Yale Psychiatry Grand Rounds: "Transforming Mental Health Care via Science-Based Digital Therapeutics"

Transcript

• 00:00It's just a, it's just an honor
• 00:03to be here and to join all of you.
• 00:05And I have so many wonderful friends
• 00:07and colleagues at Yale and I see
• 00:09some familiar names here and very
• 00:11excited to be with you all today.
• 00:12So I'm going to go ahead and
• 00:15share my screen here.
• 00:19So as you heard,
• 00:20I'm going to be talking about digital
• 00:23health as applied to mental health.
• 00:25And I know several of you on this
• 00:28call are working in this space and
• 00:31doing really pioneering work and
• 00:33exciting work in this space.
• 00:35And really excited to have a
• 00:37continued dialogue at the end of
• 00:39this lecture today and and hear what
• 00:41questions and comments you have,
• 00:42but also to learn more about what
• 00:43you all are building in this space.
• 00:45And I'm going to talk today about
• 00:46just sort of where we are in terms
• 00:48of the state of the science of
• 00:50applying digital health to mental
• 00:52health and addiction and give you
• 00:54a snapshot of sort of the scope
• 00:56of the science and how we can
• 00:58implement these tools in real,
• 00:59real world settings to really
• 01:01transform models of care.
• 01:03And then I'll tell you a little bit
• 01:04more about our Research Center.
• 01:05We have an Ida funded center as
• 01:08Stephanie mentioned that's entirely
• 01:09focused on the application of
• 01:11digital health to to the realm of
• 01:13addiction and and mental health.
• 01:15And we have a lot of resources
• 01:16and and and opportunities for
• 01:17collaboration and and if anyone here
• 01:20is interested in exploring that more,
• 01:21we would be just delighted to do so.
• 01:25So in first I want to acknowledge
• 01:27some of the funding that I'll be
• 01:28referencing in our work today,
• 01:30including our center grant from the
• 01:32National Institute on Drug Abuse as
• 01:34well as several other NIH grants.
• 01:36I'll be showing some data from today.
• 01:40And then I also wanted to acknowledge
• 01:42that in addition to my academic
• 01:45affiliation at Dartmouth College,
• 01:46I also have an affiliation with a
• 01:48few small businesses that are working
• 01:50in the digital health space as well
• 01:52as Burying or Ingelheim that's
• 01:53working in the digital health space.
• 01:55And I'll tell you a bit more
• 01:56about our center at the end.
• 01:57But you know,
• 01:58although we are a Research Center
• 01:59and really focused on bringing
• 02:01science to the space of digital health,
• 02:03we are really building out strategic
• 02:06partnerships to scale the best,
• 02:08most potent and engaging digital
• 02:10health tools and doing that in
• 02:12a strategic way with partners.
• 02:13And I'll tell you a bit more about that.
• 02:17So just starting with terminology,
• 02:20so digital health is a term that you
• 02:22probably are hearing more and more
• 02:24of it's it's increasingly a a a key
• 02:26part of many models of healthcare.
• 02:27And you know I'm convinced that undoubtedly
• 02:29it's a a a very key part of the future
• 02:32of how we're going to see healthcare
• 02:34delivery particularly in mental health.
• 02:36So I think it's really critical that
• 02:38we sort of embrace and understand it.
• 02:41It's it's going to be a key part of the
• 02:42work that we also do together as well
• 02:44as the next generation of clinicians
• 02:45and and and healthcare providers.
• 02:48So digital health is a is a term
• 02:50that often refers to using digital
• 02:52technology not unlike a a smartphone
• 02:54that's within arm's reach of most of us,
• 02:57most of the most moments of the
• 02:59day to do a few things.
• 03:00One is to get new insights into people's
• 03:03health behavior in their daily life.
• 03:05And then also you can use these types of
• 03:08digital platforms to provide therapeutic
• 03:11tools to people anytime and anywhere.
• 03:14And this is a term called
• 03:15digital therapeutics.
• 03:16And I'm going to spend some time talking
• 03:18about what that means and what these
• 03:19are and what kinds of clinical effects
• 03:21we can see with these types of tools.
• 03:23So it's really around using
• 03:25digital technology for assessment
• 03:27and for therapeutic delivery,
• 03:28which can be quite personalized
• 03:32and and also widely accessible.
• 03:35So starting with this term,
• 03:36digital therapeutics,
• 03:37this is a term that refers to software
• 03:43that is effective in preventing,
• 03:45treating or managing a health condition.
• 03:47So it's a clinical grade
• 03:50intervention delivered via software.
• 03:52So it's it's sort of going above
• 03:54and beyond what we think of as
• 03:55health promotion or Wellness apps.
• 03:57And you're taking the active therapeutic
• 03:59ingredients of a clinical intervention
• 04:01and delivering it through the
• 04:04functionality and the content of software.
• 04:06So I'll give you any examples in the
• 04:09talk today, but this could include,
• 04:10for example,
• 04:11cognitive behavioral types of
• 04:13interventions that are entirely
• 04:15delivered through software.
• 04:16So this is not telehealth,
• 04:17but it surely can complement and
• 04:19extend what we do with telehealth.
• 04:20But as we know with telehealth,
• 04:21we have synchronous communication
• 04:23with the clinician and this is
• 04:25something that's accessible anytime,
• 04:27anywhere 24/7,
• 04:28kind of like a clinician in your pocket,
• 04:31but you know,
• 04:33but that is available on demand and
• 04:36perhaps at at times of greatest need.
• 04:39And so you're packaging this model of care
• 04:41into the seamless digital delivery platform.
• 04:44And there are a number of benefits
• 04:46of this approach and we've surely
• 04:48seen this play out in the data.
• 04:50So first of all,
• 04:51these types of tools can extend the
• 04:53reach and the impact of clinicians,
• 04:55right.
• 04:56These can be additional tools in the
• 04:58toolbox of clinicians that can sort of
• 05:01supercharge our clinician workforce
• 05:02and extend their reach and provide
• 05:04resources to people even when they're
• 05:06not working with their clinicians.
• 05:08So it can reinforce and
• 05:10extend the work that you
• 05:12might be doing with the clinician
• 05:14and and and I'll show you data,
• 05:16but we've definitely seen now across many,
• 05:18many health domains,
• 05:19particularly mental health.
• 05:20Much of this work to date has been
• 05:22in the realm of mental health,
• 05:24although there are many growing
• 05:27applications in a wide array of
• 05:29aspects of preventative health
• 05:30to chronic disease management.
• 05:33But we have seen very robust and
• 05:35replicable effects on all kinds of
• 05:38health behavior and health outcomes.
• 05:40We can be assured that these
• 05:43tools can deliver interventions
• 05:44with fidelity to best practices.
• 05:47So this can be treatment interventions,
• 05:48this can be prevention interventions
• 05:50and we can really ensure that we
• 05:53are delivering this with reflecting
• 05:54sort of state of the science
• 05:56models of intervention delivery.
• 06:00And surely we know that not everybody in
• 06:03the world yet has access to digital devices.
• 06:06All the all the data, all the trends
• 06:08show that the majority of the world's
• 06:11population either has access now to
• 06:13mobile devices and or is expected to
• 06:16get access to these mobile devices.
• 06:18And we we work with all kinds of populations
• 06:20in this country including some you know,
• 06:22traditionally underserved
• 06:23populations in this country.
• 06:25But we also do a lot of work
• 06:26in other parts of the world,
• 06:27low and middle income countries and you know,
• 06:30we might work with communities that
• 06:32don't have clean water or you know,
• 06:33a landline infrastructure but
• 06:35often have a mobile device.
• 06:37And so it's an it's an amazing opportunity
• 06:39to harness the widespread availability
• 06:42and growing availability of these types
• 06:44of tools to give people resources,
• 06:47healthcare resources in new ways
• 06:49through these types of platforms.
• 06:52It's scalable.
• 06:52I think that's one of the most
• 06:54exciting things is that you can
• 06:56have really widespread reach and
• 06:57impact with these types of tools.
• 06:59And as you all know very well,
• 07:01during the the COVID crisis,
• 07:03we definitely saw a big surge in
• 07:05demand for remote models of care and
• 07:08that definitely included telehealth.
• 07:09But we also saw,
• 07:10and I'll tell you a bit more
• 07:12about this later,
• 07:13a big growth in demand for
• 07:15these types of digital tools,
• 07:17digital interventions.
• 07:20And as I mentioned,
• 07:21we see we can get a big impact on on
• 07:24lots of different health outcomes,
• 07:25including health costs.
• 07:27And there's some striking data
• 07:28including some recently released data
• 07:30that really showed not only can this
• 07:32impact people's lives and their functioning,
• 07:35but can have huge implications
• 07:38to healthcare expenditures.
• 07:40So these are some of the the benefits of it.
• 07:42And this slide is just a snapshot
• 07:44of sort of the state of research
• 07:47in the application of digital
• 07:49therapeutics to behavioral health.
• 07:50And we've been,
• 07:51I've been doing this work for decades now.
• 07:54And but there's, you know,
• 07:55really decades of of really
• 07:57robust literature focused on,
• 07:59you know,
• 08:00how do you best develop and and
• 08:02test and implement and sustain these
• 08:04types of tools to really have value.
• 08:06And this slide sort of gives you the
• 08:08big picture of that body of of research.
• 08:10And So what we generally see in the
• 08:13literature is that if you develop
• 08:14these tools, well development's huge,
• 08:17it's it's really huge.
• 08:19We could spend a lot of this time just
• 08:21talking about development in terms
• 08:22of how do you really develop a tool that is,
• 08:24you know,
• 08:25reflective of of the needs and and
• 08:27cultures and values and and and brings
• 08:29clinical utility to your target audience.
• 08:32But if you embrace sort of best
• 08:34practices and really have something
• 08:35of value to to your target audience,
• 08:38we see that these tools can be
• 08:40highly useful and acceptable to
• 08:42lots of different populations.
• 08:44We see we can have a very large
• 08:46impact on a wide array of health
• 08:49behaviors and health outcomes.
• 08:50We also have seen now in many studies
• 08:53that digital interventions can produce
• 08:56outcomes that are as good as or better
• 08:59than clinician delivered interventions.
• 09:01And you know some people you know
• 09:02sort of bristle at that and and
• 09:04worry that we're trying to replace
• 09:05clinicians with these types of tools.
• 09:07But as you all know very well in the
• 09:09work that that you do you know it
• 09:11we really have a capacity challenge
• 09:13in many pockets of the world in
• 09:15terms of you know really having
• 09:17sufficient mental health workforce
• 09:19to meet our population level needs
• 09:21or or addiction treatment workforce
• 09:22to meet our population level needs.
• 09:24And so it's it's I,
• 09:26I think of great value to know that
• 09:28the data support that these types
• 09:30of digital tools can really produce
• 09:32meaningful clinical effects that can
• 09:35extend the workforce that we have
• 09:37and can help increase capacity and
• 09:40reach for prevention and treatment
• 09:43of various health conditions.
• 09:45We've also seen you can increase reach,
• 09:47you can increase personalization of care.
• 09:49I'll talk a bit more about some
• 09:50of the economic data,
• 09:51but now there's a growing body of
• 09:54literature showing economic benefits and
• 09:56these don't always have to work the same way.
• 09:58It doesn't have to be 8 sessions of 1
• 10:00intervention or 12 sessions of another.
• 10:02You can really embrace what technology
• 10:04can do and have very adaptive types of
• 10:08interventions that can change over time,
• 10:11that can be very personalized to
• 10:13whatever an individual's needs
• 10:14and preferences are in the moment.
• 10:15But that can again be adoptive in
• 10:17an ongoing way as people's clinical
• 10:19trajectories and needs change over time.
• 10:22And there's a lot of exciting
• 10:23research in that space,
• 10:24including in our center and I'll
• 10:26I'll speak a bit about that.
• 10:27But I think that's,
• 10:29I think that we have a lot of
• 10:31promise for precision interventions,
• 10:33precision delivery of mental health
• 10:36interventions delivered anytime and
• 10:39anywhere through capture of digital
• 10:41data at the individual level and
• 10:43then very responsive interventions
• 10:45provided on digital platforms.
• 10:49I'm going to talk about, first of all,
• 10:52I'm going to talk about a
• 10:53digital intervention that we did.
• 10:55We developed the first iteration of
• 10:56a long time ago, actually the first,
• 10:58first iteration of it was in
• 10:59the late 90s and it was not,
• 11:01it was web-based at the time and not
• 11:03a mobile intervention at the time and
• 11:05has evolved over that period of time.
• 11:08But I talk about this as an
• 11:10exemplar of a digital therapeutic,
• 11:12just to highlight what one
• 11:13of these can look like,
• 11:14but also what kinds of clinical
• 11:16effects you can see when you
• 11:18use these tools different ways.
• 11:20So I'm going to give you examples
• 11:22of different ways you could
• 11:23apply this type of approach,
• 11:25But I know there are many others.
• 11:26In fact, Yale, you know,
• 11:27has developed fantastic tools in the space,
• 11:30including CBT for CBT,
• 11:31and there are many others for
• 11:33substance use and for mental health.
• 11:35So this is just an example.
• 11:37And briefly,
• 11:38this is a pretty intensive behavioral
• 11:42treatment for substance use disorders
• 11:45that's entirely delivered in this
• 11:48interactive self-directed way.
• 11:49Again started web-based and then
• 11:52morphed into a a mobile tool.
• 11:54It just briefly I'm not going to
• 11:57talk about the details of this.
• 11:59I'm happy to chat more about this
• 12:00if folks have specific questions.
• 12:02But this takes one of our very
• 12:04potent behavioral treatments for
• 12:06substance use disorders called the
• 12:08community reinforcement approach
• 12:09to substance use disorder treatment
• 12:12and takes the active ingredients
• 12:13of that therapeutic approach and
• 12:15delivers it on a digital platform.
• 12:17So it's very interactive at
• 12:20the individual level.
• 12:21It focuses on helping people understand
• 12:24their specific sort of pattern of
• 12:27of behaviors of cognitions that
• 12:29maintain self defeating patterns or
• 12:30drug taking behavior and how you can
• 12:33understand and disrupt those patterns.
• 12:35And and and develop a new skill set
• 12:36and a new behavioral repertoire that
• 12:38can help you initiate and maintain a
• 12:40recovery process and how to leverage
• 12:43different resources in that process.
• 12:46And there is an optional piece
• 12:48to this intervention that is
• 12:50motivational incentives piece,
• 12:51which is often called contingency
• 12:54management where you give people incentives,
• 12:57prizes, rewards,
• 12:58contingent on different milestones,
• 13:00different successes in a recovery
• 13:01process in this case.
• 13:02And that that's all automated
• 13:04in this tool and is an optional
• 13:06component of this broader platform.
• 13:09So again, happy to talk about this more,
• 13:10but just just wanted to give you
• 13:12a snapshot of what this is it.
• 13:14It,
• 13:14it's not just about sort of
• 13:15enhancing motivation to change.
• 13:16It really is intended to be an
• 13:19intensive behavioral treatment
• 13:20that that really helps people
• 13:22build the skills and capacity for,
• 13:25for change,
• 13:26particularly around substance use.
• 13:28So what I'm going to do now is just
• 13:30give you a little bit of data and
• 13:32some examples of the kinds of effects
• 13:34you can see with these types of tools.
• 13:37So one of the really early studies
• 13:40that we did was I'll just briefly
• 13:42describe the study and I'll mention
• 13:43that we have lots and lots
• 13:45of papers on all of this.
• 13:46So if anyone has any interest,
• 13:48please reach out.
• 13:49I have my e-mail address on the last slide,
• 13:52Please reach out and I'm happy
• 13:53to share any and all additional
• 13:55information that might be useful.
• 13:57But in the interest of time I'm
• 13:59just going to sort of give you a
• 14:01flavor of of some of this work.
• 14:02So, so this is this particular trial
• 14:05Nida funded study was a three arm
• 14:07randomized clinical trial and these
• 14:09were all adults entering outpatient
• 14:11treatment for opioid use disorder.
• 14:14And every every single participant
• 14:16in the study received medication.
• 14:19We all know that medication is a
• 14:21critical component of effective
• 14:22treatments for opioid use disorder.
• 14:24This is a sample here that all received
• 14:28buprenorphine medication as part of care.
• 14:31But the randomization occurred on the
• 14:33type of behavioral treatment that people
• 14:35received on top of the pharmacotherapy.
• 14:37So if you went into the condition that
• 14:40is reflected in the blue column here,
• 14:43you were randomly assigned to a therapist
• 14:45and you met with this therapist three
• 14:47times a week in individual sessions.
• 14:49And this therapist delivered to
• 14:51you the community reinforcement
• 14:53approach to behavior therapy for
• 14:55substance use disorder treatment.
• 14:57And there were all kinds of fidelity
• 14:59checks in place to make sure it
• 15:00was being done in accordance with
• 15:02sort of state of the science
• 15:03approach to this therapeutic model.
• 15:04And so it was a pretty intensive one-on-one
• 15:08therapeutic approach with a clinician.
• 15:10If you went into what's
• 15:12reflected in the red column here,
• 15:14you had a therapist and you saw them
• 15:16every other week just to check in.
• 15:18But your, your therapy,
• 15:19therapy was offloaded to a
• 15:22digital delivery platform.
• 15:23So this is a group that in this
• 15:26particular study actually went to a
• 15:28computer lab on site at the treatment
• 15:30facility and interacted 3 * a week
• 15:33with a digital version of this
• 15:36community reinforcement approach,
• 15:38behavioural therapy approach.
• 15:39And then if you went into what's
• 15:42reflected here in the Gray column,
• 15:43you received what was considered
• 15:46treatment as usual standard treatment
• 15:48for opioid use disorders in the
• 15:50US at the time and it wasn't the
• 15:53community reinforcement approach.
• 15:54So basically this slide shows that
• 15:56even when you offload the bulk of this
• 15:59therapeutic approach to a digital platform,
• 16:01you can get comparable clinical
• 16:03outcomes to what you observe from
• 16:06exclusively clinician delivered care.
• 16:08And then both versions of this
• 16:10you know very effective and potent
• 16:13behavioral therapy produce better
• 16:15outcomes than our standard treatments.
• 16:17And this particular slide is on a
• 16:20objectively captured data through
• 16:22urine toxicology testing looking
• 16:24at abstinence from opioids and
• 16:26cocaine in the sample.
• 16:27So.
• 16:27So that was one example of a way that
• 16:30you could apply this and and and
• 16:32you can see the kinds of benefits
• 16:34you get clinically from that.
• 16:36But now I'm going to show you
• 16:37different examples.
• 16:37So this was a study that we did
• 16:40in New York City and
• 16:42these were all adults again with
• 16:44opioid use disorder that were
• 16:46entering outpatient treatment.
• 16:48This was done in methadone treatment
• 16:51systems and when people came in
• 16:53to treatment they either received
• 16:56treatment as usual in methadone
• 16:58treatment setting which as you
• 17:00likely know includes daily methadone
• 17:03medication and some therapy support
• 17:06from counselors in the system.
• 17:08So they went either into that
• 17:10condition or they were randomized to
• 17:12this condition which was basically
• 17:15a condition where they they they
• 17:18they had daily methadone access,
• 17:20they had a clinician.
• 17:21But the only difference between
• 17:23the two conditions is that in
• 17:24the one reflected here in blue,
• 17:26those participants had a therapist
• 17:30but their clinician patient time
• 17:32was cut in half and the other half
• 17:35of that time was spent interacting
• 17:38with the digital therapy.
• 17:40So let's say instead of doing a 60
• 17:42minute session with their counselor,
• 17:43they did a 30 minute session with
• 17:45their counselor and then they
• 17:46interacted for the rest of 30 minutes
• 17:48in this interactive one-on-one
• 17:49way with this digital treatment,
• 17:51this community reinforcement
• 17:53approach treatment.
• 17:54And what we find is that when
• 17:56you cut in half patient clinician
• 17:58contact time and replace it with
• 18:00this digital intervention,
• 18:01we had significantly greater
• 18:04documented abstinence from opioids
• 18:06in that sample versus the sample who
• 18:10received standard methadone treatment.
• 18:13We had the opportunity in this study,
• 18:14this was Nida funded.
• 18:16We had the opportunity to track these
• 18:19outcomes for 12 months per participant
• 18:22and the differential here you see
• 18:24persisted for that 12 month window.
• 18:27We saw this benefit from including
• 18:29the digital treatment as part of the
• 18:32care model and there's lots to say
• 18:33about why we think this is the case.
• 18:35But you know this is an intervention
• 18:37that you know is being delivered
• 18:39with fidelity every time and
• 18:40it's very responsive to what
• 18:42people are understanding or not,
• 18:44what their needs are or not.
• 18:45So it's very personalized in the way
• 18:47that it delivers the interventions
• 18:49to a a particular individual.
• 18:51And and we know there's a
• 18:53lot of variability right, in,
• 18:54in terms of what happens in counselling
• 18:56in different in therapeutic settings.
• 18:58And so this is very encouraging that this
• 19:01can really have this kind of robust effect.
• 19:05And then one more snapshot,
• 19:07There's lots more data to
• 19:08share beyond these studies,
• 19:10but one more snapshot just to
• 19:11show you a different way that a,
• 19:13a clinical setting could embrace
• 19:15a digital therapeutic.
• 19:16And that is to say, OK,
• 19:18we're not going to touch the
• 19:19underlying model of care.
• 19:19We're just going to add this
• 19:21on as a supplement.
• 19:21We're going to say over and above our
• 19:23treatment as usual our care model.
• 19:26What if we offered a digital intervention
• 19:28as an adjunct to care and that's
• 19:31what's reflected in this study.
• 19:33So again,
• 19:34this is another study with
• 19:36adults with opioid use disorder
• 19:38entering outpatient treatment.
• 19:40This is another study in a
• 19:42methadone treatment setting.
• 19:43And the participants either again
• 19:45receive standard methadone treatment or
• 19:48as an adjunct to that they were
• 19:50given when they joined the study,
• 19:52access to a mobile version
• 19:54of this digital intervention.
• 19:56And when you gave folks this
• 19:58mobile version on top of the
• 20:00underlying treatment model,
• 20:01we were able to keep those
• 20:03people in treatment much longer,
• 20:05much higher percent of those people
• 20:07retained in treatment compared to
• 20:09those who received standard treatment.
• 20:10So right now we're looking at
• 20:12what happens in the first three
• 20:14months of your treatment episode.
• 20:16And we found much higher percent of
• 20:18patients who got the app as part of
• 20:21care were retained in that window
• 20:22of time versus those who didn't.
• 20:24And you know, this is really important.
• 20:26We know that treatment retentions are really.
• 20:28Important predictor of all kinds
• 20:30of other clinical outcomes in
• 20:31substance use treatment.
• 20:33And if we can bump up and and you know,
• 20:35increase our retention,
• 20:36particularly in these early windows,
• 20:39you know of when dropout can be quite high,
• 20:42you know this can be a
• 20:43really meaningful effect.
• 20:44And the same pattern I'm showing
• 20:45you here showed up in the urine
• 20:48toxicology data where if you offered
• 20:50this mobile tool as part of treatment,
• 20:52you had more documented abstinence
• 20:55from opioids than if you didn't.
• 20:58And again,
• 20:59I'm just giving you examples of data there.
• 21:01There's lots of really compelling
• 21:04literature on the on the utility,
• 21:06clinical utility of these types of
• 21:08tools for substance use disorder
• 21:10but also for other types of mental
• 21:12health conditions.
• 21:13And I I just wanted to mention this is
• 21:16something that if you are a clinician,
• 21:18if you don't know about this,
• 21:19you you should because it's undoubtedly
• 21:21a big part of of what is going to be
• 21:24a part of our future of healthcare.
• 21:26And surely you know our our
• 21:27residents and then the trainees,
• 21:29medical students should should
• 21:30surely be aware of this.
• 21:31So it's the case now in this country
• 21:34that software can be prescribed by
• 21:37doctors and there's a there's a
• 21:39category of medical devices that
• 21:42the US Food and Drug Administration
• 21:44calls software as a medical device
• 21:47where you can go to the FDA with
• 21:49data from a clinical trial seeking
• 21:52a label saying this software is
• 21:54effective in the prevention,
• 21:56treatment or management of some
• 21:58disease or disorder.
• 21:58And you have to meet a lot of
• 22:00different requirements.
• 22:01But if you if you meet the
• 22:03requirements and and you get sort
• 22:05of authorization for that,
• 22:07for that claim,
• 22:08you become what's called a prescription
• 22:11digital therapeutic and that software
• 22:13is an eligible to be prescribed
• 22:16by clinicians in this country.
• 22:18And the first time that happened
• 22:20was in 2017 and that happened to be
• 22:24a this computerized intervention
• 22:26I just described to you for this
• 22:29community reinforcement approach
• 22:31to behaviour therapy for substance
• 22:33use disorders.
• 22:33And then the second one which was
• 22:36FDA cleared in 2018 was a specific
• 22:39indication of that intervention for
• 22:41treatment of opioid use disorder.
• 22:43Now we have a growing array of
• 22:46digital therapeutics that
• 22:47are available for prescription.
• 22:49Most of them are in the mental health space,
• 22:51not all and one includes a
• 22:55pediatric indication for ADHD.
• 22:57So again FDA is not going to look at your,
• 23:00your your software if it's if it's
• 23:03a general Wellness app or a health
• 23:05general health promotion type of tool.
• 23:08Those are excluded from regulatory oversight.
• 23:09But it really has to be you know,
• 23:11software driven,
• 23:12evidence based and seeking to make a
• 23:14claim of this is a potent intervention.
• 23:16Some people call these digital pills.
• 23:19So you know, there's been a lot of
• 23:22excitement around this evolution in in
• 23:25the regulatory space and there are other,
• 23:27I'll speak later,
• 23:28there are multiple other paths
• 23:29to deployment as well.
• 23:30But what we have seen, you know,
• 23:32having worked in this space for a
• 23:33long time and having clinicians come
• 23:35to us and say I'm excited about this,
• 23:36I'm excited about the data,
• 23:37my patients are interested in this,
• 23:39What should I offer?
• 23:40If I go to the App Store,
• 23:42there are huge numbers of mental
• 23:43health apps out there.
• 23:44How do I navigate that?
• 23:45How do I know what's effective,
• 23:46what's not,
• 23:47what could be harmful.
• 23:48And so there are there are multiple
• 23:50ways to do that.
• 23:51But one benefit of this particular
• 23:53pathway is that you know if something
• 23:56has gotten this designation by
• 23:58FDA that it's really you know,
• 24:00been carefully vetted and really you know,
• 24:03had to meet all kinds of criteria
• 24:05to say indeed this is you know,
• 24:07safe and effective in preventing,
• 24:08treating or managing a health condition.
• 24:10So this is a growing, growing area,
• 24:12rapidly growing area.
• 24:14But I just want to tell you that you
• 24:16know I I've highlighted some examples
• 24:18of digital therapeutics in the realm
• 24:19of substance use disorder treatment.
• 24:21But we have all kinds of examples
• 24:23now of robust clinical effects
• 24:24for lots of health conditions,
• 24:26heavily mental health.
• 24:27So we've seen as you see here on the
• 24:30side decreases in mental symptoms
• 24:32in things like ADHD, anxiety,
• 24:35depression, PTSDOCD, schizophrenia.
• 24:37We've seen you can improve remission
• 24:40rates in in some types of mental health.
• 24:43We've done a number of studies with
• 24:46chronic pain patients and you know,
• 24:49giving people a digital tool to
• 24:51help them better manage chronic
• 24:53pain and to help prevent chronic
• 24:55pain from ruining their lives,
• 24:56ruining their relationships,
• 24:57preventing them from achieving goals
• 25:00and and giving them resources and to
• 25:02help them help them in these areas.
• 25:04And what we find is that you know we
• 25:06can not only improve pain management
• 25:08and sort of goal directed activity
• 25:10among chronic pain patients.
• 25:12So we've also seen that we can
• 25:13reduce Ed visits, right.
• 25:14So when you have something in your
• 25:15pocket that in the moment can help you,
• 25:17when maybe you're catastrophizing about pain,
• 25:20perhaps that tool can help you
• 25:21instead of you know needing to go to
• 25:24the Ed as as the response to that.
• 25:25And so you know surely there
• 25:28this could reduce cost
• 25:30of of healthcare utilization
• 25:32including Ed visits.
• 25:34And we've also seen some really
• 25:36compelling data in reducing
• 25:38healthcare costs for panic disorder,
• 25:39substance use disorder
• 25:40and opioid use disorder.
• 25:41For example,
• 25:43Mass Health Massachusetts Medicaid
• 25:47just recently published data with
• 25:51these tools that I just described
• 25:53for treating digital therapeutics
• 25:54for treating substance use
• 25:56disorder that they used with their
• 25:58first cohort of patients in the
• 26:00state who got access to those.
• 26:02And they showed that they dropped Ed
• 26:04utilization by 45% with when these
• 26:07tools were part of care models and
• 26:09they dropped hospitalizations by 64%.
• 26:11Very compelling data.
• 26:12I was very excited to see it.
• 26:15You know this is in the real world
• 26:17where things are is in the wild what,
• 26:18what kinds of clinical effects you
• 26:20can see but also economic effects.
• 26:22And we've seen also another
• 26:24application of these types of
• 26:25tools is in promoting medication
• 26:27adherence and also adherence to
• 26:29various types of medical regimen
• 26:31and then also functional outcomes,
• 26:34you know,
• 26:35really helping people have meaningful lives.
• 26:37So this is just a snapshot of
• 26:40what's evolving in this space.
• 26:41There's a lot of exciting work in
• 26:43the prevention space and really
• 26:45you know really robust effects in
• 26:47in building up protective factors
• 26:48and reducing risk factors for
• 26:50lots of health conditions,
• 26:52mental health,
• 26:53substance use as well as chronic
• 26:56disease management.
• 26:58So I think it's a really interesting
• 27:00time of opportunity.
• 27:01I've been doing this for a long
• 27:03time and you know I have seen a
• 27:05whole confluence of factors recently
• 27:07that I'm pretty excited about that
• 27:09I think positions us as a field to
• 27:12really envision and help shape I
• 27:15think a very promising future for
• 27:17for digital health and digital therapeutics.
• 27:20So we surely know we've seen growing
• 27:22demand for remote models of of
• 27:24care and and intervention delivery.
• 27:26We also unfortunately no across
• 27:29the globe we've seen a big surge in
• 27:33behavioral health needs in this country,
• 27:35in many other parts of the world,
• 27:37in youth, in adult populations.
• 27:40You know the statistics are really,
• 27:42really alarming and and and we
• 27:44also sadly know that a lot of
• 27:47people either won't access mental
• 27:49health care or can't.
• 27:51And that's not just true in you know,
• 27:53low and middle income countries.
• 27:54That's true in rural America.
• 27:55That's true in in many communities
• 27:57as you all likely well know.
• 27:59And so there's this big population
• 28:02level need and and and you know how can
• 28:05we scale up capacity to to achieve that.
• 28:07And we at the same time have been
• 28:09seeing a pretty striking growth
• 28:10in the digital health industry.
• 28:12It it calmed down a bit in the
• 28:13last year and a half or so,
• 28:15but it is been growing at at at
• 28:19great just exponentially really in
• 28:21terms of start-ups in the space,
• 28:24in terms of venture investment in the space.
• 28:26But also as you may know there are
• 28:29a number of global pharmaceutical
• 28:31companies that are heavily investing
• 28:33in digital therapeutics and building
• 28:36out digital
• 28:37health formulas, Digital therapeutics in
• 28:39their portfolio that sort of complement
• 28:41extend what they traditionally do in
• 28:43the medication space and sometimes
• 28:45that these are these are digital
• 28:47therapeutics that are intended to
• 28:49have synergistic effects with some of
• 28:50their medications and or promote you
• 28:52know more adherence to medications.
• 28:54But sometimes these are stand alone
• 28:56tools for a health condition that can be
• 28:59agnostic to you know if they're with,
• 29:01if they're used with or without
• 29:03various medications.
• 29:04So there's a lot of interesting growth
• 29:06in the pharma space that you may be
• 29:08aware of and glad to talk more about that.
• 29:11And then additionally,
• 29:11there's A at the same time all
• 29:13of this is happening,
• 29:14we're seeing more paths to deployment, right.
• 29:16So I talked about one which is
• 29:18this FDA regulatory pathway.
• 29:19But we also know that in this country,
• 29:23you know there are employers
• 29:24that are building out their own
• 29:27offering to their employees.
• 29:28There are pharmacy benefit
• 29:30managers that are doing that.
• 29:32There are groups like CVS,
• 29:34Caremark and and others.
• 29:35So there's a growing array and there's
• 29:38even over the counter offerings now as
• 29:41well as this prescription model I mentioned.
• 29:45And then the final piece to
• 29:46this of course is payment.
• 29:48How does this get paid for it?
• 29:49This is a huge issue and this
• 29:51has been evolving as well.
• 29:53I think that many people overestimated
• 29:55the pace at wish reimbursement
• 29:57would kick in for these.
• 29:59So even if you get approved by FDA
• 30:02and your prescribable software,
• 30:04it may not get paid for.
• 30:05So there are definitely some
• 30:07private payers paying for this.
• 30:09They're definitely some employers
• 30:11paying for this.
• 30:14There are a number of states that have
• 30:16Medicaid coverage like I mentioned
• 30:18Massachusetts as an example for this,
• 30:20but but it's it's just it's a minority
• 30:23of states that have Medicaid coverage
• 30:25and there's no national coverage
• 30:27framework by Medicare at this point.
• 30:30There is,
• 30:30though,
• 30:31a lot of hope that a pending legislation
• 30:35right now called the Access to
• 30:38Prescription to Digital Therapeutics Act
• 30:42that it may get past timelines unknown.
• 30:45There was hope it was going
• 30:46to happen last year,
• 30:46but I know there's a lot of
• 30:48advocacy efforts for this year.
• 30:49And if that gets passed,
• 30:51then we would have national
• 30:52Medicare coverage of digital
• 30:54therapeutics in this country.
• 30:55And and some colleagues feel like
• 30:58that would then expedite the pace at
• 31:01which Medicaid would kick in as well.
• 31:03So there's a lot happening in this country.
• 31:06There's a lot happening in other countries.
• 31:07There's entire countries that have
• 31:09national coverage frameworks,
• 31:10like Germany and the UK.
• 31:12Asia has been exploding in this space,
• 31:14many countries in Asia.
• 31:16So it's a really interesting time and
• 31:19there's a lot still to sort of work
• 31:20out in terms of like what's out there,
• 31:21because there's there's unfortunately
• 31:23a lot of hype,
• 31:24but there's also a lot of
• 31:25really potent tools.
• 31:26And I'm very optimistic
• 31:27that we're going to get to
• 31:28a landscape where we really have a much
• 31:30more widespread access to these tools
• 31:32that people can use in their daily lives.
• 31:35So what I thought I would highlight
• 31:37at this point is a a project that we
• 31:40were able to do that shows you you
• 31:42know so going beyond the clinical
• 31:44trials that I highlighted to you,
• 31:45you know those are promising effects.
• 31:46But what if you really wanted to scale
• 31:49up the use of digital tools and really
• 31:52think about new healthcare delivery
• 31:54models that centrally leverage digital
• 31:56health tools as part of the care
• 31:59model and what would the impact be.
• 32:01So I I'm going to tell you about an
• 32:02example of the study we've done in
• 32:04this space and this was a project
• 32:06funded by the US National Institute
• 32:07of Mental Health and it was a project
• 32:11we did in Latin America and we
• 32:14started in the country of Columbia.
• 32:17Columbia, as you may know,
• 32:18is a part of the world that has a very
• 32:21high mental health burden and very limited
• 32:25workforce capacity to tackle that burden.
• 32:28So you know,
• 32:29Columbia had generations of armed conflict
• 32:32and and if you know all the data show that,
• 32:36you know every community has been touched
• 32:39by this very high rates of depression,
• 32:41very high rates of alcohol use,
• 32:43soft and accompanied by domestic violence
• 32:45and so very high mental health need.
• 32:47But if you look at the
• 32:48mental health workforce,
• 32:49you have, you know,
• 32:50a handful of psychiatrists in Bogota
• 32:52or in some of the more urban settings.
• 32:54But if you go into rural farming communities,
• 32:56you have we you have no,
• 32:59no access to sort of outpatient psychiatry.
• 33:01There's really only inpatient psychiatric
• 33:03hospitals sprinkled throughout the country.
• 33:06So this is just an example of a part
• 33:07of the world and there are many
• 33:09other examples we could think of
• 33:11where you know you have this problem,
• 33:13right,
• 33:13you have this big need and you have
• 33:15limited capacity to meet the needs.
• 33:16So what what do you do?
• 33:18So what this project did is to leverage
• 33:23an integrated suite of digital health
• 33:26tools to seek to scale across the
• 33:29country screening and treatment for
• 33:32mental health conditions in a way
• 33:35that could help meet this need.
• 33:37So it was basically a digitally
• 33:39enhanced model of care that we did
• 33:41in partnership with Primary Care
• 33:43Systems across Columbia.
• 33:45And so Primary care in the country
• 33:47of Columbia had never talked about
• 33:49mental health.
• 33:49It wasn't part of any routine
• 33:51screening or included in care at
• 33:54all before this project.
• 33:55We know that's not true in
• 33:56other parts of the world,
• 33:57but it happened to be true there.
• 34:00But this,
• 34:01the benefit is that primary care was much
• 34:03more accessible in many parts of the country,
• 34:07unlike psychiatric care.
• 34:08So there was surely some training of
• 34:10primary care providers in understanding,
• 34:12you know what,
• 34:13what is mental health?
• 34:14Why is mental health important
• 34:16in the whole as you
• 34:18think about whole care models
• 34:20for the patients you serve.
• 34:22So it's definitely some clinician
• 34:24training and and support to primary
• 34:26care providers around embracing this.
• 34:28But then basically we we integrated
• 34:30into primary care across the country
• 34:33and integrated suite of tools that
• 34:35first included taking clinically
• 34:38validated digital screeners for mental
• 34:41health and alcohol use was also part
• 34:44of this and deliver that entirely in
• 34:46a digital assessment way that then
• 34:48directly fed into a digital clinical
• 34:50decision support tool that providers
• 34:52could use when interacting with the
• 34:54patient in front of them and to help
• 34:57with a diagnosis and then care models.
• 35:00And then also every single patient
• 35:03who met criteria for one of the
• 35:06conditions that we're screening for
• 35:07was also given a digital therapeutic.
• 35:09So that was on top of what happened
• 35:11in primary care.
• 35:12They had this tool that they could
• 35:14use every day in their daily lives to
• 35:17support to provide a mental health care.
• 35:19And so these are some photos of some
• 35:21of the tools we used for screening for
• 35:25the clinical decision support that the
• 35:28providers used and for the digital
• 35:30therapeutic that the patients use.
• 35:32This is a digital therapeutic that we
• 35:35developed based on a couple of decades
• 35:37of different NIH funded projects
• 35:39with different populations where we
• 35:41took you know sort of core sort of
• 35:42science of behavior change in the
• 35:44core active ingredients in helping
• 35:45people initiate and maintain health
• 35:47behavior changes and and embedded
• 35:49it in a transdiagnostic platform.
• 35:50So we could flexibly provide
• 35:52therapeutic tools to people depending
• 35:55on whatever combination of needs and
• 35:57preferences they have in mental health.
• 36:00And we have lots and lots of data from this.
• 36:03It was a big project.
• 36:04We have wonderful partners across
• 36:06the whole country work on this not
• 36:09just research partners but you
• 36:10know really Ministry of Health and
• 36:13industry payers and patient advocacy
• 36:15groups and healthcare leadership
• 36:17really working on this in order to
• 36:19be that was really critical in order
• 36:22to really scale this in the way we
• 36:23were able to across the country.
• 36:25So in one,
• 36:26this is a snapshot of a couple
• 36:28of years right before COVID.
• 36:30We saw in that couple years we
• 36:32went from screening no,
• 36:33No 10 patients for mental health and
• 36:35primary care to screening over 22,000 people.
• 36:39And then of that we had 22% and
• 36:41positive screens and then 8% diagnosis
• 36:43of depression or unhealthy alcohol
• 36:46use in this case.
• 36:47And so you know this is the those
• 36:508% would have gone undetected.
• 36:51You know in the traditional models,
• 36:53these very simple slide here,
• 36:54but I just want to give you this was
• 36:56mostly an implementation science study,
• 36:57but we we were able to track patient
• 37:00outcomes for one year every single patient.
• 37:02We had a whole team tracking patients
• 37:05for a year and to to to document sort of
• 37:09their experience clinically for a year.
• 37:10So these are just some examples of
• 37:12lots of data that we have including
• 37:15you know showing a market reduction
• 37:17in depression symptoms over that year.
• 37:19And that was true even if you had
• 37:21pretty severe depression at baseline.
• 37:24We saw even those,
• 37:25you know with high versus moderate
• 37:26versus mild depression at baseline were
• 37:28able to benefit from this therapeutic
• 37:30approach and reduce depression symptoms.
• 37:33Same similar pattern I should
• 37:36say with problematic alcohol use
• 37:38over that course for the sample
• 37:40that had high levels of drinking.
• 37:42And then even for those who had really
• 37:45high levels of alcohol use versus moderate
• 37:48to maybe some lower risk drinking,
• 37:51again we saw some value for
• 37:53for all of those folks.
• 37:55We have a lot of data on how
• 37:56did this impact the functioning
• 37:58of the healthcare system,
• 38:00the clinical workflow,
• 38:01how people spend their time and
• 38:04these are just some examples of
• 38:06some data from those analysis.
• 38:08So we looked at you know from
• 38:12administrator point of view,
• 38:12from provider point of view,
• 38:14you know what do you think about
• 38:15adopting this in your setting,
• 38:17is it is this accessible to do so,
• 38:19is it appropriate for the context,
• 38:20is it feasible etcetera.
• 38:21So what we found is in this two
• 38:24year window I'm showing here even at
• 38:25baseline and this was after that we
• 38:27had trained a lot of the primary care
• 38:29folks about what we're about to launch.
• 38:31We had pretty high rates of buy
• 38:33in and you know on on many of
• 38:35these dimensions at baseline which
• 38:37persisted post launch and for two
• 38:39years post launch for for most of
• 38:41the data that we've got here there's
• 38:43some new ones to talk about here.
• 38:45But in the interest of time generally
• 38:47we we found that people felt like
• 38:49it was a value we as part of
• 38:52the data collection did a pretty
• 38:55detailed costing assessment.
• 38:57So you all may know this time driven activity
• 39:00based costing metric that first came out
• 39:02of Harvard and it gives you this very,
• 39:04it's,
• 39:05it's a very lengthy process to do,
• 39:06but it's very valuable where you
• 39:08map the cost of every process
• 39:10in a clinical workflow.
• 39:12And then you can see when you introduce
• 39:14some innovation in a clinical workflow,
• 39:16how does it impact costs,
• 39:17what's cost difference.
• 39:18So the bottom line of this very long
• 39:21process was that after we implemented
• 39:23this model in primary care in the
• 39:25country to screen and treat mental health,
• 39:28the cost per patient per year was $1.89 U.S.
• 39:33dollars higher than the what the prior model,
• 39:36sort of the baseline model before we
• 39:39before we introduce this new model.
• 39:40So there's a lot of excitement about
• 39:42that because of the value that
• 39:44they saw in doing so and and sort
• 39:46of the limited cost per patient.
• 39:48And so you know there's there's a
• 39:51lot of optimism that that that they
• 39:54could continue to grow capacity in
• 39:56the region with this type of tool
• 39:59and and also grow it to embrace
• 40:01other areas of health and including
• 40:02other types of mental health but
• 40:04other preventative
• 40:05health promoting interventions and and
• 40:07other chronic disease management tools.
• 40:09And again this was the country of
• 40:11Colombia and now we've expanding our
• 40:13partnership to Chile and Peru, but.
• 40:15The nice thing is that this is an
• 40:17exemplar of a part of the world
• 40:19where you know we could show value,
• 40:21maybe we could do so in rural
• 40:23America or other countries.
• 40:24And you know it's been exciting to see the
• 40:27interest in in the region in scaling this up.
• 40:30Now you know this the funding,
• 40:32the research funding has ended and now
• 40:34they are are offering it clinically
• 40:37and seeking to expand it even more so.
• 40:40So I'm happy to share papers on that,
• 40:43but I'm going to shift gears just a
• 40:45little bit in our remaining time and
• 40:47I'm watching the clock to make sure we
• 40:49have enough time here for discussion.
• 40:50But I want to talk a little bit now
• 40:54about digital health assessment, right.
• 40:57So I talked a lot about therapeutics,
• 40:59but we can learn a lot about people through
• 41:02digital data capture about their daily lives,
• 41:04about their, you know,
• 41:06really granular data even day-to-day about
• 41:08people's needs in in their real world,
• 41:10right in their daily lives and and and
• 41:13provide a lot of insights into people's
• 41:16clinical status and trajectories.
• 41:17But then that data,
• 41:18particularly we can build at
• 41:19the individual level,
• 41:21predictive models that help us
• 41:22understand when someone might benefit,
• 41:24benefit from an intervention,
• 41:25can help us then push sort of
• 41:27digital therapeutics to people when
• 41:29they might most benefit from them.
• 41:31You know,
• 41:32when someone's at risk of relapse
• 41:33or panic attack or psychotic episode
• 41:35or whatever it is,
• 41:36could we in that moment give them some
• 41:39meaningful therapeutic intervention.
• 41:41So there's a lot of exciting work
• 41:43in the space.
• 41:44Most of it is in mental health,
• 41:47although that's evolving.
• 41:48But people are looking at digital biomarkers,
• 41:52things you can capture in vivo again about,
• 41:57you know,
• 41:59sort of in people's environment
• 42:01and neurodevelopmental context.
• 42:02That can be done through the very
• 42:05rich array of sensors you've got
• 42:07on smartphones and or wearables
• 42:10like smartwatches.
• 42:12People are even making smart jewelry
• 42:14and all kinds of different all kinds
• 42:17of different ways to capture this rich
• 42:20information in People's Daily lives.
• 42:22And and some people call this
• 42:25digital phenotyping.
• 42:26And that's just really this very
• 42:28detailed granular quantification of
• 42:30these individual level data in the
• 42:32real world collected through digital devices.
• 42:34And it could be either passively
• 42:36collected through sensing like I
• 42:38mentioned or you can prompt people
• 42:40to answer brief queries about,
• 42:42you know, their pain, their craving,
• 42:44their their mood state, their sleep,
• 42:46whatever the question is.
• 42:49And and you can do that through
• 42:51on digital platforms using these
• 42:54ecological momentary assessments.
• 42:56And so the idea is that maybe we
• 42:58can understand for a given person
• 43:00what confluence of factors might
• 43:01predict clinically meaningful events.
• 43:03And then this sort of some people
• 43:06call it just in time adaptive
• 43:08interventions or just in time
• 43:10delivery of a therapeutic.
• 43:11There's a lot of exciting
• 43:12work happening in this space.
• 43:13I'm going to give you a little
• 43:15snapshot of first of all what's
• 43:16happening in substance use disorder
• 43:18space and then more broadly mental
• 43:19health and then tell you a bit about
• 43:21some work we're doing in this space.
• 43:22So some of the really early work
• 43:25in looking at these digital
• 43:28biomarkers in substance use were
• 43:30heavily with smoking populations.
• 43:32So, so Shiffman,
• 43:33you may you may know his work well
• 43:36we did some early work in this where
• 43:38where you're tracking people's mood
• 43:40in a pretty detailed way and they
• 43:43had a whole line of research here.
• 43:45It's just one example showing that
• 43:46lapses to smoking among smokers trying
• 43:48to quit were associated with increases
• 43:50in negative mood for many days and
• 43:52not just hours before a smoking lapse.
• 43:54So it was this sort of,
• 43:56you know, sort of this,
• 43:57a more prolonged negative mood that
• 43:59seemed to be associated with lapses.
• 44:01Kenzie Preston who I
• 44:03understand recently retired,
• 44:04but there's still a lot of fantastic work
• 44:07coming out of her former lab at the Nida
• 44:10Intramural Research Center in this area.
• 44:12So one of those studies showed that
• 44:15craving predicted imminent drug use,
• 44:18but self reported stress was much
• 44:20less predictive than craving.
• 44:21So, you know,
• 44:22they have this whole literature around,
• 44:24you know,
• 44:24do these types of data help us
• 44:27understand more nuance between,
• 44:28you know,
• 44:29between different triggers for drug use
• 44:32including things like stress and craving.
• 44:34And they also the same lab,
• 44:37David Epstein's part of that group show that,
• 44:40you know,
• 44:41drug triggers,
• 44:41things that for a given person like
• 44:43exposure to drug cues or mood changes
• 44:46increase for hours before cocaine use events.
• 44:48But we we saw very different
• 44:49pattern with heroin use events.
• 44:51So they're also,
• 44:51you know,
• 44:52building out a literature suggesting
• 44:54that this type of data might give
• 44:56us new insights into different
• 44:58sort of risk profiles or different
• 45:00sort of triggers for different
• 45:02types of substance use in the
• 45:05personalized intervention space.
• 45:06Here's some early studies, again,
• 45:09including again with smoking.
• 45:10So if you track people smoking risk,
• 45:13this is what was done in
• 45:15this particular study.
• 45:16You know their risk for smoking.
• 45:18And then and then you trigger a
• 45:20tailored message responsive to that,
• 45:22like, you know,
• 45:22when someone seems like they are at risk,
• 45:24advise them to a piece of nicotine gum.
• 45:26Those types of tailored messages were
• 45:28more engaging and effective than usual care.
• 45:30Similar study where you give
• 45:32adaptively tailored advice for
• 45:33managing withdrawal symptoms
• 45:34when people are going through
• 45:36nicotine withdrawal symptoms and
• 45:38and medication side effects.
• 45:39We found that that very responsive
• 45:41in the moment advice was used more
• 45:43often and was more acceptable among
• 45:45smokers seeking to quit than usual care.
• 45:47And then we had a grant from the NIH
• 45:50Science of Behavior Change Initiative
• 45:53out of the Office of the Director's
• 45:55Office and it was on self regulation.
• 45:57And one piece of that included
• 46:00developing A momentary self regulation
• 46:02scale where we can in the moment assess
• 46:06different aspects of self regulatory
• 46:08capacity like emotion regulation.
• 46:10And we found in this study that
• 46:14digital interventions can impact
• 46:15momentary self regulation that then
• 46:17can in turn impact health behavior
• 46:19across various populations like
• 46:21people with binge eating disorder,
• 46:23like heavy smokers.
• 46:25So very these are just again a
• 46:27snapshot of of what's evolving.
• 46:29There's a lot of exciting work
• 46:30happening in this space now,
• 46:31including I I I know some folks at Yale
• 46:33are doing some great work in this space.
• 46:36We recently finished a study funded
• 46:39by Nida run on NIDA's clinical trials
• 46:42network platform that we're part of.
• 46:44It was in partnership with
• 46:46Kaiser Permanente and IBM.
• 46:47And this study was really trying
• 46:50to understand the utility of
• 46:52digital data capture with people in
• 46:55treatment for opioid use disorder.
• 46:57So we asked outpatients in buprenorphine
• 46:59treatment for opioid use disorder
• 47:01if they want to join the study.
• 47:03And if they did,
• 47:05we asked them to not only answer questions,
• 47:09we asked them through these prompts,
• 47:10these Emas on on a mobile device.
• 47:12We asked them if we could passively get
• 47:15data from smartwatches and smartphones.
• 47:17And we asked them if you have social media,
• 47:19can we can we take your social media
• 47:22data and can we look at that data?
• 47:24And the idea here was to understand,
• 47:27are some of these data meaningful
• 47:30in People's Daily lives to help us
• 47:33understand when people might relapse or
• 47:35maybe not take their medication today,
• 47:37for example, right.
• 47:38We know a lot about you know we
• 47:40know medication treatment is a
• 47:42very effective and literally life
• 47:44saving for opioid use disorder.
• 47:45And we also know that we
• 47:48have you know relapses,
• 47:49we have dropout,
• 47:50we have non medication adherence and
• 47:52we have clinical insights into some of
• 47:55the factors that contribute to that.
• 47:56But the idea here is if we get
• 47:58this day-to-day data that's
• 47:59outside of a clinical setting,
• 48:01could we get any new insights into
• 48:03what when someone might be at risk
• 48:05of these things and where are there,
• 48:07where is there redundancy
• 48:08in the data capture, right.
• 48:10So maybe you know it's just a snapshot
• 48:12of these data that are really the the
• 48:14main sort of data to extract that are
• 48:16meaningful for predicting these things.
• 48:18That's the big picture of the the
• 48:20study we we in the passing sense,
• 48:23passive sensing.
• 48:24You get all kinds of rich information from
• 48:27literally not asking people to do anything.
• 48:29It's just passively collected
• 48:30in an unobtrusive way.
• 48:32You can get features that give you
• 48:34insights into people's activity levels,
• 48:36into their sleep and quality of sleep,
• 48:38into their sociability,
• 48:39into their light exposure.
• 48:41Lots of different kinds of data you can get.
• 48:44And then we asked them these brief queries
• 48:46as you see on the right here about,
• 48:48you know, their sleep stress,
• 48:49their pain, craving,
• 48:52withdrawal symptoms,
• 48:53etcetera.
• 48:54And so these are really brief
• 48:57questions we prompt them to do
• 48:58on a mobile device.
• 48:59And then if they had social
• 49:01media data from Twitter,
• 49:03Facebook or Instagram,
• 49:03we could look at those data.
• 49:05And so we could look at postings,
• 49:07we could look at sentiment analysis,
• 49:09we could look at topology of
• 49:10social networks, the ideas.
• 49:12Are any of these data really
• 49:14strong predictors of things like
• 49:17when someone might relapse?
• 49:19So this first day was heavily A
• 49:22feasibility study and we found
• 49:24that participants in outpatient
• 49:26treatment for opioid use disorder
• 49:28carried the phone on 94% of days.
• 49:30We've been very encouraged by that,
• 49:31wore the watch 74% of days.
• 49:34We had a average response rate to our
• 49:37questions of 70% and we were actually
• 49:39quite surprised about this last one.
• 49:41We thought it'd be lower but 88% agreed
• 49:43to share their social media data and
• 49:46then we have a lot of data including
• 49:47a lot of evolving data from this.
• 49:49But I'm just going to give
• 49:50you a little flavor of some
• 49:52of the things we're finding.
• 49:53This slide is from our EMA data.
• 49:56So this is again the these questions
• 49:58that we asked people to answer and
• 50:0311 predictor that seemed among the
• 50:06strongest in predicting next day
• 50:08opioid use is this momentary self
• 50:10regulation metric that I mentioned
• 50:12to you that we this the scale that
• 50:15we developed and validated and
• 50:16particularly when you track momentary
• 50:20risk taking about 24 hours prior seems
• 50:24to be a a pretty strong predictor of
• 50:28next day non prescribed opioid use.
• 50:31That's just one example.
• 50:33Here's another from some of our
• 50:35passive sensing data and this is
• 50:37just looking at two channels.
• 50:37This is looking at heart rate
• 50:39data from wearables and smart
• 50:41smartphone conversation detection.
• 50:42So not what people are saying,
• 50:44not the content of what they're saying,
• 50:45but detecting instances of communication.
• 50:49And again we're seeing some promising
• 50:53utility of these passive data channels
• 50:56in predicting next day opioid use.
• 50:59We also have seen similar pattern in
• 51:02predicting stress and predicting craving.
• 51:04Lots of ongoing analysis,
• 51:05but this is just to give you a flavor.
• 51:08So why did Nida fund this?
• 51:09So Nida was interested in thinking about
• 51:12you know, you know in clinical trials,
• 51:13let's say we have these great
• 51:15clinically validated assessments
• 51:16we do in an episodic way during
• 51:18the course of clinical trial.
• 51:19Maybe we're looking at you know the
• 51:21effects of a novel pharmacotherapy
• 51:22or or something else.
• 51:23Could this type of data be meaningful
• 51:25to add an outcomes measurement and what
• 51:27type of data from digitally derived data
• 51:30capture could be meaningful as part
• 51:31of outcomes measurement in clinical trials.
• 51:33So that's a big part of it,
• 51:35that's a big question.
• 51:37But then, you know,
• 51:38surely there's a lot of excitement
• 51:39around if indeed we can get these
• 51:41models good enough to understand,
• 51:43you know, when someone might
• 51:44be at risk and we can really,
• 51:47you know,
• 51:47trial the,
• 51:48the utility of these very responsive in the
• 51:52moment interventions to help prevent relapse,
• 51:54for example.
• 51:55So I think this is an exciting space,
• 51:58you know,
• 51:59from the discovery science space
• 52:00and looking at digital biomarkers
• 52:01to really translational science,
• 52:03I think in terms of informing
• 52:05intervention models,
• 52:06the the literature is compelling,
• 52:07but there's a lot of proof of
• 52:09concept out there for folks who
• 52:10are working in this space.
• 52:11But it's growing.
• 52:12But I think,
• 52:13you know,
• 52:13there's a lot of opportunity for
• 52:15more rigor in this work right now
• 52:17and more validation of measures.
• 52:18Lots of people look at different
• 52:20features that you extract from
• 52:21sensing and other things.
• 52:22We don't have a lot of replicability yet,
• 52:24reproducibility of results
• 52:25or control studies as of yet.
• 52:27We have some and it's growing
• 52:29particularly mental health.
• 52:30But it's a it's an interesting and
• 52:33exciting space and I think that
• 52:35it's promising for mental health,
• 52:37but also really just thinking
• 52:39about behavior broadly and it's in
• 52:41in sort of transcending disease
• 52:43specific types of behaviors to
• 52:45really understanding in new ways
• 52:47the complexity and interrelatedness
• 52:48of different clinical conditions.
• 52:50And so in the last couple of minutes
• 52:52before we jump to discussion,
• 52:53I just wanted to mention again
• 52:56our center and this
• 52:58is a Nida funded center and we,
• 53:00we are really devoted to bringing
• 53:03science to the space, right.
• 53:05Bringing science to the development,
• 53:07evaluation and implementation of
• 53:09digital health tools from prevention to
• 53:12treatment for heavily for substance use.
• 53:15And mental health is a critical mass of
• 53:16our group in terms of the work that we do.
• 53:18But and we do work you know ranging from
• 53:21precision prevention of cancer to you know,
• 53:23chronic disease management and diabetes
• 53:24and and and lots of other areas.
• 53:26So lots of you know, rigorous science.
• 53:29We're housed at Dartmouth,
• 53:30but we work with partners across
• 53:31the country and internationally.
• 53:33But it's not just about the science, right.
• 53:34It's great to be able to do the
• 53:36rigorous science and publish on
• 53:37and share it with your colleagues.
• 53:39But our our goal is really to have
• 53:41impact and how do we bring the
• 53:42science to People's Daily lives?
• 53:44How do we scale things so that the most
• 53:47how effective and engaging tools are
• 53:49what people can access and we have a
• 53:51lot of resources if you're interested
• 53:53in this work or doing this work.
• 53:56Some of the current things we're up
• 53:58to particularly in our recent center
• 54:00grant renewal is a focus on some of our
• 54:03transdiagnostic digital therapeutics,
• 54:04some of the adaptive digital therapeutics
• 54:07I mentioned some of our faculty are
• 54:10are are doing some really pioneering
• 54:12work in the realm of artificial
• 54:14intelligence as applied to mental health.
• 54:16We've we've launched several
• 54:19partnerships with training programs
• 54:21for underrepresented minority
• 54:23scholars who are working with us in
• 54:26digital in training for for becoming
• 54:28digital health scholars.
• 54:29And then as I mentioned at the beginning,
• 54:32you know we are really working on
• 54:35strategic partnerships with with
• 54:37lots of different partners in in the
• 54:40regulatory space and policy and industry
• 54:42investors and and and not just about
• 54:45bringing the science to that community,
• 54:47but really understanding what's happening
• 54:50in those in that area and what kinds of
• 54:53questions and data people want, right.
• 54:54So when payers decide to pay
• 54:56for a digital health tool,
• 54:57what do they care about?
• 54:58You know when the FDA is looking at data,
• 55:00what do they care about so that
• 55:02you know our research community
• 55:03can also be capturing meaningful
• 55:05data that are meaningful to a lot
• 55:07of stakeholders in this space.
• 55:08So to that end we've done several things.
• 55:10We've launched in the annual Digital Health
• 55:12Summit with a whole array of partners
• 55:14with that goal for shared dialogue to it,
• 55:16with a goal of together increasing pace
• 55:19of access to the most effective tools.
• 55:23We launched a Dartmouth Innovation
• 55:25Accelerator in digital Health in
• 55:27partnership with our Magnuson Center
• 55:29for Entrepreneurship at Dartmouth.
• 55:31Again,
• 55:31it's about getting things out there
• 55:33that work and having a path in
• 55:36the right partners and knowledge
• 55:37and expertise to bring to that.
• 55:38So this is a big priority of ours.
• 55:41And again, happy to chat about it more,
• 55:43but I want to pause here so we can have
• 55:46some discussion.
• 55:47I included my e-mail,
• 55:49our website for our center.
• 55:51We have a pretty lively,
• 55:52particularly Twitter life.
• 55:53If you all are interested in this,
• 55:54please follow us.
• 55:56And then I had to mention we have some
• 56:00assistant professor level faculty positions
• 56:02available in our center right now,
• 56:05so if anyone has any interest
• 56:07in learning more about that,
• 56:08please feel free to reach out.
• 56:10So anyway, thank you for the
• 56:11opportunity to share this.
• 56:12I'm going to stop sharing my screen.