Identifying Adolescents at Risk for Substance Misuse Using Digital Tools: Why, Where, When and How

February 23, 2023

YCSC Grand Rounds February 21, 2023

Kammarauche Aneni, MD, MHS, Assistant Professor of Child Psychiatry, Yale Child Study Center

Information

ID: 9551
To Cite: DCA Citation Guide

Download Transcript

00:00Doctor Anani who used to be Doctor Isuzu
00:03but now she has her taken her married
00:07name and Annie is a good friend and and
00:10really one of our someone we're so proud
00:13to have as a trainee and now on faculty.
00:17Uche went to medical school in her
00:20native Nigeria, the University of Ibadan,
00:22and then in the states.
00:25She's been winding her way up
00:27the eastern seaboard,
00:28from Miami to Duke to Johns Hopkins.
00:35All of them are a bunch of losers
00:36because she ended up here with us,
00:37so we're delighted.
00:39And Uche has worked with a lot of the
00:42people in the Who's who of psychiatry,
00:45including I was just looking at
00:47her CV again with Doctor Charlie
00:49Nemeroff when he was in Miami.
00:51And it's just remarkable looking at your CV,
00:54which how quickly you've been publishing,
00:57getting grants, coming into your
00:59own right as an investigator,
01:01which is what you're going to show us today.
01:05Among her many interests,
01:07Uche has done some really important
01:10work on race, racism, race relations,
01:13including here in our inpatient unit.
01:17I was joking that I had the
01:19privilege of publishing, I think,
01:21Doctor Isuzus last paper,
01:23because Doctor Azusa is now Doctor Anani.
01:25But that paper is really important
01:27with many of our colleagues,
01:29including David Rees and Laurie Cardona,
01:31who's here in Amanda Calhoun,
01:33a qualitative study about racism.
01:35Race relations in an inpatient unit,
01:38a pretty brave type of work
01:41that is much needed.
01:43And Uche has moved into the digital
01:46world in a in in a major way.
01:50She is now part of a number of
01:53consortia and grants all together
01:55with the digital team here.
01:58She is now the associate director of the
02:00play to Prevent Group that we're going
02:02to be hearing about that brings technologies.
02:05To clinical needs and
02:08using all sorts of G Wiz.
02:11You know,
02:12toys towards very important purposes.
02:15So it's wonderful to be here.
02:17Oh,
02:18the most important thing is that I
02:19understand because I saw the list
02:21that her husband is watching you,
02:22so you better do a good job.
02:24And but the most important thing is
02:26that that which is also the proud mom
02:28of two beautiful kids who we have been
02:30seeing growing here, Daisy and Damien.
02:33So would you take it away?
02:44Thank you for that kind introduction.
02:49Umm.
02:54Ohh sorry. 2nd.
03:03All right. So I'm excited to talk to
03:06you all about my work investigating
03:09the utility of digital tools,
03:12not only for risk assessment,
03:13which is the focus of this talk,
03:15but also for intervention
03:18development and delivery,
03:20specifically focused on addressing
03:22adults and substance misuse.
03:25And all of this really is framed.
03:27Under the umbrella of increasing access to
03:30care, I think a lot about access to care.
03:33I grew up in a country where access
03:35to care was pretty challenging,
03:37and so I thought a lot about it growing
03:40up also as a resident and trainee,
03:44I was also really struck by how delaying
03:47access to care really leads to adolescence,
03:52presenting very late in disease,
03:54but also how difficult it is
03:56to manage when kids present.
03:58So within that framework is where
04:00where I where I land and how I
04:03will be framing the talk today.
04:05And I have an outline thinking
04:07together with you all about why,
04:09when,
04:09where and what are the barriers
04:11with identifying kids early.
04:13And I will focus mainly on 2 digital
04:15tools which have which I'm working on,
04:18which is game based and the electric the
04:20use of the electronic health records.
04:24And so when I say substance misuse,
04:25I mean unhealthy substance.
04:27Use of alcohol or drugs to relieve stress,
04:31alter reality or bring about pleasure.
04:33Using any way not prescribed by a doctor.
04:35Use without one's own prescription,
04:37using greater amounts, small often,
04:38or longer than told to take.
04:41And this affects 3.7 million adolescents,
04:43as is the national Survey of Drug
04:45Use and Health, which equates to
04:46about one to two in $10 cents.
04:48So it's common, although the average
04:50age of onset is about 15 to 17 years,
04:53it can occur earlier.
04:54And we know that younger age of onset
04:56is associated with a greater likelihood
04:59of developing a substance use disorder.
05:01Outcomes are worse.
05:03The consequences are dire.
05:04So in the last two years we've seen
05:07we're dealing with a crisis, right?
05:09And drug overdose rates has
05:11risen by 1 / 100%.
05:13If we look at the media
05:15monthly overdose deaths,
05:16it's associated with overdose,
05:17which can happen at the first time,
05:19the first time someone misuses a substance
05:22associated with comorbid mental disorders.
05:24Both as as increases the
05:27risk for substance abuse,
05:29but also as a consequence and
05:31of course associated with.
05:33Or functioning like school dropout
05:35legal problems for relationships.
05:37Also,
05:38the time between when disease staff
05:42seems sad and initial treatment seeking.
05:45Initial treatment seeking is on average.
05:48This study done by Tesla,
05:49which has reached really fund did they lead?
05:52The National Comorbidity Survey was 16 years.
05:57And I know you will agree that that's
06:01that's unacceptable and it underscores the
06:04importance and need to identify people.
06:07Adolescence, early substances used
06:09typically occur starts in adolescence,
06:11which is which is why the focus.
06:14Also less than 10% in need of
06:16treatment receive it in 2021.
06:18The number for adults since
06:1912 to 17 years was 3.5%.
06:22So.
06:23Adolescent substance misuse is a major
06:25public health problem problem and most in
06:28need of treatment are not receiving it.
06:31If we've got to think about how might we,
06:33you know, start to solve this problem,
06:35one of the things that we
06:36might think about like where,
06:37where,
06:38where are adolescents?
06:4196% are enrolled in schools,
06:43not necessarily attending schools,
06:44but I6I enrolled in school,
06:46so there might be a way to
06:48like engage contact them.
06:5095% have access to a smartphone,
06:5291% are seen in primary care annually.
06:5590% are lined daily and 90% play video games.
06:59So, umm, I think we need a systemic
07:01model for addressing substance misuse.
07:04And what I mean by systemic model is a model,
07:07a national model that
07:08screens all adolescents,
07:10triages them based on their risk of use.
07:12No substance misuse,
07:13and so therefore it needs primary prevention,
07:16at least substance misuse,
07:18secondary prevention,
07:18substance use disorder treatment. Umm.
07:22And I think what digital tools can do,
07:28we'll talk a bit about that,
07:29how they can help with these.
07:30But one of the questions that come up is,
07:32well we already have a lot of
07:34kids who are struggling and we
07:36don't have enough providers.
07:37So this is only going to increase the
07:40number of kids who who were identifying.
07:42But I I would say that I think that
07:44the fact that we're not identifying
07:46them early is contributing to the
07:48number of kids that we're seeing
07:49who are really ill and at that time.
07:52It's really difficult to address symptoms
07:55as opposed to like if you catch them early.
07:58One of the systematic reviews we did earlier,
08:00some years ago,
08:02showed that interventions digitally
08:04delivered interventions that deliver
08:06universal or targeted interventions were
08:08actually more likely to be effective,
08:11which I think speaks to other
08:13findings from other studies that
08:16data interventions can adequately
08:18target adolescents who have present
08:20with mild to moderate symptoms.
08:23As opposed to like severe disorders
08:25which need which you you need more than,
08:28you know, standalone digital intervention.
08:32And so, you know,
08:33the car as opposed to sustained model,
08:36what we do, what we have,
08:38where the model that we have is
08:40routine recommendation by Samsung,
08:41the American Academy of Pediatrics
08:43to screen routinely at annual
08:45primary care visits.
08:47And the way this happens is you,
08:49you know through the expert
08:50model where you screen,
08:51you have a proof intervention
08:53and then you refer to treatment.
08:54All of this is required.
08:56The primary care provider does this.
08:58Some systems do differently where there's
08:59someone in there who can deliver.
09:01Of intervention, but many,
09:02many times it falls on the shoulders
09:04of the primary care provider.
09:05There are no universal screening in schools.
09:07Schools do schools once,
09:08but they do whatever they can.
09:10It's not systemic at all.
09:13So what are the barriers?
09:15Many providers are not screening
09:17for substance misuse.
09:18There are time constraints,
09:20there's lack of reimbursement.
09:21It seems that every time we want
09:23to do something else that is
09:25prevention or preventive wise,
09:27we add some one more thing that
09:29primary care providers have to do.
09:31And so primary care providers have
09:32to triage and decide what they're
09:34going to prioritize in their visit,
09:36which might be appropriately informed
09:39by the presenting complaint and so.
09:42Provide us some providers have
09:44reported lacking knowledge
09:46about what screens to use the system.
09:48The workflow is in there to
09:50actually make this happen.
09:51What are the resources?
09:52I don't have resources of identifying
09:55how to figure out what to do.
09:56Occurrence not always aware of of of
09:59substance misuse among their adolescence.
10:01Adolescence are worried about disclosing
10:02this for the first time with their
10:05parents being present so they're not
10:07always forthcoming for many reasons.
10:08The stigma there's no privacy in the clinics.
10:12Umm, and so I'm saying I'm suggesting
10:14that you talk to us, mere Canadians,
10:16why some of these barriers?
10:18There's wide reach.
10:19You can automate this.
10:21You can alleviate provider burden.
10:23This can happen at any time.
10:26The study by night at all showed
10:28that adolescents are more honest
10:29about the substance misuse when
10:31completing digital screeners and
10:33paper screens compared to interviews,
10:35and there's potential for electronic
10:37health record integration.
10:39We could potentially build an
10:41automated system that identifies
10:42risk as it as it emerges,
10:44which would be ideal.
10:46Funnel appropriately,
10:47deliver an intervention if it's
10:48a standalone for multi moderate,
10:50figure out a way to deliver or funnel
10:53to to treatment for those who need.
10:56Specific treatment by a trained personnel.
10:59So what might an ideal digital
11:02Screener look like?
11:03I would suggest that it would be up,
11:05you know,
11:06objective as opposed to self report
11:07will be effective at identifying
11:09what you're trying to measure.
11:11So substance misuse in this case.
11:12It will be scalable and it will
11:15be accessible at all times by
11:17whoever needs needs to access it.
11:20So my my proposition is that games may
11:25offer an objective and effective method
11:27for identifying at risk adolescents.
11:29And this is based on the premise
11:32of of metrics which experts call
11:34digital biomarkers that are that are
11:37captured by the by the game software.
11:39And I like this definition of game
11:42digital biomarkers which is which
11:43defines it as consumer generated
11:45physiological and behavioral measures
11:47collected through connected digital
11:49tools that explain influence or
11:51predict health related outcomes.
11:53So one example that has been used now
11:56is being used is motor performance
11:58in games and the identification
12:00of Ellie Parkinson's. Umm.
12:02And so could we do something similar?
12:08Games collect a lot of metrics and
12:09we'll talk a little more about the
12:11work that I've been doing around this.
12:13But games collect a lot of metrics,
12:15and some of the metrics for example
12:17are like time to complete a task,
12:19accuracy of choices.
12:20Those task may be informed by
12:24cognitive processes such as maybe
12:26working memory or inhibitory control,
12:28which we know are implicated in development
12:31of substance misuse and are also in
12:35in impacted by misuse of substances.
12:38And so if we're going to use games to
12:40measure cognitive function or identify
12:42kids who are at risk for substance misuse,
12:45are they valid?
12:46So we did a systematic review and meta
12:48analysis to assess the validity of game
12:51based assessments of cognitive function.
12:53This has been accepted in
12:55progress in brain research.
12:57We looked at studies examining game
12:59based assessments among children
13:01and adults and zero to 17 years,
13:03but four major questions
13:05general game characteristics,
13:06cognitive functions that were measured,
13:09how, what was the validity
13:10and how did they compare.
13:11They used in the studies used pairwise
13:13correlations and we're looking at factors
13:15that may influence the validity of.
13:17Game just assessments would define
13:20validity by criterion validity.
13:22So how well does a new measure
13:25compared to an to a previously
13:27validated measure using you know?
13:30Here using pairwise correlation,
13:32so pairwise correlations between games
13:33that measured specific cognitive
13:35functions and traditional assessments.
13:37We did a meta analysis of specific
13:39to these games had many tasks and
13:41so we only did a meta analysis of
13:44games that measured specific tasks.
13:46So an impact task for working
13:48memory to a traditional and back
13:51task measuring working memory.
13:52We're also interested in things that would
13:55affect validity and we organized this
13:56around things at the person level like age,
13:59sex, race,
13:59how the game was delete,
14:02where the game was delivered,
14:03home school clinic,
14:04and how the how the game itself.
14:07Operated one of the things I'll talk
14:09about the scarring analytical method.
14:11So you could you could you could
14:13do a task and basically measure
14:15for the impact test for example
14:17how many errors or go no go task,
14:20how many errors were made when
14:21this person was doing the test.
14:23As opposed to like using collecting
14:25all the metrics in a game and
14:28using an analytic machine learning
14:30predictive model to predict cognitive
14:32function for example.
14:34So we extracted all of these metrics,
14:36type of game, duration of gameplay,
14:38narrative, storyline.
14:39So one ways in which people are trying
14:43to make screen assessments more
14:45palatable is to gamify, for example.
14:47And one way in which they gamify it
14:49is to include a narrative storyline,
14:51follow Mr.
14:51X as it goes on a plane,
14:53and then while that is happening
14:56you have different cognitive tests.
14:57And so I was interested in whether
15:01this influenced the validity of game.
15:03Especially since I'll talk about my
15:05study which actually uses a narrative
15:07based game and we extracted this
15:09current method like I talked about
15:11the study side mode of delivery and
15:14this year's traditional validated tests
15:16like the West, Kaufmanns, Baileys.
15:19There were eighteen games across
15:2020 studies, 17 serious games,
15:22which means that they were specifically
15:24designed to measure cognitive function,
15:26and one commercially available game,
15:28Minecraft, which was assessed for its
15:30utility in assessing cognitive function.
15:32And compared with traditional assessments.
15:34The duration of Gameplay varied,
15:37So we can do much, much more with that.
15:39There were five games to use narrative
15:41story Line 6 studies of 20 studies,
15:43which is the predictive model.
15:45You can see the wide range of sites,
15:47the delivery mods. Words.
15:49Most of them were through computers
15:52and we extracted shout out to Megan
15:54and ISA for helping with extracting
15:57all of this coral correlations.
15:59But we extracted.
16:02375 pairwise correlations across the
16:04street difference through 20 studies.
16:0775% of these were significant.
16:10Working memory was the most common
16:13cognitive function measured,
16:15followed by attention,
16:16inhibitory control and visual spatial skills.
16:19The meta analysis just quickly
16:22while on the low to medium range,
16:23which you might say compares
16:25to other validation studies.
16:27But for attention it was
16:280.3 inhibitory control 0.3,
16:32my working memory at the best at 0.4.
16:36We did very basic frequency high
16:40square frequency comparisons by by
16:42correlations by these different
16:44factors and we found no differences
16:48by sites and format of delivery.
16:50But we found that adolescents tended to
16:52play older adolescents tend to play better.
16:54As you would expect.
16:55As you grow older,
16:57you would you your cognitive
16:58process will get better and you
17:00will play better in the game.
17:01And also as you would expect,
17:03a producer of a prediction model was
17:05more likely to yield significant.
17:07Solutions as you would expect,
17:08since they were specifically
17:10identifying metrics, right?
17:13But also the inclusion of a narrative
17:15story lines seem to be more associated
17:17with non significant correlations.
17:18So I wonder about whether
17:21that was distracting.
17:22All this to say in general is that
17:25that that that there are factors
17:27that influence the the how valid
17:29game based games are for assessing
17:31cognitive function is is the takeover.
17:34We were interested in assisting for
17:37race and none of the studies did
17:41reviewed reported on race and ethnicity.
17:45Most studies as you can see here
17:48found effect differences for age.
17:49Gender was really around spatial
17:52reasoning and ability,
17:53which I think relates to that.
17:56Boys are more exposed to buy their
17:58toys special task as opposed to
18:00like a biological difference and
18:02then prior exposure to gaming.
18:04Technology also influence which
18:06we'll talk about also.
18:08So if.
18:09If you can learn from the game,
18:11is it is it still?
18:13Can it still be useful as a screening tool?
18:18And so I talked about this unexplored
18:20factors which we we need to be thinking
18:23about and we need to be assessing for
18:26for widespread we're talking about
18:28scalable and scalability of this tools.
18:30So in summary, many adults and such risk
18:33for substance misuse are not identified.
18:35The use of digital tools
18:37can alleviate some barriers.
18:38Cognitive dysfunction is associated
18:40with substance misuse.
18:42Video games can measure cognitive function.
18:45So it may be a potential tool for
18:47identifying adult center to risk
18:48for substance misuse via measure of
18:50indices that correlates with cognitive
18:52functions that are also implicated
18:54in development of substance misuse.
18:57So this is one of the basis for our
18:59proof of concept study and the central
19:01question is can data collected during
19:03gameplay be used to identify adult
19:05centered risk for substance misuse?
19:08Umm.
19:09Central hypothesis is that adolescence
19:11with higher risk of substance misuse
19:13will perform worse on the video game
19:15and demonstrate poorer cognitive
19:16function compared to adolescence at
19:18a lower risk for substance misuse.
19:20We've already established that
19:22cognitive function influences how you
19:24perform in a game and also influences
19:26your risk for substance misuse.
19:28So we're using play forward game
19:29developed in the play to Prevent Lab
19:31in narrative based game initially
19:33designed to target HIV and high risk
19:35behaviors like substance misuse.
19:36It has 12 levels and five mini games
19:39here shown at the bottom here.
19:41Each mini game has 10 levels.
19:43We'll talk a little bit about that
19:45and players and stars and points has
19:47been previously tested where we had
19:49160 where they were $166 cents each,
19:5311 to 14 who played play forward.
19:55There were 43% black adolescents.
19:5815% fifty 6% Hispanics, Hispanic adolescents.
20:03So in the I will try and walk through
20:05the point to show how perhaps some
20:08of these metrics may correlate
20:10with cognitive processes.
20:11And so in the no sense mini game
20:14an adolescent has a challenge and
20:16they have to decide the statement
20:18presented to them about drug use
20:20or drug misuse and they have to
20:22decide if this statement is true
20:24false and or an opinion and so you
20:27can imagine that they have some of
20:29it is influenced of course by their
20:31their prior existing knowledge but.
20:33How quickly can they retrieve
20:35the information they know?
20:36Are they going to do that or are
20:38they going to just choose an option?
20:40Impulsivity, perhaps,
20:41and how does that influence how well
20:44they perform as they're playing in the game?
20:47They have feedback,
20:48they get feedback.
20:49And so you can imagine that if that
20:51people will do better over time,
20:53unless they don't really don't
20:55care whether to do better or not.
20:56And can we seize those differences apart?
21:00Umm,
21:00in the People's Sense mini game this
21:03this player has to decide where to
21:06place their friend on friendship
21:08circles and how they decide that ideally
21:11should be influenced by people's skills,
21:13which may reflect whether they
21:15are low risk or high risk.
21:17So for example, how do you observe
21:19people's skills while Jaden is
21:20always hanging out with Dante,
21:21but Dante happens to be so
21:23wasted at the party last night.
21:25Is he someone you wants and
21:27you're very close circle?
21:29So when you decide that's the play,
21:31it gets more complex as you
21:33go through higher levels,
21:34but you can imagine that a player
21:36has to keep that information.
21:37You can say they can always go back
21:39and click and look at the risk,
21:41but how quickly you advance in this
21:43game depends also on how much of
21:46that information you can keep within.
21:48And when they finish placing
21:49friends in their circle,
21:51they then have invites that they
21:53get and the invites that they get.
21:56They have to decide whether
21:57this is a good invite.
21:59Very risky invites and depending
22:01on whether they decline or accept,
22:03they get stars.
22:04So if you accept like
22:05invites that are very risky,
22:07you can have three strikes in your house.
22:11And so I'm we myself,
22:15Megan Isabella played the game and
22:18page by page of the game reviewed.
22:21What are specific metrics in the game that
22:24may be influenced by cognitive processes?
22:27And so, for example,
22:28in the deciding your friends,
22:30for example,
22:30in people's sense,
22:31checking out like this time spent
22:34checking PS characteristics and
22:36there's you have to correctly set
22:38peers into right social circles,
22:39and we're hypothesizing that
22:42these domains are probably
22:44influencing those processes.
22:47And also when you are accepting and rejecting
22:50invites correctly accepting or declining.
22:53Um,
22:53that these processes,
22:55this cognitive processes and domains
22:57are influencing how well adolescence
23:00perform in this tasks in the game.
23:03You can also see that there are constructs,
23:05so there's a time construct,
23:07and there's also an accuracy conduct
23:09time constructing yellow and the
23:11accuracy constructing green.
23:14And so. The the first game from
23:18this study be butanol, that is,
23:20can we identify them metrics in the
23:22game that are predictive of substance
23:25misuse and can we derive a prediction
23:28model using those identified metrics?
23:30We used 166 participants.
23:32Like I talked about.
23:33We had two outcomes,
23:35substance misuse and self
23:36efficacy to refuse drugs.
23:38Sometimes it's used was already measured
23:40using the youth Risk Behavior Survey.
23:42There were twenty questions related
23:44to alcohol and drug use and we'll
23:46talk a little more about this,
23:48but some of those questions included
23:49if I tried using the cigarettes,
23:51which I think contributed to this
23:53being overall a low risk group in
23:55terms of like if you used once you
23:58were you were considered high risk.
24:00Um, based on just how many?
24:02The variance in the sample and then
24:06self efficacy to refuse drugs was
24:08measured using the door scale and
24:10we dichotomized these two outcomes.
24:14We use the the variance
24:16threshold metal method to drop
24:18all metrics with zero variance.
24:19So if they have no variance then they
24:21are likely to differentiate the two
24:23groups between low and high risk.
24:25And we also dropped some variables
24:27that had very high multicollinearity
24:30and used this machine learning
24:32technique called cross validation
24:33which splits the data and then splits
24:35the data even more and and checks
24:37to make sure that how the model
24:39performs in one set of data is the
24:41same across multiple sets of the data.
24:43It's a very small sample for people
24:45who do machine learning statistics as
24:47one single is like very small sample.
24:49Usually you want to be using
24:52samples 600 and above.
24:53And so we were limited in the amount of
24:56like can we examine race for example,
24:59which is something that I wanted
25:00to examine other other models,
25:01the same between black and
25:03white adolescents for example.
25:05And then we tested these six different
25:08models and computer AUC values.
25:10AUC value tells you how well
25:12your model is doing.
25:13And 0.5 usually means that
25:16it's not doing anything,
25:180.6 means that it's performing moderately OK,
25:20and 0.7 usually means this
25:22is a pretty good model.
25:25So we excluded 6 um log files that were
25:30corrupted or were from adolescence.
25:33We had there were mostly mostly corrupted
25:36or were incomplete and based on the final
25:39sample one in three adolescents had high
25:41risk or causing the high risk of substance
25:44misuse and wanting about wanting to
25:46have poor self efficacy to refuse drugs.
25:48We ultimately had 285 in
25:51game metrics after cleaning.
25:53So our first outcome,
25:55a model was not good.
25:57So the model didn't predict what didn't
26:01predict substance misuse among adolescents.
26:04Umm. And but the second outcome
26:08self efficacy to refuse drugs.
26:10The logistic regression model seem
26:12to perform relatively well across
26:15multiple and was stable across multiple
26:17cuts of the data with an AUC of 0.6.
26:22When we looked at the model,
26:24when we looked at what was contributing,
26:26what metrics were contributing
26:28to this prediction?
26:30What I take from here is that most
26:32of them happened at the beginning.
26:34So zero level 0, there were ten levels,
26:36zero level 0 to 9 and most of these
26:38were happening at the beginning
26:39of the of the game.
26:40Which again speaks to like can these be
26:43used as a can just really be used as a
26:46screening if it identifies if it can,
26:48if your performance can change over
26:50time and is if your performance
26:52changing over time.
26:53Is that reflective of an improvement
26:55in function that actually influences
26:57substance misuse or not?
26:58Many questions are raised right?
27:01So in summary I think there
27:02are outstanding questions.
27:03I think we found certain game metrics
27:06were predicted of self efficacy to refuse
27:08drugs among adolescents aged 11 to 14,
27:10but not drug misuse.
27:11I think this was an overall
27:13overall low risk sample.
27:15Game based features may be more
27:17useful as monitoring metrics
27:18during an intervention for example,
27:20like if you embed them as opposed
27:22to screening, but if you embed.
27:24Them into an intervention and you
27:26use machine learning algorithms
27:28to personalize interventions,
27:30and you've documented at baseline
27:32where people have deficits.
27:33Could you then use that as a monitoring
27:35over time of the improvement?
27:37But then you also have to show
27:39that the the improvement is related
27:42to actual improvement in risk.
27:46And then we probably need better game
27:48behavior that is more reflective
27:50of substance misuse.
27:52Some of the work that people are
27:54beginning to think about is like
27:56are there can you embed cues within
27:59a game and and can you use more
28:01biometric measures and would that
28:03be more reflective as opposed to,
28:05you know,
28:06how people are performing in a game,
28:08for example. So there's definitely more work.
28:11I think further investigation is
28:13needed at this algorithm is going to be.
28:15Finally,
28:15valid whether the hypothesis may
28:17be at play that account for these
28:20predictions algorithms performing
28:21similarly between among black
28:23adolescents as opposed to white adolescents.
28:26Umm.
28:28One of the things we're going to do so,
28:29so far we've we've looked at a precise
28:31prediction for substance misuse,
28:32right. But we're our hypothesis is that
28:35this is influenced by cognitive processes.
28:37But we haven't tested
28:38for cognitive processes.
28:39So we're going to embark on a
28:41pilot to see if those same mackers,
28:44we can compute the score using the
28:47logistic regression model and if those
28:49are actually associated with executive
28:51functioning measured by actual tasks
28:53like the impact test and the go,
28:55no GO task among.
28:5714 to 15 years old.
28:59So we'll see.
29:00We'll see what that data tells us.
29:04So in addition to games,
29:07we talked a lot about games.
29:09I think I think I got you know,
29:10I presented the slide where I say let's
29:13meet adolescence wherever they are,
29:14how they are engaging with the world.
29:17And so you know the games I one of
29:19the areas that I've been thinking
29:21about is how do we use the electronic
29:24medical record to identify risk.
29:26Umm, and our rush risk of substance
29:29misuse rationale is that the you know,
29:32the HR is already in use.
29:34It has vast amounts of data and
29:37these that are routinely collected.
29:39We don't need a different process
29:40for collecting this data.
29:41It's already happening.
29:44There are two types of data
29:46that occur that you can.
29:47I mean there's it.
29:48There's a large debate about
29:49the kinds of data in the HR,
29:51but largely there's two
29:52kinds of data in the EHR,
29:54structured data and unstructured data.
29:56Structured data you might say like things
29:59that someone selects from pre populated,
30:02it's already in the system
30:04and you select all.
30:05This person has an alcohol use
30:07disorder you selected as opposed to
30:09like a structure where a provider is
30:11imputing what they think and data.
30:14The type of data is important from
30:18a from a from the point of trust.
30:20And we talk a little bit about about trust,
30:23like what data can we trust?
30:25Is the problem,
30:26can we trust the problem list?
30:28Is it always complete 80%?
30:32There is not.
30:34And is it nice thing to load?
30:39Um, can we,
30:40you know or or do we trust the
30:42clinical notes at the clinical notes,
30:45what kind of information we
30:47get from the clinical?
30:48Are they more predictive?
30:49The consensus is that we should be
30:52using both all kinds of data that
30:54we can get from the EHR as long as
30:57we're intentional about why we're
30:59using them and also intentional and
31:01thoughtful about what we find from,
31:04you know, whatever models that we find from,
31:06from the use of this data.
31:08Prior studies have shown that
31:10we can use EHR data to predict
31:13mental health outcomes as you
31:15side health services research,
31:17suicide prediction, depression,
31:18anxiety and alcohol.
31:19And these are findings from two different
31:22studies on the left here as one study here,
31:25which was among young adults
31:27and this was among adolescents.
31:29And the models were pretty pretty good.
31:32Good enough, I would say,
31:34in identifying these disorders
31:36among adolescents.
31:38So it can be used.
31:40I think there are limited
31:41studies among the adults and
31:42population,
31:43particularly using unstructured data.
31:45Most of the studies use structured data.
31:48And I'm, I'm very interested, you know,
31:51I showed this slide about the median
31:54time between onset of symptoms and
31:58actual initial treatment contact.
32:00Is it possible to derive a model
32:05that identifies risk as the mergers
32:09and then funnel an adolescent
32:12appropriately to intervention?
32:14It would mean that you have a way
32:16to to to determine time to event or
32:19time between the onset of symptoms
32:22and when has recurrently defined
32:25substance use disorder, for example.
32:27So I'm I would like to explore
32:30that and investigate that and I
32:32think there's a lack of focus on
32:35disparities and why disparities.
32:37Um.
32:38Umm.
32:42Lack of focus on disparities can
32:45cause harm and machine learning.
32:46You know, a few years ago we said
32:48we thought machine learning was
32:49going to solve all our problems.
32:51There was truth in it and we would be
32:53able to identify every insoluble problem.
32:56And now we're finding that machine
32:58learning algorithms are inherently biased.
32:59And some are. Some are racist and.
33:03And so on the left,
33:04here we have Google apologizing for
33:06having an algorithm that then gets a bug,
33:08but then this bug uses for some reason.
33:12However it does this, it's able to.
33:14It's now identified that now
33:16identifies black people, wrongly,
33:18as guerrillas.
33:20And on the right is this famous people
33:23might have known about this study by Obama.
33:27Yeah, which?
33:29Was looking at the models used in a program,
33:33where the program was designed to
33:37automatically funnel adults into
33:39a program that helped them manage
33:42comorbid chronic conditions.
33:44And so the algorithm computer the score
33:47and the risk score was at 97 percentile,
33:51and if you reach that based on
33:53the number of chronic conditions,
33:54you were automatically funneled
33:56into this program.
33:58And so you can see that the the the
34:01couple line is for blacks and the
34:03orange line or white is for the yellows
34:06for whites that whites at a lower level,
34:09lower number of active chronic
34:11conditions were being funneled into
34:14the program earlier than blacks.
34:16And so, umm,
34:17when they when they risk
34:19wasn't accounted for.
34:20Race is not exactly a very good
34:23because we're learning it's not a very
34:25good metric for assessing racism.
34:27But I think it's a, it's a good start,
34:30especially if you're intentional
34:32about examining disparities.
34:34But they didn't account for that here.
34:36They simply just deployed an
34:38algorithm based on data that existed.
34:40But when they examined,
34:43they realized they realized that the.
34:46Metric, which is why I,
34:48you know,
34:49examined the metric like what
34:50metrics are actually contributing
34:52to our prediction model.
34:54So when they examine the metrics that
34:56we're contributing to this model,
34:57they found that the one of the largest
35:01contributor of the model was cost
35:03and that whites were more likely to
35:06spend more per chronic condition than blacks.
35:10Why?
35:10Some of the things that like there
35:12are many competing priorities,
35:14Blacks may not be able to take time off work.
35:17To go and see their doctor,
35:19whites were more likely to have procedures
35:23and inpatients appointment large.
35:25This is to say that if if
35:27we're not intentional like.
35:29Algorithms can do a lot of good,
35:32but they can also cost ham
35:34and we need to be thinking.
35:36Or at least I,
35:36as someone who is doing a lot
35:38of machine learning research,
35:40needs to be thinking about how
35:43how these models are used.
35:44What informs these models and can we,
35:46before we deploy them for, you know,
35:48deploy them for prime time?
35:50And so the question that we're asking is,
35:53can data collected routinely
35:55in the electronic health record
35:57be used to identify adolescents
35:59at risk for substance misuse?
36:01And now they are algorithmic
36:03differences by racial ethnic groups?
36:07We will identify electronic health
36:09record data features that predict
36:11substance use disorder will derive a
36:13model will determine if electronic
36:15health record features predictive
36:16of substance misuse disorder.
36:18So use this sort of default by racial
36:20ethnic groups and then we'll try to derive
36:22a lot of time so these these features
36:25are collected at different time points.
36:27When do at what point do you have
36:29enough features in the model that
36:31you can actually see this person
36:33should be further assessed and.
36:36What is the length of time between
36:38when those at documented and the first
36:41determination of substance use is so
36:43that they exist right now and also
36:45referral for behavioral health services
36:47and also determine if that length of
36:49time there any racial ethnic differences
36:51in those in that length of time?
36:53We're going to use data from the
36:56Fairhaven Community Healthcare.
36:56They have about 100 / 100,000
36:59records and we're going to use that
37:02training model for predicting SD.
37:04We will validate that model among
37:06adolescents 12 to 17 years and we'll use
37:09both structured and unstructured data.
37:11So right now we have IRB approval and
37:14we're working through data use agreements.
37:19So hope, hope, hopeful to have some
37:21of this data and stats get into the
37:24nitty gritty nephew in a few weeks.
37:27So, in conclusion, a dozen substance
37:31misuse is a major public health problem.
37:34There are myriad of barriers that
37:36preclude early identification.
37:37We need to identify adolescents
37:39wherever they are and as risk emerges.
37:44Talked specifically about video games.
37:46I'm interested in all things digital tools.
37:49Phones and a lot of there's a lot
37:52of work on ER and that's ecological
37:56momentary assessment MMA and so that
37:59there's there's a wide variety of
38:01how we can use digital tools mid
38:04adolescence where they are social
38:06media for example I was I was I was
38:09trying this I was looking yesterday
38:10as I was preparing and Googling on on
38:13Google like self harm and immediately
38:15I Google self harm like the 1st
38:18988 if you you know like there's.
38:20Regarding working,
38:21that is trying to like identify risk and
38:24trying to like deliver an intervention.
38:28And so I think,
38:29you know we need to be thinking about
38:31all of these different strategies have
38:34I think ideally have a systemic model.
38:37And umm,
38:38and refine the ones like there's some.
38:42I think there's a lot of work to do
38:44to refine the use of video games,
38:46refine the use of the electronic
38:49health record.
38:50So there's more research to be
38:51done in refining these tools.
38:53And ultimately we want,
38:54we want the adolescents to
38:56live healthy lives.
38:57We want them to live highly functional lives.
38:59And yeah,
39:00whatever we can do to make that a reality.
39:04So is all about. So thank you.
39:07I will.
39:12My family patients,
39:14study participants by mentors,
39:16collaborators, funders,
39:17members of the play to prevent lab.
39:20Shout out to Jenny and Fiza who
39:23keep the wheels running and yes,
39:26I'll take questions.
39:33Yes.
39:36Stolen.
39:46Richard, thank you so much.
39:48So as a CL psychologist, I'm absolutely
39:50thrilled by this study where
39:52you're looking at the electronic
39:54health record in primary care. So
39:57that is an overwhelmingly rich
40:01source of data, everything from,
40:03you know, social workers notes
40:05to the standardized instruments
40:07that are being used at Fairhaven.
40:10Do you have a sense of in that vast
40:12data set which initially are going to be
40:16part of your first pass
40:18because we know that at Fair Haven they are
40:20using standardized measures of depression,
40:22anxiety, suicide risk, social work notes.
40:25So I'm wondering how you're prioritizing
40:28that vast data in terms of your first pass.
40:32Are you using your kind of clinical intuition
40:34of what's because you're a clinician,
40:36you're amazing clinician of
40:37what's most likely to result?
40:39In that higher yield, right, right.
40:42I do, yes, we're using the,
40:44we're using the scales validated skills.
40:47So they use craft to identify, to identify,
40:50to identify the outcome as you know,
40:52as an outcome for prediction,
40:54like who is misusing substances.
40:57But we're also going to use like
40:59all primary care notes, I find.
41:01I think the notes are more something
41:03I find like the notes are more
41:06informative about presenting complaints.
41:07Sometimes you don't use the problem lists.
41:10Umm.
41:10But we're also going to use the problem
41:12lists and then we're going to use similar
41:15things that have been used in different
41:18studies like vital signs and encounters.
41:20I mean encounters they had in a year,
41:23did they go to the Ed,
41:24what did they go to the
41:26Ed for inpatient visits.
41:27So we're going to be using all of those
41:30and those are going to definitely be
41:32informed by our clinical knowledge.
41:34But also you know the I think
41:37the the thing about that is.
41:39Useful about machine learning is that
41:41it's at its nature is exploratory and
41:44hypothesis generating and so informed by
41:47that you also want to like get all that
41:49you can get because you can learn you.
41:55On the one hand you can your your clinical
41:58knowledge informs hypothesis which are
42:00already existing and you're testing them.
42:03But also there may be things that
42:04you haven't thought about that
42:06the machine learning helps you
42:07generate or think about them.
42:09Um, that we.
42:10So we're balancing,
42:11we're balancing those and we'll
42:13be collecting as much information
42:15as we have access to.
42:16Yeah.
42:23That was great Bouche and and you
42:27know it's such a huge problem that
42:29that we really wish you success
42:31because it's it's so important.
42:33So I was wondering what you thought
42:35about kind of what the factors are
42:38the skills are that help adolescents
42:40you know say no to drugs or or or
42:42or lessen their use and and we
42:44know that fear doesn't work right.
42:46That's been tested forever
42:48that that doesn't work.
42:49But is it I'm stuck with this is it
42:52like learning things or is it just
42:55peer group influence and and you know
42:57are there really things we could teach
43:00that get kids to you know that you
43:03could teach on a game or is it the
43:05way we get them to think differently
43:07after they're playing the game?
43:09I'm, I'm I'm just interested in what you
43:11what you think you're going to find.
43:15So I think that based on based on
43:17other studies that have been done,
43:19I think that you can one, you can model,
43:22you can model behavior in like the
43:24same ways that you expect, you know,
43:27adults, parents to model behavior.
43:29You can model those behaviors in game,
43:31in games. You can also teach within the
43:34game because they like in the mining games,
43:36they are practicing skills like
43:38they're practicing how do you refuse?
43:41Someone says, oh,
43:41let's go to like how do you say no,
43:43what things can you say?
43:45Um to to circumvent this.
43:48Um, sometimes you may not
43:50succeed and if you don't succeed,
43:51what are the consequences?
43:53And the idea is that if you're if
43:56you're engaging your cognitive
43:58processes in practicing this in
44:00within the veteran environment
44:02that you may be able to translate,
44:04depending you may be able to translate
44:06that you will translate this into real life.
44:08But I think also there's the
44:10part about habit,
44:11like the dose like how much,
44:12how much practice are they getting?
44:16Similarly to how I might learn math,
44:18for example.
44:18Like if I if I did more of the
44:20work of problem sets,
44:21then the likelihood that I would know
44:23what to do if I'm presented with a
44:25different problem set will be high.
44:27So how much? How much dose is enough?
44:29How much dose translates to
44:30a lowering your risk,
44:31how much of that then translates to the
44:33idea to the fact that these adults and
44:36might then practice this in in real life.
44:38But also there's also,
44:40I think he also alludes to a a big
44:43the issue of like this culture.
44:45Because there's a huge influence of culture,
44:48so you may learn all of this,
44:50but like the stigma that's peer like.
44:53I might confident enough that I can
44:56do that within all of the scenarios
44:59and we in view and raise the level of
45:02confidence within the games to then
45:05ensure that they can also translate
45:06this when there's high pressure.
45:08That's something that's I think
45:10that's something we need to test
45:11because indeed it is complex, I agree.
45:16Hi, nice talk.
45:19I had a couple clarifying questions
45:21about the study about the game, so.
45:25Can you explain again how you
45:27classified whether they were
45:29classified as using substances?
45:31Did you say it was one time
45:33using one cigarette? No, no, no.
45:35I there were many questions.
45:36There were twenty questions one
45:38and and it included cigarettes,
45:41alcohol and drugs and it included ever
45:44used and also past 30 days of use.
45:47OK. Yeah. And all of those.
45:49So the, it was such a low risk
45:51sample that all the all those
45:53who said no for all of them were
45:55essentially in the low risk group
45:57and anyone who endorsed any of
45:58that went into the high risk group,
46:00which yeah which is why we're
46:02going to do it more with the pilot.
46:04We're going to have a more high risk
46:06sample like everyone will be misusing
46:08substances at some degree of frequency.
46:10And are you thinking of because
46:12I also was thinking about the
46:14age range from 11 to 14 but.
46:16Yes there are some kids that
46:17are starting to use that young
46:19unfortunately more all the time but
46:20I think that they'll start you know
46:22there are a whole group of kids
46:24that don't start until they get
46:26to high school and that's really
46:28when it when there's more risk.
46:29So I just wondered if you're thinking
46:31of going up a little little higher
46:33in the age range we are so the
46:35pilots is going to be between 14
46:37to 15 year olds and we wanted to
46:39be careful not to you know we're
46:40I I showed how age can in the age
46:43can influence how they perform in
46:45the game and such that if it's.
46:46If you're using older kids,
46:47gonna be so easy that you're really
46:49not getting at the processes.
46:51And so we're we're looking at 14 to
46:5315 also because of the feasibility
46:55like you can at least get all
46:57of them in a high school,
46:58but they would be high school
47:00students who are 14 to 15 years.
47:02Last question, sorry.
47:03I just thought too about the the
47:06reasons that kids start to use right,
47:08so that we've talked a lot,
47:09it sounds like we're talking a
47:10lot about the pressures, right,
47:11of other in the peer situations,
47:13but there are kids that that's not
47:15necessarily how or why they start.
47:17And so I just wonder if that's
47:19something you've thought about
47:21in terms of how to integrate?
47:22Those sorts of risk questions
47:24related to coping strategies
47:26or stress levels or you know,
47:28reasons that kids start,
47:29you know,
47:30nipping out of their parents
47:31cabinet at home or stuff like that,
47:32that doesn't have anything to do with
47:34them being at a party or being with friends.
47:37Right.
47:37I think the interventions do all like address
47:40a lot of the different risk situations.
47:43We just,
47:43yeah,
47:43this was just a sample of,
47:45but intervention interventions
47:46usually address a wide range of of
47:49risk and influence by like
47:51focus groups that make.
47:52The game story lines more
47:54reflective of their own
47:56experiences, but yes, agreed.
48:01Sort of PBA on two of the previous questions.
48:05This is a very important initiative for
48:08us to be hearing about those of us who
48:11have been around and been struggling
48:13with this issue for 50 years or more.
48:15It's easy to get very habituated
48:18to the discouraging components of
48:21how we have failed repeatedly this
48:23population no matter what we've tried.
48:26So the the fresh creativity that you
48:28bring to this is extremely welcome and
48:31don't let anybody talk you out of it.
48:35And one of the questions that
48:37I'm raised in my mind is that.
48:39I think some of the best.
48:44Diagnostic work about substance abuse
48:46in this age group is done by peers.
48:50They're often extraordinarily accurate
48:52talking about their friends and talking
48:55about what they see in their friends,
48:58and I wondered if you have a multiplayer
49:00game in your future where you could expose
49:03this to a larger group of problem solvers.
49:08Love to I would love to. Yes.
49:11Um, multiplayer family based games.
49:13Yes. Yes I think I I think we should
49:16be meeting adolescence where they are.
49:19We should, it should be informed
49:20by what we know about risk.
49:22I think we should use multi modality.
49:24And yes, absolutely curious.
49:28We have a. Sure. Kim,
49:33can you hear us? Can you unmute?
49:38Hi, uchi. I have a question for you.
49:44I was really interested what
49:45you were saying about like uh,
49:47it was really fascinating that you
49:48were able to pick about apart a
49:50lot of this by kind of thinking
49:52through how kids need to adjust to
49:53game play before they, you know,
49:55thinking about how those biomarkers
49:57might be relevant to kind of address
50:00you know or identify at risk players.
50:03So I'm curious your thoughts when
50:05you have like if you have a brief
50:08intervention like a brief one or
50:10two hour game based intervention,
50:12how you might get over that barrier?
50:14Um, do you in terms of like collecting
50:16data to try to kind of gather that
50:18information around at risk youth,
50:20are you suggesting that?
50:21It may not be a good idea to look
50:23at that early game play and maybe
50:25looking like at the full game
50:27plays to change over time.
50:28Or are you or are you thinking
50:30it may be more valuable to look
50:31after somebody kind of salad and
50:33learn the ropes and gotten through
50:35several levels later in the game?
50:37I I think we should look at all
50:39of it because I I think that.
50:41So one of the questions I'm I'm
50:44wondering about is that is the
50:46difficulty of overcoming that initial
50:48difficulty is that influenced by
50:51difficult you know difficulties
50:53and cognitive processing or is it
50:55just that we're just trying to get.
50:57I'm just trying to learn how this game works.
51:00We haven't really tested if if there's
51:02a difference with that and if they settle,
51:06if they settle.
51:07Like is that?
51:09The rate of settling, is that also
51:12influenced by baseline cognitive function?
51:15I don't know.
51:16I think that those are all questions
51:18that need to be, need to be,
51:19and need to be answered for us
51:21to determine when.
51:23I do think, though,
51:25that if learning occurs.
51:27If learning from the game occurs,
51:29and it doesn't influence risk like that,
51:32that that risk doesn't get better as you're
51:35learning like it's the couple from it,
51:37then it's not a good metric for
51:39measuring risk for substance misuse.
51:46Thank you, Kim and thank you for
51:48helping being such a good mentor
51:50to which we have another question
51:52from Doctor Christine Emmons.
51:54Christine and if you
51:56do you have plans to develop
51:58games that tag your treatment.
52:00Oh, sorry about that.
52:00Christine do you want to
52:01ask your question and if you if you.
52:05Yes. So my question is do you have
52:07any plans to to create games at
52:10Target treatment or integrated
52:13diagnostics with treatment?
52:16Um. I mean, ideally we would have a model,
52:19we'll have. Personally,
52:22I think about prevention. I do.
52:25But but if we think about the
52:27problem of substance misuse,
52:28we should all be thinking about
52:30how we also target treatment,
52:32and so I think that those are
52:35possibilities, especially.
52:36If we can, if there's a model,
52:39you know my mind,
52:41there's a model that funnels
52:43appropriately and but we also need
52:45you know I also mentioned how self
52:48standalone digital interventions may
52:50not be very effective for treatments,
52:53you know, for a severe substance
52:55use disorder and treatments.
52:57So I think we need more.
52:59Most of the studies that have
53:00been done so far are showing that
53:02they are useful as adjunctive,
53:03especially if you're having like
53:05a maybe zoom televideo treatment.
53:07And then you have these as perhaps
53:10assignments at John Adjunctive that
53:12help a person practice some of these
53:14skills that we're talking about.
53:16So as an adjunctive treatment,
53:18I think those are,
53:19those are areas where due to interventions
53:21can be really high yield, I think, yeah.
53:25But of course I'm open.
53:27I'm open to a possibility.
53:30So maybe building on some of the
53:32comments earlier on and you talked
53:34about possibly using other digital
53:35technologies and integrating them
53:37into your future research program.
53:38I was very taken by reaching recent
53:41nature I think biotechnology paper that
53:43looked at Fitbit data in the context
53:46of COVID and predicted infection like
53:49many days before symptom onset and that
53:51was in a relatively small sample size,
53:53I think it was around 64 participants.
53:55And then the all of US initiative,
53:58you know they're recruiting.
54:00Million people,
54:01but their approach is just to say well
54:03if you have a Fitbit you know will
54:04you allow us to access your data.
54:06So they're not even providing Fitbits,
54:08but they receive the data.
54:09I just wondered if anyone is
54:11integrating wearable tech like Fitbit
54:13data in substance use in adults
54:15and whether or not that might be of
54:17value in in this population as well.
54:19I think I think they're doing.
54:20I think there's actually some research from.
54:23Integrating Fitbit or at least
54:26wearable technology, I would love to.
54:29I would love to integrate that especially.
54:34Because you know, one of the ways
54:35I think about it is that if, if,
54:37if there are areas where I think that
54:39where they've used them before in adult
54:41studies is that there are areas that.
54:44For example, if you're close to a
54:45shop where you can buy a vape right,
54:47like and then something will,
54:50you will get a notification that
54:51you're in a place where you might
54:53engage in a high risk behavior.
54:54What might you do?
54:56What resources do you have that
54:58you can employ in this moment?
55:00So I would I would love to be
55:02able to integrate the wearable
55:06technology in monitoring.
55:08Kids who are high risk and
55:10thinking about how we might,
55:11how we might measure those, how,
55:13how and what will be most useful
55:17as measures for identifying
55:19who might be struggling,
55:21who is struggling at the moment,
55:23who would be a risk for substance misuse.
55:25Yeah,
55:26and if the kids are bringing their
55:27own Fitbit, it would be quite
55:29cost effective. Question from you.
55:37Thank you very much. That
55:38was terrific presentation
55:39of of your amazing work.
55:42I had a weird idea that that I
55:44don't it's not terribly well formed,
55:46so I apologize for that.
55:47But on my mind is the recent CDC
55:51report about the prevalence of.
55:53Of anxiety and depression in
55:55youth in the United States,
55:57especially amongst.
55:59Girls and young women and but
56:03across the board I think really,
56:05and what occurred to me is that probably
56:09there's a connection between those
56:11dysphoric experiences and substance use,
56:14I mean, at least broadly.
56:16And I just wondered whether.
56:18You know,
56:19you may not have an answer right now,
56:20but whether in the data you collect,
56:23is there some way of collecting
56:26data about about those dysphoric
56:29experiences at the same time you're
56:31collecting things that might lead
56:33to directly to substance use?
56:35Right.
56:37So with the electronic
56:39health record data data,
56:41we're also going to be looking at
56:43predictions for depression and ID.
56:48So we, we would look at,
56:50we would look at any relationships and
56:52see if those features are also predictive.
56:54If the features that are predictive
56:56of substance misuse disorder are
56:58also the feature same features that
57:00are predictive of major depression,
57:01there's major depression occur
57:03before substance use disorder.
57:05Could we use that as a way to also screen,
57:07you know, for for substance misuse?
57:12I do think that there in terms
57:14of games or digital technology,
57:16there are ways in which you can.
57:20Measure like the ways in which behavior
57:23in a game can be reflective of affect.
57:27And so if, if, if, we can.
57:34If we can embed some of those as
57:37we develop games and we use that
57:39as monitoring for like if if we can
57:42monitor a change in your effects
57:44is that also improving your risk
57:46or lowering your risk.
57:48So I do think I mean and these
57:49are highly commoditized like the
57:51depression anxiety increase you're
57:52repeating as they increase your
57:54risk of substance misuse.
57:55So we do need to be addressing the
57:58both but also yes I agree thinking
58:00about how we how we use what we
58:03know about the physiological.
58:04Presentations,
58:05physiological manifestations of
58:07this disorders,
58:08how do we embed those in games
58:11that measure and monitor over time
58:13and how do we use that to monitor
58:16improvements as we as we address,
58:18as we address this underlying disorders?
58:22Which I think that there
58:23would be many more questions,
58:24but we're going to finish here.
58:26But thank you so much.