model showed
  • 21:26higher sensitivity and specificity
  • 21:27and detecting sleep than the
  • 21:29quote non sleep staging model.
  • 21:30So the more recent ones are the ones
  • 21:33that will show you the rehmann light
  • 21:35sleep and deep sleep as opposed to the
  • 21:37ones that I showed earlier that are
  • 21:39only soaring sort of sleep and restless time.
  • 21:43And so there was what they said in this
  • 21:45article is promising performance definition,
  • 21:48wake from sleep and their
  • 21:50convenient and economical.
  • 21:51But there are limited specificity
  • 21:53and not a substitute for PSG.
  • 21:56Which is of course not terribly surprising.
  • 21:59And so here's something that just
  • 22:01came out late last year looking at
  • 22:03Fitbit Charge Two and Fitbit Alta HR,
  • 22:06which are again fairly recent models
  • 22:08recognizing they're not the most recent.
  • 22:10But again,
  • 22:11they showed acceptable sensitivity
  • 22:12but poor specificity,
  • 22:13and so they are not accurate enough
  • 22:16from a clinical perspective to
  • 22:18replace what we do in the laboratory.
  • 22:21I'm not sure that they want to replace
  • 22:23what we do in the laboratory as of yet,
  • 22:25but I will say I suspect some of those
  • 22:27things may be coming down the line.
  • 22:30So as one of my favorite movies,
  • 22:33they quote 60% of the time.
  • 22:35It works every time, right?
  • 22:37That's that's kind of where
  • 22:38Fitbit is at currently.
  • 22:40But I think they continue to
  • 22:43refine and improve and it will
  • 22:45be better than this overtime.
  • 22:48So the world continues to evolve, right?
  • 22:50They? It's not just Fitbit, right?
  • 22:52Fitbit is part of this?
  • 22:54And why is Fitbit part of this?
  • 22:56Will Fitbit isn't just Fitbit anymore?
  • 22:58Fitbit is now Google and so how do you?
  • 23:01Why does Google by Fitbit?
  • 23:03Well, I think you have a sense right?
  • 23:06Here's millions of people with millions
  • 23:08upon millions of nights of data.
  • 23:10And what does Google do really?
  • 23:12Well they manage data and so
  • 23:14clearly you can see where these
  • 23:16interconnections are going to continue.
  • 23:19But Google Plus Fitbit is going in
  • 23:22going to create some interesting
  • 23:24data streams I would imagine.
  • 23:26But Google and Fitbit are not the
  • 23:28only people in the marketplace right?
  • 23:31There are.
  • 23:31Garman there's the band,
  • 23:33the subscription model for many
  • 23:34the athletes are using these days.
  • 23:36Then of course the Apple Watch which
  • 23:39is actually stayed on the sideline of
  • 23:41those sleep world for the most part,
  • 23:43but is clearly increasing their
  • 23:46interest in that area based
  • 23:48on what we can see so.
  • 23:50These devices.
  • 23:59You're muted, Doug.
  • 24:02Somehow you got muted. That's
  • 24:04interesting. I don't know when I got muted,
  • 24:06I didn't do anything,
  • 24:07but somebody muted me.
  • 24:09OK, they didn't like what I was saying.
  • 24:11Maybe it's Google. Sure would.
  • 24:15Some point after Rupan so the Apple Watch.
  • 24:19I will say they had stayed on
  • 24:21the sidelines for the most part.
  • 24:23On the sleep world, for the most part,
  • 24:26but they're slowly entering
  • 24:27this marketplace as well.
  • 24:29They also have oxygen monitoring
  • 24:31capabilities on the newer Apple Watch
  • 24:33is so recognizing that they're going
  • 24:35to enter this this space as well that
  • 24:37the players are beginning to change,
  • 24:39but the outcomes aren't changing
  • 24:41and the technology.
  • 24:43Of the devices is going to change
  • 24:45faster than what we can keep up with.
  • 24:48OK, let's see now that I'm.
  • 24:50OK, so let me then shift a little
  • 24:52bit and erables so these are
  • 24:54devices like the res Med S plus.
  • 24:56Now this device was an older device,
  • 24:58it was fancy and expensive backrow
  • 25:00days and then it got less expensive
  • 25:02because people weren't using it.
  • 25:03'cause it seems sort of bulky
  • 25:05and complicated to have a device
  • 25:06that's near your bed as opposed
  • 25:08to something you could wear.
  • 25:09But this is actually the data
  • 25:11that I had from this device.
  • 25:13They actually loan me one to try
  • 25:15out at a time and you can kind of
  • 25:17see you would get a score and you
  • 25:19get what your mind would charge.
  • 25:21Your body would charge and I
  • 25:23told him that this was.
  • 25:25A fairly a bunch of who high and
  • 25:28there really wasn't a good reason
  • 25:30to give me a score of some kind,
  • 25:32but they felt it was important and
  • 25:34now you can see that most people
  • 25:36in some way or getting a score what
  • 25:39that score means is hard to assess,
  • 25:41but it is something that is.
  • 25:44Element that patients are following
  • 25:46this and you can kind of see
  • 25:48the data you would get off.
  • 25:49And S plus again a hypnogram similar to that.
  • 25:52And this is an amazing way something
  • 25:54that's not even touching you,
  • 25:56right?
  • 25:56It's able to stage your sleep based
  • 25:58on what it can actually assess by
  • 26:00being near you and also provide
  • 26:02information on light and temperature.
  • 26:05And you can kind of see where
  • 26:06you get your score from,
  • 26:08but it's some combination of,
  • 26:09well, how much were you disrupted
  • 26:10and how much you know how?
  • 26:12Like how much light was in there,
  • 26:13and you know what is actually
  • 26:15not light light sleep.
  • 26:16How much sleep did you have and
  • 26:17how much deep sleep did you have,
  • 26:19etc.
  • 26:22But it's beyond that right that the
  • 26:24beds can be trackers to sleep, IQ,
  • 26:26sleep number beds have data that kicked out,
  • 26:28and so this is information from one
  • 26:30of my colleagues is asleep doctor.
  • 26:32She was willing to share some of
  • 26:34the data for her again looking at
  • 26:36her breath rate, her heart rate.
  • 26:38You know what kind of restful sleep she had.
  • 26:41I will say at least she was getting.
  • 26:44You know she was in bed for a good
  • 26:4710 hours almost, which is great.
  • 26:49You know some of that time.
  • 26:51Clearly she's not sleeping,
  • 26:53but but in the end she was
  • 26:56practicing what she was preaching.
  • 26:58But you can track it for your kids too.
  • 27:00For those people who really want to
  • 27:02know what's going on with their kids.
  • 27:04Beyond the miracles,
  • 27:06there are obviously the apps,
  • 27:08and so in terms of the apps.
  • 27:13There are obviously sleep apps galore.
  • 27:17Mobile health apps in general.
  • 27:19You know they can promote Wellness
  • 27:21relatively inexpensively and maybe help
  • 27:22with management of chronic diseases.
  • 27:24There are bewildering number of
  • 27:25apps that are available and it's
  • 27:28difficult to see which ones are the
  • 27:29safest or most effective and at
  • 27:31least according to the JAMA article.
  • 27:33It would be nice to have some sort of
  • 27:36unbiased review and certification process,
  • 27:38but that has yet to occur.
  • 27:42So what kind of apps are
  • 27:43out there regarding sleep?
  • 27:45I thought I would cover a couple of them.
  • 27:47This is one that was built in combination
  • 27:49with the VA and Stanford which is CBT.
  • 27:51I coach I still in fact use this in
  • 27:53my clinic for some of my patients,
  • 27:55'cause it's free and it has some things
  • 27:58that are useful in terms of tools.
  • 28:00But you can see it has things like
  • 28:03a progressive muscle relaxation
  • 28:05or or wind down exercise.
  • 28:07It has tools for sleep Diaries
  • 28:10and ability to chart some things.
  • 28:12It's a useful tool and and best of all,
  • 28:16it's free.
  • 28:18There are circadian applications,
  • 28:19like for instance in train,
  • 28:21which you can gather a whole bunch
  • 28:23of information about in terms of
  • 28:25people who are traveling, right?
  • 28:27This was a particularly useful thing
  • 28:29if you're going to Tokyo in a world
  • 28:31in which we are in covid may be less
  • 28:34useful 'cause we're not traveling very much,
  • 28:36but the idea is that you can use
  • 28:38some of the information we know
  • 28:40scientifically and put it out in
  • 28:42a way to make patients ability to
  • 28:44travel somewhat easier, right?
  • 28:46When should you get bright light?
  • 28:47When should you get dark,
  • 28:49and how can you prep?
  • 28:51Your trip to make your trip is
  • 28:53as helpful as possible and make
  • 28:55your sleep as good as possible.
  • 28:57And this is one of the classic apps.
  • 29:00The Sleep cycle app.
  • 29:01This is one that you could stick under
  • 29:03the sort of sheet and it would kind
  • 29:05of give you a report of your sleep
  • 29:08depth at some basic level and you
  • 29:09can see this is some what we would
  • 29:12see is a fairly normal looking cycle,
  • 29:14right?
  • 29:14That somebody is in deep sleep
  • 29:15and then in lighter sleep and deep
  • 29:17sleep in longer sleep in cycles of
  • 29:19somewhere between 90 and 120 minutes.
  • 29:24Where this starts to get more interesting
  • 29:26is in the fact that there have been a
  • 29:30number of applications used for snoring.
  • 29:32And there was an article published
  • 29:34in 2016 looking at several of these
  • 29:37apps overtime and effectively showed
  • 29:39that there was excellent positive
  • 29:41predictive value for snoring detection
  • 29:43in the in the populations used.
  • 29:47So here's an example.
  • 29:48The smart alarm that I just showed you
  • 29:50also has an ability to track snoring,
  • 29:51and you could actually listen to those
  • 29:53snores that pop pop up during the night.
  • 29:55All those little circles or
  • 29:57periods where you could listen.
  • 29:59Another app called quits.
  • 30:00Morning where you can see it was in this
  • 30:03particular case compared to Poly sonography,
  • 30:05and so you can see kind of the how
  • 30:07they synced based on time the arrows.
  • 30:10Anne and.
  • 30:13Showed effectively in this particular app,
  • 30:16again, the positive predictive value
  • 30:18of these three patients at versus PSG.
  • 30:21Was upwards of somewhere between 93 and 96%,
  • 30:23which is really pretty good.
  • 30:25And why is that?
  • 30:26Well, because snoring is a pretty easy
  • 30:28thing to track for the most part.
  • 30:29It may be difficult if there's
  • 30:31more than one person there, right?
  • 30:32So remember,
  • 30:33if you have a bed partner,
  • 30:35you may be picking up on snoring of
  • 30:37two people, not just one person,
  • 30:38but broadly speaking,
  • 30:39if you're sleeping alone,
  • 30:40it should have a pretty good chance
  • 30:43of picking up snoring and giving
  • 30:45you a sense of how bad it is.
  • 30:47Snore Lab is another app that
  • 30:49has been used and I'll show you
  • 30:51why I highlighted this one.
  • 30:53This is a patient of mine.
  • 30:56Who was an MIT engineer?
  • 30:57And of course you know had all
  • 30:59sorts of charting here, but he had,
  • 31:01you know,
  • 31:02tracked his snoring when he used
  • 31:03nothing in the top left and then with
  • 31:06his old kind of mouthpiece that he
  • 31:08had built for him on the top right
  • 31:10and then with CPAP on the bottom.
  • 31:11And he did a bunch of calculations and
  • 31:14you can even see where he marks off
  • 31:16that he took where he C Pap was on and wear.
  • 31:19His see.
  • 31:19Pap was often kind of noises that were made.
  • 31:23And so, broadly, you can see how these
  • 31:25apps can be used to assess progress, right?
  • 31:28We always talk about how, for instance,
  • 31:30and use of dental devices.
  • 31:31You know it's all kind of a little
  • 31:34bit it hit or miss, right?
  • 31:36You know, is the snoring better?
  • 31:37Is it not?
  • 31:38Better?
  • 31:39Was the bed partner say,
  • 31:40well,
  • 31:40you know,
  • 31:41would using an app in some basic
  • 31:43level be useful in terms of tracking
  • 31:45whether a mouthpiece is or is
  • 31:47not effective and how effective
  • 31:49is for controlling aspiring?
  • 31:50Well, sure,
  • 31:51something like this could be done.
  • 31:53And and it's not an unreasonable
  • 31:55approach for some of these things.
  • 31:57It doesn't obviate the need for follow up.
  • 32:00Testing at some point to ensure
  • 32:01that sleep apnea is controlled,
  • 32:03but if you're trying to titrate somebody,
  • 32:05this is not an unreasonable
  • 32:06way to consider it.
  • 32:09So moving beyond snoring,
  • 32:11then can you assess sleep
  • 32:12apnea via phones and apps?
  • 32:14And so this was sort of the original
  • 32:16study that I could find using a
  • 32:19phone and there actually strapped
  • 32:21the phone to somebody's chest,
  • 32:22which wasn't obviously going
  • 32:24to be a long term solution,
  • 32:26but more or less with this group showed
  • 32:28in 2014 was that in fact they could
  • 32:31show with reasonable sensitivity and
  • 32:33specificity that the the RDI was decent
  • 32:35for picking up and hi greater than 15.
  • 32:39But it moves beyond this, right?
  • 32:41So the University of Washington there
  • 32:43was an app which I'll show you here.
  • 32:46Where they actually had a
  • 32:48again an app where they didn't.
  • 32:51In fact, having the phone touching
  • 32:53anybody and so they in a contactless way,
  • 32:56was able to identify respiratory events
  • 32:59when compared that versus Poly sonography.
  • 33:02And so this technology ended
  • 33:04up in the durable.
  • 33:06The Sleep score Max.
  • 33:09And so you know you can see the evolution
  • 33:11of the S plus into this sleep score,
  • 33:14where now not just to you
  • 33:16assessing sleep quality which was
  • 33:18effectively with the S plus did.
  • 33:20But now you are able to assess
  • 33:22to some level you know.
  • 33:24The grieving someone might have.
  • 33:28Now they have done a lot of work
  • 33:30with this particular device,
  • 33:31however,
  • 33:32I haven't seen a lot of the
  • 33:35data percolate out of this.
  • 33:36And then there's apps like this,
  • 33:38which is sleep check which hasn't
  • 33:41yet hit the American market.
  • 33:43But is it is in Europe and this is
  • 33:46a device that was able to identify
  • 33:50OSA with a sensitivity of 85%.
  • 33:53For mild OSA,
  • 33:5483% for moderately and 83% for
  • 33:57severe OSA compared to full but
  • 34:00unattended Poly sonography?
  • 34:02And in the patients home,
  • 34:03and so you can see,
  • 34:05you know this is the wave of
  • 34:06where this is moving now,
  • 34:08whether they're going to be truly
  • 34:10diagnostic or pre pre assessments
  • 34:11before people are actually
  • 34:12willing to go and spend the money
  • 34:14to get formally diagnosed.
  • 34:15But this is the stuff that is
  • 34:17coming right that we are going to
  • 34:19move from a world in which they
  • 34:21are just assessing sleep to
  • 34:23assessing sleep and breathing.
  • 34:24And I think we have to be prepared for.
  • 34:29So you gotta prepare yourself for this world.
  • 34:31The world is going to change.
  • 34:33Continue to change in front of us.
  • 34:35We need to be ready for that. So.
  • 34:39Is all this technology good or is it bad?
  • 34:42I think in the end. It's neither.
  • 34:48That adaptation and evolution of
  • 34:50us of the practitioners of Sleep
  • 34:54Medicine is going to be a key
  • 34:57aspect of what we need to do.
  • 34:59My belief is that there are.
  • 35:04Numerous patients out there
  • 35:07that are undiagnosed.
  • 35:09Who have a sleep problem and this
  • 35:11may be way this technology to get
  • 35:14them to take that next step to come
  • 35:18to a Sleep Medicine practitioner.
  • 35:20The problem is,
  • 35:21there's not a lot of clinical teams right.
  • 35:24The number of sleep practitioners is
  • 35:26growing at a fairly slow rate and retirement.
  • 35:30Of sleep doctors and clinicians
  • 35:32is a also stable rate,
  • 35:35which means that we're not going to
  • 35:39be increasing our team size with.
  • 35:42Board certified sleep doctors.
  • 35:44Very quickly,
  • 35:45the world of Aips nurse practitioners
  • 35:47NPS is going to grow rapidly
  • 35:49and nurses and artizen are PSG.
  • 35:51Tees are all going to be increasing
  • 35:54as we look at the at the sleep team,
  • 35:57but still there are not going to
  • 36:00be a lot of growth in the world
  • 36:03doctors and so we need a way of
  • 36:06assessing and managing these patients.
  • 36:08And where are these devices going to go?
  • 36:10Is it going to be identification
  • 36:12or is it and then send the to the
  • 36:14clinical team for a formal diagnosis
  • 36:15or they're actually going to try
  • 36:17and diagnose them at home?
  • 36:20And without the care of a local doctor right,
  • 36:24you can see this with some things
  • 36:27like the hair.
  • 36:30The world of hair growth or erectile
  • 36:32dysfunction, where there's all done
  • 36:34via these sort of apps and monitoring
  • 36:36right that they monitor you?
  • 36:38They connected with the doctor
  • 36:40for a quick consultation and and
  • 36:42then you get your treatment right.
  • 36:44The medicine is delivered to your door.
  • 36:46Well the same could be true. Firstly,
  • 36:50back again and it and it may come to that.
  • 36:52We're not there yet.
  • 36:54Is technology going to hurt the sleep lads?
  • 36:57Well, maybe maybe not.
  • 36:58Depends on how many more patients
  • 36:59come in versus how many patients
  • 37:01do you lose because they're
  • 37:03being diagnosed somewhere else?
  • 37:04And I think that that's hard to predict,
  • 37:06but in the short term,
  • 37:08I think more patients come in
  • 37:10then were before because they're
  • 37:11looking at their sleep.
  • 37:12Please,
  • 37:13I think that in some cases there may
  • 37:15be too many patients right that wait
  • 37:17times will continue to increase if
  • 37:19too many patients are identified and
  • 37:21you've got to be able to manage that,
  • 37:23that comes to that Internet
  • 37:26and clinical team question.
  • 37:27And what about patients who have
  • 37:29incorrect diagnosis right there,
  • 37:30identified on technology that is black box?
  • 37:32Or that you're not sure about,
  • 37:34you know, is there a problem there?
  • 37:36Is there something we have to worry about?
  • 37:38What if patients are told that
  • 37:40they don't have sleep apnea,
  • 37:41when in fact not only do they have
  • 37:43sleep apnea, but it's bad sleep apnea?
  • 37:45How comfortable can we be with
  • 37:47some of the things that are going
  • 37:50on behind the scenes?
  • 37:51And then Lastly treatment paradigm, right?
  • 37:53So it's not just about diagnosis and
  • 37:55then this is kind of where I'm going to.
  • 37:58I'm going to close the talk a little bit,
  • 38:00but it's recognition that the diagnosis
  • 38:02of sleep apnea or diagnosis of a sleep
  • 38:05problem is not the only thing we do.
  • 38:07It's about the long term management
  • 38:09of the patient,
  • 38:10and I think that that's really
  • 38:11crucial to recognize.
  • 38:15So what are the ASM juice the
  • 38:18ASM brought together a consumer
  • 38:20sleep technology task force.
  • 38:23They came out with the position
  • 38:26statement in 2018 and largely
  • 38:30said that these applications.
  • 38:33Purport to measure an any,
  • 38:35maybe even improve sleep and that we,
  • 38:37as you probably all are aware
  • 38:38we encountered this consumer
  • 38:40sleep technology in practice.
  • 38:41And though there's not
  • 38:42always validation data,
  • 38:43you gotta understand a little bit about
  • 38:46what these devices are and what they do.
  • 38:49You need to sort of recognize that
  • 38:51many of them are not validated.
  • 38:53Many of them don't have FDA clearance,
  • 38:56and you really shouldn't use them at
  • 38:58this point for diagnosis and treatment
  • 39:00of sleep disorders at the at the time,
  • 39:02but this may change in coming
  • 39:04years would be my guess,
  • 39:06but I think if it talking about
  • 39:08that data when somebody says hey,
  • 39:10do you want to see my Fitbit data?
  • 39:13I almost never will say no.
  • 39:15I think it's recognition that hey,
  • 39:17they're bringing another data point.
  • 39:18How useful is it? Hard to know.
  • 39:21On the other hand, saying no to that.
  • 39:26Doesn't invalidate the patients
  • 39:27feeling about that data,
  • 39:28and so it's worth opening that data
  • 39:31looking through their screen and talking
  • 39:33to them for a minute or two about that data.
  • 39:37And so you can kind of see
  • 39:38where the symptoms come down,
  • 39:39which is to say, hey,
  • 39:41the more we get validation,
  • 39:42the more we get raw data and
  • 39:44understanding algorithms the better.
  • 39:45We're going to be comfortable
  • 39:46with how these things go.
  • 39:48Yes,
  • 39:49I'm also created a asleep technology
  • 39:51section on their website and part of
  • 39:54what this task force did besides coming
  • 39:57up with the position statement that you.
  • 40:00See is they also built and have
  • 40:03continued to build out a database.
  • 40:06And so in the sleep technology,
  • 40:07if you were an ASM member,
  • 40:09you can log in and look at it.
  • 40:11For instance,
  • 40:12you can look up the outer ring
  • 40:15and so this is a ring that is.
  • 40:17Purports to monitor things.
  • 40:18It's obviously a wearable
  • 40:20'cause it's on your finger.
  • 40:21It's small and it's easy to
  • 40:23sort of have on through the
  • 40:25night for at least some people,
  • 40:27and you can kind of see.
  • 40:30All the sort of tags of data
  • 40:32that are compared here.
  • 40:33You can sort of see that there was
  • 40:35compared to PSG that you can't look at
  • 40:37the raw data and it is not FDA cleared,
  • 40:40but the idea here is that there
  • 40:42are summaries of all these data.
  • 40:43So if you have a question or
  • 40:45if a patient brings in a device
  • 40:47you're not familiar with,
  • 40:48you can quickly look up to see if
  • 40:50it's in the ASM sort of database.
  • 40:52This is not a.
  • 40:55There is no.
  • 40:56The SM is trying not to give any credence
  • 40:59to anyone device more than another.
  • 41:01It's just trying to gather the data
  • 41:04in a way that makes it easy for
  • 41:06you to track and so for instance,
  • 41:09here is the data around the
  • 41:11aura ring from 2019
  • 41:13and you can again see how
  • 41:15PSG versus the ring kind of.
  • 41:17They're fairly similar.
  • 41:18They're not exactly the same,
  • 41:20but you can see again that there is
  • 41:22fairly good correlation with sleep,
  • 41:24but a lot of source scatters, right?
  • 41:27So the scatter plot is certainly higher.
  • 41:29I think for the ordering that it is for PSG,
  • 41:33and I think that's part of the challenge.
  • 41:35With this is.
  • 41:36Yeah, it looks good in some cases,
  • 41:39but it's still not meeting up to
  • 41:41the gold standard necessarily.
  • 41:45And so I thought,
  • 41:46I closed here just sort of saying,
  • 41:49you know, this data is still coming.
  • 41:51This is an article that has not yet been
  • 41:54formally published and has been accepted
  • 41:56for print and sleep for this year.
  • 41:59And you can see again now, four different
  • 42:02wearables and three non wearables.
  • 42:04Where they were testing them
  • 42:07versus Actigraphy and PSG and you
  • 42:10can kind of see where there is.
  • 42:12Again some good sensitivity,
  • 42:14but problems with specificity and the
  • 42:16sleep stage comparisons are mixed,
  • 42:19and the worst the sleep is,
  • 42:21the worst the devices do,
  • 42:23and I think it's worth recognizing.
  • 42:27That this is the problem, right?
  • 42:29That our population is not a normal sleep.
  • 42:32When you see a lot of the validation
  • 42:34data is often comparing normal PSG.
  • 42:37It's me patients who are normal
  • 42:39between PSG and the wearable or
  • 42:41niarbyl that that's under study,
  • 42:43but we recognize we don't see
  • 42:45normal people in our clinic.
  • 42:47We see people who are abnormal
  • 42:50and that's really the issue.
  • 42:53So how am I going to close?
  • 42:55I'm going to close by giving
  • 42:57you sort of the tag line to the
  • 42:59editorial I wrote in JC SM.
  • 43:01Just a short period of time ago,
  • 43:04but to say to seize this opportunity
  • 43:05Miss Re Orient are thinking expanding
  • 43:07beyond sleep apnea detection to
  • 43:09fully embrace the holistic importance
  • 43:11of sleep for disease prevention
  • 43:13and management productivity and
  • 43:14satisfaction and safety and well being.
  • 43:16And health system that demands
  • 43:18greater efficiency,
  • 43:18we need to redefine the problem instead
  • 43:21of asking how we can get more people.
  • 43:23Into the Sleep laboratory we determine
  • 43:26how to manage millions of people
  • 43:28with undiagnosed sleep apnea through
  • 43:30collaborative care models with.
  • 43:32Primary care doctors,
  • 43:33other specialists and our own sleep team.
  • 43:37And finally we need to redirect our
  • 43:39field by leveraging the disruptive
  • 43:40technology like the consumer
  • 43:42technology we're talking about today,
  • 43:44to help us improve patient access to
  • 43:46sleep care and improve their health,
  • 43:48and by refraining Sleep Medicine,
  • 43:50we can provide greater value payers,
  • 43:52patients and employers.
  • 43:55So.
  • 43:55In summary, obviously the evaluation of
  • 43:57sleep is evolved significantly overtime.
  • 44:00It's not unique to Sleep Medicine,
  • 44:02pathology, radiology,
  • 44:02other fields are going
  • 44:04through this change as well.
  • 44:05It's just that the consumer side.
  • 44:07This is been particularly notable
  • 44:09in sleep just because so many people
  • 44:11are interested in your sleep,
  • 44:13and that's great.
  • 44:14It means the word has gotten
  • 44:16out that sleep is important.
  • 44:18On the other hand,
  • 44:19we have to deal with all this consumer
  • 44:22technology as patients bring it in.
  • 44:25Number two that the scientific data around
  • 44:28consumer sleep technology is growing rapidly,
  • 44:30though consumer technology is still not gold,
  • 44:33standard right is validated
  • 44:34against PSG in normal sleepers,
  • 44:36really that same population would care about,
  • 44:39probably not,
  • 44:40and so it's really validating against PSG,
  • 44:42and patients who have disease,
  • 44:44that,
  • 44:45to me would seem to be what eventually
  • 44:48we need to get to and then three.
  • 44:51We,
  • 44:51as clinicians need to understand,
  • 44:53adapt and evolve to this incoming technology.
  • 44:56Because it's only going to get to be a
  • 44:59bigger piece of what we deal with, right?
  • 45:01I didn't even get into things like my air,
  • 45:04right where patients are looking
  • 45:05at their own CPAP data and giving
  • 45:07getting some feedback from it.
  • 45:09Is that disruptive technology?
  • 45:10No, it's actually great.
  • 45:11Technology is helping our patients
  • 45:13be more successful and it's adding
  • 45:15to things that we already do,
  • 45:16but this is the world in which
  • 45:18we are going to see continued and
  • 45:20constant growth and staying on top
  • 45:22of it's going to be difficult.
  • 45:24I know the ASM is working to
  • 45:26try and keep people.
  • 45:28Educated as much as they can about
  • 45:30devices as they come out and apps and
  • 45:32all the things that are out there.
  • 45:33But even for them,
  • 45:35it's going to be difficult,
  • 45:36and I think it's important to recognize
  • 45:39if we just shut this all down.
  • 45:41Say, you know I'm not dealing
  • 45:43with any of that.
  • 45:44I think that that's going to lead
  • 45:46you down the path where you're
  • 45:48eventually going to be left behind.
  • 45:50That these folks who do technology
  • 45:52work are very smart and they are
  • 45:54moving much more rapidly than I think
  • 45:56we feel sometimes comfortable with.
  • 45:58But eventually we'll get to a
  • 46:00place where I think we will meet
  • 46:02in a both comfort and use.
  • 46:04Perspective,
  • 46:04so with that I wanted to thank
  • 46:07everybody for their time and attention.
  • 46:09I'm happy to take questions
  • 46:10at this point and if anybody
  • 46:12has questions after the fact,
  • 46:14my emails on the bottom of the slide.
  • 46:16So thank you.
  • 46:19Thank you John drive.
  • 46:21That really was very timely appreciated.
  • 46:23I'm gonna ask people if they have questions.
  • 46:26Just make some sort of notation
  • 46:29in the chat box and will call you.
  • 46:32I'll start you off with a multipart
  • 46:35question given your knowledge of
  • 46:37and working with the organization.
  • 46:39Are there any current
  • 46:41collaborations between the Academy,
  • 46:42an industry and what that looks like?
  • 46:45What are the opportunities for the
  • 46:48people here to collaborate with?
  • 46:50Industry and is the Academy providing any
  • 46:53structure slash funding for any of them.
  • 46:57So that's a that's a really wide question,
  • 47:00Larry, but but thank you for making it.
  • 47:02I'm obviously less well connected that I
  • 47:04once was as I've rotated off the board.
  • 47:06But I will say my belief is that The
  • 47:09Academy Is still working towards.
  • 47:11A partnership in which there is clarity on
  • 47:14how how the data the technology can be used.
  • 47:17Right so I think the challenge that
  • 47:19the Academy has these technology
  • 47:21companies like Fitbit or Google
  • 47:23or really large and we asleep.
  • 47:25Doctors are not a very large group.
  • 47:27They are interested in working with us
  • 47:30but sometimes on their terms more than
  • 47:32our terms and I think that that is a
  • 47:35challenge that we will continue to face.
  • 47:38I think it is true also of things like AI,
  • 47:41sleep staging.
  • 47:42Right that that there is,
  • 47:44the Academy has some data on sleep scoring
  • 47:46that AI companies would probably like
  • 47:48to have to use this comparison points.
  • 47:51So I think that there are partnerships
  • 47:54that are being worked on and I
  • 47:56think that there is interest from
  • 47:58the research perspective.
  • 48:00I think if you look at the foundation
  • 48:02they're always looking for technology they
  • 48:04don't want people to be trying to promote
  • 48:07their own technology with foundation funds.
  • 48:10But I think that they are
  • 48:12looking for research that is.
  • 48:14Interesting within that
  • 48:15particular technology world,
  • 48:16so I think the Academy recognizes
  • 48:19that the challenges are out there
  • 48:21and I think they're really open
  • 48:23to ways trying to work together.
  • 48:26But at the same time want to protect the
  • 48:29sleep clinician as much as possible.
  • 48:32Recognizing that you know we don't
  • 48:34want to be replaced by the robots.
  • 48:39And then.
  • 48:42We have other questions. Yeah, can I
  • 48:45ask Doug a question or make a comment
  • 48:48so there are millions of people
  • 48:51that are using oximeters.
  • 48:54At home, and it turns out that the ox,
  • 48:58the standards in order to
  • 49:00sell an oximeter do not take
  • 49:02into account different pigment types.
  • 49:05And so there's a huge as a matter
  • 49:09of fact that couple of senators
  • 49:11Elizabeth Warren and Cory Booker
  • 49:13have asked the FDA to look into
  • 49:16this issue because there has not been any
  • 49:19validation of these devices on people
  • 49:21with different skin colors. So this
  • 49:24is, you know there are millions of these
  • 49:27out there an we just need to
  • 49:29keep that in mind that a lot of these devices
  • 49:32have really not been validated.
  • 49:34I think that's an excellent point.
  • 49:36I mean, I think they certainly oximeters
  • 49:38have blown up in the setting of kovid, right?
  • 49:40That there's more and more of them
  • 49:42being sold, which interesting,
  • 49:43of course, is that patients will also
  • 49:45use that in the clinical sense to say,
  • 49:47hey, I don't have sleep apnea,
  • 49:49'cause every time I check my oximeter,
  • 49:51it's normal, so I couldn't have sleep apnea,
  • 49:53to which I say yes, but you're awake.
  • 49:57And to your point around validation,
  • 49:58I think the issue is,
  • 49:59you know there's been a.
  • 50:00Real push to get these things out
  • 50:03because people wanted to have them.
  • 50:05And yet at the same time knowing how much
  • 50:08variability there may be and whether
  • 50:10skin type or pigmentation or whatever, right?
  • 50:13That variance among population
  • 50:14really does matter,
  • 50:15and that's true for the oximeters,
  • 50:17but it's also true for all these wearables.
  • 50:20Yeah, now I think that the wearables
  • 50:22obviously they have tried to gather,
  • 50:24you know, for instance,
  • 50:26Fitbit has so many users that they
  • 50:28probably have looked at some variances.
  • 50:30Around skin color if they have
  • 50:33access to that.
  • 50:34But again,
  • 50:34who knows because that data is not always
  • 50:37being published in a scientific way.
  • 50:40It's in their back rooms
  • 50:41being discussed amongst them,
  • 50:43which is a challenge.
  • 50:51River question from Karen Johnson.
  • 50:53You wanna unmute and ask your question?
  • 50:57So I just want your thoughts on sort of,
  • 50:59you know when we think about
  • 51:01public health issues like daylight
  • 51:02savings time or school start times,
  • 51:04what can we get from you know Fitbit
  • 51:07and Google and where do you think
  • 51:09it'll sort of help answer some of
  • 51:11these sort of unanswerable questions
  • 51:12about the effects on sleep and
  • 51:14potentially sleep disparities and such.
  • 51:16Well, I think that's a great question.
  • 51:18I think the real issue is will we
  • 51:20have access to that data, right?
  • 51:23I think.
  • 51:24The number of people who have a wearable
  • 51:27device is growing continues to grow.
  • 51:30As these devices get more
  • 51:32and more affordable.
  • 51:34The companies will have more
  • 51:35and more data points right?
  • 51:37And so just like I showed
  • 51:38you with an earthquake,
  • 51:40you know it's not just that right,
  • 51:42it's what is your sleep look like
  • 51:44the day after some big event, right?
  • 51:46You know the inauguration, you know,
  • 51:48for whether you are for or against the
  • 51:50current President you know was that you know?
  • 51:53Was the sleep better or worse, right?
  • 51:55Is it on a school day,
  • 51:57all 17 year olds?
  • 51:58How much sleep are they short
  • 52:00and how much does it matter?
  • 52:01And how much can we involve
  • 52:03ourselves in that right?
  • 52:04I think.
  • 52:05This data is not as of yet publicly
  • 52:09available, and if it became so,
  • 52:11I think it would be a goldmine of research,
  • 52:15but as it currently stands,
  • 52:17it's all behind firewalls and I think.
  • 52:21The more that the Academy and
  • 52:23these technology companies are
  • 52:24able to work together, the better.
  • 52:26I think we chance we have of
  • 52:28being able to use that data for
  • 52:30public health initiatives.
  • 52:32I think it would be lovely.
  • 52:33I just don't know that we can
  • 52:36plan on having that access.
  • 52:44I was wondering if I could ask a question.
  • 52:47This is under his truck over from Yell,
  • 52:49really nice talk.
  • 52:50Thanks for talking about a topic
  • 52:52that is very actual for patients
  • 52:53and and a little bit distant for us.
  • 52:56So I like your point of acknowledging
  • 52:58that you know we should be looking
  • 53:00at these devices and looking at the
  • 53:02data that patients are presenting.
  • 53:04And I'm faced with that everyday.
  • 53:05I've I've seen patients bring in every
  • 53:07single app report that you have shown
  • 53:09in others and so I don't curiosity.
  • 53:12You know what?
  • 53:13Which one of these apps?
  • 53:14And do you actually use and how
  • 53:16do you use them?
  • 53:18So I mean, I've used nor lab for, you know,
  • 53:21titration off the back on the back and
  • 53:23maybe oral appliance stuff as well.
  • 53:25I think I've found a little bit more
  • 53:27difficult to find devices that measure
  • 53:29oxygen reliably and also devices that
  • 53:31measure position reliably or not.
  • 53:33Devices, apps,
  • 53:33I should say the measure position reliably,
  • 53:35so was wondering what what do you do
  • 53:38with information that comes in from
  • 53:39the sleep cycle or it comes in from
  • 53:42another particular like circle for example?
  • 53:44Right,
  • 53:44so I think I think that's an
  • 53:47excellent question and I think I
  • 53:49think we're all faced with this.
  • 53:50Is is the challenge of how do we use this
  • 53:53data and what do we use in our own clinics?
  • 53:57So what I'll tell you is,
  • 53:58at least in, at least in my clinic,
  • 54:01I don't tend to use much more than
  • 54:03sort of generics, which is to say,
  • 54:06hey,
  • 54:06if you download an app such as quit snoring,
  • 54:09or snore lab,
  • 54:09it might tell us how bad your snoring
  • 54:12is with that custom mouthpiece or
  • 54:14without the custom mouthpiece.
  • 54:15And I will say the number of times
  • 54:17I get that feedback back from the
  • 54:19patient with that recommendation is small,
  • 54:21right?
  • 54:21But sometimes they just never come
  • 54:23back 'cause they're tracking their own data.
  • 54:24Sometimes they come back and
  • 54:26they never did it.
  • 54:28Similarly, I think one of the things that we.
  • 54:32We may have use for is the
  • 54:35patients and I have seen several
  • 54:37of these recently who are really.
  • 54:40Not well scheduled and we don't
  • 54:42have a great sense of what that
  • 54:44schedule for sleeping looks like,
  • 54:45so I've had several these recently where
  • 54:47I've made a recommendation to say look.
  • 54:49You know I'd love to get an actigraph on you,
  • 54:52but the wait time for our actigraph is
  • 54:55long and ones out 'cause it's broken.
  • 54:57And so why don't you buy yourself
  • 54:59a device of some kind and actually
  • 55:01look at schedule?
  • 55:02I don't even care what the sleep
  • 55:04quality looks like,
  • 55:05it can be the cheapest non
  • 55:07sleep staging Fitbit.
  • 55:07But I just need to know when you're
  • 55:10going to bed and getting up every day.
  • 55:12And to me,
  • 55:13the most valuable information
  • 55:14I can get from these devices is
  • 55:16really that is how well scheduled
  • 55:18are these 'cause the people
  • 55:19for whom they are bringing
  • 55:20these devices?
  • 55:21A lot of the time and want to talk about it?
  • 55:24Or the people who aren't sleeping well.
  • 55:27And a lot of the time that understanding
  • 55:30that schedule to me becomes so crucial
  • 55:32and suspected is for you as well.
  • 55:35And you know, even then I can
  • 55:37scan through that data and take a
  • 55:39couple of screenshots and upload
  • 55:41it to their medical file, but.
  • 55:43I have no good way of summarizing
  • 55:46that data in a one page snapshot of.
  • 55:48Well, here's their bedtime and wait
  • 55:50time average and their variances.
  • 55:51And wouldn't that be really nice and useful?
  • 55:54And that's kind of what we told Fitbit
  • 55:56when we went to meet with them.
  • 55:58We have yet to see that.
  • 56:03Thank you. I think another opportunity
  • 56:06is for respironics and res Med to put
  • 56:09in some smaller emitters in the masks
  • 56:10so we have a sense of what's happening
  • 56:12with the patients and rash leaping in,
  • 56:14and I think that there is a
  • 56:16wealth of options out there.
  • 56:18There is no question and I think I
  • 56:19will say I think technology companies
  • 56:21some of them are more willing
  • 56:23to listen to other than others.
  • 56:25Some of them are just trying to provide
  • 56:26the best consumer experience they have.
  • 56:28Some of them are just kicking out scores
  • 56:31that have no relevance to anything.
  • 56:33The ones that are at least willing
  • 56:36to collaborate a little bit and
  • 56:38sit down in some way as much as
  • 56:40a big company can sit down,
  • 56:42but you know there was a sleep scientist
  • 56:44who spoke at a from Fitbit who spoke
  • 56:47at an Academy American Accounting
  • 56:48medicine conference like to me,
  • 56:50those are the small steps and what
  • 56:53they can release is small and they
  • 56:55can talk about the algorithm and they
  • 56:57have NDA's and all these things,
  • 56:59but it shows at least that they're
  • 57:01willing to take step step to meet.
  • 57:04Somewhere in the middle we may never
  • 57:05be thrilled with having an algorithm
  • 57:07determine what we see about somebody sleep.
  • 57:09At the same time, I'm using a watch
  • 57:11path that does the exact same thing.
  • 57:13How comfortable might with that?
  • 57:15OK, 'cause there's a lot of data behind it,
  • 57:17but.
  • 57:19Probably the Fitbit has more data,
  • 57:20we just don't know what it looks like, right?
  • 57:22So the more that there is openness
  • 57:25about the data, the better.
  • 57:27I think we care about,
  • 57:28but Fitbit as a general rule,
  • 57:30doesn't they want best health but their
  • 57:33company they want to sell things right?
  • 57:35And so how?
  • 57:36What is that validation do for them?
  • 57:38Maybe not a lot.
  • 57:41Because they're already selling
  • 57:42millions of the devices,
  • 57:43right?
  • 57:46On that. On that note,
  • 57:48I think we're getting close to the end here.
  • 57:51Clearly this is something that
  • 57:53we will be talking about a lot
  • 57:56for the foreseeable future,
  • 57:58so thank you for coming on.
  • 58:00Starting our conversation on it and.
  • 58:04Andrea, don't know there's anything
  • 58:05you want to do to wrap up.
  • 58:08I know I just wanted to thank
  • 58:10everybody who participates in
  • 58:11these the joint conferences.
  • 58:12I think. So far it's been a great
  • 58:14success and we're looking forward
  • 58:15to more and more of these talks.
  • 58:17And thank you Doug,
  • 58:18for making the time for us and just
  • 58:20for everybody else for next week
  • 58:22will have another session of the
  • 58:24yields the conference at 2:00 PM,
  • 58:25so we'll see you then.
  • 58:28Say very much everybody.
  • 58:29Take care everybody.