Building digital mental health approaches to prevention and early intervention

October 04, 2022

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YCSC Grand Rounds September 27, 2022
Nicholas B. Allen, PhD
Ann Swindells Professor; Director, Center for Digital Mental Health; Associate Director of Clinical Training, University of Oregon Department of Psychology

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00:00Good afternoon, everyone,
00:03and welcome to Grand Rounds.
00:04Please enjoy the coffee and
00:06take your seats when you can.
00:08And it's a pleasure to welcome you
00:10back here to the Cohen for grand rounds.
00:12And I'd like to just start with echoing
00:14the sentiment expressed by Linda and
00:16Tara and their message last week.
00:19And I want to apologize on behalf of
00:21the entire Grand Rounds Committee
00:22for scheduling grand rounds when
00:24some members of our community
00:25are observing Rosh Hashanah.
00:27And sure, I would like to wish everyone
00:29who is observing Rosh Hashanah.
00:31Uh, Shana tova, if you're watching
00:33this back on the recording,
00:35I hope you'll take us up on our
00:36offer to schedule a virtual meeting
00:38with Doctor Allen and to connect
00:40with him about his research.
00:41And I'd like to thank Doctor Allen for
00:44making himself available to meet with
00:46interested faculty later on this week.
00:48Now, just a reminder.
00:49Next week we will be back here in
00:52the Cohen Auditorium for our first
00:54compassionate care rounds of the semester,
00:57led by Laurie Cordona, Dr Laurie Cordona.
00:59So please do join us for that.
01:01And then.
01:01So all of our sessions.
01:03Our ground rounds sessions for the month of
01:05October will be in person here in the Cohen.
01:08So join us for a coffee as we come
01:10together and as we learn together.
01:12Now today it's a great pleasure
01:14to introduce our speaker,
01:15Doctor Nicholas Allen.
01:16And so Nick is the founding director
01:19of the Center of Digital Mental Health
01:21at the University of Oregon where
01:22he's the Ann Swindells professor in
01:24clinical psychology and Nick trained in
01:26clinical psychology in the University
01:28of Melbourne with postdoctoral
01:29training here in the United States.
01:32With Peter Levinson and before
01:33returning to Melbourne to take a
01:35faculty position where he worked for
01:37over 10 years in the National Center
01:39of Excellence in Youth Mental Health
01:41before returning to the University of Oregon.
01:44And as many of you will have
01:46read in Nick's research,
01:48he applies innovative digital technologies
01:50both wearable and mobile to try
01:53and predict or prevent suicide and
01:55to better understand risk states in
01:57the context of adolescent depression.
01:59He's disseminated his research
02:00widely in journals.
02:02Including in Nature in PNAS.
02:05And in 2019, he founded Kasana Health,
02:08which is a company whose mission
02:10is to accelerate the translation of
02:12digital technologies to improve the
02:14care of individuals mental health.
02:16So, with no further ado,
02:18I'd like you to join me in welcoming Dr.
02:20Nick Allen.
02:29Well, thank you, Kieran,
02:30and thank you everyone for making
02:32time to come and listen today.
02:33I really do appreciate it and let me
02:37just get my share screen working.
02:43There we go. Good. And it's
02:47great to be here at Yale.
02:50I, as you can tell I did
02:52not grow up in this country.
02:57So I have a competition with my wife
03:00about which states we've visited,
03:02and the definition of visiting a state
03:04for the purpose of this competition
03:06is that you have to have slept there.
03:09And I just ticked off Connecticut.
03:12So thank you for that.
03:16Anyway, I want to talk to you about
03:18our work on adolescent depression
03:21bringing in digital methods.
03:23Hello? Why is it not responding?
03:28There we go. All right.
03:30We'll start with the disclosure,
03:31which you've already heard,
03:32which is that I have founded a
03:34company called Kasana Health,
03:35a digital mental health company,
03:37and I have an equity
03:39interest in that company.
03:44Now this is not necessarily a
03:46smart thing to do, but there's this
03:48website and what you can do on this
03:50website is you can go and pick a
03:52particular date and it will give you.
03:54Data on the burden of disease for
03:57different categories of disease
03:58on different dates and so I.
04:03In a crazy experiment,
04:04I went and looked at this,
04:05and I and I put in the age I was when I
04:07graduated with my PhD in clinical psychology.
04:10And then I put in today's date.
04:13And what this showed is that although
04:16we've made really great progress in many
04:18areas in medicine with things like this,
04:22particularly infectious diseases.
04:25Obstetric issues and so forth.
04:29Mental health problems have not shifted
04:31at all in terms of the burden of disease.
04:34Nothing, not a jot,
04:36and to make matters worse.
04:39I have spent my entire career
04:41focused on mental health and youth,
04:42so adolescents and young adults.
04:45And as you can see,
04:47this is a stage of life where the
04:50burden of disease of mental disorders
04:53is at its peak across the lifespan.
04:56And things are actually getting worse.
04:59We think for these, this group of people,
05:01so you can see here some data on depression.
05:07Symptoms across across time from
05:09two different studies showing that,
05:11particularly for.
05:12Young girls,
05:13there's an increase in these problems,
05:16particularly over the last decade
05:18and 1/2 to two decades.
05:20And moreover,
05:21suicide is increasing in this country,
05:24not all countries,
05:25and has overtaken homicide as the
05:28most common as as a as a more common,
05:31sorry form of death amongst young people.
05:34So.
05:37So what have I been doing all this time?
05:40I've been working on the problem
05:41and we're not making progress.
05:43So that's that's a that's a concern.
05:48Now part of the problem is
05:50we know it's a hard problem.
05:52We know it's a wicked problem, so.
05:55And one of the difficulties is it's hard
05:58to know how to scale up our efforts in
06:01a way that will really have impact.
06:04So this diagram here comes from a
06:06report that I was involved in for the
06:08World Innovation Summit for Health,
06:10so on digital technologies and mental health.
06:12And what this this graphic points out is
06:17that if you calculate it a certain way,
06:19you could argue that the current mental
06:21health system is not effective for 90% of
06:23the people with a mental health problem.
06:25The way you get there is you say
06:28OK for every. 10 people in need.
06:32Only four people actually access treatment.
06:35And for those,
06:36only about 1.5 of those people
06:38will access what we would call
06:40minimally acceptable treatment,
06:41so treatment of adequate quality.
06:44And evidence base.
06:45And then even though evidence based
06:48treatments will get variability and outcome,
06:51So what this highlights is that we have
06:54a number of kind of grand challenges
06:56in the field that we need to solve if
06:58we're really going to bend the curve,
07:00this curve that seems to be
07:02bending in the wrong direction. So.
07:05We need to think about prevention.
07:08So how do we prevent these people
07:10from getting into that first group
07:12in the 1st place?
07:13We need to think about access
07:15to mental health services.
07:17We need to think about the quality of the
07:19mental health services that people receive.
07:22And we also need to improve the
07:24effectiveness of our services.
07:25So this is a nomenclature
07:27that I'll come back to,
07:28but I think is really critical in terms
07:30of thinking about how we solve the
07:32problem and start to make progress.
07:37So. My own work has been largely
07:42informed by throughout most of
07:44my career by an an interest in
07:47prevention and early intervention.
07:49And of course, we know that the mental
07:52health system has traditionally been designed
07:54to deal with chronic and end state illness,
07:58to respond to crisis rather than
08:00to be preventative in orientation.
08:04And so, so this is a a paradigm shift.
08:06It's occurring in many contexts
08:08around the world.
08:09But this is the one that I've been
08:11interested in and the and I guess
08:13if you if you if I think about my
08:16program of research and clinical
08:17activity over most of my career,
08:19you know the argument that I would make
08:21in my grant applications and things
08:23like that would go something like this.
08:25We need to understand what these
08:27pre morbid indicators of risk are.
08:30We need to have practical and
08:32scalable methods for monitoring
08:34these and screening people.
08:36We need to have modifiable factors,
08:39so just because something is a
08:42predictor doesn't mean it's modifiable.
08:44And and then we have to have effective
08:46and scalable methods of intervention,
08:48early intervention or prevention and
08:50also it's been very much informed by an
08:53interest in developmental inflection points.
08:54So are there ways to target our intervention
08:58efforts developmentally so that we're
09:01getting a greater impact of the,
09:03so that we're meeting the,
09:04the plasticity,
09:05if you like,
09:06of the process with an intervention
09:07that's well timed?
09:11And I want to give you and you
09:12know we did we did lots of work.
09:13We, I've, I've run a couple
09:16of large longitudinal studies
09:18that have looked at a range of
09:20different risk factors including.
09:23You know, brain biomarkers have
09:25obtained through neuroimaging,
09:27genetics, neuroinflammatory processes,
09:29neuroendocrine processes,
09:31puberty as a developmental process.
09:33We've also done a lot of work on
09:36family interactions where we've used
09:38micro social observation of family
09:40interactions in adolescence to try and
09:42understand patterns of interpersonal
09:43relating and how they react.
09:44And so we've, we've we've looked at lots
09:47of different risk factors and there's
09:49many things I could say about all that.
09:52But, but we were searching for this
09:54thing that was a really strong predictor
09:56of outcome and that was modifiable.
09:58And so that's when.
09:59And so as a result of this we
10:01got quite interested in sleep.
10:03Because sleep is is a potent marker
10:06and it is modifiable so as and it's
10:11also developmentally sensitive.
10:13So particularly in early adolescence
10:15as many of you will know,
10:16there's a shift in the timing
10:19of the circadian process.
10:20Such that it's it's pushed later.
10:23And so there's less sleep
10:25drive early in the evening,
10:26there's more sleep drive in the morning.
10:28And that combines with various
10:32lifestyle factors such as homework.
10:35Sporting activity, but also the big one,
10:39social media, right? Being online.
10:41The fact is that your peer group is now
10:44available to you online almost 24/7.
10:46It's a very.
10:48Historically unusual.
10:50Context.
10:53And then we've got early
10:55start times for school.
10:57So it's very easy for adolescents
10:58to build up quite a lot of
11:00sleep debt during the week.
11:01And then they engage in catch
11:03up sleep on the weekend.
11:05So they sleep in catch up.
11:08But of course, then they shift
11:10the circadian oscillator further,
11:11and then when Monday comes around,
11:12they've got what some people
11:14call social jet lag. Right.
11:16So they feel awful because they're
11:17trying to reset the circadian office.
11:19So even though they've dealt with
11:20the sleep debt and the sleep drive,
11:22now there's circadian system
11:23is telling them hang on.
11:25This thing is getting up at 7:00 AM
11:27or whenever it is on Monday morning.
11:28That doesn't feel good.
11:34So this is a known issue in adolescence.
11:38But the other thing we know about it is
11:40that it's related to a lot of stuff,
11:41a lot of outcomes.
11:43It's a broad transdiagnostic risk factor
11:45for all sorts of difficult outcomes.
11:47Depression, suicide, substance use,
11:50cardiovascular disease later in life,
11:53or risk factors for cardiovascular
11:55disease that emerge during adolescence,
11:57neurocognitive functioning
11:58in school performance.
11:59I won't belabor the point,
12:00I'm sure you get it,
12:02but this is a predictor both in
12:04a distal and in approximal way.
12:05There's some very interesting
12:07studies that have.
12:08Been done on day-to-day variability
12:09and sleep and how it correlates to
12:11day-to-day variability in some of these.
12:13Phenomena.
12:16We also know from experimental
12:17studies such as this one from
12:19Allison Harvey's group at Berkeley,
12:21that by experimentally manipulating sleep
12:25you can actually see effects on mood.
12:29Pretty much immediately.
12:30And there's A and there's a
12:32developmental sensitivity to it.
12:34So if we look over here,
12:35let me see if we get the cursor to wake up,
12:38you can see that the early adolescence.
12:40Are experiencing the much stronger
12:43effect of the sleep deprivation
12:45than the late middle adolescence.
12:48So early adolescence is a
12:51sensitive period developmentally.
12:53So. We're in business facts.
12:56We've got an early risk factor
12:58that's developmentally specific.
13:00It's modifiable.
13:03Well,
13:03here's a bit of data that we
13:05still haven't published yet.
13:06It's in the process,
13:07but this is also looking at an individual
13:09difference called chronophage,
13:11so whether you're a night owl
13:13or an early bird.
13:14And what what this I want drag
13:16you through this complex graph,
13:18but what it's basically showing
13:20you is that the chronic phase
13:22prospectively this is a four
13:23wave longitudinal study and the
13:25chronic phase is prospectively
13:27predicting changes in depression.
13:29On two out of the three waves,
13:31depression doesn't predict
13:33chronic phase on any wave.
13:35So we also see that's a
13:37prospective market if you're a,
13:38if you're a an evening phenotype,
13:41then you're probably going to have a
13:43a a worse version of that problem I
13:45talked about before of the delayed
13:47sleep and then the sleep debt
13:49and then the the social jet lag.
13:53So.
13:55We did this, we developed an intervention.
13:58And we and we,
14:00it's called the Census project.
14:02This was done when I was in Australia.
14:05And we had a we ran an RCT,
14:08we went into high schools,
14:11what's called high school in Australia.
14:13I should point out in Australia High
14:15School refers to year 7 to to 12.
14:17So that's what what it was actually
14:19more what would be called middle school
14:20in the most of the US and we compared,
14:23we developed,
14:24we it was a group delivered intervention.
14:27We had one that was asleep
14:28intervention and the other one
14:30was a study skills intervention.
14:32So we tried to build something
14:34of a plausible placebo.
14:36For the intervention,
14:37we were very careful to not
14:40communicate to people what the.
14:43What the study was all about.
14:45And and.
14:47In general terms,
14:49it worked. We were able to see
14:51that it actually improved sleep.
14:53So this is a there's a self report of
14:56sleep and you can see that there are
14:59variables and you can see that the.
15:01Global sleep problems were lower in the
15:04intervention group sleep onset latency.
15:05So how long it takes you
15:07to fall off to sleep?
15:09Shorter in the intervention group
15:11and daytime sleepiness slower.
15:13We also see that with objective data,
15:15so this comes from wrist actigraphy.
15:18And we can see that once again,
15:19the sleep onset latency was shorter,
15:22there was less,
15:22and there was less variability in sleep.
15:24So kids became who in the
15:26intervention group became more
15:27regular with their sleep behaviour.
15:29So less variability and sleep onset latency,
15:32less sleep efficiency variability
15:34and less variability in bedtimes.
15:36So that's so good.
15:38We also find that it.
15:41They were less anxious.
15:44They experienced less pre sleep
15:45arousal or anxiety and they and
15:48they knew more about sleep.
15:49Full disclosure, the intervention
15:51did not impact on depression.
15:53So we didn't find an effect there.
15:56So. OK. We've done it.
16:03Take right we found a modifiable risk factor.
16:07We developed an intervention. We tested it.
16:09We published it in good journals.
16:14But this was a career crisis for me.
16:17And I'll tell you why.
16:19Because I could not give it away.
16:23How do I get it out there?
16:27So the problem was, as I said,
16:29we had found an effective approach,
16:32potent premorbid refactor,
16:33a modifier etiological factor,
16:35and effective measure of intervention.
16:36We developed it.
16:38It had a plausible
16:39developmental inflection point.
16:40But what about practical methods
16:42of screening and monitoring?
16:44It was so much effort to go into
16:46these schools and to screen the
16:48kids and to find them and then to
16:50get them to come to the groups.
16:51I had, you know we had millions of
16:54dollars of funding for study from
16:56the Australian NHMRC and and and it
16:59was tough and people worked hard
17:01and you know like, so that was it.
17:03And then secondly,
17:04how can we deliver this intervention
17:06at scale?
17:06It's a group delivered intervention
17:08making those scheduling the intervention.
17:10It's kind of about,
17:11you know the whole thing.
17:12So so I was sort of like I felt
17:15like I'd kind of done what I said I
17:18was going to do and and I you know
17:20I was still frustrated it wasn't
17:22having the impact and so that's
17:24when amongst other things I started
17:26to get interested in in digital.
17:28So and and hopefully you'll see why.
17:31Partly because I was focusing
17:33on adolescence and adolescence
17:34obviously used their phones and
17:36and other digital devices,
17:38but especially their phones.
17:41Extensively, right.
17:42So we've got this adolescence
17:43is an interesting point.
17:44We've got this combination of factors.
17:47We've got the emergence of onset of
17:49many forms of mental health problem,
17:51particularly depression.
17:52Eating disorders,
17:53substance use disorders later in adolescence,
17:56various forms of early psychosis.
18:00And other problems,
18:02you've got intensive use of mobile
18:05computing for a particular purpose.
18:08Social connection. Right.
18:11It's a device of social connection,
18:13and there's a developmental
18:14reason why people at this age
18:16are so interested in this device,
18:18because it is a is a tool
18:20of social connection,
18:21and we've got high plasticity
18:23and learning going on as well.
18:26So I've mentioned this why is
18:28technology so compelling from
18:29a developmental point of view?
18:31Well, it fits with a lot of the
18:33developmental tasks of adolescence.
18:34It's about connecting socially.
18:36You can experiment with identity.
18:39So you can have, you know,
18:40as you might know,
18:41kids often have different accounts
18:42for different aspects of their
18:43identity that for instance,
18:44and the sisters and the, you know,
18:46that sort of thing.
18:46So you actually can experiment with
18:48what you want to present to the
18:51world and to different audiences.
18:52You get a lot of peer based
18:54information and feedback.
18:55It's private. Especially from parents.
18:59Adolescents do not care much about
19:02what Google knows about them.
19:05But they care a great deal what
19:07their parents know about them.
19:08Right.
19:08And the interesting thing is,
19:10when I was young, it's a long time ago,
19:12and I wanted to call up my girlfriend.
19:15I had to talk to her parents first.
19:18It was absolutely mortifying.
19:20And so,
19:21and they would have to get through,
19:22run that gauntlet to get to talk to
19:24her because there was one phone in
19:25the house and it was connected to the wall,
19:27right?
19:28And so this idea that I could have
19:30directly spoken to her without the
19:32parents having any clue would have
19:34been absolutely mind blowing, right?
19:37So that's pretty important.
19:39And of course there's a there's
19:40a literature on board and
19:42pronouncing adolescence,
19:42which is also relevant because
19:46it's a divisive entertainment.
19:48Now The thing is, this is 1.
19:50This is a picture for the real old folks,
19:52but.
19:55The. The interesting thing about this device,
19:58though, is that it's full of sensors.
20:01It's collecting information constantly.
20:05And you don't have to do a damn thing
20:08to get people to contribute because
20:10they're contributing just with their
20:12naturalistic use of the device.
20:13And of course, this is exactly why Google
20:15and Facebook are profitable companies,
20:17because what they do is they use that
20:19data to target people with advertising.
20:23But what if we used it for something good?
20:26All right. That's the intriguing possibility.
20:28So suddenly we've got this new kind
20:30of data in terms of the feasibility,
20:32it's objective, it's unobtrusive.
20:34It can be collected without bothering
20:36people like you do with questionnaires.
20:39It's individualized.
20:39You can get a very long baseline and an
20:42individual person understand their personal
20:44variability and deviations from it.
20:46It can be collected.
20:47It creates the possibility of real time
20:49responses to things that are going on.
20:52And of course,
20:53it's highly scalable because most people.
20:55Even in most countries around the world now.
20:58Have some kind of device like this.
21:01So that's pretty intriguing.
21:04So what's wrong with self report?
21:05This is what we've built our.
21:08Our whole business on in mental health and
21:11there's nothing wrong with self report.
21:13Self report is good data,
21:14it's interesting data,
21:15it's important data,
21:16but it's never ever complete data
21:19because we know that whenever we
21:21measure something with self report and
21:23with objective data at the same time,
21:25we find that there is either a very
21:27moderate correlation or sometimes
21:29no correlation.
21:31So we know that from studies such
21:32as there's studies on condom use,
21:34for example,
21:35that have shown this variability.
21:38Sleep.
21:38This is an area that I know a bit
21:40about when we use wrist actigraphy
21:42and we compare it to self report
21:44substance use where you look at.
21:46Say you're in screens compared
21:47to self report,
21:48you always find you get a different data
21:50set from the objective and the subjective,
21:52so having them together is really helpful.
21:54And like I said,
21:56the teenagers are pouring data into this.
21:58This is now quite out of date.
22:00You know that one of the challenges
22:01in this whole world is that.
22:042018 is a million years ago.
22:06In terms of the products that are
22:08available and how people are using them,
22:10but even there you can see this
22:12massive rise in the ownership of
22:15smartphone engagement with social media.
22:18The preference of how you like
22:21to communicate. So texting is
22:23now more popular than in person.
22:25Communication, social media and
22:27video chatting more more popular.
22:29So as a result of this,
22:30we developed a research platform called
22:32EARS that stands for effortless assessment
22:35research system, but it actually.
22:37There's also good reason to use the Bunny.
22:41And so we've got our little Bunny logo there.
22:43And so it's a, it's a,
22:44it's a research tool that people can use.
22:46You can download it onto your
22:49iOS or Android phone.
22:50When you when you're on board,
22:51you get a code from the research study,
22:53and that tells you that.
22:55Tells us that you've consented and that
22:57you're and tells us which study you're in.
22:59It emphasizes using the phone only,
23:01so you don't need to.
23:02No one needs to own a wearable
23:03or anything like that.
23:04You don't have to get them out to people,
23:05active graphs,
23:06or other weird things that
23:08you might want them to wear.
23:10They there's no special instructions,
23:12just use your phone as you normally
23:14would and so and it also is is
23:18collecting data continuously.
23:19And we can collect a lot of
23:20different kinds of things.
23:21There's, you know, there's the raw sensors.
23:23And then really what we've been
23:25working on in our research work is
23:27how to extract meaningful behavioural
23:29features from the rural sensors.
23:30So, for example,
23:31we can look at patterns of phone and
23:34app usage, language and cognition.
23:35I'll present you some data on
23:38that in a second.
23:39Not as much detail as you'd
23:40get from a wrist actigraph,
23:42but you can certainly estimate
23:44sleep onset and sleep offset from
23:46the phone usage with some accuracy.
23:49Had some physical activity,
23:51geographic location.
23:53We've done some work on facial
23:55expression and selfies put
23:56asterisks on that one bit hard.
23:58Most people use Snapchat to take
24:00selfies and Snapchats got its
24:01own camera that we can't get to,
24:03so in fact,
24:04we don't get a lot of selfies
24:06for that reason.
24:07What music people are listening to.
24:09Circadian patterning have some methods for
24:12collecting acoustic voice data as well.
24:14So that's a lot of stuff that you can get.
24:16So I want to,
24:17you know,
24:17so we could go on about this
24:19stuff is a great deal,
24:20but let me let me focus on one
24:22particular thing is that we,
24:23so we've got a method for
24:25collecting the language that types
24:27that's typed into the keyboard.
24:30And every keystroke is marked with
24:31a time and date stamp and also
24:34what app is in the foreground,
24:36so we're able to actually look
24:38at different language patterns
24:39in different apps and so forth.
24:41One thing that you'll notice
24:43here is this is a,
24:45this is a youth population.
24:46This is. There's enormous variability in
24:48how much people type into their phones.
24:50Some people type an enormous amount. This is.
24:53This is average daily communication,
24:54so we have one participant here
24:57who's typing in nearly 3000 words
24:59a day on average. On average.
25:01And most of them are going into social media.
25:04The rest are going into SMS.
25:06Almost nothing in e-mail.
25:08Right, So e-mail is definitely your
25:12grandfather's way of communicating.
25:15So I wanted to show you a little bit of
25:17data from one of my graduate students,
25:18Elizabeth Mcneely.
25:19This is a paper that's in press
25:21and clinical psych science.
25:23And this is with a group of
25:2513 and 14 year olds.
25:26So there are people who haven't
25:28had their phone for that long.
25:29And we we collected these messages and we
25:33also collected them across a period of time.
25:37Uh.
25:38And we would.
25:39And we what we did is we not only
25:41had baseline data on the sample,
25:42but we also had daily reports of mood.
25:46And what we can and we could,
25:48we restricted it to the the language
25:51that was typed into social applications,
25:54so either social media or messaging.
25:57Instant messaging.
25:57So wasn't search not that search,
25:59search date is very interesting,
26:01but we we did we we didn't include that here,
26:03but it's different.
26:04You've got a different audience.
26:06So a couple of interesting findings.
26:08First of all, we found that kids who
26:12were had lower levels of of well-being.
26:15I generally typed more into their phone
26:19and that that was particularly true
26:21if we looked at the daily word count.
26:23There's an interaction here where the
26:26kids who are have low well-being in
26:29general are particularly likely to type
26:31more words when they're having a bad day.
26:34OK, so there's an interaction
26:36there that's interesting.
26:37So overall,
26:38word count is important.
26:40First person pronouns turn out
26:42to be a really important marker,
26:44and what we're seeing here is once again,
26:46the kids who are more depressed in general
26:50have more first person pronoun use and.
26:53On days when you're reporting
26:55lower well-being you,
26:57you tend to use first person pronouns more.
26:59So what we're seeing here is the the heavy
27:01line is the average regression line.
27:03But the interesting thing to me is that
27:05all these others are the individual
27:07regression lines for each participant.
27:08And you can see that that slope is there
27:11for almost everybody in the sample.
27:13So it's quite consistent within
27:16person phenomena.
27:17The other interesting finding is we
27:19found that present focused words,
27:21so present tense.
27:22Is also more common when you're feeling.
27:27On when you're a person who
27:28doesn't feel as well in general,
27:30and also on days when you're
27:32not feeling as good.
27:35Now you may be interested,
27:36you may be wondering why why aren't we
27:37looking at positive and negative words?
27:39You know, these valence and actually
27:41they they don't have as strong a
27:43relationship as these patterns do.
27:45And in fact this is not,
27:46we're not the first people to observe this.
27:48This effect with first person pronouns
27:50has been well established and published.
27:53There's a meta analysis on it in the
27:55literature where one of the first
27:56to look at it in this kind of data,
27:58but in other language data this
27:59is a common factor.
28:00And in fact there's a there was a
28:03really cool paper published by Eric.
28:05I'm sure many of you know.
28:08In PNAS earlier this year and they were
28:10looking at therapy transcripts from
28:12I think it's talk. Talks talk space.
28:14There's a talk life in the talk space.
28:17I get the muddled up sometime.
28:18This is a an online text based
28:20therapy platform and they found and
28:22they looked at this concept of what
28:25they called psychological distance
28:27and psychological distance was
28:28the inverse of using first person
28:31pronouns and present tense words.
28:33So in other words,
28:34they saw it as a good thing.
28:35They saw it as a capacity
28:37to decentre or to regulate.
28:39And sure enough, they saw that.
28:43There was an effect across treatment.
28:47That symptoms got better.
28:50Linguistic distance defined as I
28:54just described. Improved, improved.
28:56And that there was a relationship between
28:59this linguistic distance and symptoms,
29:02both between subjects and within subjects.
29:06So it's cool paper,
29:07definitely worth checking out.
29:08So that's a kind of a replica,
29:09you know,
29:10a consistent finding to the one I've
29:12just presented you from Elizabeth's work.
29:14So the interesting thing about this is that.
29:20One of the unique affordances of this kind
29:23of approach is the ability to actually look
29:26at day-to-day variability within a person.
29:28And this turns out to be something
29:31that's quite of interest to clinicians.
29:33This is from a blog that they published on.
29:38Google X, which is their research arm and
29:41what they were doing this work on a on an
29:44EEG system that would diagnose depression.
29:47And as part of that they went out
29:48and they talked to clinicians,
29:49they did what they called in
29:51business customer discovery.
29:52So they went and talked to the
29:53clinicians and they said, you know,
29:54would you like to have this EEG system
29:56that could help diagnose depression.
29:57And interesting thing was most
29:59of them said not really.
30:00Because I actually, I'm pretty,
30:02I think I'm pretty good
30:03at diagnosing depression.
30:04You know, I've been doing it for a long time.
30:06I've been trained well to do it,
30:07you know, and it's it's it's, you know,
30:10probably in some ways not that hard,
30:12you know, to diagnose depression.
30:14And so they said,
30:15but what we would really love and this
30:17is where we really have a problem, is.
30:19Using technology as a tool
30:21for ongoing monitoring,
30:22knowing what's working and not working,
30:24not what's not working.
30:25How is my patient doing now?
30:28How are they?
30:28How is their trajectory of what's going on?
30:30Do I need to change my therapeutic strategy?
30:33You know,
30:33things like that is actually where
30:35the clinicians were much more
30:36interested than that first thing.
30:37And so we've got a technique here that at
30:40least potentially can contribute to that.
30:42So let's go back to our grand challenges.
30:46Prevention, access. Quality, effectiveness.
30:50And the fact is, if you read most things
30:53that people write about digital technology,
30:56they're going to say, why is it exciting
30:58in mental health services as well?
31:00Because we can address the access problem.
31:03All right, so we can give people
31:04access an app, you can download it,
31:06everyone can have access.
31:07We can send it all around the world.
31:09Software solution,
31:09take the human out of the loop. Brilliant.
31:14There's a problem. First of all.
31:17We know that increasing access.
31:20In and of itself does not help.
31:24And how do we know that?
31:25Because in high income countries like the US,
31:28the UK, Australia and Canada,
31:31there has been a massive increase in access
31:33to mental health services over the last
31:35few decades with no concurrent reduction.
31:39In the burden of disease associated with.
31:43Mental health problems,
31:44so we're pouring more money in.
31:47It's a good thing I've supported.
31:50But we're not getting a better outcome.
31:52So access itself is not a solution
31:54and when you come to digital products,
31:56there's a very clear reason for that
31:59is that is that they're not sticky.
32:01So this is a curve of how many
32:03people keep using an app.
32:04This is mental health apps
32:05specifically over a period of time,
32:07and you can see within three to four days,
32:1080% of your people you've lost.
32:12So this is The Dirty little
32:14secret of digital health, right?
32:15The fact is that you're actually,
32:17it's what David Moore has.
32:20Brilliant on this issue calls
32:22the denominator problem.
32:24You know,
32:24when you run an RCT with a digital product,
32:26you can show effectiveness,
32:28but often they don't tell us
32:30how many people wouldn't use it.
32:33And so that's a big problem.
32:35We do see a little signal over here.
32:38See this Gray line?
32:39These are the apps that have a
32:41social element. Oh my goodness.
32:45Someone's gonna go away.
32:50Umm. Yeah. And anyway,
32:52so we'll move along and so this and so we.
32:55And so one of the themes that's coming
32:57through with digital technology and
32:59it's usefulness and mental health is
33:01that having a human in the loop in
33:04some way seems to be really important.
33:07It seems to make a difference
33:08to effectiveness and engagement
33:10and of course, effectiveness,
33:11engagement are related to each other.
33:14So we can't, you know,
33:15so I think we need to reframe the problem
33:17from one where we're saying like,
33:19how can we just scale things
33:21and get the humans out.
33:24To say like,
33:25let's try and understand what the humans are
33:27trying to do and help them to do it better.
33:30And that's, I think,
33:31a better version of how we can
33:33move forward with digital.
33:35Hello, I've lost my.
33:39Kinko's got rid of my control of the screen.
33:41OK, so back to our problems.
33:44So we really do need to solve
33:45the quality and effectiveness
33:46problem along with the access
33:48problem or we're not doing,
33:50we're not having much impact.
33:54And, you know, one of the
33:56interesting things is that we. Umm.
33:59This is term they use in digital
34:02technology called scudo morphism
34:04is anyone heard that skeuomorphism
34:06is what Apple products used to
34:08do when they when they had,
34:10when they gave you a calendar,
34:11the little icon for calendar looked
34:13like a literal calendar, right.
34:15And so that's that's that's.
34:17And so in some ways,
34:19one of the problems we have with digital is
34:21that we're we're using skewed amorphism.
34:23This is not my idea.
34:24This is Stephen Schuler,
34:25University of California,
34:26Irvine made this point in a brilliant paper.
34:29We're we're saying like what we
34:31need to do with digital is make
34:34it like a clinical consultation,
34:35like make it like the way we
34:38currently do things.
34:39But the but the thing about that
34:41is that actually the way we do
34:43things now actually might be
34:45quite poorly matched to the actual
34:47thing we're trying to do.
34:48Let me explain what I make that
34:50didn't come out very clear at all.
34:51Let me explain what I'm trying to mean.
34:53We know.
34:55Pretty well what the principles
34:58of behavior change are.
35:00It's something like this.
35:01You start with a clear description
35:03of the new skills to be learned,
35:05along with modeling of those skills so
35:07present to know what they want to do.
35:09Then they've got to have chances
35:11for practice,
35:12repeated practice with timely
35:13feedback so that they can actually
35:16keep trying and failing,
35:17but getting good feedback,
35:18and so that they learn.
35:20And then you have to have specific
35:22procedures to make sure that
35:23the skill generalizes to the
35:25environment you care about.
35:26So it doesn't matter whether it's throwing
35:28a football or driving a car or whatever.
35:30The skill is.
35:31The formula is something like this,
35:32and the things that we're
35:34actually good at teaching people,
35:35we tend to follow this formula.
35:37Now I put it to you that the current way.
35:40We do psychotherapy is a little bit
35:43like a football coach who says?
35:46Listen, I'm not going to come to the game.
35:49And I'm not even gonna watch
35:51tape of the game.
35:52What I'm going to do is I'm going
35:53to sit in my office and I'm going
35:54to have you come and tell me once
35:56a week how you think you played.
35:57And then I'll give you some verbal
35:59suggestions about how you could
36:01play better and then you can
36:02go out and play again and then
36:04come back next week and repeat,
36:06repeat the process, right.
36:07So that's.
36:08So in that sense,
36:09what I'm trying to point out is
36:11the way we deliver services now
36:13in these kind of appointments,
36:15punctuated appointments is actually
36:16very poorly matched with the process of.
36:19Behaviour change with what we know
36:21about the fundamentals of behaviour change.
36:24So this is where I think digital
36:26technology has an affordance to actually
36:29think about doing things differently.
36:31Because we can take that therapy
36:33process out of the office,
36:35into People's Daily lives,
36:36and it can be portable and it can be in
36:39your pocket and all that kind of thing.
36:41So we've been working on a long after years
36:45which is still being used by many people,
36:48we've developed a new system called Vera.
36:52And what this is,
36:54is it's a system that's designed to bring
36:56this capacity for continuous remote
36:59patient monitoring in behavioral health.
37:01And the capacity for ongoing
37:04continuous behaviour change support
37:06as a tool set to the clinician
37:09and so it goes kind of like this.
37:11You have this patient app.
37:15The person downloads and puts
37:16it on their phone.
37:17It collects data continuously.
37:19If the person wishes to,
37:22they can then share those data
37:24with a practitioner who has the
37:26complementary so well they only software.
37:28It's a web portal,
37:30so they need to log in and then the
37:33practitioner and what's happening is.
37:35What we're doing is we are collecting
37:38data on mood and behaviour across
37:41time within an individual and
37:43then we're building a model.
37:45A data model of each individual.
37:48And what are the particular
37:50behaviour patterns that support
37:51good mood and well-being for them?
37:53So this is simple.
37:54This is very simple stuff folks.
37:56This is called behavioral activation, right?
37:58It's an old technique, but one with a great.
38:02Pedigree in terms of effectiveness,
38:04but when I was a lad and we were learning,
38:07what would we do? We give people these.
38:11Matrix,
38:11matrix questionnaires.
38:11And they'd have to fill in what they
38:13did every hour and how they enjoyed it
38:15and whether it gave them a sense of mastery,
38:17as it was called then and so forth.
38:19And and and of course people didn't do it,
38:21you know,
38:22or they'd fill it in in the waiting
38:23room before they came to see you.
38:25All that kind of stuff, right?
38:26So this takes the effort out of it,
38:28because we're actually measuring behavior
38:30continuously and and objectively,
38:32and we're also correlating it
38:34with mood variability.
38:35And then for each interpersonal we
38:36are able to tell you which aspect of
38:39mood variability is most important.
38:42You. Then.
38:43That is fed back to the practitioner.
38:46The practitioner can look at
38:48those suggestions.
38:48She's not getting much sleep.
38:51Umm, you know,
38:52you can look at patterns like rigid
38:54thinking and the language and so forth.
38:56And it will also make some suggestions like,
38:58well, look,
38:59when this person gets more sleep,
39:00their mood is better or when they're more,
39:02when they have more social connection
39:05or if they are more positive in their
39:08thinking style or if they are more
39:11physically active or whatever it might be.
39:13And so that's fed back to the clinician.
39:15The clinician then build a,
39:17an intervention that is supported
39:20by just in time nudges.
39:22So they actually build out.
39:24They discussed this with the
39:25person and of course you know
39:26the idea of nudging this term.
39:27It comes from behavioral economics.
39:29The important thing about a
39:30nudge is that the person's got
39:32to match a goal the person holds.
39:33You can't manipulate people's
39:35behavior with a nudge,
39:36but if someone's trying to do something,
39:37then the nudge might connect their
39:40current self with their future
39:41self for one of a better term.
39:43You know, connections to that
39:44long term goal that they have.
39:46And so we know that nudging is not perfect,
39:48but it does.
39:48You know, the behavioral economics
39:50literature suggests that, you know,
39:52in all these different areas where
39:54they've looked at vaccines and, you know,
39:55various things, eating behavior,
39:56there's a lot of studies.
39:58Meta analysis tend to suggest it works.
40:00It's good.
40:01It helps people more often,
40:02more of the time follow through
40:04with their behavioural intentions.
40:06So you can build this in and this then
40:08arrives on the phone as a notification.
40:10Person can click through on that,
40:13they can let us know if they did it or not.
40:17And they can then and then we can learn.
40:21And the other thing that and the
40:23thing that actually gets me very
40:25excited about this is that what we're
40:27also doing is we are digitizing
40:29all this stuff in the workflow.
40:31Right.
40:31So this is so the clinician doesn't
40:34have to spend more time on.
40:35This is something I use when
40:37they're with the patient.
40:37And so suddenly we can then roll that up to
40:40the healthcare organization and they can
40:43see in real time are people getting better?
40:46What are my clinicians doing?
40:48Because at least in most
40:50evidence based therapies,
40:50some kind of homeworkers
40:52usually part of the deal.
40:55You see which kind of you can see what's
40:57working with our population here.
40:58Not some RCT across the other side of the
41:01country or the other side of the world,
41:03but with my population,
41:04with its particular characteristics,
41:06what is working and so that I
41:08can use that for supervision,
41:10for training, for service reform,
41:12all that kind of stuff.
41:14So suddenly we're creating a learning system,
41:17and one of the things that I think is
41:19exciting about this is the fact that
41:21we actually know more about traffic.
41:22Than we do about mental health.
41:25Because we get real time data and
41:28we adjust policy and interventions
41:30on the basis of it.
41:32And we don't get that in mental health.
41:34So that's that's so that's what we're
41:36working on and we're we're currently
41:37where we are with that is it's built,
41:40it's working and it's needs to work better,
41:42but it's but we're working on it.
41:45We've got a series of partnerships
41:47with different groups to test this out
41:49with different kinds of populations
41:51and and certainly if anyone here is
41:53interested in talking about this,
41:55I'd love love to chat to you.
41:58Umm.
42:00So. So what are the next steps?
42:04Well, the next steps are.
42:08At the moment we decided to start with
42:10a system where this kind of nudging
42:12procedure is controlled by clinician.
42:14But there are, and you know,
42:16there are various reasons for that.
42:17We want to learn more about it.
42:18We want to collect data.
42:19We want safety.
42:21Etcetera, but the.
42:23There's a lot of people talking about
42:25this concept of just in time adaptive
42:28interventions and so these are more
42:30automated things that are driven off.
42:33Machine learning,
42:34artificial intelligence, say.
42:35Just like that.
42:36But here's a definition,
42:37an intervention design that aims to provide
42:39just in time support by adapting to the
42:41dynamics that have been individuals,
42:43internal state, and context,
42:45which is measured continuously.
42:47Italics are mine. So.
42:51The point is that with mobile sensing.
42:54There's a lot of problems.
42:55Let me be clear.
42:56In case I'm sending to evangelical,
42:58I want to be clear.
42:59There's a lot of unresolved
43:01problems with mobile sensing.
43:02All right,
43:03we've got a lot of work to do to
43:05get better at it and to understand
43:06more what the data means.
43:08But it but it but at least it
43:10gives us line of sight on that.
43:13Problem.
43:13So let me give you a very,
43:15very simple example,
43:16one that I'm excited to be
43:18working with some colleagues on
43:19who are interested in people
43:21who are addicted to opiates.
43:23We're thinking about a procedure where.
43:26Participant could.
43:28Identify locations.
43:30Dropping a pin on a map that they
43:33are now a danger locations for them.
43:35So it could be the doctor where
43:37you get prescribed your opiates.
43:38It could be the,
43:39you know where you score your heroin.
43:41It could be, you know, like,
43:42so it could be wherever.
43:43It could be your friend who you go
43:44and visit and you used together,
43:45you know.
43:46So there's various things and you
43:47could drop those in if you wish to.
43:48This is all you know,
43:50totally only if the person wants to do it.
43:52And then it's not a hard technical
43:55problem to then geographically ring fence
43:57those locations and give the person a nudge.
44:00Whenever they're nearby.
44:03They're still free to do
44:04whatever they want to do.
44:04We're not controlling their behaviour,
44:06but we're just reminding them that if
44:08they do have a commitment to sobriety.
44:10That this is a decision point for them.
44:13And that we're raising that awareness,
44:14like I say,
44:15connecting your future self
44:17with your current self.
44:18So that's the kind of thing
44:21that's actually very tractable.
44:23The other one that I haven't
44:24talked about a lot today,
44:25but is that is really this was
44:26the use case that got me excited
44:28in the 1st place about it,
44:29which was suicide prevention.
44:32This is a harder problem.
44:34But we know that all the interventions
44:37that we have that are effective for
44:39suicide prevention work because
44:41they have an impact at the time of
44:43high risk and the time of high risk
44:45where there's an intention to act,
44:46and the time of high risk for
44:48most people is relatively short.
44:50Might even be only 10 or 15 minutes long,
44:52but the things that work are things
44:54that put some friction in at that point.
44:57Don't have a gun in the house?
44:59Keep your ammunition away from the gun.
45:02Put pills into blister
45:03packs instead of bottles.
45:05Put bridge, barriers up, etcetera.
45:08Don't, don't give people access to
45:10agricultural chemicals that have,
45:11you know, they're highly toxic.
45:12There's a whole range range of
45:14these things that have all been
45:15shown to be effective at the point
45:17is they all have an impact on at
45:18the at the moment of high risk.
45:20So it's a harder problem than
45:22the one I just described for
45:23the substance use scenario.
45:25But if we could get even some
45:27probabilistic estimate of when
45:29people's mood if someone's at risk.
45:31And then their mood is shifting
45:33in the particular direction,
45:34then the capacity to reach out at
45:36that time might be something that
45:37could really be a game changer.
45:39So that's pretty exciting. The.
45:43So the ultimate vision for this,
45:44also in terms of access,
45:46is to actually build a system that provides.
45:51Access to what people call stratified care,
45:54right.
45:54So one of the problems we have with
45:56our current mental health system is
45:58that we use the same solution for
46:00almost every problem most of the time.
46:02So it's expensive.
46:03People like me, you know,
46:04and and you know,
46:06it's the 50 minute hour and you know,
46:07we have that kind of,
46:08it's not always true,
46:09but but there's a tendency to
46:11use that one solution and what
46:13we want to be able to do is.
46:20Give people, through a digital tool, access
46:23to a range of different levels of care.
46:27That they can access. Without friction.
46:32Mental healthcare systems full of friction.
46:34It's hard to get in,
46:35hard to know where to go,
46:36hard to get an appointment,
46:38hard to know if you're not doing well
46:40because it's your fault or the therapist.
46:41You know? There's just like
46:43fiction everywhere and so.
46:45Once you're in,
46:46you can use the app for self-care.
46:48You can interact with automated nudges.
46:50You can get a text based coaching model,
46:53health coaching model,
46:54which is more scalable than a
46:56traditional face to face therapy model.
46:57You can also access telehealth.
47:00Consultations and face to
47:01face therapy and if necessary,
47:03hospitalization and inpatient care.
47:07A daycare, sorry.
47:10But the idea is,
47:11wouldn't it be cool if someone
47:12who had a mental health challenge
47:14could plug into this system at
47:15the right level for them and then
47:17move up and down according to what
47:18they needed at a particular time,
47:20knowing that they can still move back
47:23up or move back down relatively easily?
47:26So that's that's the kind of the goal.
47:28So we're starting on.
47:30This part and this part and this part,
47:34but we want to kind of build it out overtime.
47:37All right.
47:38Thank you for listening.
47:54Thank you very much, Nick.
47:55And questions for Doctor Allen.
48:02Carla.
48:07So I'm very interested in this.
48:09I do a lot of work with people
48:11with significant trauma
48:12backgrounds and a lot of emotion.
48:13Emotion dysregulation,
48:15difficulties, irritable,
48:16angry, outbursts, violence.
48:18And I'm thinking about this
48:20in terms of applications to
48:22someone being able to monitor
48:24who isn't very well in touch
48:26with their internal States and
48:27being able to monitor them
48:28and get pings that they need to use
48:30coping strategies. Does that seem
48:32like something that is?
48:35A million years away,
48:37or potentially feasible sooner? So.
48:40Well, let me go back to the microphone.
48:47So when I did my PhD research,
48:49I did it in psychophysiology and
48:52so this is not a new problem,
48:54this is an old problem and I was particularly
48:56in the psychophysiology of emotion.
48:58And so one of the problems we know that
49:00we've got with the psychophysiology
49:02or behavioural signatures of
49:03emotion is that there's, it's a,
49:05it's a kind of a many to one problem.
49:07There's lots of different.
49:09Signatures that can occur that
49:11can be associated with emotional
49:13experience across individuals.
49:15So one person might show high autonomic
49:17arousal and another person might not,
49:19and another person might express
49:20it in language, you know,
49:21so there's lots of so.
49:23So I want to say that as a background
49:25that that there's a lot of people
49:26in this digital health area and you,
49:28if you read the digital tech press and
49:30the saving some of the mainstream press,
49:32you'll see these things occasionally.
49:34Does your smartphone know when you're
49:36depressed, you know, or something like that?
49:37And it's always simple.
49:39Have simplified.
49:41Because of this many to one problem
49:44this multifinality issue but.
49:46There is a,
49:47I think a tractable version,
49:49which is that the system has to
49:51learn about the individual right.
49:53It has to learn at the individual level.
49:55And I think that's more tractable.
49:56If you could actually track someone
49:58across time and understand their
50:00mood variability and then look
50:01at the particular signatures that
50:03are associated with them,
50:04that's what we do as clinicians, right?
50:07We try to learn what for each
50:09individual intervention, you know,
50:10if they're working with the clinician,
50:11trying to identify what are their signals,
50:13then you could input it into
50:15the device exactly.
50:16So for example.
50:17You know, there's lots of examples.
50:19The one that you mentioned,
50:20another classic is someone who
50:22experiences bipolar disorder.
50:24And you know,
50:24when you work psycho therapeutically
50:26with someone with bipolar disorder,
50:27one of the main things you do is
50:29try to help them to learn early,
50:30particularly in the manic phase to pick up.
50:33That's something shifting,
50:34which they often don't notice until too late.
50:38But over repeated episodes
50:39they can start to learn.
50:40And so if you could have something like
50:42that that is monitored and fed back,
50:44then that's very empowering
50:46to the individual and I.
50:47And I just, I'll dwell on that point,
50:50empowering for a moment.
50:51Because one of the things I
50:52suspect some of you are thinking,
50:53if not all of you is.
50:55It's a common reaction.
50:56I get people go, this is a little creepy,
50:58nick, this is a little creepy.
51:00You know,
51:01you're tracking people and things like that.
51:03And I think there's a couple of,
51:05there's good reasons for that, but
51:07I think one thing that's important is that.
51:10Data is power.
51:13And the question is,
51:14who is it empowering?
51:16Now,
51:16our most common experience with
51:18digital data at the moment is that.
51:20It empowers large tech companies who
51:23use it to manipulate what we buy.
51:27But there is actually a use of data,
51:29I believe, that is empowering to the
51:31individual if you set it upright.
51:33And this is the kind of use
51:34case you're talking about.
51:36Like, it's empowering to me to understand.
51:39My patterns of behaviour and how they
51:41are going to affect my long-term health.
51:45It's empowering to me to be able
51:47to share those data with someone
51:48I want to share it with,
51:50as well as take the data back when I don't
51:52want them to have it anymore, right?
51:54Which our system does.
51:55So I do think.
51:58Yes, it's potentially creepy and you've gotta
52:00and you know you've gotta manage it, right?
52:02But I think if you if we ignore it,
52:05then we're missing out on an
52:07opportunity to empower people with
52:09greater knowledge and self-awareness.
52:10Yeah.
52:15Susie?
52:21Thank you so much for this presentation.
52:22I have multiple questions,
52:24but I'm going to go with one and that is
52:27I'm given the deluge of funding that is
52:29coming out of the federal government,
52:31whether the bipartisan infrastructure
52:33law or the safety bill,
52:37there's huge resources for digital work.
52:41The question is whether you are interfacing
52:44with policy to show this additional,
52:46this additional value,
52:48this hugely important.
52:49Value that is offered through this.
52:51And so are you doing it or are you
52:53interested in playing a policy role?
52:55So when you say you,
52:58you mean me as an individual,
52:59I mean you as an individual,
53:01University of Oregon, your private company.
53:03The time is now,
53:04the money's out and and and I
53:05think that there would be a lot of
53:07interest in what you're doing because
53:09you're showing an additional value
53:11to bridging that digital divide.
53:13So the quick answer to that is.
53:16I'm trying,
53:16but it's not something I'm necessarily
53:19well trained in or good at.
53:21So, for example,
53:22I mentioned briefly this report that we
53:25did for the world Innovation Health Forum.
53:28I've just finished working on a white
53:30paper with the National Scientific
53:32Council on the developing adolescent,
53:34which is a a group I'm involved in
53:37which is trying to influence policy.
53:41I'm trying to think of some other examples.
53:43You know I've been to meetings with
53:44UNICEF and the World Economic Foundation
53:46and other sort of policy related things.
53:48So trying, but I will say I'm a
53:52clinical psychologist, you know,
53:53I like seeing patients and collecting
53:55data and building software now, but I,
53:57you know, it's it's an interesting skill set.
53:59I don't know that I'm, I'm,
54:01I need people to help me.
54:04In the water, yeah.
54:05So now if you start swimming, yeah.
54:07No, but I think you're absolutely right.
54:09And of course, as you know,
54:11whenever there is an A deluge,
54:13as you describe it,
54:14of government funding, there's always,
54:16even when you like the topic they're funding,
54:18there's always that feeling like.
54:20How much of it is going to get wasted?
54:22And we know it's some,
54:25but how can we minimize the
54:26amount that's going to get wasted?
54:27And there's, of course,
54:28the moment the government,
54:29there's a bucket of money
54:30from the government,
54:31all sorts of hucksters and so forth
54:32will be running at it along with the
54:34people who've actually got solutions.
54:35So I think partly the digital area has
54:40been very fatty and and influenced by that.
54:43I think the good news is that what I'm
54:45seeing even in the business part of it
54:47and Inventure venture funding and so forth,
54:49there's a,
54:49there's a maturing.
54:50Where people are starting to ask
54:53better questions of people who've
54:54got some new app and they're asking
54:57for evidence and they're asking for.
54:59You know,
54:59implementation feasibility and
55:01all that kind of stuff.
55:02So I think we're getting better,
55:03but there has you know it,
55:05it there's definitely a potential for.
55:08Poor spending.
55:10Angie has a question online.
55:14Thank you. Thank you so much for this talk.
55:17It actually is very refreshing to
55:20actually have data behind things that
55:23you clinically see in the day-to-day.
55:26So a lot of the research that we do
55:30is suicidality and adolescence and
55:33so you know just anecdotally things
55:35that you know that is that your your
55:38patients listen to more dark music.
55:41You know, is there a way,
55:42is there a way with which we could
55:44track their Spotify and see, you know,
55:47kind of what they're listening to so that
55:49we can then send them a nudge and say,
55:51hey, you know,
55:52you're listening to too much of that
55:55music or whatever it is that they're.
55:58But then one of the things that
56:01we've come across is from,
56:04from the up down perspective of healthcare.
56:10Jokingly said e-mail is the your
56:13grandfathers but but right now that is
56:16how we're allowed to communicate with
56:19our patients when clinically we know.
56:22Just from just from our day-to-day
56:24interactions I know that I'm better
56:27able to reach out to my my participant,
56:30my adolescent participant.
56:31If I text them, more likely to text me back.
56:37From what I do in research,
56:39then applying that to outpatient services,
56:43there's a big divide because
56:45obviously HIPAA distractions.
56:47You know how how do we protect
56:50the patients information?
56:52How does this interface all
56:53of the data that is collected?
56:55How does that interface with
56:57EPIC for example as a, as an ER?
57:01So I'm I'm wondering.
57:04How are you or how do you envision
57:09taking some of the the data that you've
57:12acquired to convince some of these
57:16larger echelons of healthcare to allow
57:19for us to have a better relationship,
57:22even if it is through these technological
57:26devices with our adolescents,
57:28because developmentally that's
57:29where they're at and that's
57:31what they're expecting from us.
57:33We need to be mobile.
57:35For for our patients.
57:37Yeah, great. 2 great points.
57:40I'll take the second one first.
57:41So the. Yeah, you're absolutely right.
57:44I don't know if you've ever called
57:45one of your adolescent kids on the
57:46phone and they picked up and said,
57:48why didn't you text me?
57:49You know, because they really like
57:51interacting via text. And so,
57:53but this is actually a solvable problem.
57:55I mean, our system is fully HIPAA compliant,
57:57so you can build it to a
57:59HIPAA compliant standard.
58:00And the problem is that and and
58:02so then it means, you know,
58:03but a lot of the solutions that the
58:05that the adolescent is using for their
58:07regular texting or instant messaging.
58:09Or not.
58:10And so that's one of the advantages of
58:12having a kind of an app like ours is
58:14that you've actually got a HIPAA compliant.
58:17Messaging system within it and that
58:18the apps already on their phone.
58:20So there are there are ways to deal
58:22with that, but I agree and and and.
58:25Patients love it.
58:26I love having that kind of capacity
58:28to check in and it's a little bit
58:31like when Marshall Lanahan started
58:32you know having a therapist and
58:34DBT carried phones with them and
58:36everyone said like Oh my God,
58:37you can't do that.
58:38These, you know these clients
58:39will be just constantly ringing.
58:40You turns out not to be true
58:42most of the time.
58:43You know most clients actually use it very
58:46responsibly and so and that's even with the,
58:48you know, the borderline clients
58:49that they were working with,
58:50with the PT.
58:51So.
58:51So I think that you know,
58:53patients really appreciate having greater
58:55access and they and they tend to use it.
58:58Very um, but but you know.
59:01Legal systems can be built to be
59:03HIPAA compliant and so I think it's a
59:04question of how you how you do that.
59:05Now just remind me what
59:06your first question was.
59:07I thought it was really interesting and.
59:09Wanted to say
59:10it was about checking their Spotify.
59:14Really? Yeah, we can totally do that.
59:15So we can look at the music
59:18and there's actually Spotify.
59:20We actually use their engine because
59:21they have an engine for music
59:23recommendation and they have every song
59:25in the world listed and they rated on
59:27these musical and emotional qualities.
59:28But once again, the important thing is
59:30not all kids who listen to Norwegian
59:33death metal are about to kill themselves,
59:35right? It it it.
59:36But if you've got a kid who who
59:38doesn't normally listen to that,
59:40so it's about the within person change,
59:42right?
59:43And if they suddenly are listening to it?
59:45A lot that might be,
59:46I mean this is a speculation at this point,
59:49but so it's important not to say it's,
59:52I think it's more state like than
59:53trade like you have to understand
59:55the usual pattern of listening.
59:56We don't want to say everyone who
59:58loves ex kind of music is you know,
60:00got a mental health issue or in need.
01:00:02But I think if you see changes,
01:00:03dynamic changes across time.
01:00:05We do know anecdotally in clinical
01:00:06circumstances where sometimes kids
01:00:08who are getting very are having
01:00:10a lot of suicidal ideation will
01:00:11listen to a certain kind of music.
01:00:13That for them is very much associated
01:00:15with that mood state and if there's
01:00:17a lot of listening to that,
01:00:18that could be a marker.
01:00:20Yeah.
01:00:22So we are just about at time,
01:00:24but I would just like
01:00:25to thank Doctor Allen for
01:00:27a wonderful presentation.