"Consumer Sleep Technologies-Potentials, Pitfalls, and the Future of Ambulatory Sleep Tracking" Cathy Goldstein (01.06.2021)

January 11, 2021

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00:20Alright hi everybody.
00:22Welcome, my name is Lauren Tobias.
00:26I'd like you to welcome to welcome
00:28you back to our Yale Sleep Seminar
00:30for the Winter Spring semester.
00:32I have a few quick announcements
00:34before I introduce today's speaker.
00:36So first, please take a moment to
00:39ensure that you're muted in order to
00:41receive CME credit for attendance,
00:43please see the chat room for instructions.
00:46You can text the unique ID for
00:48this conference anytime until 3:15
00:50PM Eastern Time if you're not
00:52already registered with DLC and me.
00:54You do need to do that.
00:561st, If you have any questions
00:58during the presentation,
00:59I encourage you to make use of the
01:01chat room throughout the hour and
01:03will also have an opportunity for you
01:05to unmute yourself and ask questions
01:07directly to Doctor Goldstein at the end.
01:09We do have recorded versions of
01:11these lectures which are going to
01:13be available online at the link
01:15provided in the chat and then finally,
01:17please feel free to share our
01:18announcement for a weekly lecture
01:20series to anyone else who you
01:21think may be interested,
01:23or contact Debbie Lovejoy to
01:24be added to our email list.
01:26So this afternoon I have the pleasure
01:29of introducing Doctor Kathy Goldstein.
01:31Doctor Goldstein received her medical
01:33degree from the Medical College of Georgia.
01:36Didn't ask her about her feelings
01:39about events of this morning yet.
01:41She completed a neurology residency
01:43at the University of Colorado and
01:46her sleep fellowship at Northwestern,
01:48followed by a Masters in clinical research,
01:51design,
01:51and statistical analysis at
01:53the University of Michigan.
01:55She's currently a clinical associate
01:57professor in neurology at the
01:59University of Michigan, Ann Arbor.
02:01Her research interest is in sleep and
02:04circadian interactions with health
02:05and for several years she's been
02:08interested in the role of consumer
02:11sleep technologies and Sleep Medicine,
02:13including the role of wearables and
02:15smartphones, sleep tracking acts, apps.
02:17She's an active clinical educator.
02:19She lectures nationally and and
02:21she is slated to be Co.
02:24Chair of the ASM Sleep Trends
02:26Conference for the upcoming year.
02:28She's authored ASM position statement
02:30on the role of consumer sleep tracking.
02:33And also artificial intelligence
02:34and Sleep Medicine.
02:35And just recently she published an
02:38article that encourage everybody to
02:40check out in JC SM that shares the
02:42results of a large national survey
02:44examining the impact of COVID-19
02:46stay at home orders and sleep,
02:48health and working patterns,
02:50which I found really interesting.
02:52So with that I am delighted to have
02:55Doctor Goldstein here to discuss
02:56a topic that I know many of us are
02:59eager to learn more about consumer
03:02sleep technologies potentials.
03:03Pitfalls in the future of ambulatory
03:05sleep tracking? Thank you.
03:07I will turn myself off Doctor
03:09Goldstein and turn it over to you.
03:12Awesome,
03:12thank you so much Lauren and big virtual.
03:15Hello to everybody.
03:16I'm going to go ahead and get started here.
03:20This is your code to text to get your CME.
03:24And I have no conflicts
03:27of interest to disclose.
03:29OK, so here's our road map
03:32for today's discussion.
03:34Casino what is to come?
03:36And I do want to disclose the fact that
03:39this is not gonna be a review of all
03:43the available consumer technologies.
03:45The entire landscape that
03:47I'm going to present to you,
03:50a general framework with which you can
03:53approach consumer sleep technologies
03:55in general and with the specific use
03:57case of wearable sleep estimation
04:00devices that use motion and heart rate.
04:03So first of all.
04:05Why do we even care bout tracking sleep?
04:09So we have this really big paradox
04:11in Sleep Medicine and research where
04:14we know that sleep deprivation and
04:16sleep disorders cause their detriment
04:18through chronic exposure every
04:20night to disturbances in sleep,
04:23quality, erratic sleep timing or
04:25reduce sleep duration.
04:26That how do we measure sleep with an
04:30overnight polysomnogram is our gold
04:32standard which is great to diagnose.
04:34Sleep apnea, certain parasomnias
04:36and movement Santa titrate.
04:38Happy,
04:38but this is really impractical
04:40beyond one to two nights or abuse.
04:44So how do we track sleep objectively
04:46so that we can get more than one
04:49to two nights of sleep recording?
04:52Well, we do have an option.
04:54I am the currently accepted option for
04:57clinical Sleep Medicine and research
05:00is actigraphy anac trigger fee in the
05:03traditional sense is cleared by the FDA,
05:05and it uses a wrist warm accelerometer
05:08to monitor motion during 24 hour period
05:11with the rationale that we move more than.
05:14When we're awake,
05:15then we do when we're asleep.
05:17So the way this works is you give
05:20an actigraph to your patient.
05:22They wear the device and there's
05:24a piezo electric accelerometer or
05:26Mens accelerometer in the device
05:28which collects motion,
05:29which is then digitized into
05:30activity counts the data from the
05:33devices then downloaded.
05:34And a software package uses an
05:37algorithm to determine sleep from wake,
05:40and we like actigraphy because Actigraphy
05:43performance has been compared to PSG,
05:45and the findings of that
05:48performance published in many,
05:50many research publications, so.
05:52What's the problem?
05:53If we have this small, unobtrusive
05:55way to measure sleep over days to weeks,
05:58well, there's quite a bit.
06:00As you can tell.
06:02So first of all, actigraphy,
06:04typically just measures movement.
06:05Some devices measure light.
06:07The data acquisition methods and storage
06:09methods are really outdated with actigraphy,
06:12so before you give the device to somebody,
06:15you have to initialize it
06:17and to recover the data.
06:19You typically have to plug
06:20it back in via USB ports,
06:23and so we can't really transmit an.
06:26Evaluate the data in real time.
06:28I've even seen researchers male actor
06:31graphs back and forth to each other,
06:33which is pretty prehistoric
06:35in this day and age.
06:37Always talk about Actigraphy is validated,
06:39it's validated it's validated,
06:41and yes,
06:42it's been compared to polysomnogram
06:44many times, but it's not that great,
06:47so it has a high sensitivity
06:49but a low specificity for sleep,
06:52which means that the algorithms
06:53applied to actigraphy data typically
06:55misinterpret nonmoving wakefulness,
06:57as sleep, and So what that means is
06:59these have very high accuracy and
07:02healthy sleepers during nighttime sleep.
07:05However, as sleep disruption
07:06increases throughout the night.
07:08There's more wake after sleep onset,
07:10increased sleep onset latency,
07:12the accuracy of the sleep metrics that are
07:15output by the software are going to go down.
07:19And Speaking of software that is
07:21another pitfall of actigraphy.
07:22The traditional actigraph software does
07:24not interface with the electronic health
07:27record or any other digital health platforms,
07:30so that makes it difficult to kind
07:32of track sleek sleep along with
07:35other health metrics in an easy,
07:37meaningful, practical way.
07:38The use of actigraphy requires
07:40quite a bit of data cleaning.
07:42Given that I'm in bed intervals need
07:44to be set for appropriate analysis,
07:47and this interpretation,
07:48as well as the set up the patient
07:50recording even though we have a CPT code.
07:53This is typically not reimbursed,
07:55so actigraphy is not going to be practical
07:58for a clinical workflow in most cases,
08:00and it's going to be burdensome
08:03on the research team.
08:05Additionally,
08:05actor graphs are expensive.
08:07There are at least $800 a device
08:09for the FDA cleared devices,
08:11so this is not really practical
08:13for large population studies,
08:15and it restricts use clinically,
08:17particularly if there is a sleep
08:19clinic in an underserved area.
08:21One big thing that people don't talk
08:23about is it these devices are owned by
08:26the health system where the research
08:28team they're not owned by the patient,
08:31so even though we consider actigraphy
08:33to be longitudinal sleep recording.
08:35These typically don't go beyond 1
08:37two weeks as opposed to devices
08:40that patients might own and use
08:42themselves that are worn over weeks,
08:44days, months, even years.
08:47So despite the strong wording
08:49that we see here,
08:51that Actigraphy should be used before
08:54the MSL T in circadian rhythm disorders.
08:58What happens when we stop following
09:01the icst three and start getting real?
09:04The real world Sleep Medicine clinic
09:07and research we ask about sleep
09:10cross sectionally.
09:11So for example,
09:13will say how many hours do you
09:16sleep on average?
09:18At face value,
09:19this seems like a very kind of easy,
09:21simple question,
09:22but obviously this can be
09:24interpreted in a multitude of ways.
09:26For example,
09:26how many hours do you sleep at night?
09:29How many hours do you sleep at night,
09:31plus how many hours do you sleep during naps?
09:34What time do you go to bed and when
09:37do you wake up and do the math?
09:40Is this during the work week during the
09:42weekend so we can see that this can
09:45be misconstrued if we use lanja tude?
09:47No self report?
09:48With the sleep diary.
09:50So daily self report recording of sleep.
09:52That might be a bit better,
09:54but there's problems with that as well.
09:56This is burdensome to our patients
09:58and is oftentimes incomplete,
09:59or the classic example of people filling
10:01these out in the waiting room before
10:03they come in for their PSG MSL T.
10:06This could be difficult for certain
10:08populations specifically like the very
10:09young or the older with cognitive problems.
10:11And again, even if your patients to
10:13fill this out, what did they do?
10:16Fax it to you or FS,
10:17send it as an email attachment.
10:19So not really relevant for the technical
10:23capabilities that we have in 2020.
10:25Now, so why is this important?
10:28Does this really affect
10:29our clinical practice?
10:31Well, we suspect it does,
10:33so the American Academy of Sleep Medicine
10:35did do a systematic review and found
10:38that the objective information from
10:40Actigraph recordings is beneficial.
10:42An provides distinct information over
10:44sleep logs in a variety of disorders,
10:47but the evaluation and management.
10:50Additionally,
10:51the group from Harvard has already
10:53asked researchers to stop querying
10:55self reported sleep duration.
10:57As you can see here,
10:59and that's because objective sleep
11:01recording is also incredibly
11:03relevant for research because the
11:05in accuracies that are possible
11:07with self report sleep duration
11:09can depend on patient demographics,
11:11whether or not they have comorbid
11:13sleep disorders.
11:14When the question is asked an way,
11:17the question is asked, so this can lead.
11:20Tobias in research.
11:22Another problem with using self
11:24report sleep duration and research
11:27is that the whole construct of
11:29sleep duration as an equivalent
11:32for sleep health which we know is
11:35probably not the case,
11:36and there's growing evidence to support this.
11:39For example,
11:40something called intraindividual variability.
11:42So the variation of sleep
11:44parameters around the mean,
11:46which has been shown relevant
11:48for a variety of outcomes,
11:50including psychiatric, metabolic,
11:51and cardiovascular.
11:52This is going to require
11:54longitudinal tracking which.
11:56Is difficult to do with self report and so
11:59a passive objective method is required.
12:03Additionally,
12:03as we said that sleep duration
12:06doesn't equate to sleep health eh 7
12:09domain definition has been proposed,
12:11and that's a combination of both self
12:14report and primarily objective measures,
12:17including in addition to duration,
12:19continuity, timing,
12:20rhythmicity regularity and
12:21subjective sleepiness and quality,
12:23as well as a composite of extreme values.
12:27And when you combine,
12:29combine all seven domains an.
12:31Additionally,
12:31the number of composite extreme values.
12:34We can see that this has a much
12:37bigger impact on mortality then
12:39just sleep duration alone,
12:41which I don't know if you
12:43can see my cursor here,
12:45but here are the seven domains combined
12:48as well as the number of extreme values,
12:51and then we have sleep duration is
12:54all the way over here when used as
12:57an isolated sleep variable to predict
12:59mortality in a random forest model.
13:02So it seems like longitudinal objective
13:05sleep measurements are pretty important.
13:07So if we can't use actigraphy
13:09because of the limitations,
13:10what are options?
13:11It seems like we sure have a lot
13:14with consumer sleep tracking technology.
13:16We have wrist worn devices,
13:18as you can see here from a variety
13:21of manufacturers.
13:22More and more rings are
13:24coming on to the market dry,
13:26e.g headbands mirabal's that are
13:28either at the bedside or under the bed.
13:32So before we can really talk about
13:35the possibilities and the problems,
13:36that is consumer sleep technologies hold.
13:38We have to understand a little bit
13:41more about how they work or how
13:44do we get from signal to sleep.
13:46And I'm not sure I can't see everybody,
13:49so it's hard to know Peoples ages.
13:51But for those of you that
13:53remember Schoolhouse Rock,
13:54the best schoolhouse rock is.
13:56How does a bill become a law?
13:58And here we have Bill,
14:00and he's just sitting here on Capitol Hill.
14:03And just like a bill, is not a law.
14:06Smartwatches,
14:06rings etc aren't direct measures of sleep.
14:09There are a bunch of steps in between,
14:11so this is an oversimplification,
14:13but this does give you some
14:15idea about how these.
14:17Actually work,
14:17so your device is going to
14:19or most risk form devices.
14:22At least they're going to typically
14:24include a Tri axial accelerometer,
14:26which is going to record motion
14:28in three directions, XY and Z,
14:30and that's going to give you raw signal
14:33to the motion we see pictured here,
14:36which is actually from an Apple Watch
14:39and then optical photoplus Mogra Fi,
14:41and that is a visual,
14:43light based way to estimate blood volume,
14:46which is then converted into
14:48pulse Anna variety.
14:49Of computed features from pulse,
14:50including things like heart rate variability
14:53and then your motion and your heart rate.
14:55Features are going to go into an
14:58algorithm which is going to even be there
15:00through an app or a cloud based server,
15:03and that algorithm is typically black box.
15:05As you can see from the figure and then
15:08the sleep information is returned to the
15:10user both visually and in summary metrics.
15:13As you can see in the example below.
15:16So it's a myth that your watch track sleep,
15:20but what the truth is is that algorithms
15:23estimate sleep from wearable sensor,
15:25acquired physiological
15:25signals and arrived features.
15:27But sometimes that's a bit difficult
15:29to discuss with their patients.
15:32Now, these algorithms that estimate sleep.
15:34Where do they come from?
15:36So let's start by taking a look
15:39at our traditional actigraphy,
15:41so the algorithms that are
15:43used by FDA cleared ACTIGRAPHY,
15:45and the associated software typically are
15:47ones that are more than 2025 years old.
15:50So on the top left you can see
15:53the Respironics actor algorithm,
15:55and on the bottom right that's the cool
15:58Kripke algorithm in the Respironics Act.
16:01Aware Algorithm E sub zeros
16:03the epic being scored.
16:05And same with cold Kripke,
16:06we have a sub zero,
16:08so those are the epic of interest.
16:10And then there's a weighted sum and
16:13the negative numbers are the epochs
16:15that are before the epic at hand,
16:17being scored and the positive
16:19numbers or the epics after the
16:21epic being scored and you can see
16:23these received lesser weights and
16:25then if the sum crosses a threshold
16:27it's going to get scored.
16:29That epic is going to get Squared's
16:31wake and if it is below that
16:33threshold it'll get scored as sleep.
16:35So weighted sounds.
16:36Now how is this changed?
16:38So we use much more sophisticated
16:41methods to classify,
16:42sleep and wake from wearable sensor data.
16:45At this point in time an I worked
16:47with a couple mathematicians at
16:49University of Michigan Humane,
16:51Olivia Walsh and Danny Forger and
16:54we wanted to know if we could
16:56actually make our own algorithms
16:58to train models to classify sleep
17:01versus wake from an off the shelf
17:04consumer sleep technology and we
17:06actually use the Apple Watch and
17:08the reason was is that was the.
17:11Only one that we could get our hands
17:13on where we had access to the motion.
17:16An heart rate data for modeling
17:18purposes and this was funded by
17:20our athletics Department because
17:21they are very interested in sleep
17:24and sleep technology to improve
17:26recovery and performance,
17:27and for those of you interested in
17:29college basketball, it was pretty fun.
17:31Because Jordan Poole and Isaiah
17:33Livers did the UI for our app.
17:35So really neat project that we're
17:38grateful we have the funding for.
17:40And what we did is we had people
17:43wear Apple Watch is for a seven
17:45day ambulatory period,
17:46and the reason we use that ambulatory
17:49period is Danny Anna Livia.
17:51They're computational biologists
17:52that are focused on the modeling
17:54of the circadian Clock,
17:55and there is a very well validated
17:58differential equations model that you
18:00can pass activity through to get a
18:03Clock proxy that we used as an input,
18:05and then on the final night
18:07of this seven day recording,
18:09we had our subjects come in and they slept.
18:12Simultaneously with the Apple Watch
18:14and synchronized with Polysomnogram,
18:16and we obtained the rock celebration data.
18:19And at PPG heart rate from their
18:22Apple Watch on that same night,
18:25we use that Clock proxy feature as
18:27well as activity counts derived from
18:30the acceleration and PPG heart rate
18:32and arrived standard deviation of
18:35heart rate to train algorithms against
18:38scored PSG to classify wake versus sleep,
18:41and we use regular old logistic regression.
18:44As well as machine learning
18:46techniques for our classifier,
18:48I'm not going to go into the details of that,
18:53but our final algorithm performed as
18:55well as actigraphy or what's been
18:58historically cited for actigraphy
19:00in regards to sensitivity and
19:02even improved with specificity.
19:04But you guys can read about that
19:07in the paper.
19:08But I do wanna show you is that there are
19:11a few other groups that have done this.
19:14When the access to raw signal from consumer
19:17sleep technologies to been iaccessible,
19:18so Zang used a Microsoft Band,
19:20Roberts,
19:21used an Apple Watch Ann
19:22Beattie at all used to Fitbit.
19:24I believe they worked with Fitbit,
19:26so they can have access to that data and I
19:29won't go through all the details of this.
19:32But what I want you to kind of
19:35recognize is the breath of the
19:37features that are used.
19:38So we went to previous algorithms with.
19:40FDA cleared actigraphy that were just
19:43using weighted sums of activity counts
19:46to a variety of features from motion,
19:48an heart rate, an various computed features.
19:51As you can see here and then,
19:54these are going into machine
19:57learning algorithms to train their
19:59capability to predict sleep and wake
20:01so much as this is changed overtime,
20:04and we're really looking at
20:06different sleep wake classifiers
20:08that were previously accustomed to.
20:10But what everyone wants to know is
20:13what about the algorithms that are
20:15actually associated with the apps
20:17that come with the devices that your
20:20patients or research subjects actually use.
20:22So these these algorithms that other
20:25people have come up with aren't
20:27typically available in a usable way.
20:29Ours is open source which
20:31can be obtained from GitHub,
20:33but it's not enough.
20:35Closed app right now,
20:36so the problem is we don't really know
20:39the the apps that are available now
20:42and be associated with the CST's that
20:45are available on the market market.
20:47We don't really have details
20:49about the features that are used,
20:52the populations that the algorithms
20:53were trained on, or the methodology.
20:56So it's pretty unclear.
20:58Which makes us dependent on the already
21:01processed sleep stage output from these
21:04devices and their associated algorithms.
21:06So the question is,
21:08how good is this data?
21:10Which brings us to the
21:13question on everybody's mind,
21:14which is validation.
21:16And this is the position statement that the
21:19American Academy of Sleep Medicine put forth
21:22in regards to consumer sleep technology.
21:25And you can see that we
21:28talk about validation a lot.
21:30So what is this validation that's so
21:33important for consumer sleep technologies?
21:35So what's validation in general?
21:37So validation is the act,
21:39process,
21:40or instance of validating,
21:41especially the determination of the
21:44degree of the validity of a device.
21:46So what's validity,
21:48the quality or state of being valid,
21:50such as the quality of being well grounded,
21:53sound or correct in regards
21:56to a new method of testing?
21:59So how do we validate consumer
22:01sleep technologies?
22:02So there was a group that was
22:06formed to discuss just that.
22:08An last year there was a manuscript
22:11that described the outcomes of
22:13that discussion and made some
22:16recommendations on how to quote unquote,
22:18validate,
22:19consumer sleep technologies which might
22:21be better described as determining
22:23performance in a specific situation,
22:26but will use the term validation for now.
22:30So PSG was cited as the gold standard
22:33to which the CST must be compared,
22:36so not actigraphy as the sole competitor
22:39must be. Sleep is defined by PSG.
22:42The scoring of PSG should be manual,
22:45not automated,
22:45right 'cause it's not just PSG signal,
22:48that's ground truth, it's scored PSG.
22:51That is our ground truth or gold standard,
22:54ideally double,
22:55scored portable PSG is acceptable
22:57as compared to full.
22:59These protocols also cited that the CS
23:02team needs to be recorded simultaneously.
23:05Obviously with the PSG so that you're
23:08comparing the same time interval
23:10on PSG to the same time in revolt
23:13that was scored by the Consumer
23:16sleep technology algorithm.
23:17Additionally, it was recommended that
23:20Epic by epic data from the Consumer
23:23Sleep technology outputs be sought
23:25out for the epic by epic analysis,
23:28and I just want to go into a little bit
23:31more detail about why that's important.
23:34So whenever you're reading a consumer
23:37sleep technology validation paper,
23:39you're aware of this,
23:40but what an epic by epic analysis
23:43is is that you're comparing the
23:46same 32nd increment of stage sleep.
23:48By the Consumer sleep technology
23:51algorithm to that exact 30
23:53seconds in time of polysomnogram
23:55that was scored by a technician.
23:59So you're comparing simultaneous data
24:01in the same increment of time and that
24:05supposed to comparing summary data
24:08over the course of the night to each.
24:11So how do you analyze these
24:14epic by epic comparisons?
24:16This takes us back to college
24:19or medical school.
24:204 by 4 tables where the typical use
24:24was assessing a new test against a
24:27gold standard for disease of interest.
24:30But in the context of validating
24:32consumer sleep technologies against PSG,
24:34sleep is the outcome of interest and
24:37the population is actually not people.
24:40It's the 32nd FX at hand.
24:43So how does that look?
24:45So let's look at our 4 by 4 table here.
24:49So we have horizontally or gold
24:52standard which is scored PSG.
24:54And then vertically we have the new test
24:57which is the output of the CST algorithm.
25:00So if you have a CST epic that
25:03is scored as sleep and that
25:05same epic is actually PSG sleep,
25:07that's going to be a true positive.
25:10If you have CST output for a given
25:1330 seconds that scored his wake,
25:15but it's actually sleep,
25:17that would be considered a false negative.
25:19If you have a PSG wake,
25:22epic that the CST interprets is sleep.
25:24That's going to be a false positive,
25:27and if you have a CST epic or CST
25:30scored Wake Epic, that's actually wake.
25:33That's going to be considered
25:34a true negative.
25:36And again, these are true positive,
25:38false positive,
25:39false negative,
25:40true negative,
25:40because the outcome of interest is sleep,
25:43so the negative state is not sleep or wake,
25:47and so then you can derive sensitivity and
25:50specificity from this epic by epic analysis.
25:52So sensitivity here is the fraction
25:55of PSG sleep.
25:56Epics that are correctly designated
25:58as sleep by the CST while specificity
26:01is the fraction of PSG wake ethics
26:04that are correctly designated as wake
26:07by the Consumer sleep technology.
26:09Oftentimes will also get a report
26:11on accuracy and what accuracy
26:13is in this setting.
26:15Is is the number of epochs correctly
26:18identified by the CST as a correct
26:20state divided by all the epics
26:22being considered and accuracy
26:24can be very deceiving and healthy
26:27sleepers because these studies
26:28typically take place at night and
26:31what a healthy sleepers do at night,
26:33they sleep and they have
26:35high sleep efficiency.
26:36So say you have an algorithm
26:38and the developers.
26:40They just want to have a startup company,
26:42so the algorithm goes through and scores.
26:45Everything is sleep if you test it algorithm
26:47in the lab and you determine accuracy in
26:50somebody with a sleep efficiency of 90%,
26:53the accuracy is going to be 90% despite
26:56the fact that the algorithm was arbitrary.
26:58So that can be a very deceiving
27:01metric in these studies.
27:03So how does performance of consumer
27:06sleep technologies look overall?
27:07So in the left panel you'll see
27:10sensitivity and in the right
27:12panel you'll see specificity.
27:14And the black bars here are
27:17consumer sleep technology,
27:18wearable devices and then the
27:21Gray bars are standard actigraphy,
27:23so CST's tend to have sensitivity
27:26greater than 90%.
27:28Pretty similar to Actigraphy,
27:30and specificity's that are more
27:33widely ranging about 30 to 60%.
27:35So again, similar to standard Actigraphy,
27:38sometimes outperforming and
27:40sometimes underperforming.
27:41So, just like actigraphy,
27:43these consumer sleep technologies
27:46overall tend to not miss any sleep,
27:48but they may misclassify awake
27:50during the sleep period and sleep,
27:53which results overall,
27:55and an overestimation of sleep and
27:58decreased accuracy when people have more
28:01wake during the time in bed period.
28:04So if we have validation, are we good to go?
28:08I mean based on this American Academy
28:11of Sleep Medicine position statement,
28:13this is really the main barrier, Sir.
28:16I'm just checking my time here.
28:18So once a consumer sleep technology
28:20is validated,
28:21can we start implementing it in
28:24clinical practice and research?
28:26Well,
28:26there unfortunately a lot of threats
28:30to extrapolation of these in lab
28:34validation studies to the real
28:37life environment. Quite a few.
28:39So for example we have problems
28:42with our ground truth,
28:43so these algorithms are trained
28:46to predict sleep as defined by
28:48scored PSG and is squared PSG.
28:50Really a gold standard.
28:52It is our best gold standard now,
28:54but there are a lot of problems with it.
28:58How many people do we need to score the PSG?
29:02What is an experienced technologist?
29:04We know that even good RP SG teams
29:07have imperfect interscore reliability.
29:09So again,
29:09making her ground truth not
29:11necessarily perfect.
29:12Automated scoring is something that
29:14could be beneficial in the future
29:16to make this more more systematic.
29:18And you know we have differences
29:20in laboratory,
29:21we might have better quality of our data,
29:24but out of center is going to
29:26allow us to have subject sleep in
29:28the home environment,
29:30which may be more reflective
29:32of sleep overtime.
29:34Another problem with the current
29:36validation processes we have it is we
29:39don't really consider the fact that
29:41these consumer sleep technologies
29:43are probably measuring an entirely
29:45different construct of sleep.
29:47So in addition to the fact that with
29:50the CST you have an algorithm that's
29:53determining the state as opposed to a person,
29:57we're also measuring entirely different
29:59physiological metrics with the
30:01consumer sleep technology typically
30:03being based in motion and heart rate.
30:06And a PSG being based in EG.
30:09And we accept these different contract
30:11the constructs for sleep logs versus PSG,
30:14but we don't practice graffiti and
30:18consumer sleep technologies which
30:20we hold for this to the same
30:23standard as polysomnogram.
30:24Another problem with taking those
30:26validation studies done in the lab
30:29and extrapolating them to use in
30:31the wild is that I'm in bed issue,
30:34which is another under
30:36recognized threat to validity.
30:37And when these studies are done in
30:40the sleep lab that I'm in bed is set
30:44manually by lights out to lights on.
30:47However, that there not is not how
30:49these devices work when used in reality.
30:52So previously with some of the older
30:56consumer sleep technologies you did
30:58had to put the device into sleep mode.
31:00So then the sleep detection
31:03classifiers would be diploid.
31:05However, now this is all passive,
31:07so even if you have a consumer
31:10sleep technology algorithm that
31:12performs awesome in the sleep lab,
31:14you have high sensitivity without
31:17sacrificing too much specificity.
31:18If that time in bed detection
31:21algorithm isn't good,
31:22those sleep wait classifiers are not
31:25going to be deployed at the correct time,
31:28and that's going to result in sleep
31:31summary metrics that are off.
31:33So real world utility.
31:35Of the consumer,
31:36sleep technology is not only based
31:39on the performance of the sleep
31:41classifier into sleep lab against PSG,
31:44but also if those algorithms are
31:46deployed in the correct window,
31:48which is something that's really study.
31:52What about environment?
31:53So we have our external environment
31:56and things that are not going
31:58to be problems in the lab.
32:00For example, bed partners, pets.
32:02Those aren't going to be in the labs when
32:05the sleep period is placed, so most CST.
32:08PSG comparison studies are going
32:10to be during the night time,
32:12so not during naps dot during daytime.
32:15Sleep in shift workers,
32:16not during this time.
32:18Sleep and circadian rhythm,
32:19sleep wake disorders,
32:21and these are situations.
32:22Where we can really benefit from lanja tude.
32:25No sleep tracking.
32:26Additionally,
32:27things like substance use alcohol,
32:28most people aren't going to use alcohol
32:31or drugs if they are enrolled in his
32:34study of their scent tracking device
32:36against PSG is often times these are
32:39exclusion criteria's for the study,
32:41so this might change the performance
32:44when we look at these devices.
32:46In the real world and the
32:48other thing is reliability,
32:49so we've already said that
32:51the beauty of these devices,
32:52their whole need,
32:53is the fact that they can be used night
32:56after night after night overtime.
32:58And we don't know if the performance
33:00is reliable overtime given the
33:02validation studies typically
33:03only take place over one night,
33:05and there's a possibility that
33:06overtime the sensors aren't as good.
33:08That's something we don't know as well.
33:12We also can't verify what we don't
33:14understand and many consumer sleep
33:16trackers come out with these
33:18numbers or scores that they don't
33:21disclose the definition of.
33:23And I know our patients really like
33:25these an you know this manufacture
33:28on the right probably should get
33:30the Nobel Peace Prize because they
33:33somehow know what an individual's
33:35exact sleep need is.
33:37Here cited at 6 hours and
33:3951 minutes so we don't know.
33:42The algorithms behind these metrics
33:44there's really no way we can compare them
33:48to anything that is based in science.
33:51Population is also a big problem here,
33:54so once a consumer sleep technology
33:56is validated in a certain age group,
33:59that doesn't mean that that
34:02performance will translate to
34:03different age groups or individuals
34:05with other comorbid sleep disorders,
34:08particularly as we've seen
34:10in hypersomnia and insomnia.
34:12We also don't know if heart conditions
34:14or certain medications could potentially
34:16be problematic given that these
34:19algorithms are highly dependent on heart
34:21rate and derived metrics from the heart rate,
34:24like heart rate variability.
34:26Additionally, user also algorithms
34:27that are based in motion,
34:29so how about individuals with spinal
34:32cord injuries strokes patients
34:34in the ICU who sleep?
34:35We'd like to track.
34:37We probably can't extrapolate the performance
34:40data that we see to these individuals.
34:43Now, problems with accuracy can even
34:45or validity can even extend past.
34:48Just looking at in accuracies
34:50in the device output.
34:52So this is a patient of mine who
34:54wore an Apple Watch the same night
34:57of his home sleep apnea test.
35:00I'm sorry,
35:01trying to get everything on one page so
35:04you can't see these oximetry values here,
35:07but with these high pop Mia's
35:10he's desatting as low as 88% and.
35:13None of these were captured
35:15by his Apple Watch oximeter,
35:17and this is an individual with
35:19dark skin and we now know that
35:23these pulse oximetry in general,
35:25and these pulse oximeters,
35:27particularly on CST,
35:28which were unsure of the general accuracy of,
35:31could be discrepant an that could augment.
35:35Problems that we see are ready
35:37with P in individuals with poor
35:39social determinants of health.
35:41If were collecting massive amounts
35:43of data from these devices and
35:45using this as a basis for research,
35:48we might augment some major
35:50health disparities.
35:52Now another problem,
35:53when we compare a consumer sleep
35:55technology to polysomnogram and
35:56there's some performance metrics cited,
35:58it takes time to do the study.
36:01Then the studies written up and
36:03it goes through a peer review
36:05process for publication,
36:07and then someone will say,
36:09Oh yes,
36:10that device has been validated against PSG.
36:13Um,
36:13that performance metrics that are
36:16output from that study are going
36:18to be specific to the sensors
36:21of the device that was used,
36:23plus the algorithm of the current
36:26firmware and software iteration,
36:27and these are things that change,
36:30and they change with actigraphy as well,
36:33so that's not something you,
36:35but oftentimes with consumer
36:37sleep technologies,
36:38the hardware of the device is updated.
36:41There's new models that come out or.
36:44They could spend all this time on
36:46a validation study and the company
36:48goes out of business and so that's
36:50another threat to generalizing
36:52the results of these studies.
36:54And what we really need to kind of
36:58move passed through these oneof.
37:01You know small convenience samples
37:03and lack of transparent data in
37:06regards the performance of these
37:09consumer sleep technologies is
37:11really large datasets with some
37:14disclosure of the methodology behind how
37:17this sleep classifiers are developed.
37:19An also information about the population.
37:23And we are getting closer to that.
37:26So the Somnia project is a project
37:29in the Netherlands where in their
37:31individuals who are receiving clinical
37:34polysomnogram there recording a variety
37:36of other sensors and these are sensors
37:39that could be incorporated into consumer
37:41sleep technologies or that already are,
37:44for example, respond PPG,
37:46an actigraphy under the bed sensor,
37:48microphones, and what this is
37:50going to establish is a data set
37:53that includes Co recorded.
37:55Polysomnogram with all of this different
37:58sensor signal so that the best
38:01predictors of sleep can be determined
38:03an also that these can be tested.
38:06These algorithms can be tested
38:09on different population subsets.
38:12So what does our future hold in regards
38:15to consumer sleep technologies?
38:17So although it's tempting to
38:19just Subs in for actigraphy,
38:21I believe these could be much more valuable.
38:25I think that they could change the
38:27logistics of Sleep Medicine and
38:29completely transform our practice.
38:31So instead of seeing a patient and
38:34taking this leap history that's
38:36primarily retroactive,
38:37and then maybe sending them home
38:40with an actigraph or sleep Diaries,
38:42we can have a long term recordings
38:45that were performed prior to their
38:48first appointment.
38:48Because they own the consumer sleep
38:50technology and have those available
38:52at our initial evaluation.
38:54So what I've heard this compared to
38:56that you would never hear a woman come
38:59to her first obstetric appointment
39:01without an over the counter pregnancy test.
39:04So this could be something equivalent
39:06to that where even if it isn't
39:08an FDA cleared device,
39:10we have some idea of the sleep wake
39:12patterns from over the counter source.
39:15Also,
39:15this can be used in between visits
39:18and given our move to Tele medicine.
39:20The fact that the patient's own these
39:23devices really makes us a completely no
39:26contact way to practice Sleep Medicine.
39:29Consumer sleep technologies would
39:30allow us to personalize interventions
39:32to patients actual sleep patterns
39:34as opposed to what we think they are
39:36or what they try and remember them
39:38to be at the clinic visit and those
39:41interventions could be made in real time.
39:44The API with the apps associated with
39:46CST might also make it easier for us
39:49to leave link sleep metrics to other things.
39:53So,
39:53for example,
39:54mobile apps really lend
39:55themselves to something called
39:56ecological momentary assessment,
39:58and that's when you evaluate self
40:00report symptoms in real time in
40:03response to a push notification,
40:04which might give us a better understanding
40:07of how sleep interacts with other symptoms.
40:10Additionally,
40:10we can get health and non health information.
40:13Things like Geo location, social media use.
40:16Alongside sleep through other apps,
40:18potentially use of smart devices
40:21like glucometers scales,
40:22blood pressure,
40:23cuffs with our consumer sleep
40:25technology to help see how sleep
40:28affects those objective parameters,
40:30and then obviously ideally these
40:32would interface with our electronic
40:35health records so the sleep provider
40:37could see this data
40:39in a seamless fashion and hopeful
40:42that one day sleep with what we've
40:45learned longitudinally could even.
40:46Act as a vital sign and we'd be able
40:49to see changes in the signature of
40:51consumer sleep technologies before
40:53an acute health event occurs,
40:55so we can see an impending deterioration
40:57in our patients and potentially intervene
40:59before they end up in the hospital.
41:01And that's the case.
41:03I hope with Pap data as well.
41:06So this is 1 example of a consumer sleep
41:09technology that has been FDA cleared
41:11up for personalized treatment and you
41:14guys have probably heard of this.
41:16So this is the night where app and this
41:19is an app for individuals with nightmares,
41:22primarily individuals with PTSD
41:23and what this entails is you charge
41:26your Apple Watch during the day
41:28and then you wear it at night.
41:31This device doesn't.
41:32Outright claim to track sleep.
41:34It says it kind of learns the
41:37the cardiac and motion patterns
41:39during sleep over the course of
41:42days and then based on that,
41:44it identifies departures from that
41:46that the app thinks that the algorithm
41:49thinks are nightmares and there's
41:51a stimuli of vibration or that goes
41:54through the watch to arouse the patient
41:57just slightly from the nightmare
41:59interfere with the nightmare without
42:01completely waking them for sleep.
42:03And there's trials going on
42:05right now with this device.
42:07Now we talked about the need for this
42:11truly longitudinal recording of sleep
42:14to really understand sleeping disease
42:16an when you look at the data that we
42:20currently have, it's pretty short,
42:22so active graphic recordings in studies
42:25that associate sleep timing duration,
42:27intraindividual variability with
42:29health outcomes have been 5 seven days.
42:32And if we're looking at contribution
42:34of sleep to chronic disease,
42:37we're going to learn much more.
42:40If a consumer sleep technology
42:42that's worn for weeks, months,
42:45seasons, years can be used.
42:48But the other thing is that these devices
42:51they are inexpensive and they're pretty
42:54widespread in regards to their use,
42:57and that gives us the ability to perform
43:00sleep research with objective data at scale.
43:03Colleagues at U of M used an app to globally
43:06quantify sleep in circadian rhythms.
43:09So across the world.
43:11Additionally,
43:11there's studies that have been
43:13done in interns who have a really
43:16unique sleep challenge,
43:17and because of the taxing nature
43:20of the medical training program,
43:22it would be difficult to assess
43:24their sleep otherwise.
43:25And there was really unique study
43:28by Eric to pollen colleagues where
43:30Fitbit data was looked at in a state.
43:34Aggregate level Anne used to
43:36determine the likelihood of influenza
43:38like illness outbreaks,
43:39so these are things that couldn't
43:42really be easily conducted with use
43:45of actigraphy and are very promising.
43:48And of course, what about Covid?
43:50So Covid has been now used in a few studies.
43:54These are the three big ones.
43:57Excuse me,
43:57CST's have been used in Covid studies
44:00are the three big ones to determine
44:03if consumer wearable technologies
44:05can help predict covid both with
44:07and without the incorporation
44:09of symptoms, and these have been promising
44:12these primarily used resting heart rate
44:14and heart rate variability and activity,
44:16and some of them you sleep.
44:19To predict covid, they've also been found
44:22to predict other viral illnesses and has
44:24shown that the data from the sensors is
44:27beneficial above and beyond the symptom data.
44:30And here is a nice picture from one
44:33of these investigations and you can
44:35see that symptom onsets day zero here,
44:38and your blue tracing is respiratory rate.
44:41Your red tracing is heart rate,
44:43and your green and pink are
44:46heart rate variability measures.
44:47This is all believe this one is.
44:50Yep, this ones from Fitbit and if you
44:53look at Day Zero which is symptoms start,
44:57you're already seeing a change from
44:59baseline and these consumer sensor
45:01parameters and so this could be
45:04very helpful in identifying people
45:05who are going to have a viral
45:08illness specifically coded.
45:09And if all else fails,
45:11you can put your wearable
45:13tracker on your pet.
45:14So I thank you for your attention
45:16today and I'm happy to take any
45:19questions and thank you for having me.
45:23Thank you so much for a fantastic
45:26overview of that topic.
45:28Doctor Goldstein, that was really
45:30just a really great in depth
45:32perspective on all of these devices,
45:35how they work algorithms,
45:36the data, everything. Thank you.
45:38So I'll open it up for questions.
45:41And while people are getting ready for that,
45:44I'll just start with one.
45:46I love your optimism vote kind of
45:48embracing the practical advantages
45:50of consumer sleep technology
45:52over traditional actigraphy,
45:53and obviously in the setting of research.
45:56I think you've shown great
45:58examples of how this can be used,
46:01especially for collecting large
46:02amounts of data that are just not
46:05possible with with actigraphy.
46:07What I'm struggling with.
46:08Is what the mechanism is that we
46:10as asleep community could use
46:12to actually encourage companies
46:14to be transparent about their
46:17algorithms to facilitate validation.
46:19And I am not sure how I could see
46:21that there ever will really be a great
46:25financial incentive for validation
46:27to hold companies accountable.
46:29I mean, by what means?
46:31Does Apple I mean have to to
46:34actually broadcast that these
46:36things are accurate to consumers?
46:38Pay attention to this,
46:40how can?
46:41How can we put pressure on them or what
46:43is their their ill and incentive to do
46:46this? Yeah, and I you know
46:47I I don't know the answer.
46:49There's obviously a lot of suggestibility,
46:51so once the patient starts wearing the
46:53device but we often see is that they take
46:56the information from that's reported
46:57obviously by the app more than they
46:59take their own perception of their sleep.
47:01So there becomes, you know,
47:03I would think that most patients would
47:05want these to be as accurate as possible,
47:07but there also interacting with the
47:09device in that way that they are
47:11taking into account what it says.
47:13So they might take that above their own.
47:16Own subjective impression of their sleep,
47:18and so it's hard to know.
47:20I don't know if it will be on the
47:22order of the customer because of that.
47:25That pushes the companies to more validation.
47:27I don't think that we can do.
47:30We can currently continue on with
47:32the validation framework as it is if
47:34we want to engage with corporations,
47:36it just doesn't.
47:37It doesn't work.
47:38It's too slow to wait till these
47:41are companies come out in a peer
47:43reviewed publication.
47:44I don't know if they'll have,
47:46you know, I know some of them.
47:48You know there might be occupational
47:50programs and insurance company programs
47:52and fitness for duty programs that
47:54might be more attractive than healthcare.
47:57You know,
47:57we already don't get reimbursed
47:59for FDA cleared Actigraphy,
48:01so I don't think that you know
48:03from the hospital from the Medical
48:05Center standpoint we're really
48:07going to move the needle on that.
48:10I would hope that they are
48:12interested in what we have to say,
48:14and they seem like they are.
48:17I mean, I've had.
48:18Positive interactions with industry,
48:20you know, and we don't need to have.
48:22You know when you're using
48:24machine learning in general,
48:25it's you're not going to know
48:27everything about the algorithm, right?
48:29But we do need some trends,
48:31so that can you know,
48:32maintain trade secret,
48:33but we do need some information
48:35about the population.
48:36And just like we were going to
48:38recommend with AI based sleep stage,
48:40and we need to understand what
48:42population it was trained on
48:44hasn't been independently tested.
48:45Things like that,
48:46or things that we would like to know.
48:49From the companies.
48:50Before we use these and you know,
48:53rely on these, but you know,
48:55I don't really know what's going
48:57to push the needle. That's that.
48:59I don't have any answer to it's
49:02a great question.
49:03Thanks.
49:05Anyone else who wants to ask a
49:07question to Doctor Goldstein?
49:10Hi, it's Brian. I have a question.
49:16So yeah, I agree with Lauren
49:19that's so great talk an I myself,
49:21I'm sort of wondering about using various
49:24different technologies in my own research,
49:26so one one comment and then one question just
49:29based on what you guys were just saying.
49:32I wonder if. I mean if they if the this
49:35you know ASM sleep research society,
49:38could we have any leverage with these
49:41companies to say you know the more
49:43transparent you are about your technology,
49:45it how it works,
49:47the more likely researchers already
49:49use it in their own research.
49:51I mean what I've thought about is,
49:53I've looked at these different technologies
49:56is am I going to have access to the raw data?
49:59I think that's really important
50:01from a reach research perspective,
50:03and so perhaps some companies you know.
50:06They want uptake of these devices.
50:08Might be receptive to that kind of argument.
50:11Just a thought.
50:12My question is Doctor Goldstein,
50:14have you whether you've looked at some
50:17of these consumer technologies that
50:19actually look at EEG as opposed to you,
50:23know actigraphy,
50:23and if you have any thoughts or
50:26comments about those technologies.
50:28So I haven't in detail, I'll say it.
50:31Probably many people miss in this room
50:34zoom room whatever we call it now.
50:37Have tried them.
50:38I mean,
50:39I haven't been impressed physically
50:40trying those myself and we don't have.
50:43There's very limited,
50:44limited data on those as well
50:47an I haven't seen much.
50:48I haven't personally as far as
50:50like the dry e.g head mansized and
50:53that's where you're talking about.
50:55I haven't seen them interact with
50:58academia as much as the heart rate and.
51:01Motion based wearable devices.
51:03You would think that they would be better,
51:07but I'm not sure.
51:08There's definitely a disconnect and
51:09they want to make it so patient
51:11can just put it on,
51:12but that it also records the EG
51:14and I just don't know if the dry
51:16e.g technology is there yet.
51:18You should try some of these
51:19out and I think you'll agree.
51:21I looked like I was.
51:22I was in some kind of like I was
51:25awake and I was like in Delta.
51:27I mean they're just they don't.
51:28I mean 'cause you when you think
51:30about the work that guys end
51:32up putting on e.g leads,
51:33it just doesn't translate.
51:35I mean you can have like.
51:36Single lady e.g.
51:37But these are embedded in the headband,
51:39so I don't know.
51:41I just don't think we're
51:42quite there yet with that.
51:49I'm just going to scan the chat for any
51:52question I had actually question then
51:54this is that you know where how you
51:56doing Kathy good to see you again.
51:58At Norwalk hospital.
51:59So yeah, we talk and it's a very,
52:02you know, sort of hot topic issue.
52:04You know when I think about this
52:06I I think about how in terms of
52:08population health is probably where
52:10it may have its biggest effect and
52:12I think of sleep deprivation, which
52:14is really an epidemic that
52:16we don't talk much about in
52:17Sleep Medicine as much as we probably should.
52:20And so when people track their sleep,
52:22you know one thing I find similar
52:24to like the step trackers you know
52:26they'll look at and they'll say,
52:28oh, I've done, you know?
52:305000 steps OK, Great
52:31and you don't do anything
52:33with the information,
52:34so I guess the question is can we
52:36can we use these sleep trackers?
52:38ANO Validation is important,
52:39but maybe sort of the more
52:41important issue is can the
52:43individual person use it to
52:44sort of improve their health,
52:46get more sleep and
52:47you know? And so is there.
52:49I think that maybe a sense of
52:52kind of focus in on a population
52:54in because a lot of people have
52:56these these trackers now so you
52:58aware of anyone who is using.
53:00Consumer technology in an algorithm
53:01to try to help people either get
53:03more sleep or you know similar
53:05to maybe diagnosis or like
53:06a screening for sleep apnea.
53:07I can't do what they
53:09have for the Apple Watch,
53:10which is this A-fib detection,
53:11which is quite interesting. Where
53:13that would sort of clue them in that
53:15they may have a sleep problem.
53:16So I guess the question is,
53:18are you aware of any studies
53:20that are looking at using the
53:21zoom technologies to increase
53:22total sleep time in a population?
53:24So this is it.
53:25This is a great question and a great point.
53:27And as of this time you know
53:29both for fitness and sleep.
53:31There there's not good evidence to
53:33suggest that tracking improves behavior.
53:35There is some work that's going on though,
53:38so Kelly Baron and colleagues
53:39at University of Utah I think.
53:41Or maybe she did this back in Chicago,
53:44but they used a consumer sleep technology.
53:47They developed an app and they did
53:49sleep extension in people with short
53:51sleep duration and hypertension,
53:53and they did improve sleep times
53:55and they did improve blood pressure
53:57control in those individuals.
53:59So an I think that's the key.
54:01In how we use these is there
54:03never gonna be exactly like e.g
54:06defined sleep like they will just.
54:08It's your measuring something
54:10totally different.
54:10Magni Eunice told me once he was like
54:12I don't understand he's like these
54:15sleep trackers there monitoring things
54:17that we don't stay asleep with that
54:19they're totally different, right?
54:21So but maybe it's not,
54:23you know,
54:23getting to this perfection level of accuracy.
54:26Maybe it's using it as a completely
54:28different construct of sleep and seeing
54:31how interventions applied to that.
54:32Can change your still both your
54:34sleep outcome in other health
54:36outcomes totally agree.
54:40Thank you, ******
54:46Hi Kathy, this is Andres
54:47and Chuck really nice talk.
54:49Hey, thank you hear hear your thoughts
54:51and agree with you and Ian about you
54:54know these are probably not going to
54:57be perfect matches to what we measure
54:59in the PSG and I wonder if you know
55:02we can use the metrics from these
55:04devices as biomarkers and correlate
55:06them or establish a relationship with
55:08meaningful outcomes and overtime.
55:09They might be actually more relevant
55:11than e.g signals that we get.
55:13I mean sleep is defined by e.g but we're
55:16only looking at superficial layer.
55:18Of the cells and you know that some of
55:20that sleep occurs supportively, right?
55:22And so.
55:23It's hard to say what we what
55:26is the truth here,
55:28but I was going to ask you question about the
55:32what do we do with all of this information.
55:35So for example,
55:36if we're tracking patients over days,
55:39weeks, months,
55:39how do you analyze that
55:41data in a meaningful way?
55:43And how do you relate it to an outcome?
55:46I think it's been a challenge 'cause so far.
55:49We've really mostly been used
55:51to metrics that are very.
55:52Sort of Abacus like where we count
55:55things overtime or we you know measure
55:58some intensity area under the curve
56:00and so the data is being collected.
56:03Now at these devices is vastly
56:05different and so even with just sleep
56:08duration or sleep period written ecity
56:11like have you seen any unique ways
56:13that that data has been analyzed?
56:16And so not from consumer wearables
56:18and not for extended amounts of time.
56:21And that's where we'll obviously need
56:23people with your level of expertise
56:25in AI to help distill the data,
56:28because there's going to be a
56:30lot of Co dependencies,
56:31there's going to be just a vast number of,
56:34and this is not even.
56:36The machine learning algorithms
56:37to determine sleep from wake
56:39for everyone who's listening.
56:40This is the once the post process
56:43data is just so much bigger
56:45and faster than anything else.
56:47So we will need more flexible
56:49models to deal with that.
56:50I think the closest that we
56:52get is the work that Meredith.
56:54I think there's no.
56:55I don't think pen is here that
56:58Meredith Wallace has done,
56:59but that was using actigraphy for
57:01about 5 days, but they were able.
57:03At least you know it's a start
57:05for looking at these different
57:07parameters of sleep,
57:08at least at the same time simultaneously,
57:11duration rhythmicity timing,
57:12but this is going to be even
57:14way more than that,
57:15so it'll be interesting to see
57:17how that data is dealt with.
57:19When we get to that point.
57:23Great, thank you so much again,
57:26Doctor Goldstein.
57:26I think will will close there.
57:29I just want to let everybody know
57:31that our talk next week we were
57:34going to have as we have on the 2nd
57:37Wednesday of each month adjoint Yale,
57:40Harvard Sleep Conference and we're
57:42having a kind of unique talk.
57:44James Nestor is an author of the book
57:47Breathe the new science of a Lost Art,
57:50and so he's going to be giving
57:53next week stock so please.
57:55Join us for that and again,
57:57thank you so much Doctor Goldstein
57:59and stay well everybody will see you
58:01next week. Sounds good. Thanks,